DWQ_final_report_v3

NC DEQ Project - Task Order # CW28707

Project Title:
Jordan Lake Water Quality Model Development

Final Report

Submitted by

James D. Bowen
Civil and Environmental Engineering Department
University of North Carolina at Charlotte

A Report to the NC Division of Water Resources
Raleigh, North Carolina

March 15, 2024
1
EXECUTIVE SUMMARY
A three-dimensional mass balance-based water quality model of Jordan Lake, North Carolina is
described and is used to test a range of nutrient reduction scenarios intended to reduce
phytoplankton abundance as measured by chlorophyll-a concentration. Jordan Lake is a
physiologically unique lake in the piedmont of North Carolina that has two distinctly different
lake regions within the Haw River and New Hope Creek watersheds. A review of observed
chlorophyll-a concentrations in Jordan Lake indicates that the lake regularly exceeds the water
quality criteria for chlorophyll-a, but the magnitude of the exceedance differs significantly
between the Haw River and New Hope Creek arms of the lake. A loading analysis of the lake
indicates that Haw River provides 75% of the water to the lake, 65% of the total nitrogen, and
55% of the total phosphorus. Benthic sediments are an important source of nutrients, providing
40% of the bioavailable phosphorus and 85% of the ammonia incoming to the lake water
column.
Several model updates were conducted to improve the Jordan Lake model from its original
implementation in 2019. Water surface elevation prediction was greatly improved through
development of a method for estimating inflows from the ungaged portions of the watershed.
Inflow estimates from the ungaged watersheds and outflows from the dam now use a water
balance method implemented on a daily time step. Loading estimates of dissolved inorganic
phosphorus (DIP) were improved by developing a temperature dependent ratio of organic
nitrogen to organic phosphorus that can estimate DIP from the nitrogen and total phosphorus
data. The model time period chosen (January 2014 – February 2016) has an extensive
chlorophyll-a observed data set that was used to calibrate the model. These efforts have greatly
improved the model’s prediction of chlorophyll-a and nutrient concentration throughout the lake
compared to the 2019 version of the model. A model use-specific calibration objective based
upon the model’s intended use for predicting exceedance of the chlorophyll-a (chl-a) criteria for
various levels of nutrient load reduction was implemented to ensure that the model’s exceedance
fraction of the chl-a criteria for various regions within the lake matched that of the observed data
for the 2014-2016 model time period.
Nutrient load reduction scenarios examined the lake’s response with respect to chlorophyll-a
concentration to nitrogen and/or phosphorus load reductions from zero to seventy percent over
the two-year model time period (2014-2016). A hydrologic analysis of this time period was
used to compare the model time period to corresponding values from 1980-2018 with respect to
rainfall, streamflows, and nutrient loadings. Since the 1980s annual rainfall has trended upward,
yet cumulative surface water inflows to the lake have trended downward. The model time period
years (2014, 2015, 2016) were approximately 10% above the long-term trend line for both
rainfall and cumulative surface water inflow. Over the 1980-2018 time period cumulative
nitrogen loadings for three Jordan Lake watersheds (Haw River, Morgan Creek, New Hope
Creek) are relatively flat while phosphorus loadings have decreased. Two of the model time
period years (2014, 2016) were very close to the trend lines for total nitrogen and phosphorus
loading while the third year (2015) was approximately twenty percent above the trend line. The
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lake’s chlorophyll-a response was calculated by looking at seventeen monitoring stations across
the lake. These seventeen stations were grouped into three subsets of stations (Haw River,
Middle New Hope Creek, Morgan and Upper New Hope Creeks) for the load reduction analysis.
The entire set of stations was also considered during the load reduction analysis. In general, as
expected, the median chlorophyll concentrations were reduced over the full range of nitrogen
and/or phosphorus load reductions simulated. The fraction of predicted chlorophyll-a
concentrations above the 40 µg/L water quality criteria was also reduced as the nutrient loads
were reduced. When all Jordan Lake water quality monitoring stations were considered, median
chl-a concentration decreased from 20.5 µg/L to 14.2 µg/L or from 20.5 to 10.2 µg/L with
phosphorus only or nitrogen only reductions ranging from 0% to 70%. Reducing both nutrients
by 70% further reduced the median chl-a concentration in all stations to 8.9 µg/L. Decreasing
nutrient loading reduced the fraction of chl-a values above 40 µg/L from 0.26 to 0.17 when just
phosphorus was reduced by 70%. Reducing just nitrogen loading by 70% decreased the
exceedance fraction to 0.08 (below the target value of 0.10) from the base case value of 0.26.
When both nutrients were decreased the exceedance fraction was further reduced to 0.04 when
the entire set of Jordan Lake stations was considered.

There were significant spatial differences in the predicted chlorophyll-a concentrations across
different regions of Jordan Lake and differences in the sensitivity to nitrogen and phosphorus
load reductions. These were examined, as mentioned earlier, by looking at three different
groupings of stations across the lake (Haw River, Middle New Hope Creek, Morgan and Upper
New Hope Creeks). The Morgan and Upper New Hope Creek station group had the lowest
median chlorophyll-a concentration of 12.5 ug/L. The Haw arm station group had the second
lowest median chlorophyll-a concentration (16.1 ug/L). The highest median chl-a concentration
was seen in the middle New Hope Creek region (25.2 ug/L). More significant than the
differences in chl-a concentrations were the station group differences in sensitivity to load
reduction. The Haw arm station group had very similar responses to reductions in nitrogen vs.
phosphorus with respect to median chlorophyll-a. For the exceedance fractions in the Haw arm
station group, the results were slightly more sensitive to phosphorus load reduction than nitrogen
load reduction. Several combinations of load reductions a phosphorus only reduction of 40%, to
several P+N% reduction cases (e.g. 30% P + 30% N), to a just nitrogen reduction case (70% N
reduction) were sufficient station to reduce the fraction of exceedances below the targeted 0.10
value. The New Hope Creek arm stations were very different from the Haw arm stations, with
much more sensitivity to nitrogen load reduction compared to phosphorus reduction. Nitrogen
load reductions of 50% to 70% were necessary for these station groups to lower the exceedance
fraction to of the chlorophyll-a criteria to 0.10. The higher sensitivity to nitrogen load reduction
in the New Hope Creek arm of the lake as compared to the Haw River arm may be due to
physical factors like differences in average water depth and water residence time.

3
Acknowledgements
This project report is based upon work supported by the North Carolina Policy Collaboratory and
The North Carolina Department of Environmental Quality. The author gratefully acknowledges
the hard work of two UNC Charlotte graduate students (Babatunde Adeyeye and Savannah
Fraleigh) and one research engineer (William Langley) who provided significant contributions to
the research, analysis, and report writing that are presented in this technical report. The author
would also like to thank and acknowledge four colleagues at the North Carolina Department of
Environmental Quality (Jing Lin, Pam Behm, Rich Gannon, Rishi Bastakoti) for their patient,
thoughtful, and constructive review of project work and the documentation found in this report.

4

Table of Contents
EXECUTIVE SUMMARY ................................................................................................. 1
Acknowledgements ............................................................................................................ 3
Table of Figures ................................................................................................................ 8
Table of Tables ................................................................................................................ 11
1. INTRODUCTION ..................................................................................................... 13
1.1. Background ................................................................................................................... 13
1.1. Study Objectives ........................................................................................................... 13
1.2. Organization of the Study Report .................................................................................. 15
2. MODEL SETUP ....................................................................................................... 16
2.1. Model Description ......................................................................................................... 16
2.2. Description of Jordan Lake Basin .................................................................................. 19
2.3. Previous Modeling of Jordan Lake ................................................................................ 19
2.4. Model Grid and Bathymetry ......................................................................................... 19
2.5. Monitoring Data Used for Model Setup and Calibration ................................................ 24
2.5.1. Data Sources ........................................................................................................................ 24
2.5.2. Data for Nutrient Response, Flow, and Temperature Models ............................................... 27
2.5.3. Nutrient Loading Analysis ................................................................................................... 29
3. SPECIFICATION OF MODEL INPUT FILES .......................................................... 32
3.1.1. Riverine Input Flow Specification ........................................................................................ 32
3.1.2. Adjustment of Flows from Ungaged Watersheds and the Dam ............................................. 33
3.1.3. Temperature and Concentration Specification ..................................................................... 36
3.1.3.1. Estimation of Dissolved and Organic Phosphorus Concentration ......................................... 37
3.1.4. Water Treatment Plant Outflows ......................................................................................... 41
3.2. Benthic Inputs ............................................................................................................... 41
3.3. Meteorological Inputs .................................................................................................... 43
4. MODEL CALIBRATION .......................................................................................... 45
4.1. Description of the Calibration Time Period ................................................................... 45
4.2. Hydrodynamic Model Calibration ................................................................................. 48
4.2.1. Modeled and Observed Water Surface Elevations ................................................................ 49
4.2.2. Temperature Model Calibration .......................................................................................... 52
4.3. Water Quality Model Calibration .................................................................................. 54
5. MODEL EVALUATION ........................................................................................... 71
5.1. HYDROLOGIC ANALYSIS ......................................................................................... 71
5
5.1.1. Annual Rainfall at RDU Airport .......................................................................................... 71
5.1.2. Yearly Average Streamflows ................................................................................................ 72
5.1.3. Annual Average Total Nitrogen and Total Phosphorus Loading ........................................... 73
5.1.4. Comparisons of Nitrate, Total Nitrogen, and Total Phosphorus Concentration .................... 75
5.1.4.1. Haw River ....................................................................................................................... 75
5.1.4.2. New Hope Creek .............................................................................................................. 78
5.1.4.3. White Oak Creek ............................................................................................................. 80
5.2. Model Verification ........................................................................................................ 83
5.3. Analysis of Simulated Dye Releases ............................................................................... 85
6. PREDICTION OF WATER QUALITY CHANGES WITH REDUCED NUTRIENT
LOADING ....................................................................................................................... 90
7. SUMMARY, DISCUSSION, AND CONCLUSIONS ................................................. 100
8. REFERENCES ......................................................................................................... 103
Appendix 1. Creating Constituent Time Series at Inflow Model Boundaries .................. 108
Appendix 2. Observed vs. Model Predicted Temperature Time Histories at Each Jordan
Lake Monitoring Station for the 2014-2016 model time period. ........................................ 121
Appendix 3. Observed vs. Model Predicted Chlorophyll-a Time Histories at Each Jordan
Lake Monitoring Station during the 2014-2016 model time-period. ................................... 130
Appendix 4. Observed vs. Model Predicted Nitrate + Nitrate (NOx) Time Histories at Each
Jordan Lake Monitoring Station during the 2014-2016 model time-period. ....................... 139
Appendix 5. Observed vs. Model Predicted Ammonia (NH4+ + NH3 (aq)) Time Histories at
Each Jordan Lake Monitoring Station during the 2014-2016 model time-period. .............. 148
Appendix 6. Observed vs. Model Predicted Total Kjeldahl Nitrogen (TKN) Time Histories at
Each Jordan Lake Monitoring Station during the 2014-2016 model time-period. .............. 157
Appendix 7. Observed vs. Model Predicted Total Phosphorus (TP) Time Histories at Each
Jordan Lake Monitoring Station during the 2014-2016 model time-period. ....................... 166
Appendix 8. Observed vs. Model Predicted Dissolved Oxygen (DO) Time Histories at Each
Jordan Lake Monitoring Station during the 2014-2016 model time-period. ....................... 175
Appendix 9. Observed vs. Model Predicted Cumulative Distributions Functions (CDFs) for
the Seven Parameters in the Observed Data Set (Temperature, Chl-a, NOx, NH4, TKN, TP,
DO) 184
Appendix 10. Observed vs. Model Predicted Scatter Plots for the Seven Parameters in the
Observed Data Set (Temperature, Chl-a, NOx, NH4, TKN, TP, DO. ................................. 188
Appendix 11. Mean Monthly Nutrient Concentrations at Six Locations in the Jordan Lake
Watershed from an Analysis of Available Monitoring Data from 2000 – 2018. .................. 192
Appendix 13. plot_ts_gui (v11) Users Manual ............................................................ 195
1. Choose System to Plot back to GUI page .............................................................. 198
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2. Julian Day 0 Date back to GUI page .................................................................... 199
3. Plot Layers (bot to top) back to GUI page ............................................................ 199
4. Set Plotting Options back to GUI page ................................................................. 199
4.1. Use month for x-axis tic (yes/no) ........................................................................... 199
4.2. Set x-axis values? (yes/no) back to GUI page ................................................. 199
4.3. Set y-axis values? (yes/no) ..................................................................................... 200
4.4. Save figures? (yes/no) ............................................................................................. 200
4.5. Make a cumulative freq. fig? (yes/no) back to GUI page ................................... 200
4.6. Make log plots? (yes/no) .......................................................................................... 200
4.7. Save DO sat or Travel Time to file? (yes/no) back to GUI page .......................... 200
4.8. 15-step filter preds? (button) back to GUI page ................................................ 200
4.9. Stats Only? (button) ................................................................................................. 201
4.10. Plot data? (yes/no) back to GUI page ........................................................... 201
4.11. Save station fit stats? (yes/no) ........................................................................... 201
4.12. Plot top/bot differences (yes/no) back to GUI page ........................................ 201
5. Folder with Output Files back to GUI page .......................................................... 201
6. Observed Data Files and Sheets (1=Hydro, 2=WQ), in same directory ........................ 202
6.1. Choose folder for observed data spreadsheets back to GUI page ..................... 202
6.2. Select file and set sheet (file 1) back to GUI page .............................................. 202
6.3. Select file and set sheet (file 2) back to GUI page .............................................. 202
7. Choose One back to GUI page ........................................................................... 202
7.1. Hydro back to GUI page .................................................................................... 203
7.2. WQ back to GUI page ........................................................................................ 203
7.3. WQ, EE back to GUI page .................................................................................. 205
8. Choose Constituent (Hydrodynamics, DO, NH4) back to GUI page ......................... 205
9. Choose Constituent (Water Quality) back to GUI page .......................................... 205
10. Choose Stations to Plot back to GUI page ........................................................ 205
10.1. Plot All Stations .................................................................................................... 205
10.2. Station Range ........................................................................................................ 205
10.3. Station List ............................................................................................................ 206
11. Run Control Buttons back to GUI page ............................................................ 206
11.1. Update Preferences .............................................................................................. 206
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11.2. Make Plot ............................................................................................................... 206
11.3. Quit back to GUI page .................................................................................. 206
Appendix 14. Model input file EFDC.INP file for base case. ...................................... 208
Appendix 15. Model input file WQ3DWC.INP file for base case. ................................ 238
Appendix 16. Model input file BENFLUX.INP file for base case. ............................... 250
Appendix 17. plot_ts_gui results output file ptsg_results.txt for base case. ................... 251
Appendix 18. Observed vs. Model Predicted Temperature Time Histories at Each Jordan
Lake Monitoring Station for the 2014-2016 model time period without the 15-step time filter
applied to model predictions (layers 2 or bottom and 24). ................................................. 260

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Table of Figures
Figure 1. Jordan Lake watershed within the Cape Fear River basin of the piedmont region of North
Carolina. ....................................................................................................................................................... 14
Figure 2. Box and arrow diagram showing the EFDC water quality state variables and the mass flows
between them. See Table 1 for the constituent names and abbreviations. .................................................. 17
Figure 3. Volume-elevation curves for the 393 cell and 407 candidate model grids and the corresponding
Jordan Lake volume-elevation curve available from the Army Corps of Engineers .................................. 20
Figure 4. Lake surface area vs. elevation for the 393 and 407 cell Jordan Lake model grids as compared to
lake surface area data available from the Army Corps of Engineers. ......................................................... 21
Figure 5. The 407 Cell Jordan Lake Model Grid with Each Cell Shown Colored by Bottom Elevation in
Meters. ......................................................................................................................................................... 22
Figure 6. Mask location on Jordan Lake 407 cell model grid. .................................................................... 23
Figure 7. Jordan Lake wet and dry model cells. .......................................................................................... 24
Figure 8. Locations of 18 Jordan Lake Monitoring Stations Sampled by the NC Division of Water
Resources. The newly developed 407 cell EFDC model grid is also shown. ............................................ 27
Figure 9. Pie charts showing 2014-2018 daily average inflows (a), daily average outflow (b), daily
average total phosphorus (TP) load (c), and daily average total nitrogen (TN) load for Jordan Lake, NC.30
Figure 10. Pie charts showing 2014-2018 daily average ammonia load (a), daily average phosphate load
(b), and the daily average nitrate load (c) for Jordan Lake NC. Panel d shows the daily average nitrate
load for the New Hope Creek arm of the lake. ............................................................................................ 31
Figure 11. Pie charts showing the 2014-2018 daily average ammonia (a) and phosphate (b) load into the
New Hope Creek arm of Jordan Lake, NC. ................................................................................................. 31
Figure 12. Model flow boundary locations. ................................................................................................. 34
Figure 13. Time history comparisons of observed and model predicted water surface elevations at the
Jordan Lake dam for the 2014-2016 model time period (left panel) and the 2017-2018 time period (right
panel). From: (Bowen, Langley, & Adeyeye, 2019). .................................................................................. 35
Figure 14. Relationship between the TON/TOP ratio vs. temperature (top panel) and the inorganic
P/TP ratio vs. temperature (bottom panel) in the EFDC input files for Tenkiller Lake, OK. ..................... 39
Figure 15. Predicted vs. actual DIP using the temperature variable P/TP ratio (top panel) and the
temperature variable TON/TOP ratio (bottom panel) in the EFDC input files for Tenkiller Lake, OK. .... 40
Figure 16. Predicted vs. actual DIP using the fixed TON/TOP ratio using the Redfield ratio value in
the EFDC input files for Tenkiller Lake, OK. ............................................................................................. 41
Figure 17. Haw River daily streamflows (in cfs, blue) and historical monthly averages (red) for the 2014-
2016 model time period. .............................................................................................................................. 46
Figure 18. New Hope Creek daily streamflows (in cfs, blue) and historical monthly averages (red) for the
2014-2016 model time period. ..................................................................................................................... 47
Figure 19. Modeled vs observed water surface elevations using 393 cell grid model. ............................... 49
Figure 20. Scatter plot of modeled vs observed water surface elevation (2014-2016). .............................. 50
9
Figure 21. Modeled vs observed water surface elevation for the 2014-2016 model time period. .............. 51
Figure 22. Time history comparisons of observed and model predicted surface and bottom
temperatures at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time period. ....... 53
Figure 23. Scatter plot of observed and model predicted surface and bottom temperatures at all monitoring
stations in Jordan Lake, NC for the 2014-2016 model time period. ............................................................ 53
Figure 24. Time history comparisons of observed and model predicted surface and bottom chl-a
concentrations at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time period. .... 56
Figure 25. Observed vs. Model Predicted Chl-a Cumulative Distributions Functions (CDFs) at Seventeen
Jordan Lake Stations Over the 2014-2016 time period. .............................................................................. 57
Figure 26. Time history comparisons of observed and model predicted surface and bottom NOx
concentrations at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time period. .... 58
Figure 27. Scatter Plot of Observed vs. Model Predicted NOx Concentrations at Seventeen Jordan Lake
Stations Over the 2014-2016 time period. ................................................................................................... 59
Figure 28. Time history comparisons of observed and model predicted surface and bottom NH4
concentrations at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time period. .... 60
Figure 29. Scatter Plot of Observed vs. Model Predicted NH4 Concentrations at Seventeen Jordan Lake
Stations Over the 2014-2016 time period. ................................................................................................... 61
Figure 30. Time history comparisons of observed and model predicted surface and bottom TKN
concentrations at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time period. .... 62
Figure 31. Scatter Plot of Observed vs. Model Predicted TKN Concentrations at Seventeen Jordan Lake
Stations Over the 2014-2016 time period. ................................................................................................... 63
Figure 32. Time history comparisons of observed and model predicted surface and bottom TN
concentrations at station CPF055D (left) and CPF086C (right) for the 2014-2016 model time period. .... 64
Figure 33. Observed vs. Model Predicted Total Nitrogen Concentration Cumulative Distribution
Functions (CDFs) at Seventeen Jordan Lake Stations Over the 2014-2016 time period. ........................... 65
Figure 34. Time history comparisons of observed and model predicted surface and bottom TP
concentrations at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time period. .... 66
Figure 35. Scatter Plot of Observed vs. Model Predicted TP Concentrations at Seventeen Jordan Lake
Stations Over the 2014-2016 time period. ................................................................................................... 67
Figure 36. Time history comparisons of observed and model predicted surface and bottom DO
concentrations at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time period. .... 69
Figure 37. Observed vs. Model Predicted Dissolved Oxygen Concentration Cumulative Distribution
Functions (CDFs) at Seventeen Jordan Lake Stations Over the 2014-2016 time period. ........................... 69
Figure 38. Total Annual Rainfall Measured at the RDU Meteorological Station, 1980-2018 (in). Years
2014 to 2016 are shown with a red oval. ..................................................................................................... 72
Figure 39. Annual Average Cumulative Flow for Three Jordan Lake Watersheds (Haw River, Morgan
Creek, New Hope Creek), 1983-2016 (m3/s). Years 2014 to 2016 are shown with a red oval. ................. 73
10
Figure 40. Annual Cumulative Nitrogen Loading (kg N/yr) from Three Jordan Lake Watersheds (Haw
River, Morgan Creek, New Hope Creek), 1983-2018 (using data from Obenour Lab). Years 2014 to 2016
are shown with a red oval. ........................................................................................................................... 74
Figure 41. Annual Cumulative Phosphorus Loading (kg P/yr) from Three Jordan Lake Watersheds (Haw
River, Morgan Creek, New Hope Creek), 1983-2018 (using data from Obenour Lab). Years 2014 to 2016
are shown with a red oval. ........................................................................................................................... 74
Figure 42. Haw River Measured and Estimated Total Nitrogen Time History. .......................................... 75
Figure 43. Haw River Measured and Estimated Nitrate Time History. ...................................................... 76
Figure 44. Haw River Measured and Estimated Total Phosphorus Time History. ..................................... 76
Figure 45. Haw River Measured Vs. Estimated Nitrate Concentration. ..................................................... 76
Figure 46. Haw River Measured Vs. Estimated Total Nitrogen Concentration. ......................................... 77
Figure 47. Haw River Measured Vs. Estimated Total Phosphorus Concentration. .................................... 77
Figure 48. New Hope Creek Measured and Estimated Nitrate Time History. ............................................ 78
Figure 49. New Hope Creek Measured and Estimated Total Nitrogen Time History. ............................... 78
Figure 50. New Hope Creek Measured and Estimated Total Phosphorus Time History. ........................... 79
Figure 51. New Hope Creek Measured Vs. Estimated Nitrate Concentration. ........................................... 79
Figure 52. New Hope Creek Measured Vs. Estimated Total Nitrogen Concentration. ............................... 79
Figure 53. New Hope Creek Measured Vs. Estimated Total Phosphorus Concentration. .......................... 80
Figure 54. White Oak Creek Measured and Estimated Nitrate Time History. ............................................ 81
Figure 55. White Oak Creek Measured and Estimated Total Nitrogen Time History. ............................... 81
Figure 56. White Oak Creek Measured Vs. Estimated Total Phosphorus Time History. ........................... 81
Figure 57. White Oak Creek Measured Vs. Estimated Nitrate Concentration. ........................................... 82
Figure 58. White Oak Creek Measured vs. Estimated Total Nitrogen Concentration. ............................... 82
Figure 59. White Oak Creek Measured Vs. Estimated Total Phosphorus Concentration. .......................... 83
Figure 60. Water age model estimates (days) at station CPF055D for May 1 to Oct 31, 2015. ................. 86
Figure 61. Inflows at USGS Gaging Stations 02096960 Haw River and 02097314 New Hope Creek. ..... 86
Figure 62. Water age model estimates (days) at station CPF0880A for May 1 to Oct 31, 2015. ............... 87
Figure 63. Water age model estimates (days) at station CPF087B3 for May 1 to Oct 31, 2015. ............... 87
Figure 64. Station Groupings for Analysis of Nutrient Reduction Scenarios ............................................ 91

11
Table of Tables
Table 1. EFDC Water Quality State Variables (Tetra Tech 2007). Abbreviations refer to constituents as
shown in Figure 2. ....................................................................................................................................... 16
Table 2. Recent Water Quality Modeling Reports Used as a Basis for this Study. ..................................... 18
Table 3. EFDC Input Files and Data Sources. ............................................................................................. 25
Table 4. Monitoring Stations Used to Calibrate the Jordan Lake Model. ................................................... 26
Table 5. Analysis of 2014 – 2018 Photic Zone Chl a Measurements at selected stations within four
regions of Jordan Lake. ................................................................................................................................ 28
Table 6. Specification of Surface Water Inflow or Outflow Time Series in the QSER.INP file ................ 33
Table 7. State variables for water quality model ......................................................................................... 37
Table 8. Temporally Varying Temperature Corrections for Benthic Fluxes ............................................... 42
Table 9. Time average benthic nutrient fluxes to the water column. .......................................................... 43
Table 10. Sky condition observations and corresponding W2 cloud cover input. ...................................... 44
Table 11. Monthly Average Haw River Streamflows and Streamflow Ratios for 2014 and 2015. ........... 47
Table 12. Monthly Average New Hope Creek Streamflows and Streamflow Ratios for 2014 and 2015. . 48
Table 13. Statistical comparison of modeled vs observed water surface elevations for the 2014-2016 time
period. .......................................................................................................................................................... 51
Table 14. Statistical comparison of modeled vs observed surface water and bottom temperatures for the
2014-2016 time period. ................................................................................................................................ 52
Table 15. Summary of Phytoplankton Kinetic Parameters (taken from July 2023 Base Case wq3dwc.inp
file, folder name = 18_baseANCc_pt167_CPprm1_38_best_final, see Appendix 15) ............................... 55
Table 16. Statistical comparison of modeled vs observed chlorophyll-a concentration using all Jordan
Lake monitoring stations for the 2014-2016 time period (lower and upper model layers = 20 and 24, with
a 15-step time filter applied). ....................................................................................................................... 57
Table 17. Statistical comparison of modeled vs observed NOx concentrations using all Jordan Lake
monitoring stations for the 2014-2016 time period (lower and upper model layers = 2 (or bottom) and 24).
..................................................................................................................................................................... 59
Table 18. Statistical comparison of modeled vs observed NH4 concentration using all Jordan Lake
monitoring stations for the 2014-2016 time period (lower and upper model layers = 2 (or bottom) and 24).
..................................................................................................................................................................... 61
Table 19. Statistical comparison of modeled vs observed TKN concentration using all Jordan Lake
monitoring stations for the 2014-2016 time period (lower and upper model layers = 2 (or bottom) and 24).
..................................................................................................................................................................... 63
Table 20. Statistical comparison of modeled vs observed TN concentrations using all Jordan Lake
monitoring stations for the 2014-2016 time period (lower and upper model layers = 2 (or bottom) and 24).
..................................................................................................................................................................... 65
12
Table 21. Statistical comparison of modeled vs observed TP concentration using all Jordan Lake
monitoring stations for the 2014-2016 time period (lower and upper model layers = 2 (or bottom) and 24).
..................................................................................................................................................................... 67
Table 22. Statistical comparison of modeled vs observed DO concentration using all Jordan Lake
monitoring stations for the 2014-2016 time period (lower and upper model layers = 2 (or bottom) and 24).
..................................................................................................................................................................... 70
Table 23. Statistical comparison of modeled vs. observed water temperatures for three separate model
time periods (Bowen, Langley et al. 2019). ................................................................................................. 84
Table 24. Statistical comparison of modeled vs. observed chlorophyll-a concentration for three separate
model time periods (Bowen, Langley et al. 2019). ...................................................................................... 84
Table 25. Estimates of Water Age at Selected DWR Monitoring Stations of Jordan Lake, NC for the May
2015-Oct 2015 Model Time Period. ............................................................................................................ 85
Table 26. Time-average simulated dye concentrations at locations across four Jordan Lake regions.
Higher concentrations indicate a higher contribution from Haw River inflow. The average concentration
for a region is based upon all stations within that region. ........................................................................... 88
Table 27. Number of Model Predictions Analyzed for to Calculate Median Chlorophyll-a Concentration
and Fraction of Predictions Above Water Quality Criteria Value ............................................................... 91
Table 28. Chl-a to Carbon Adjustment Factors for Each Station Group ..................................................... 92
Table 29. Median predicted Chl-a concentrations (µg/L) at all stations for nitrogen and phosphorus load
reductions from 0% to 70% for the 2014-2016 model period. .................................................................... 93
Table 30. Median predicted Chl-a concentrations (µg/L) at Haw arm stations for nitrogen and
phosphorus load reductions from 0% to 70% for the 2014-2016 model period. ......................................... 93
Table 31. Median predicted Chl-a concentrations (µg/L) at Morgan and Upper New Hope stations for
nitrogen and phosphorus load reductions from 0% to 70% for the 2014-2016 model period. .................... 94
Table 32. Median predicted Chl-a concentrations (µg/L) at the Middle New Hope stations for nitrogen
and phosphorus load reductions from 0% to 70% for the 2014-2016 model period. .................................. 95
Table 33. Fraction of model predicted Chl-a concentrations above 40 µg/L at all stations for nitrogen and
phosphorus load reductions from 0% to 70% for the 2014-2016 model period. ......................................... 96
Table 34. Fraction of model predicted Chl-a concentrations above 40 µg/L at Haw Arm stations for
nitrogen and phosphorus load reductions from 0% to 70% for the 2014-2016 model period. .................... 97
Table 35. Fraction of model predicted Chl-a concentrations above 40 µg/L at Morgan and Upper
New Hope Creek stations for nitrogen and phosphorus load reductions from 0% to 70% for the 2014-
2016 model period. ...................................................................................................................................... 98
Table 36. Fraction of model predicted Chl-a concentrations above 40 µg/L at Middle New Hope stations
for nitrogen and phosphorus load reductions from 0% to 70% for the 2014-2016 model period. .............. 99

13

1. INTRODUCTION

1.1. Background

The Jordan Lake Nutrient Response Model, reported on herein, evaluates (1) the lake’s potential
for eutrophication relative to nutrient loads, streamflow patterns, and climate, for both current
conditions and future scenarios, and (2) the potential for nutrient mitigation by implementing
best management practices, regulatory measures, and restoration efforts. The model was
originally developed in 2019 as part of the Jordan Lake Nutrient Management Study organized
and funded by the North Carolina Policy Collaboratory. During 2019, a number of research
projects, referred to collectively as the Jordan Lake Nutrient Management Study, were initiated
under this provision. The resulting scientific findings were integrated into a three-dimensional
mass-balance-based simulation model of Jordan Lake (Bowen, Langley et al. 2019, Langley and
Bowen 2020). This work updates that model and performs a nutrient load reduction scenario
analysis done with the updated model.

The Jordan Lake watershed lies within the Cape Fear River basin in the Piedmont region of
North Carolina (Figure 1). The Jordan Lake Nutrient Response Model is a numerical simulation
of physical, chemical, and biological processes in the lake and underlying sediments. Water,
nutrients, and organic matter constitute input loads to the lake at its inflow boundaries. Within
the lake and its sediments, physical, chemical, and biological transformations occur under the
prevailing conditions of heat and light. The quality of water in the lake and its outflow are
transformed as a result.

1.1. Study Objectives
This report describes the setup, calibration, and scenario testing with a newly developed Jordan
Lake Nutrient Response Model. The model presented here is based on a model implementation
that originally used a five-year monitoring dataset (2014-2018) (Bowen, Langley et al. 2019,
Langley and Bowen 2020). The model was developed to test how reductions in watershed
loadings of nutrients, specifically nitrogen and phosphorus, would be expected to affect the water
quality conditions in the lake. Of primary interest were the chlorophyll-a concentrations in the
lake for various load reduction scenarios. Chlorophyll-a is an essential pigment in phytoplankton
cells that is commonly used as quantitative measure of algal abundance. A second project
objective was to better understand the interactions between the Jordan Lake watershed, the
underlying benthic sediments of Jordan Lake, and the physical, chemical, and biological
conditions in the water column of Jordan Lake.

14

Figure 1. Jordan Lake watershed within the Cape Fear River basin of the piedmont region of North
Carolina.

The work follows an earlier study (Tetra Tech 2002, Tetra Tech Inc. 2003) that used monitoring
data from 1993-2001 to create a nutrient response model of the lake as a coupled EFDC/WASP
application (Hamrick 1992, Ambrose, Wool et al. 1993). The model described in this report
takes advantage of a large amount of newly collected physical, chemical, and biological
information on the lake, and reflects the latest conditions with respect to development within the
Jordan Lake watershed. The newly developed model also takes advantage of advances in the
capabilities of mass-balance based water quality models. The Jordan Lake model developed here
utilizes a new computational model grid and an improved prediction of loadings of water and
nutrients to the lake. These advances allow for a better accounting of the short and long-term
responses that would be expected under a scenario that significantly reduces nutrient loading to
the lake.

15
1.2. Organization of the Study Report
The following section provides a description of the numerical model (EFDC) that was used as
the basis of the Jordan Lake nutrient response model. A literature review of similar EFDC
modeling projects is also provided. Some background information on Jordan Lake and a
summary of the observed chlorophyll-a concentrations for the original 2014-2018 model time
period are provided in the system description section. Specification of model input files and a
section on calibration describes the data sources used and the methods for calibrating the model
to the observed data on the physical, chemical, and biological conditions during the model time
period. A loading analysis is also presented that quantitatively compares the sources of water
and inorganic and organic forms of phosphorus and nitrogen to the lake. The next two sections
examine the model and use it to describe the functioning of the system under reduced nutrient
loading. A model evaluation section critically examines these model inputs and the model
performance during times outside those used for calibration. The model evaluation included
simulated releases of a non-reactive dye are used to determine the residence times of waters
entering the lake, and the circulation and mixing of waters in various regions of the lake.
Nutrient load scenarios look at the impacts of a broad range of possible load reductions, and
examine how that reduction differs in different regions of the lake. A discussion and conclusions
section ends the report.

16
2. MODEL SETUP

2.1. Model Description

The Environmental Fluid Dynamics Code (EFDC) model was used to model simulate the
hydrodynamics and water quality of the lake. EFDC (Hamrick 1992) is a general-purpose surface
water modeling package for simulating three-dimensional (3-D) water circulation, mass
transport, sediments and biogeochemical processes in surface waters. The graphical user
interface EFDC Explorer 8.4 (Craig 2018) was used for pre- and post-processing of data. A
number of pre and post-processing tools were also developed by the project team in Matlab.
EFDC solves numerically the three-dimensional, vertically hydrostatic, free surface, Reynold’s
averaged momentum equations for a variable-density fluid (Hamrick 1992). Turbulent kinetic
energy, turbulent length scale, salinity and temperature transport equations are also solved.
Wetting and drying of shallow areas is simulated using a mass conservation scheme (Hamrick
1992).

A 16-state variable (Table 1) version EFDC water quality model was used for this study (Tetra
Tech 2007). Five variables found in the full 21-state variable model were not included for this
study. (chemical oxygen demand (COD), total available metal (TAM), total suspended solids
TSS), and bioavailable (SA) and non-bioavailable silicate (SU). The state variables included
were able to simulate the algal dynamics using three state variables (cyanobacteria, diatoms,
green algae), nutrient dynamics using three inorganic (total phosphate, nitrate nitrogen,
ammonium) and six organic state variables (refractory and labile particulate nitrogen and

Table 1. EFDC Water Quality State Variables (Tetra Tech 2007). Abbreviations refer to constituents as
shown in Figure 2.
No. Water Quality State Variable Abbreviation Unit
1 Cyanobacteria (blue-green algae) Bc g/m3
2 Diatoms (algae) Bd g/m3
3 Green algae (others) Bg g/m3
4 Refractory particulate organic carbon RPOC g/m3
5 Labile particulate organic carbon LPOC g/m3
6 Dissolved organic carbon DOC g/m3
7 Refractory particulate organic phosphorus RPOP g/m3
8 Labile particulate organic phosphorous LPOP g/m3
9 Dissolved organic Phosphorous DOP g/m3
10 Total phosphate TPO4 g/m3
11 Refractory particulate organic nitrogen RPON g/m3
12 Labile particulate organic nitrogen LPON g/m3
13 Dissolved organic nitrogen DON g/m3
14 Ammonium NH4 g/m3
15 Nitrate nitrogen NO3- g/m3
16 Dissolved Oxygen DO g/m3

17
phosphorus, dissolved organic nitrogen), carbon cycling between algal and detrital fractions
using three additional state variables (refractory and labile particulate carbon, dissolved organic
carbon), and dissolved oxygen dynamics using one additional state variable. EFDC simulated
the spatially and temporally varying mass balance of each of these state variables and the
exchange of mass between the state variables to simulate processes in the water column such as
nutrient uptake via photosynthesis, nutrient release via respiration and predation, and nutrient
recycling between organic and inorganic forms (Figure 2).

Figure 2. Box and arrow diagram showing the EFDC water quality state variables and the mass flows
between them. See Table 1 for the constituent names and abbreviations.

Temporal and spatial variations in additional state variables (e.g., temperature, x-, y-, and z-
direction velocity) were simulated with the water-column hydrodynamic model.

A version of the EFDC code was developed by Dynamic Solutions International, LLC (DSILLC)
that simplifies the modeling process and provides links to a pre-processing and post-processing
software called EFDC Explorer. Model setup, data input, and post-processing of model results
can be performed with the EFDC Explorer graphical user interface. The model runs and post-
processing of model results can also be done with programs such as MATLAB and Python. This
project utilized a publicly-available version of the DSILLC EFDC Fortran code (version 8.5) that
was compiled using the Intel FORTRAN compiler to create executables for running on either
PCs or Macs. The model pre-processing was done either manually or with the assistance of
EFDC Explorer 8.5. Model post-processing was done with EFDC Explorer 8.5, the DSI utility
GetEFDC that converts binary output to text files, and MATLAB scripts developed by the

cxlvii

RPOC
LPOC
DOC
PO4d
PO4p
SAd
SAp
RPON
LPON
DON
NH4
NO23
DO
COD
Bc Bg Bd
RPOP
LPOP
DOP
PO4t
SU
SA
reaeration respiration
photosynthesis light TSS
FCB
Figure 3-1. A schematic diagram for the water column water quality model.
TAM
or*
* TSS from hydrodynamic model

18
project team that read, analyzed, and visualized the text-based model output from the GetEFDC
utility.

Recent reports of modeling lake hydrodynamics and water quality were reviewed in preparation
for developing the Jordan Lake model (Table 2). These reports provided technical support for
the selection of the numerous model parameters, many of which are not explicitly identifiable for

Table 2. Recent Water Quality Modeling Reports Used as a Basis for this Study.
Report and Reference Subject Specific areas considered
Tenkiller Ferry Lake EFDC Water
Quality Model (Michael Baker
2015)
EFDC model of hydrodynamics and
water quality
Water balance calibration, water
quality model parameters,
sediment diagenesis model setup
3-D Hydrodynamic and Water
Quality Model of Lake Thunderbird,
Oklahoma (Dynamic Solutions
2013)
EFDC model of hydrodynamics and
water quality
Water balance calibration, water
quality model parameters,
sediment diagenesis model setup
3-D Modeling of Hydrodynamics
and Transport in Narragansett Bay
(Abdelrhman 2015)
EFDC model of hydrodynamics and
water quality
Water balance calibration, water
quality model parameters,
sediment diagenesis model setup
Integration of a benthic sediment
diagenesis module into the 2D
hydrodynamic and water quality
model – CE-QUAL-W2 (Zhang, Sun
et al. 2015)
sediment diagenesis model
integration into CE-QUAL-W2
Sediment diagenesis model theory
and results for transient (seasonal)
periods
Falls Lake Nutrient
Response Model (Falls Lake
Technical Advisory Committee
2009, Lin and Li 2011)
EFDC model of hydrodynamics and
water quality
Consistency with local/regional
data inputs and results
High Rock Lake
Hydrodynamic and Nutrient
Response Models (Tetra Tech
2016)
EFDC model of hydrodynamics
coupled to a WASP model of water
quality
Consistency with local/regional
data inputs and results
Jordan Lake
Nutrient Response Model (Tetra
Tech 2002, Tetra Tech 2003)
EFDC model of hydrodynamics,
WASP/EUTRO model of water
quality
Consistency with local/regional
data inputs and results
Puget Sound Dissolved Oxygen
Modeling Study: Development of
an Intermediate Scale
Water Quality Model
(Khangaonkar, Long et al. 2012)
FVCOM/ CE-QUAL-ICM
model of hydrodynamics and water
quality
Water balance calibration and
water quality model parameter
selection, calibration
Total Maximum Daily Load
Evaluation for Lake Lanier
in the Chattahoochee River Basin
for Chlorophyll a (Georgia
Department of Natural Resources-
Environmental Protection Division
2017)
EFDC model of hydrodynamics and
water quality
Water balance calibration, water
quality model parameters,
sediment diagenesis model setup

19
Lake Jordan. They also provided guidance for conducting hydrodynamic and water quality
model calibrations and selecting appropriate calibration targets. Local (in-state) model reports
were also reviewed for consistency with previously reported input data and model results.

2.2. Description of Jordan Lake Basin
Jordan Lake is a physically unique lake with distinct characteristics. Some unique features
include the sharp variations in depths across the lake area, a deep and narrow section along the
Haw River Arm, and a shallow and broad section along the New Hope Arm. The Haw River
contributes the most flow into the lake, accounting for about 70 to 90 percent of the total annual
flow (NC DWQ 2007), but the Haw River Arm occupies a relatively small fraction of the lake
surface area. Most of the lake surface area is in the New Hope Creek arm of the lake but
receives only a small fraction of the lake’s inflow. As a result, there is a large difference in the
average water residence times and average water depth for the two arms of the lake. At normal
operating conditions (216 feet MSL), Jordan Lake has an area of 13,940 acres.

Another significant characteristic is the existence of a large area that alternates between wet and
dry conditions depending on the water level in the lake (Tetra Tech 2002). As water level
increases due to high inflows and precipitation, there is a significant increase in the wet area of
the lake in comparison to the normal pool level. Additionally, the influence of causeways and
natural constrictions restrict flow between sections of the lake. The influence of constrictions and
causeways across the lake caused by the Mount Carmel Church Road, U.S. 64 Highway, NC 751
Road and Farrington Road are included in this project using EFDC’s masking feature that
simulates thin flow barriers between adjoining model cells (Tetra Tech 2007, Craig 2018).

2.3. Previous Modeling of Jordan Lake
The previous modeling study performed by Tetra Tech (2002 (Tetra Tech 2002)) concluded that
algal growth in Jordan Lake is boosted by high levels of nitrogen and phosphorus input and
recycling (Tetra Tech 2002). Tetra Tech studied Jordan Lake’s response to nutrient loadings
with a linked system combining a hydrodynamic model generated by using Environmental Fluid
Dynamics Code (EFDC), which served as input to a water quality model generated by the Water
Quality Analysis Simulation Program (WASP) (Hamrick 1992, Ambrose, Wool et al. 1993). The
model was calibrated and validated to observed data between the time periods 1992-2001 (Tetra
Tech 2003).

2.4. Model Grid and Bathymetry
The first step in the model setup is the definition of the model grid. The grid should provide a
good approximation of the actual physical dimensions (morphometry) of the water body. EFDC
is set up to use a curvilinear-orthogonal grid in the horizontal plane that is stretched to provide an
approximate representation of the curvature of the actual water body. Vertical structure is
represented by specifying a fixed or varying number of vertical subdivisions for each horizontal
grid cell. CVLGrid, which is a grid generating preprocessor program alongside EFDC Explorer
modeling package and Google Earth were used to construct the horizontal model grid.
20
Bathymetric data obtained from sonar sampling in the lake by Collaboratory partners in 2019 and
LIDAR data obtained from North Carolina Flood Risk Information System (FRIS) were used to
develop and refine a new Jordan Lake model grid. Horizontal projection for the XY data used to
define shoreline and grid coordinates is UTM Zone 17 as meters. Four candidate horizontal grid
resolutions (393 cells, 407 cells, 857 cells, 1598 cells) were tested against one another with
respect to lake volume and surface area vs. elevation, model computation time for the
hydrodynamic model portion of EFDC+, and water surface elevation results. The volume and
elevation curves for each grid were calculated by EFDC Explorer and compared to data on the
lake obtained from the Army Corps of Engineers (USACE 2019). The first candidate grid (393
cells) was found to overestimate both lake volume (Figure 3) and lake surface area (Figure 4)

Figure 3. Volume-elevation curves for the 393 cell and 407 candidate model grids and the
corresponding Jordan Lake volume-elevation curve available from the Army Corps of
Engineers

for elevations above approximately 68 m. Additional spatial data were obtained from North
Carolina Flood Risk Information System (FRIS) and were combined with the bathymetry data to
update grid portions beyond the lake’s shoreline at normal pool elevation. Doing this added
another twenty cells or so, giving an updated model grid of 407 cells that more closely matched
the USACE data for water volume (Figure 3) and water surface area vs. elevation (Figure 4).
The 407-cell grid did underestimate the lake surface area for the highest end of the elevation
curve, but since the volumes were accurate to elevations above 70 m, and since these water
surface elevations were only occasionally seen in the lake, the 407-cell horizontal grid
specification was considered to be acceptable approximation to the lake’s morphometry. Two
additional grids of 857 and 1598 cells were then developed by further subdividing cells in the
407-cell grid. Hydrodynamic model testing found that, not surprisingly, the model ran much
21
more slowly using these grids as compared with the 407-cell grid and gave water surface
elevation results that varied only slightly from the 407-cell case. For these reasons the 407-cell
grid was selected for use in the Jordan Lake model.

Figure 4. Lake surface area vs. elevation for the 393 and 407 cell Jordan Lake model grids as compared
to lake surface area data available from the Army Corps of Engineers.

The Jordan Lake model grid developed from the process described above contains 407 horizontal
grid cells, with cell sizes varying from 178 m to 1105 m in long axis dimension (Figure 5).
Depth of the water column was represented with vertical layers using the SGZ (Sigma-Zed)
option available in EFDC+, with the model grid having seven minimum active vertical layers in
the shallowest portion of the lake and 25 maximum vertical layers in the deeper portion of the
lake. The maximum number of layers to use was also investigated, with candidate resolutions
between eight and 40 layers. The 25-layer model was found to have acceptable run times and
did a good job of representing seasonal temperature stratification in the deeper portions of the
lake.

The Sigma-Zed layering scheme was developed by DSI to deal with pressure gradient errors that
occur in some models that have steep changes in bed elevation (Craig 2018). This layering
scheme has some similarities to both the z-grid and sigma-grid layering systems that are used in
other models. With the Sigma-Zed scheme, depth variations across the model grid are handled by
varying the number of vertical layers (like the z-grid system). In the Sigma-Zed scheme the
number of vertical layers at a particular I,J location is, however, fixed in time and the vertical
layers are distributed uniformly across the depth (like the sigma-grid system). Reducing the
number of layers in the shallow portions of the water body also reduces the computational
22
burden in that fewer cell/layer combinations are used to define the grid compared to a sigma grid
specification. In Sigma-Zed scheme, layer thicknesses vary smoothly across the grid and vary in
time at each I,J location as the total depth for that location varies.

Figure 5. The 407 Cell Jordan Lake Model Grid with Each Cell Shown Colored by Bottom Elevation in
Meters.

The influence of causeway/bridge constrictions on inter-segment flow are also included in the
model through use of the MASK option in EFDC+. This option inserts a thin (no-flow) barrier
between adjacent cells (Craig 2018) and was used to represent the constrictions and causeways at
the Mount Carmel Church Road, U.S. 64 Highway, NC 751 Road and Farrington Road (Figure
6). Additional masks were also placed along two side arms of the New Hope Creek arm of the
lake (Figure 6).

23

Figure 6. Mask location on Jordan Lake 407 cell model grid.

Another characteristic of Jordan Lake is the presence of areas that become wet or dry depending
upon the water level in the lake. The model utilizes the wetting/drying option available in
EFDC+ with the wet depth specified as 0.1 meters and dry depth as 0.06 meters. Model cells that
contain water at normal pool are shown in blue in Figure 7, while cells that are dry at normal
pool are shown in grey. As will be seen in the section describing surface water input locations
(section 3.1.2) freshwater inputs were specified using cells that are always wet.
24
Figure 7. Jordan Lake wet and dry model cells.

2.5. Monitoring Data Used for Model Setup and Calibration

2.5.1. Data Sources
Model input data sets and observed data sets used for calibration and validation, and model
scenario testing were developed using observed data gathered from various agencies (Table 3).
Time series data including flow rates, flow temperature and pollutant loading from the drainage
areas, withdrawals from water supply intakes and releases at the dam, meteorological and wind
forcing data, and atmospheric and benthic deposition of nutrients were obtained for the original
model period of 2014 to 2018. Flow input data were obtained from US Geological Survey
(USGS) gages and US Army Corps of Engineers, meteorological and wind forcing data were
obtained from North Carolina State Climate Office (NCSCO) and National Oceanic and
Atmospheric Administration (NOAA). Atmospheric deposition of nutrients were obtained from
National Atmospheric Deposition Program (NADP) and Clean Air Status and Trends Network
(CASTNET) for nitrogen with phosphorus estimated from annual average N/P ratios for
atmospheric deposition of N and P (Willey and Kiefer 1993). Benthic deposition of nutrients for
the flux specified simulations were obtained using limited sampling data from Collaboratory
partners and model results for sediment fluxes from other lake models that included sediment
25
diagenesis (Dynamic Solutions 2013, Abdelrhman 2015, Michael Baker 2015). Water
temperature time series were derived from a regression of measured air temperatures and water
temperatures at NCDEQ water quality stations. Time histories of nutrient loading were derived
with a WRTDS watershed model (Hirsch and De Cicco 2015) developed for the Jordan Lake
watershed (Del Giudice, Aupperle et al. 2019).

Table 3. EFDC Input Files and Data Sources.
EFDC Input
Filename
Description of Data Contained in
File
Data Sources
QSER.INP Flow time series data at flow
specified model boundaries and
point source locations
US Geological Survey (Haw River,
creeks), US Army Corps of Engineers
(water treatment plant, dam
outflows)
ASER.INP Meteorological time series data (air
temp, dewpoint temp, relative
humidity, short-wave solar radiation,
precipitation, cloud cover)
North Carolina State Climate Office
(NCSCO) and National Oceanic and
Atmospheric Administration (NOAA)
TSER.INP Temperature time series data at all
model boundaries and point source
inputs
North Carolina Department of
Environmental Quality (NC DEQ) and
US Geological Survey
WSER.INP Wind time series data for magnitude
and direction
North Carolina State Climate Office
(NCSCO) and National Oceanic and
Atmospheric Administration (NOAA)
DXDY.INP Horizontal cell lengths, widths,
depths, bottom roughness
Collaboratory Partners (Bathymetry),
North Carolina Flood Risk Information
System (FRIS) and US Army Corps of
Engineers (Lake elevation at the start
of each model phase)
LXLY.INP Horizontal cell size location,
orientation relative to E-W, N-S
direction
Google Earth (UTM Zone 17 ) and
CVLGrid (Craig 2018)
WQBENMAP.INP,
BENFLUX.INP
Map of benthic nutrient and DO flux
zones, specification of NO3, NH4,
PO4, and DO flux time histories by
zone (flux specified runs only)
Calibrated values w/ information
from Collaboratory Partners (NO3 and
DO benthic flux measurements), and
recent lake model studies (Table 2)
TEMP.INP Initial condition for temperature for
every model cell and layer
created with a model spin up run,
using the EFDC restart option
CWQSRXX.INP (XX
indicates constit.
number)
Time series concentration boundary
condition at flow specified
boundaries
Collaboratory partners (TN and TP
loading), DWR data on N speciation,
MATLAB script used to create files as
described in Cape Fear Model Report
(Bowen, Negusse et al. 2009)

An extensive water quality monitoring dataset was available to support the model. Water quality
data are available at eighteen stations (Table 4) across the lake. One of these stations
(CPF086D) only had water quality data for 2017, so it was therefore not used for calibration or
load reduction scenario purposes, but had previously been used in the original implementation of
the model. The Jordan Lake data were collected by the NC Division of Water Resources and
made available to this study as a Microsoft Access database. Water quality parameters from the
26
database that were used for this study included temperature profiles, and grab samples analyzed
for nitrate+nitrite (NOx), ammonia (NH4), total phosphorus (TP), total Kjeldahl nitrogen (TKN),
chlorophyll-a (chl-a), and dissolved oxygen (DO). Stations were present in all regions of the
lake (Figure 8).

Table 4. Monitoring Stations Used to Calibrate the Jordan Lake Model.
No. Description

Grid I, J Station
Latitude in
degrees
Longitude
in degrees
1 Jordan Lake Dam 23, 6 Dam 35.6548 -79.0672
2
Jordan Lake above Stinking Creek Near
Pittsboro, NC
17, 6
CPF055C 35.6913 -79.0791
3 Jordan Lake in Haw River Bay Arm Upstream 15, 6 CPF055C1 35.6988 -79.0820
4 Jordan Lake in Haw River Bay Arm NE 16, 7 CPF055C2 35.6955 -79.0761
5 Jordan Lake in Haw River Bay Arm NW 16, 6 CPF055C3 35.6932 -79.0830
6 Jordan Lake in Haw River Bay Arm SE 17, 7 CPF055C4 35.6899 -79.0756
7 Jordan Lake in Haw River Bay Arm SW 17, 5 CPF055C5 35.6867 -79.0841
8 Jordan Lake in Haw River Arm Bay Downstream 18, 6 CPF055C6 35.6822 -79.0780
9 Jordan Lake in Middle of Haw River Arm 20, 6 CPF055D 35.6725 -79.0772
10 Jordan Lake above Dam Near Moncure, NC 22, 6 CPF055E 35.6600 -79.0700
11
Jordan Lake Downstream Crooked Creek, New
Hope Arm
18, 42
CPF081A1B 35.8365 -78.9763
12 Jordan Lake @ Mouth of New Hope Creek 18, 37 CPF081A1C 35.8162 -78.9868
13
Jordan Lake @ Mouth of Morgan Creek Near
Farrington
15, 35
CPF086C 35.8215 -78.9974
14 Jordan Lake In Upstream 11, 34 CPF086CUPS 35.8382 -79.0014
15
Jordan Lake, Downstream Morgan, New Hope
Creek Arm
18, 35
CPF086D 35.8095 -78.9974
16 Jordan Lake Near Farrington, NC 18, 31 CPF086F 35.7970 -79.0108
17 Jordan Lake at Buoy #9 Near Merry Oaks, NC 18, 25 CPF087B3 35.7652 -79.0260
18
Jordan Lake @ Mouth White Oak Creek Near
Seaforth, NC
18, 21
CPF087D 35.7386 -79.0242
19
Jordan Lake Near Mouth Beaver Creek Near
Merry Oaks, NC
19, 14
CPF0880A 35.6965 -79.0436

27
Figure 8. Locations of 18 Jordan Lake Monitoring Stations Sampled by the NC Division of Water
Resources. The newly developed 407 cell EFDC model grid is also shown.

2.5.2. Data for Nutrient Response, Flow, and Temperature Models
In the Jordan Lake nutrient response model, chlorophyll-a data are used as a measure of the
cumulative abundance of the three state variables (cyanobacteria, diatoms, and green algae)
collectively representing the phytoplankton biomass. The spatial and temporal dynamics in the
data are used to calibrate the algal growth kinetic parameters in the model. North Carolina also
uses chlorophyll-a as a numeric water quality criteria (NC Division of Water Resources 2017).
The current approved regulatory text for the State’s chlorophyll-a criteria, located at 15A NCAC
02B .0211(4), states:

Chlorophyll-a (corrected): not greater than 40 ug/l for lakes, reservoirs, and other
waters subject to growths of macroscopic or microscopic vegetation not designated as
trout waters, and not greater than 15 ug/l for lakes, reservoirs, and other waters subject
28
to growths of macroscopic or microscopic vegetation designated as trout waters (not
applicable to lakes or reservoirs less than 10 acres in surface area).

A waterbody is considered impaired if there is a 90% confidence that more than 10% of the
photic zone average chlorophyll measurements are above the regulatory limit, in this case 40
ug/L (NCDWR 2018).

Based upon a review of the chlorophyll-a monitoring data collected over the five-year (2014-
2018) model time period, every one of the eighteen monitoring stations exceeded the 40 ug/L
more than 10% of the time (Table 5). The 90th percentile (the value exceeded exactly 10% of

Table 5. Analysis of 2014 – 2018 Photic Zone Chl a Measurements at selected stations within four
regions of Jordan Lake.
Lake Region Station
Number of
Chl a
samples
Chl a median
concentration
(µg/L)
90th
percentile
Chla-a
concentration
(µg/L)
Reduction needed
for 90th percentile
Chl-a
concentration at
40 µg/L
Haw River CPF055C 74 29.0 63.7 37%
CPF055D 72 25.0 44.9 11%
CPF055E 73 28.0 44.0 9%
Above CPF081A1C 74 57.5 90.4 56%
Causeways CPF086C 74 58.5 89.0 55%
CPF086F 74 52.5 81.7 51%
Between
Causeways CPF087B3 74 34.0 52.4 24%
CPF087D 74 29.5 53.0 25%
Below
Causeways CPF0880A 74 28.0 42.0 5%
Jordan Lake
All 18
Stations 1004 36.0 72.0 44%

the time) photic zone chlorophyll-a concentration for all eighteen stations considered collectively
(1004 measurements total) for the 2014-2018 time period was 72.0 ug/L (Table 5). An overall
reduction in chl-a concentrations of 44% would be needed to lower the 90th percentile
chlorophyll-a concentration to the regulatory limit of 40 ug/L.

Frequently sampled stations in each of the four regions of the lake also had 90th percentile
chlorophyll-a concentrations above the criteria value, but the magnitude of the exceedances
varied significantly from region to region. The one station in the below causeways region of the
New Hope Creek arm of the lake exceeded the 90th percentile by only 5% (74 samples total). In
the above causeways region of the New Hope Creek arm of the lake, all three stations exceeded
29
the 40 ug/L by more than 50% (Table 5). The other two regions of the lake (Haw River,
between causeways had exceedance levels between these two extremes.

Temperature data for the lake’s surface water input locations (specified in the TSER.INP file)
were not available at a frequency necessary for the model, so a data analysis procedure was
developed to provide the temperature time series at surface water input locations. Regressions
between measured air temperatures at one meteorological station and stream water temperatures
measured roughly semi-monthly as part of routine monitoring in the Jordan watershed were
developed to estimate a daily water temperature time series for all surface water inflow locations.
This approach was considered superior to having the model interpolate the temporally sparse
water temperature record at these locations. Maximum and minimum daily air temperatures for
the weather station at Chapel Hill (USC00311677 CHAPEL HILL 2 W, NC US) from 2008 to
2018 inclusive were retrieved. Since the intent was to predict water temperatures, measured air
temperatures were processed by (a) replacing maximum and minimum air temperatures less than
32 deg F with 32.5 deg F, (b) averaging these maximum and minimum values for each day, and
finally (c) calculating an average value for each day as the average of the current day and the two
previous days. The time averaging of daily air temperatures was done to simulate the temporal
averaging of water temperatures that occurs in the streams. These final air temperature averages
were regressed against the measured water temperatures at each water quality station, where
approximately 220 daily water temperature measurements over the period 2008 to 2016 were
available. The regression equations are 2nd order polynomials with non-zero y intercepts. The
three regression constants a, b, and c (corresponding to the equation: y = ax2 + bx + c where x =
time-averaged air temperature and y = water temperature) were used to estimate water
temperatures for the original simulation period (2014-2018) based on the time-averaged air
temperature time series.

2.5.3. Nutrient Loading Analysis
A nutrient loading analysis based on the model’s input files was used to quantify the relative
nutrient load contributions from the external and internal sources of pollutants to the lake for the
original model period 2014 – 2018 (Bowen, Langley et al. 2019). The external sources include
the surface water inputs and wet and dry atmospheric deposition, while the internal sources
include the benthic fluxes of inorganic nutrients across the sediment-water interface of the lake.
A flow analysis was also used to quantify the inflow and outflow contributions to the lake for the
model period as rates (m3/day). This analysis found that the Haw River accounts for over 75% of
the inflows while the dam outflow accounts for over 95% of the outflows for the model period
(Figure 9, a & b). The nutrient loading analysis indicated that the majority of nutrients entered
the lake from the Haw River arm, and these nutrients entered majorly in organic forms that were

30
Figure 9. Pie charts showing 2014-2018 daily average inflows (a), daily average outflow (b), daily
average total phosphorus (TP) load (c), and daily average total nitrogen (TN) load for Jordan
Lake, NC.

not immediately bioavailable. The Haw River arm accounted for over 65% of total nitrogen and
55% of total phosphorus loads into the lake (Figure 9, c & d). Benthic sediments were a
significant source of bioavailable nutrients, providing more than 40% of phosphate and 85% of
ammonia to the lake (Figure 10, a & b). Benthic sediments also acted as a major sink for the
particulate fraction of organic nutrients, nitrate (Figure 10, c & d), and dissolved oxygen.
Atmospheric deposition was a relatively minor source of nutrients to the lake, accounting for less
than 5% of the total nitrogen. Through model simulated dye tracer studies, it was observed that
only a small fraction of nutrient inputs from the Haw River arm moved up into the upper reaches
of the New Hope Creek arm of the lake on a long-term basis (see section 5.3 and Table 26).
Some episodic high flow events were observed by Collaboratory partners to transport Haw River
water well upstream into the New Hope Creek arm of the lake (UNC Policy Collaboratory 2019),
but the long-term effect of these events appears to be limited with respect to loading of water and
nutrients in the New Hope Creek arm of the lake.
a b
d c
31
Figure 10. Pie charts showing 2014-2018 daily average ammonia load (a), daily average phosphate load
(b), and the daily average nitrate load (c) for Jordan Lake NC. Panel d shows the daily
average nitrate load for the New Hope Creek arm of the lake.

The majority of water and nutrients to the New Hope Creek arm of the lake (Figure 10 d, Figure
11) are provided by the local surface water sources (Morgan, New Hope, Northeast, and other
smaller creeks). For certain inorganic forms of nitrogen and phosphorus, benthic sediments were
the major supply source to the water column in the New Hope arm of the lake, providing more
than 75% of the phosphate and 90% of ammonia (Figure 11, a & b).

Figure 11. Pie charts showing the 2014-2018 daily average ammonia (a) and phosphate (b) load into the
New Hope Creek arm of Jordan Lake, NC.

d
-
c
a b
32
3. SPECIFICATION OF MODEL INPUT FILES

The EFDC lake model requires the specification of external boundary data to execute the
hydrodynamic and water quality simulations. In the initial phase of model development (Bowen,
Langley et al. 2019, Langley and Bowen 2020), time series data including flow, water
temperature, and constituent concentration from the drainage areas, withdrawals from water
supply intakes and releases at the dam, meteorological and wind forcing data, and atmospheric
and benthic deposition of nutrients were assembled for the original model period 2014 to 2018.
Three EFDC simulations were accomplished using three separate model time periods: 2014-
2015, 2016, and 2017-2018 (Bowen, Langley et al. 2019, Langley and Bowen 2020). Two time
periods were used for calibration and scenario testing (2014-2015, 2017-2018) and a third (2016)
for model verification.

For the work presented here it was decided to use only one of the three available model time
periods. As described in the model calibration chapter (Section 4) a model time period beginning
January 1, 2014 and ending February 19, 2016 was used for calibration and scenario testing. The
2014-2016 time period is far superior to the others in the number of water temperature and
chlorophyll-a measurements available for comparing to model predictions and was therefore
used for both calibration and scenario testing (see Table 23 and Table 24 for a comparison of
the number of available temperature and chl-a measurements for the three time periods). In this
section the data sources and analyses performed to develop the model’s initial conditions,
boundary condition time series, and calibration data used are presented.

3.1. Jordan Lake Riverine Inputs and Surface Water Outputs

3.1.1. Riverine Input Flow Specification
Flows to and from the lake are provided in the QSER.INP file. Fifteen inputs and outputs were
specified in the Jordan Lake model domain to represent 13 tributaries, one outflow as withdrawal
for water supply and the outflow at the dam. Inflow data were obtained from USGS gages for 5
stations, Haw River near Bynum USGS Gage 02096960, Morgan Creek USGS Gage 02097517,
New Hope Creek USGS Gage 02097314, Northeast Creek USGS Gage 0209741955 and White
Oak Creek USGS Gage 0209782609. These data were adjusted to account for additional
drainage area from the gage stations to the inflow mouth into the lake. Outflow data were also
obtained for the dam using the Haw River at Moncure USGS Gage 02098206. Additionally, to
represent ungaged inflows into the lake, a flow balance was estimated using all gaged inflows,
rainfall, and all outflows and evaporation. The flow balance was computed to minimize the
difference between measured versus modeled lake water levels over time. The resulting ungaged
composite flow was distributed to 8 boundary inflow locations to represent the remaining
tributary inflow. A total of fifteen surface water inflows or outflow (Table 6) hydrographs were
specified at point source locations throughout the lake (Figure 12).

33
3.1.2. Adjustment of Flows from Ungaged Watersheds and the Dam
Approximately 47% of the “non-Haw” Jordan Lake watershed is ungaged (Miller, Karimi et al.
2019). Since the original development of the Jordan Lake model (Bowen, Langley et al. 2019,
Langley and Bowen 2020) model development work has been done to better simulate water level
fluctuations in the lake. Out of a total of 15 surface water inputs and outputs to the lake
(quantified in the QSER.INP file), eight are from watersheds that are ungaged (Table 6). A
majority of this ungaged watershed area lies in the New Hope Creek arm of the lake (Miller,
Karimi et al. 2019).

Table 6. Specification of Surface Water Inflow or Outflow Time Series in the QSER.INP file
Name Description Data Source
QSER1 Haw R. @ Jordan Lake USGS Gage 02096960, Haw River at Bynum
QSER2 Morgan Ck. @ Jordan L. USGS Gage 02097517, Morgan Creek
QSER3 New Hope Ck. @ Jordan L. USGS Gage 02097314, New Hope Creek
QSER4 Northeast Ck. @ Jordan L. USGS Gage 0209741955, Northeast Creek
QSER5 White Oak @ Jordan L. USGS Gage 0209782609, White Oak Creek
QSER6 Ungaged, Lake Segment 4, west Ungaged Composite
QSER7 Outflow @ Dam USGS 02098206, Haw River at Moncure
QSER8 Ungaged, Lake Segment 3, west Ungaged Composite
QSER9 Ungaged, Lake Segment 3, east Ungaged Composite
QSER10 Ungaged, Lake Segment 2, west Ungaged Composite
QSER11 Ungaged, Lake Segment 2, east Ungaged Composite
QSER12 Ungaged, Lake Segment 1 Ungaged Composite
QSER13 Cary Withdrawal USACOE Operational Records
QSER14 Ungaged, Lake Segment 4, east Ungaged Composite
QSER15 Ungaged, Lake Segment 4, north Ungaged Composite

Two surface water outflows (QSER7, QSER13) are found at a water treatment plant in the lower
half of the New Hope Creek arm of the lake and at the dam that forms the lake (Figure 12). The
dam outflow is an estimated value using a downstream USGS gage at Montcure (Table 6). A
daily hydrograph of the withdrawal from the water treatment plant was provided by the US
Army Corps of Engineers.

34
Figure 12. Model flow boundary locations.

In the earlier version of the Jordan Lake model, hydrographs of the ungaged watersheds used the
White Oak hydrograph that were scaled based upon the relative watershed areas (Bowen,
Langley et al. 2019, Langley and Bowen 2020). A second iteratively adjusted, but not time
varying scaling factor was then applied to the inflows of the ungaged watershed to improve
agreement with the observed long-term water surface elevation record. This method minimized
the long-term (multi-year) mean error between observed and model predicted water level in the
lake but produced errors of more than 0.5 m for extended time periods of time (Figure 13)
(Bowen, Langley et al. 2019, Langley and Bowen 2020).
35

To improve the simulation of water surface elevation, a new method was developed to specify
flow adjustments for the ungaged watersheds and for the dam outflows that were based on a
water balance that was applied on a daily time scale. Adjustment of the dam outflow along with
the ungaged watershed inflows was considered to be a reasonable approach given the results of
the previous modeling work and the use of the downstream gaging station for the dam outflow
estimate. Additionally, this approach has been used in other recent reservoir models (Michael
Baker 2015).

The water balance analysis was used to make adjustments in the ungaged watershed inflows and
the dam outflow hydrograph to match the daily overall water balance determined from changes
in observed water surface elevation at the dam. Observed daily changes in lake water level were
combined with the volume vs. elevation curve for the model grid that was available from EFDC
Explorer package to estimate the daily change in lake volume. A Matlab script was written to
read the existing water temperature and meteorological data from the EFDC input files, and then
estimate evaporation using the Ryan-Harleman method (Rosenberry, Winter et al. 2007). All
water inflows and outflows were then available to estimate the net volume of water added to the
lake on a daily basis. An overall adjustment flow was then calculated so that the daily change in
lake volume based upon water surface changes agreed with the sum of the net inflows to the
lake. A second Matlab script (AddEVAP2ASER) also rewrote the ASER.INP to add the
evaporation time history to the model input. The Jordan Lake model than used this evaporation
information as a model input when predicting the water surface elevations rather than calculating
evaporation internally.

As described above, with all the inflows and outflows estimated, the lake water balance
calculation was used to determine a daily adjustment flow that would exactly match the observed
water surface elevation. Not surprisingly and probably due to measurement error in the many
Figure 13. Time history comparisons of observed and model predicted water surface elevations at the
Jordan Lake dam for the 2014-2016 model time period (left panel) and the 2017-2018 time
period (right panel). From: (Bowen, Langley, & Adeyeye, 2019).
36
inputs used, this adjustment flow was found to vary widely from day to day with negative and
positive values that were occasionally unrealistically small or large given the magnitude of the
inflows and outflows. To remedy this, a moving average was applied to the adjustment flow to
reduce the temporal variations in the adjustment flow. A thirty-one-day time window was
chosen iteratively to smooth out the adjustment flows. A limit was also imposed on the extent to
which the flows would be adjusted. A series of model tests used limits from 10% to 200% of the
inflow or outflow. A limit of 20% was selected to be used for flow adjustment. As will be seen
in the results section these changes improved greatly the prediction of water surface elevation
compared to the original Jordan Lake model, and these improvements were made with relatively
minor adjustments to the dam outflows. Over the entire model time period the outflow was
increased by 0.71%. The average of the magnitude of the difference between the modified and
unmodified outflows at the Dam was 14.4% over the entire model period.

3.1.3. Temperature and Concentration Specification
Each tributary inflow contributes heat energy, and thus the temperature balance in the lake is
dependent on these inflow temperatures. Water temperature data were obtained from 5 NC WQ
stations for 2008-2018, B4050000 Haw River below Jordan Dam near Moncure, B2100000 Haw
River near Bynum, B3660000 Northeast Creek near Nelson, B3040000 New Hope Creek near
Blands, B3900000 Morgan Creek near Farrington. The regression equations described in Section
2.5.2 were used to estimate water temperatures at water inflow boundaries to the Lake model for
the simulation period from 2014 to 2016. The time histories for these data are specified in the
TSER.INP file.

The water quality lake model requires the definition of water quality loading time series for each
of the external flow series entering the lake (Table 6). The lake model is forced with time series
for each flow input and for each water quality state variables (Table 7). The water quality model
as developed here had two algal groups (cyanobacteria and diatoms) eliminating the green algae
group from the original model, nine organic matter constituents (three each of organic nitrogen,
organic phosphorus, and organic carbon), three 3 inorganic nutrients (NOx, NH4, PO4), and
dissolved oxygen. Nutrient fractionation estimates for the model were based upon calculated
long-term averages of tributary concentrations that were determined on a monthly basis. A
transformation matrix approach was used to estimate the concentration time history of each
constituent needed by the model (see Appendix 1). Flows from the various ungaged watersheds
were scaled according to the individual watershed areas. The overall flow from ungaged
watersheds was estimated as described in section 3.1.2. Concentrations for the ungaged
watershed used the White Oak Creek data. The time history concentrations for these data are
specified in the CWQSR.INP files.

37
Table 7. State variables for water quality model
Description Units
Cyanobacteria mg/L
Diatoms mg/L
Green Algae mg/L
Refractory POC mg/L
Labile POC mg/L
Dis Org Carbon mg/L
Ref Part Org Phosphorus mg/L
Lab Part Org Phosphorus mg/L
Dis Org Phosphorus mg/L
Total Phosphate mg/L
Ref Part Org Nitrogen mg/L
Lab Part Org Nitrogen mg/L
Dis Org Nitrogen mg/L
Ammonia Nitrogen mg/L
Nitrate Nitrogen mg/L
Dissolved Oxygen mg/L

3.1.3.1. Estimation of Dissolved and Organic Phosphorus Concentration
A fundamental challenge in developing the time series of nutrient inputs was the lack of
dissolved inorganic phosphorus (sometimes called total phosphate, soluble reactive phosphorus,
or bioavailable phosphorus) in the monitoring data set. Although there are four state variables
for phosphorus in EFDC (labile and refractory organic, dissolved organic, and total phosphate,
(Table 7) only total phosphorus was available in the monitoring data set. The situation was
better for nitrogen, with four separate analytes in the monitoring data (total nitrogen (TN),
nitrate+nitrite, ammonia, Kjeldahl nitrogen). In the original Jordan Lake model (Bowen,
Langley et al. 2019, Langley and Bowen 2020)) total organic phosphorus was estimated by first
estimating organic nitrogen with the measured ammonia and Kjeldahl data. Total organic
phosphorus (TOP) was then predicted using a total organic N to total organic P (TON/TOP)
ratio that is assumed to be constant and equal to the Redfield N:P ratio of 16:1 on a molar basis
or 7.2:1 on a mass basis (Redfield 1958). Dissolved inorganic phosphorus (DIP) was then
calculated as the difference between total phosphorus (TP) and total organic phosphorus (TOP).

Additional research (Bowen, Fraleigh et al. 2022) critically examined the method used to predict
dissolved inorganic phosphorus (DIP) in the Jordan Lake model and investigated whether a
superior alternate approach existed. A relatively recent EFDC model study of Tenkiller Lake in
38
Oklahoma (Michael Baker 2015) provided an ideal EFDC input data set for the investigation.
The Tenkiller Lake model dataset had time history data for all of the nitrogen and phosphorus
EFDC state variables for all surface waters entering the lake. Two alternate prediction methods
were examined: 1) a temperature dependent total organic nitrogen to phosphorus (TON/TOP)
ratio, and 2) a temperature dependent DIP/TP ratio. Adding temperature as a predictor variable
was thought to provide a surrogate for seasonal effects on the nutrient ratios.

The Illinois time history data were regressed to determine the temperature dependent TON/TOP
ratio (Figure 14, top panel) and the temperature dependent (DIP/TP) ratio (Figure 14, bottom
panel). The regression parameters were then used to predict DIP using either the TON/TOP ratio
or the DIP/TP ratio. These predicted DIP concentrations determined with the two temperature
dependent ratios were then plotted against the actual DIP concentration time histories to compare
the alternate approaches to DIP prediction (Figure 15). The existing DIP prediction method
using the fixed TON/TOP (Redfield) ratio was investigated in a similar way by comparing
predicted DIP using the method to the actual DIP time history (Figure 16).

All three methods did a good job estimating DIP based upon the analysis of Tenkiller Lake
EFDC input files, but there were some differences between the methods. The two methods using
the TON/TOP ratio-based estimation seemed to perform better than the method using the DIP/TP
ratio (Figure 15). Either of the TOP/TON ratio methods did an excellent job predicting DIP
(Figure 15, Figure 16), although it seems that the temperature dependent method might perform
slightly better when the regressions were forced to a zero y-intercept value. This method was
adopted to estimate DIP in the model.
39
Figure 14. Relationship between the TON/TOP ratio vs. temperature (top panel) and the inorganic P/TP
ratio vs. temperature (bottom panel) in the EFDC input files for Tenkiller Lake, OK.

40
Figure 15. Predicted vs. actual DIP using the temperature variable P/TP ratio (top panel) and the
temperature variable TON/TOP ratio (bottom panel) in the EFDC input files for Tenkiller
Lake, OK.
41

Figure 16. Predicted vs. actual DIP using the fixed TON/TOP ratio using the Redfield ratio value in the
EFDC input files for Tenkiller Lake, OK.

3.1.4. Water Treatment Plant Outflows
Outflow data for the Cary/Apex Water Treatment Plant intake were obtained from USACE
website for Lake Jordan (https://epec.saw.usace.army.mil/jord.htm).

3.2. Benthic Inputs

Nutrient fluxes to and from the underlying sediment in a water body influence the nutrient levels
in the water column. Sediment denitrification rates and sediment oxygen demand measurements
were made in the Jordan Lake in 2019 at six lake locations, above the causeways, between the
causeways, below the causeways and along Haw River arm by Collaboratory partners (Mike
Piehler, personal communication) were used to guide the calibration of the benthic flux
specification. The EFDC water quality model provides three options for defining the sediment-
water interface fluxes for nutrients and dissolved oxygen. The options are: (1) externally forced
spatially and temporally constant fluxes; (2) externally forced spatially and temporally variable
fluxes; and (3) internally coupled fluxes simulated with the sediment diagenesis model. The
water quality state variables that are controlled by diffusive exchange across the sediment-water
42
interface include phosphate, ammonia, nitrate, silica, chemical oxygen demand and dissolved
oxygen. Option 3 provides the cause-effect predictive capability to evaluate how water quality
conditions might change with the implementation of alternative load reduction or management
scenarios. However, Option 2 was selected for the Jordan Lake model as Option 3 required a
calibrated water column model of 5 years simulation time with sediment nutrient fluxes included.
Numerous model iterations at very short time steps would be necessary to achieve calibration.
Therefore, temporally and spatially varying benthic flux specification (Option 2) was selected as
it provided for relatively fast model runt times and reliable results that are consistent with
calibration targets in the water column. A review of model results for sediment fluxes from
other EFDC models that included sediment diagenesis combined with the limited benthic
exchange data obtained from Collaboratory partners were used as a guide to estimate time series
for spatially and temporally variable fluxes in the Jordan Lake model. Sediment flux spatial
variability has been attributed to characteristics of the water quality zones (i.e., lacustrine,
riverine, or transition) and sediment particle size distribution (sand, silt, clay). Temporal
variability is mainly attributed to seasonal temperature trends.

In the Jordan Lake model the prescribed fluxes were determined primarily by calibration. The
available measured values were used as a starting point for determining the approximate time-
averaged flux and estimating differences between the two arms of the lake. Temporal variations
were considered by calculating temperature correction factors using an appropriate theta value
(1.08) for biologically mediated processes (Chapra 2008). An approximate spatially averaged
bottom water temperature time history was determined from the measured temperatures in the
lake. This time history was used with the theta value to calculate temperature based correction
factors (Table 8). From there a time-average value was determined for the New Hope Creek and

Table 8. Temporally Varying Temperature Corrections for Benthic Fluxes
JDAY Date temp (deg C)
Temp Corr
(Kc)
1065 12/1/13 10.00 0.46
1096 1/1/14 8.67 0.42
1155 3/1/14 6.00 0.34
1339 9/1/14 25.00 1.47
1430 12/1/14 10.00 0.46
1520 3/1/15 6.00 0.34
1704 9/1/15 25.00 1.47
1795 12/1/15 10.00 0.46
1826 1/1/16 8.67 0.42
1886 3/1/16 6.00 0.34

Haw River arms of the lake as part of the water quality model calibration process (Table 9). The
time history benthic flux information is specified in the BENFLUX.INP file (see Appendix 16
43
for base case version of BENFLUX.INP). A spreadsheet (benflux.xlsx) was used to calculate the
time varying nutrient fluxes.

Table 9. Time average benthic nutrient fluxes to the water column.
Location
PO4 flux
(g/m2/d)
NH4 flux
(g/m2/d)
NO3 flux
(g/m2/d)
SOD
(g/m2/d)
New Hope Creek 0.0024 0.0008 -0.0995 -1.6716
Haw River 0.0012 0.0004 -0.0497 -0.8358

3.3. Meteorological Inputs
The lake model requires information on meteorological conditions in the modeled region. The
time history for these data are specified in the ASER.INP and WSER.INP files. The required
data includes atmospheric pressure, air temperature, relative humidity, precipitation, evaporation
rate, solar radiation, cloud cover, wind speed, and wind direction. The data utilized for the
model are obtained from the Raleigh Durham (RDU) International Airport Station (WBAN
13722) through North Carolina State Climate Office (NCSCO) and National Oceanic and
Atmospheric Administration (NOAA) for the model period used originally for the Jordan Lake
model (2014 to 2018).
• Daily precipitation data obtained from RDU are assigned as a rate in inches per hour for
the model time period. The EFDC model has an option to include a conversion factor to
handle various rainfall units.
• Dry bulb temperature and wet bulb temperatures are obtained from RDU in degree
Fahrenheit and converted to degree Celsius for the model period.
• Atmospheric pressure data were obtained from NOAA for RDU in inches of mercury and
converted to millibars for the model period.
• Wind speed in miles per hour (mph) and wind direction in degrees were obtained from
NCSCO for RDU. Wind speed was converted to meters per second (m/s) and wind
direction was utilized in degrees but converted in the direction the wind is blowing from
and applied the lake model.
• Cloud cover values are calculated from sky condition codes and NOAA sky cover rating
(from 0-8) obtained for RDU weather station. Sky condition codes and NOAA sky cover
ratings were converted to a W2 cloud cover (from 0-10), with zero indicating clear skies
and ten indicating cloudy skies (Table 10) for the model period. This method has been
utilized in previous water quality models to estimate cloud cover (Bowen and Harrigan
2017).
• Solar Radiation is calculated from cloud cover data using the method from CE-QUAL-
W2, version 3 (Cole and Wells 2006) adapted into a MATLAB Code. The values are
obtained in watts per meter square (Watts/m2) and applied to the model domain.
• Atmospheric deposition has been observed to be an important source of inorganic
nutrients to a water body (NC DWR, 2016). Atmospheric deposition is represented in the
44
EFDC model with separate source terms for dry deposition and wet deposition. Dry
deposition is defined by a constant mass flux rate (as g/m2day) for constituents that settles
as dust during a period of no rainfall. Wet deposition is defined by a constant
concentration (as mg/L) of a constituent in rainfall and it utilizes the time series of
precipitation assigned in the ASER.INP file for input to the hydrodynamic model. The
Jordan Lake model is driven by specification of constant wet and dry atmospheric
deposition of ammonia as nitrogen, and nitrate as nitrogen obtained through 2018 from
the National Atmospheric Deposition Program (NADP) National Trends Network (NTN)
from Finley Farm station NC41, and Clean Air Status and Trends Network (CASTNET)
Site ID RTP 101 located in Research Triangle Park, Durham, NC. Since phosphorus
deposition data were unavailable for the CASNET and NADP sites, dry and wet
deposition of Phosphate was estimated by CASTNET and NADP data for nitrogen with
annual average N/P ratios for atmospheric deposition of N and P from Willey and Kiefer
(1993).

Table 10. Sky condition observations and corresponding W2 cloud cover input.
Condition CODE NOAA Sky Cover W2 Cloud Cover
(out of 8) (out of 10)
Clear Sky CLR 0 0
Few Clouds FEW 1 — 2 1.25
Scattered Clouds SCT 3 — 4 4.38
Broken BKN 5 — 7 7.5
Variable VV 8 10
Overcast OVC 8 10

45
4. MODEL CALIBRATION
Model calibration was undertaken in two phases: hydrodynamic and water-quality calibration.
In both cases, the lake’s model parameters are varied in order to produce the best agreement
between model predictions and observed data collected at 17 NC DWR monitoring stations and
stage observations by USACE at the dam (Figure 8, Table 4). This section describes the steps
taken for the hydrodynamic and water quality calibration of the Jordan Lake model. For the
hydrodynamic model, predictions from the model are calibrated to observations for water level
and water temperature for both bottom and surface layers. Water quality calibration will focus on
chlorophyll-a, nutrients, and dissolved oxygen. A total of six water quality parameters
(chlorophyll-a (chl-a), nitrate+nitrite (NOx), ammonia (NH4), total Kjeldahl nitrogen (TKN),
total phosphorus (TP, and dissolved oxygen (DO)) are available in the monitoring data set and
are therefore available to be calibrated to the corresponding model predictions, Calibration was
accomplished first using only the hydrodynamic model, comparing model predicted water
surface elevations and temperatures to corresponding values from the monitoring stations. For
the hydrodynamic model, specification of model inputs was done by adjusting values in the
EFDC control file (EFDC.INP see base case values in Appendix 14) Adjustment of kinetic
water quality parameters was generally done by editing the water quality control file
(WQ3DWC.INP, see base case version of this file in Appendix 15). Temporally and spatially
varying benthic fluxes of phosphate, ammonia, nitrate, and dissolved oxygen were specified in
the benthic flux control file (BENFLUX.INP, see base case version in Appendix 16). Once the
hydrodynamic model was calibrated, water quality calibration could be conducted. The major
goal of calibration for this model was to minimize mean error between observations and
predictions while achieving the highest coefficient of determination (R-squared). Seventeen of
the eighteen Jordan Lake water quality stations were used (station CPF086D had no monitoring
data for the model time period). Chlorophyll-a a calibration had special considerations that will
be discussed later in that section.

4.1. Description of the Calibration Time Period

The latest version of the Jordan Lake EFDC model presented in this report was run over a
twenty-six month time period (January 1, 2014 – February 19, 2016). The first thirty days of the
model time period (January 1 – January 30, 2014 were used for model initialization. The 2014-
2015 model time period used initially was extended until February 2016 because of the high
stream flows and water levels that occurred in late December 2015 (see page 49 on water surface
elevation calibration and Figure 13). In addition, as compared to the other model time periods
previously used (Bowen, Langley et al. 2019, Langley and Bowen 2020) for model calibration
and scenario testing (January 2017 – December 2018), the 2014-2016 model data set had more
observations of temperature and chlorophyll-a than the 2016 and 2017-2018 model time periods
combined (Table 23, Table 24) (Bowen, Langley et al. 2019). In addition, as will be seen in a
long-term comparison of rainfall, runoff, and nutrient loading (section 5.1), 2018 was a very wet
year that had unusually high annual nutrient loading. For these reasons the 2014-2016 time
46
period was therefore selected as the only model time period used for both calibration and nutrient
load reduction scenario testing.

Streamflows during the 2014-2016 time period showed considerable variability in time, with
many short periods of relatively high flows in the non-growing parts of the year for both the Haw
River (Figure 17) and New Hope Creek (Figure 18) watersheds. The 2014-2016 model time
period had many months that were either considerably drier or considerably wetter than
historical average values for watershed inflows. The monthly average Haw River watershed
inflow was above historical values from January 2014 – May 2014, but was then below average
for the next sixteen months (Table 11). The final three months of 2015 had average streamflows
that were approximately three times historical average values (Table 11).

Figure 17. Haw River daily streamflows (in cfs, blue) and historical monthly averages (red) for the 2014-
2016 model time period.

New Hope Creek monthly average streamflows showed a similar pattern to the Haw River, with
several months of below average streamflow followed by three months of high flows in late 2015
(Table 12), although the pattern was somewhat different for this watershed as there were above
average streamflows in some some summer months in 2014 that were not seen in the Haw River
flows.

0
5000
10000
15000
20000
25000
30000
1/1/14 2/1/14 3/1/14 4/1/14 5/1/14 6/1/14 7/1/14 8/1/14 9/1/14 10/1/14 11/1/14 12/1/14 1/1/15 2/1/15 3/1/15 4/1/15 5/1/15 6/1/15 7/1/15 8/1/15 9/1/15 10/1/15 11/1/15 12/1/15 1/1/16 2/1/16
Flo
w

(
c
f
s
)
Haw River Hydrology 2014-2015
Modeled Daily Average USGS Historic Monthly Average
47

Figure 18. New Hope Creek daily streamflows (in cfs, blue) and historical monthly averages (red) for the
2014-2016 model time period.

Table 11. Monthly Average Haw River Streamflows and Streamflow Ratios for 2014 and 2015.

Haw River Streamflows Just Upstream of Jordan Lake

Historical Average
(cfs) 2014/historic 2015/historic
January 1900 1.34 0.48
February 2060 0.82 0.40
March 2250 1.44 0.86
April 1700 1.27 0.81
May 1110 1.17 0.59
June 875 0.33 0.38
July 709 0.30 0.40
August 599 0.51 0.33
September 861 0.19 0.45
October 702 0.28 2.49
November 966 0.36 2.72
December 1370 0.46 3.81

0
500
1000
1500
2000
2500
3000
1/1/14 2/1/14 3/1/14 4/1/14 5/1/14 6/1/14 7/1/14 8/1/14 9/1/14 10/1/14 11/1/14 12/1/14 1/1/15 2/1/15 3/1/15 4/1/15 5/1/15 6/1/15 7/1/15 8/1/15 9/1/15 10/1/15 11/1/15 12/1/15 1/1/16 2/1/16
New Hope River Hydrology 2014-2015
Modeled Daily Average USGS Historic Monthly Average
48
Table 12. Monthly Average New Hope Creek Streamflows and Streamflow Ratios for 2014 and 2015.
New Hope Creek

Historical Average
(cfs) 2014/historic 2015/historic
January 131 1.08 1.00
February 167 1.02 0.60
March 177 1.85 1.11
April 140 1.17 0.89
May 92 2.14 0.48
June 53 0.50 0.76
July 56 1.42 0.73
August 48 1.91 0.44
September 67 0.43 0.66
October 62 0.40 2.06
November 82 0.65 2.23
December 97 1.32 4.01

4.2. Hydrodynamic Model Calibration
The processes involved in the hydrodynamic calibration of the lake model are discussed in two
parts, water surface elevation calibration, and temperature calibration. For each of these model
versus data comparisons examination, both statistical measures of calibration performance are
used with additional graphical comparisons of model fit. As explained in the following sections,
model calibration was performed by considering a number of these graphical and quantitative
measures of model fit.

49

4.2.1. Modeled and Observed Water Surface Elevations
Hydrodynamic calibration began by comparing model predictions to observed water surface
elevations data by USACOE at the dam. This updated version of the Jordan Lake model used as
a starting point daily stage data that were compared to model predictions over the 2014-2016
model time period. Initial simulation using the 393 cells grid (Bowen, Langley et al. 2019,
Langley and Bowen 2020) showed that model prediction of elevation was higher than observed
data for most of the model time period as seen with a time history comparison (Figure 19) and a
scatter plot of observed vs. predicted water surface elevations (Figure 20).
Figure 19. Modeled vs observed water surface elevations using 393 cell grid model.

50
Figure 20. Scatter plot of modeled vs observed water surface elevation (2014-2016).

Factors such as grid adjustment, bathymetric update with LIDAR data, changing the dam
outflow from USACE records to USGS gage 02098206 at Moncure, and adjustment of the
ungaged inflows and dam outflows using the flow balance method (see section 3.1.2), improved
the match between modeled and observed water surface elevations with the 407 cells grid
(Figure 21). The final grid provided an acceptable match with the coefficient of determination
values (R2) above 95%, and mean errors below 0.02 m (Table 13).

Consideration of water surface elevation time histories (Figure 21) is essential to understanding
the goodness of fit between measured and modeled water levels. The largest errors are
associated with time periods when the lake water level apparently has been drawn down and
when peak water level events occur due to high inflows. Accurate modeling of water level
drawdowns is problematic as these periods were not directly related to gaged and estimated
(ungaged) inflows, outflow at the dam, and water supply withdrawals used in the model.
Accurate modeling of peak water level events is also problematic as they are very sensitive to the
timing of inflows and outflows that are applied as daily averages in the model.

51
Figure 21. Modeled vs observed water surface elevation for the 2014-2016 model time period.

Table 13. Statistical comparison of modeled vs observed water surface elevations for the 2014-2016 time
period.
Calibration Statistic Value for
2014-2016 time period Units
Mean Error (predicted – observed) -0.02 m
Normalized Mean Error -0.0% %
Root Mean Square Error 0.23 m
Normalized Root Mean Square
Error 0.3% %
Mean Absolute Error 0.16 m
Normalized Mean Absolute Error 0.2% %
Coefficient of determination (R2) 95.7% %
Number of Model/Data
Comparisons 749 -
Nash-Sutcliffe Model Efficiency 93.5% %

52

4.2.2. Temperature Model Calibration
All statistical measures of calibration performance for temperature calibration (Table 14) such as
mean error, root mean square error, and goodness of fit measures (coefficient of determination
(R-squared) and N-S model efficiency) indicate the agreement between measured and modeled
water temperatures is good to very good. The time history comparisons for the surface and

Table 14. Statistical comparison of modeled vs observed surface water and bottom temperatures for the
2014-2016 time period.
Calibration Statistic Value for
2014-2016 time period Units
Mean Error (predicted – observed) -0.54 Deg C
Normalized Mean Error -2.7% %
Root Mean Square Error 1.91 Deg C
Normalized Root Mean Square
Error 9.7% %
Mean Absolute Error 1.42 Deg C
Normalized Mean Absolute Error 7.2% %
Coefficient of determination (R2) 95.2% %
Number of Model/Data
Comparisons 1075 -
Nash-Sutcliffe Model Efficiency 94.7% %

bottom water layers (Figure 22) are also in good agreement with the measured temperatures at
two representative locations (stations CPF087D and CPF055C) for the 2014-2016 model time
period. For these model/data comparisons, the model predictions have a 15-step moving
averaging filter applied. A set of model/data time history comparisons for temperatures without
the filter applied are also available (Appendix 18). The model results are consistent with
observed water temperature for both well-mixed winter conditions and summer stratified
conditions. A scatter plot of observed vs. model predicted temperature (Figure 23) indicate the
limited error and low bias across the full range of predicted and observed temperatures. A
complete set of observed vs. model predicted temperature time histories is available in Appendix
2. A complete set of CDFs and scatter plots for all hydrodynamic and water quality state
variables is provided in Appendix 9 and Appendix 10.

53

Figure 22. Time history comparisons of observed and model predicted surface and bottom temperatures
at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time period.

Figure 23. Scatter plot of observed and model predicted surface and bottom temperatures at all
monitoring stations in Jordan Lake, NC for the 2014-2016 model time period.

54

4.3. Water Quality Model Calibration

4.3.1. Calibration of Chlorophyll-a Concentrations
Since the numeric criteria for chlorophyll-a examines the magnitude of infrequently occurring
high chlorophyll-a values (i.e., what is the chlorophyll-a value that is exceeded exactly 10% of
the time), it is important the distribution of model predicted chlorophyll-a concentrations closely
match that of the observed values. Said another way, it is more important the model accurately
predicts the magnitude and frequency of the higher chlorophyll values than it accurately predicts
exactly where and when those high values occur. For this reason, the primary objective of the
chlorophyll-a calibration was to match well the frequency distribution of observed values. Other
quantitative calibration measures such as mean error, root mean square error and coefficient of
determination (R-squared) that assess how well the model simulates the overall dynamics of
primary production as affected by changing light, nutrient, and temperature conditions were
considered as well, but the cumulative distribution function (CDF) for chlorophyll-a was given
primary consideration.

During calibration, cumulative distribution functions (CDFs) and the other calibration statistics
were calculated using an application (plot_ts_gui, see user’s manual Appendix 13) that has been
continuously developed for visualization of model results for a number of CE-QUAL-W2 and
EFDC applications over more than twenty years (Bowen and Hieronymus 2000, Bowen and
Hieronymus 2003, Duclaud and Bowen 2007, Bowen, Negusse et al. 2009, Negusse and Bowen
2009, Froelich, Bowen et al. 2013, Harrigan and Bowen 2016, Langley and Bowen 2020). These
fit statistics are saved to a file (ptsg_results.txt) that saves the calibration statistics along with all
of the choices related to selection of the observed and model predicted constituents to compare.
Model predicted and observed chlorophyll-a data were examined for hundreds of separate model
runs for the 2014-2016 to select kinetic parameters related to algal growth and nutrient
dynamics. The ptsg_results.txt file for analysis of the hydrodynamic and water quality
constituents for the 2014-2016 calibrated base model can be found in Appendix 17.

Kinetic parameters that were considered during calibration include maximum algal growth rates
for each algal group, temperature, nutrient, and light dependence of algal growth, carbon to
chlorophyll ratio for algal organic matter, algal respiration and predation rates, benthic nutrient
fluxes of nitrate, phosphate, and ammonia, background light attenuation, and chlorophyll-and
organic matter specific light attenuation. An examination of the seasonal variation in
chlorophyll-a concentration indicated that a two algal groups might be preferable to three in that
it would allow for a step-wise calibration of the parameters. This is how the calibration was
performed, first by considering only the Fall group (cyanobacteria). Once an optimal parameter
set was chosen to match the Fall chlorophyll-a conditions, then the second group (diatoms) was
added and the calibration process repeated considering both Spring and Fall conditions. Three
groups were actually simulated in the model but the third group (greens) had zero initial
conditions and zero maximum growth rates so that the group biomass was zero at all times and
places.
55

The parameter calibrated parameters sets for these two groups does show some important
differences (Table 15) in the temperature optimum, maximum growth rate, carbon to chlorophyll
ratio, half-saturation values for nitrogen and phosphorus, and the nitrogen to carbon ratio. Time
history plots for two representative stations (CPF055D, CPF087D) for the Haw and New Hope
arms of the lake show peaks chlorophyll concentrations in the Spring and Fall with lower values
in the late Fall and Winter (Figure 24). The model predicted 90th percentile chl-a concentration
value exactly matches the observed value (Figure 25) when data from all monitoring stations are
included. Calibration statistics for chlorophyll-a show a mean error that is less than zero, a
normalized mean error of -14.1%, a normalized RMS error of 72.6%, a coefficient of
determination (R-squared) of 28.1% and a Nash-Sutcliffe (N-S) model efficiency of

Table 15. Summary of Phytoplankton Kinetic Parameters (taken from July 2023 Base Case wq3dwc.inp
file, folder name = 18_baseANCc_pt167_CPprm1_38_best_final, see Appendix 15)
No. Parameter Units Cyano
Value
Diatom
Value
1 Nitrogen half-saturation mg/L 0.06 0.01
2 Phosphorus half-saturation mg/L 0.008 0.001
3 Carbon-to-chlorophyll ratio mg C/ug Chl 0.0156 0.0237
4 Optimal depth (m) for growth m 1.0 1.0
5 Lower optimal temperature for growth Deg C 30 5
6 Upper optimal temperature for growth Deg C 39 17
7 Suboptimal temperature effect coef. For growth

- 0.006 0.0025
8 Superoptimal temperature effect coef. for growth - 0.006 0.012
9 Reference temperature for metabolism

Deg C 20 20
10 Temperature effect coef. For metabolism - 0.069 0.069
11 Carbon distribution coef. For metabolism - 1.0 1.0
12 Half-sat. constant () for DOC excretion gO2/m^3 0.5 0.5
13 Phos. distribution coef. of RPOP for metabolism - 0 0
14 Phos. distribution coef. of LPOP for metabolism - 0 0
15 Phosphorus distribution coef. of DOP for
metabolism
- 1 1
16 Phosphorus distribution coef. of P4T for
metabolism

- 0 0
17

Nitrogen distribution coef. of RPON for
metabolism
- 0.075 0.075
18 Nitrogen distribution coef. of LPON for
metabolism
- 0.075 0.075
19 Nitrogen distribution coef. of DON for metabolism - 0.25 0.25
20 Nitrogen distribution coef. of DIN for metabolism - 0.60 0.60
21 Max. growth rate 1/day 1.40 0.8
22 Basal metabolism rate 1/day 0.002 0.025
23 Predation rate 1/day 0.03 0.05
24 Settling velocity m/day 0.1 0.2
25 Phos-to-Carbon params, CPprm1, CPprm2 - 38/85 38/85
26 Nitrogen to carbon ratio, ANC - 0.167 0.100
56
-0.202 (Table 16). The full set of cumulative distribution functions for observed and model
predicted hydrodynamic and water quality state variables is available in Appendix 9. Scatter
plots for hydrodynamic and water quality state variables are provided and Appendix 10.

While the calibration statistics for chlorophyll-a don’t seem to indicate a good fit to the data, the
CDF does match well the observed data. An examination of all the time histories for
chlorophyll-a (Appendix 3) shows the extremely dynamic nature of the chlorophyll-a
distributions in the lake and the significant differences in the response between the Haw and
New Hope arms of the lake. No set of parameters was found that could accurately predict the
peak chlorophyll values, yet it is these highest values that have a significant impact on
calibration parameters such as R-squared and Nash-Sutcliffe model efficiency. The level of
calibration performance achieved is typical of models of algal growth in highly dynamic, long
residence time systems. Given the good fit to the 90th percentile value and considering how the
model is to be used, the model was considered to be adequately calibrated to chlorophyll-a
concentrations. The model is sufficiently calibrated to the observed chlorophyll-a for the
purposes of investigating the effects of nutrient load reductions on chlorophyll-a concentrations.

Figure 24. Time history comparisons of observed and model predicted surface and bottom chl-a
concentrations at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time
period.

57
Figure 25. Observed vs. Model Predicted Chl-a Cumulative Distributions Functions (CDFs) at Seventeen
Jordan Lake Stations Over the 2014-2016 time period.

Table 16. Statistical comparison of modeled vs observed chlorophyll-a concentration using all Jordan
Lake monitoring stations for the 2014-2016 time period (lower and upper model layers = 20
and 24, with a 15-step time filter applied).
Calibration Statistic Value for
2014-2016 time period Units
Mean Error (predicted – observed) -5.37 ug/L
Normalized Mean Error -14.2% %
Root Mean Square Error 27.2 ug/L
Normalized Root Mean Square
Error 72.1% %
Mean Absolute Error 18.9 ug/L
Normalized Mean Absolute Error 49.9% %
Coefficient of determination (R2) 28.3% %
Number of Model/Data
Comparisons 584 -
Nash-Sutcliffe Model Efficiency -0.184 -

58
4.3.2. Calibration of Nitrate + Nitrite (NOx) Concentrations

Time histories of nitrate+nitrite (NOx) concentrations showed a predictable seasonable pattern
with highest concentrations from December through March and lower concentrations throughout
the months when the phytoplankton are actively growing, indicating nitrogen limitation of
phytoplankton productivity. The range of concentrations and the timing of the peak values was
quite variable from station to station, with generally higher concentrations in both the upper
portion of both the Haw arm and New Hope Creek arms of the lake (Appendix 4). These spatial
and temporal patterns in the NOx concentrations are nicely modeled in the Haw arm of the lake
(Figure 26, left panel), and to a lesser extent in the lower part of the New Hope Creek arm of the
lake (Figure 26, right panel).

Figure 26. Time history comparisons of observed and model predicted surface and bottom NOx
concentrations at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time
period.

The scatter plot of observed vs. model predicted NOx concentrations show that the model’s
distribution of predicted NOx concentration (0.0 – 3.0 mg/L) is somewhat higher than the
observations (Figure 27). There are somewhat more near zero observed concentrations than
there are near zero model predicted concentrations. Judging from the scatter plot, overall the
model seems to slightly overpredict NOx concentrations (Figure 27), which can also been in the
CDF for NOx (Appendix 9).

Numerical calibration statistics tell a similar story of the model overpredicting NOx
concentrations, with a mean error (predicted-observed) of 0.14 mg/L. Root-mean-square and
absolute errors are 0.39 and 0.24 mg/L, which are 100% and 162% of the mean observed value
for NOx (Table 17). The coefficient of determination (R-squared) for NOx is 11.5% (Table 17).
Like chlorophyll-a, the NOx time histories are highly dynamic (Appendix 4), but in most cases
59

Figure 27. Scatter Plot of Observed vs. Model Predicted NOx Concentrations at Seventeen Jordan Lake
Stations Over the 2014-2016 time period.

Table 17. Statistical comparison of modeled vs observed NOx concentrations using all Jordan Lake
monitoring stations for the 2014-2016 time period (lower and upper model layers = 2 (or
bottom) and 24).
Calibration Statistic Value for
2014-2016 time period Units
Mean Error (predicted – observed) 0.14 mg/L
Normalized Mean Error 57.7% %
Root Mean Square Error 0.39 mg/L
Normalized Root Mean Square
Error 162.2% %
Mean Absolute Error 0.24 mg/L
Normalized Mean Absolute Error 100.2% %
Coefficient of determination (R2) 11.5% %
Number of Model/Data
Comparisons 563 -
Nash-Sutcliffe Model Efficiency -1.042 -

60
show a seasonal pattern that indicates nitrogen limited phytoplankton production. In general,
timing and magnitude of the peak NOx concentrations is well predicted by the model in the Haw
arm of the lake as compared to the upper portion of the New Hope Creek arm of the lake
(Appendix 4).

4.3.3. Calibration of Ammonia (NH4) Concentrations

Ammonia is one of two inorganic nitrogen species that are readily available as nutrients for
supporting phytoplankton productivity. Ammonia is generally lower in concentration in the
water column compared to the other inorganic nitrogen species (nitrate). In addition, ammonia
differs from nitrate in that both the watershed and the sediments provide a source to the water
column of the lake, rather than the nitrogen sink that comes from sediment denitrification during
times and places where bottom water dissolved oxygen concentration is low and organic matter
concentration is high. In the Jordan Lake model, the model predicted ammonia concentration
was somewhat more dynamic than was seen in the observed concentrations (Appendix 5, Figure
28), although interestingly the model tracks quite well the decline in concentrations during the
early growing season (March to April) in 2015 at both the Haw arm and New Hope Creek arm
stations (Figure 28) shown previously. The range in model predicted vs. observed
concentrations of ammonia is similar as seen in the ammonia scatter (Figure 29), although the
model seems to often overpredicted ammonia concentration. The mean error (predicted –
observed) in ammonia was relatively low in magnitude (0.07 mg/L) but large in percentage
(165%) (Table 18). Like other nutrient species that are significantly affected by sediment
processes (nitrate, TKN), the model was only able to capture a relatively small fraction of the
variability present in the observed values, with a coefficient of determination of 0.09% and a
Nash-Sutcliffe model efficiency of -3.87 (Table 18).

Figure 28. Time history comparisons of observed and model predicted surface and bottom NH4
concentrations at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time
period.
61

Figure 29. Scatter Plot of Observed vs. Model Predicted NH4 Concentrations at Seventeen Jordan Lake
Stations Over the 2014-2016 time period.

Table 18. Statistical comparison of modeled vs observed NH4 concentration using all Jordan Lake
monitoring stations for the 2014-2016 time period (lower and upper model layers = 2 (or
bottom) and 24).
Calibration Statistic Value for
2014-2016 time period Units
Mean Error (predicted – observed) 0.07 mg/L
Normalized Mean Error 164.6% %
Root Mean Square Error 0.11 mg/L
Normalized Root Mean Square
Error 238.7% %
Mean Absolute Error 0.09 mg/L
Normalized Mean Absolute Error 197.2% %
Coefficient of determination (R2) 0.09% %
Number of Model/Data
Comparisons 563 -
Nash-Sutcliffe Model Efficiency -3.865 -

62
4.3.4. Calibration of Total Kjeldahl Nitrogen (TKN) Concentrations

Total Kjeldahl nitrogen concentrations are a summation of five model state variables including
both particulate and dissolved organic species plus one inorganic species (ammonia). In natural
systems the organic forms usually dominate the inorganic. In Jordan Lake ammonia
concentrations were generally less than 0.2 mg/L (Appendix 6) with the bulk of TKN in the
organic nitrogen forms. The Jordan Lake model typically underpredicted TKN and therefore
underpredicted the organic forms of nitrogen as can be seen both in the two representative time
history plots (Figure 30) for the Haw and New Hope arms of the lake and the scatter plot
(Figure 31). Measures of model fit tell a similar story with a mean error (model predicted –
observed) of -0.40 mg/L and a normalized mean error of -46% (Table 19). Only a small portion
of the observed variability was captured by the model (coefficient of determination = 2.6%), and
with the bias in concentrations this resulted in a Nash-Sutcliffe model efficiency that was less
than zero (-4.487) (Table 19).

Figure 30. Time history comparisons of observed and model predicted surface and bottom TKN
concentrations at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time
period.

63
Figure 31. Scatter Plot of Observed vs. Model Predicted TKN Concentrations at Seventeen Jordan Lake
Stations Over the 2014-2016 time period.

Table 19. Statistical comparison of modeled vs observed TKN concentration using all Jordan Lake
monitoring stations for the 2014-2016 time period (lower and upper model layers = 2 (or
bottom) and 24).
Calibration Statistic Value for
2014-2016 time period Units
Mean Error (predicted – observed) -0.40 mg/L
Normalized Mean Error -46.1% %
Root Mean Square Error 0.47 mg/L
Normalized Root Mean Square
Error 54.0% %
Mean Absolute Error 0.41 mg/L
Normalized Mean Absolute Error 47.5% %
Coefficient of determination (R2) 2.6% %
Number of Model/Data
Comparisons 587 -
Nash-Sutcliffe Model Efficiency -4.487 -

64
4.3.5. Calibration of Total Nitrogen (TN) Concentrations
Data on total nitrogen were added to the monitoring database by adding the observed values for
TKN and nitrate+nitrate. This allows for model vs. data comparisons of a cumulative measure
of all dissolved and particulate, organic and inorganic forms of nitrogen in the model. A total of
eight model state variables (Table 1) are used to calculate model predicted total nitrogen. The
three algal variables are considered using the nitrogen to carbon ratio that is set in the water
quality input file (Appendix 15, Model input file WQ3DWC.INP file for base case.).

Time history comparisons of model predicted vs. observed total nitrogen at one representative
station in the Haw arm (CPF055D) and one station of the New Hope arm (CPF086C) of the lake
show similar concentrations and seasonal variation (Figure 32). Like some other model
predictions such as

model constituents such as chlorophyll-a, the cumulative distribution function for model
predictions overpredicts the total nitrogen magnitude at the higher end of distribution and
underpredicts at the lower end of the distribution (Figure 33). Numerical measures of
calibration performance (Table 20) are similar to other nutrient state variables. The normalized
mean error is less than 1 mg/L (-0.27 mg/L), and the normalized RMSE is 56.1% (Table 20).

Figure 32. Time history comparisons of observed and model predicted surface and bottom TN
concentrations at station CPF055D (left) and CPF086C (right) for the 2014-2016 model time period.
65
Figure 33. Observed vs. Model Predicted Total Nitrogen Concentration Cumulative Distribution
Functions (CDFs) at Seventeen Jordan Lake Stations Over the 2014-2016 time period.

Table 20. Statistical comparison of modeled vs observed TN concentrations using all Jordan Lake
monitoring stations for the 2014-2016 time period (lower and upper model layers = 2 (or bottom) and 24).
Calibration Statistic Value for
2014-2016 time period Units
Mean Error (predicted – observed) -0.27 mg/L
Normalized Mean Error -24.0% %
Root Mean Square Error 0.62 mg/L
Normalized Root Mean Square
Error 56.1% %
Mean Absolute Error 0.46 mg/L
Normalized Mean Absolute Error 41.5% %
Coefficient of determination (R2) 3.6% %
Number of Model/Data
Comparisons 592 -
Nash-Sutcliffe Model Efficiency -1.476 -

66
4.3.6. Calibration of Total Phosphorus (TP) Concentrations
Total phosphorus concentrations are a sum of both inorganic (phosphate) and organic species
(both riverine and lacustrine detrital and algal) and thus show temporal variations that are
affected both by watershed inputs and phytoplankton dynamics. Time histories of total
phosphorus at a representative Haw arm station (CPF055D) and a New Hope Creek arm station
(CPF087D) show non-growing season increases (Figure 34) that are likely due to watershed
inputs late in 2014 and 2015. At the New Hope Creek arm station (CPF087D) the model
overpredicts the late 2015 into 2016 increase in TP concentration (Figure 34, right panel) but the
model does a good job in the Haw arm station (Figure 34, left panel) as both the model and the
observed data show an increase in TP concentration of approximately five-fold from Fall 2015 to
Winter 2015/2016. Overall, the model is somewhat more dynamic than the observed data as seen
both from time history plots (Appendix 7) and from the TP scatter plot (Figure 35) with fewer
low concentrations and a broader range in concentrations. Overall, the model has a low mean
error of 0.0 mg/L, with a normalized mean error of 4.6% (Table 21). The normalized RMS error
is also relatively low (75%) compared to the corresponding nitrate, TKN, and ammonia values
seen earlier. Compared to these other N & P variables the coefficient of determination for TP is
somewhat higher, with a value of 12.6% (Table 21). The Nash-Sutcliffe model efficiency is also
somewhat higher but is still less than zero (-1.379).

Figure 34. Time history comparisons of observed and model predicted surface and bottom TP
concentrations at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time
period.
67
Figure 35. Scatter Plot of Observed vs. Model Predicted TP Concentrations at Seventeen Jordan Lake
Stations Over the 2014-2016 time period.

Table 21. Statistical comparison of modeled vs observed TP concentration using all Jordan Lake
monitoring stations for the 2014-2016 time period (lower and upper model layers = 2 (or
bottom) and 24).
Calibration Statistic Value for
2014-2016 time period Units
Mean Error (predicted – observed) 0.0 mg/L
Normalized Mean Error 4.6% %
Root Mean Square Error 0.06 mg/L
Normalized Root Mean Square
Error 75.5% %
Mean Absolute Error 0.04 mg/L
Normalized Mean Absolute Error 55.6% %
Coefficient of determination (R2) 12.6% %
Number of Model/Data
Comparisons 572 -
Nash-Sutcliffe Model Efficiency -1.379 -

68
4.3.7. Calibration of Dissolved Oxygen (DO) Concentrations
In monomictic thermally stratified freshwater lakes such as Jordan Lake, dissolved oxygen (DO)
concentrations typically show significant variations both seasonally and spatially. Because
dissolved oxygen saturation concentration decreases with increasing water temperature, and
bottom water oxygen depletion increases with increasing temperature, the lowest dissolved
oxygen concentrations are typically seen in the summer and early fall months. Water at the
deeper stations may also exhibit vertical temperature stratification, which limits transport of
dissolved from the surface waters and therefore greatly affects the vertical distribution of
dissolved oxygen in the water column. In the cooler Fall convective mixing of colder surface
waters eventually mixes and destratifies the water column, which aerates the bottom waters.
These cooler waters also have higher DO saturation concentrations, which elevates DO
concentrations thorougout the entire water column. In the spring heating of surface water
restratifies the water column, which reestablishes the stratified temperature and DO conditions.

The time history plots at one station in the Haw arm (CPF055D) and one station in the New
Hope arm (CPF087B3) of Jordan Lake show the temporal patterns in DO concentration seen
across the deeper portions of the lake . Model predicted DO concentrations peak at about 12-14
mg/L in late Winter and are similar in top and bottom waters (Figure 36). Thermal stratification
begins in early Spring (Figure 22) and bottom waters immediately begin to decline and are near
zero until the lake turns over in the Fall. Both model predictions and observed data show these
patterns. Interestingly the model is also able to simulate the partial vertical mixing events at the
shallower New Hope Creek arm station (CPF087D, Figure 36, right panel) in July 2015 that
seems to reduce but not eliminate the difference in DO concentrations between surface and
bottom waters. Summertime DO stratification is not present at the shallower stations in the
upper part of the New Hope Creek arm of the lake (Appendix 8). Comparing model predictions
across the stations shows the model generally does a good job in capturing the differences in
vertical distribution of DO concentrations across the stations throughout the lake (Appendix 8).
Surface water DO concentrations show a high degree of variability in the Haw arm portion of the
lake (Figure 36, left panel).

69

Figure 36. Time history comparisons of observed and model predicted surface and bottom DO
concentrations at station CPF055D (left) and CPF087D (right) for the 2014-2016 model time
period.

A comparison of the model predicted and observed cumulative distribution functions (CDFs) for
dissolved oxygen concentration shows the model’s ability to capture the range and frequency of
higher and lower concentrations (Figure 37). The frequency of low DO concentrations (less
than 4.0 mg/L) is approximately 15% for the observed data and 10% for the model predicted DO
concentrations. The median DO concentration are also similar between model predicted and
observed values, with a median of approximately 8.0 mg/L for the model predictions and 8.5
mg/L for the observations (Figure 37). The scatter plot for DO as well as the other water quality

Figure 37. Observed vs. Model Predicted Dissolved Oxygen Concentration Cumulative Distribution
Functions (CDFs) at Seventeen Jordan Lake Stations Over the 2014-2016 time period.

70
and hydrodynamic model state variables is available in Appendix 9. There is significant scatter
in the “middle” values of DO concentration that can be seen in the scatter plot for DO (Figure
A9). This is likely due to intermittent vertical mixing that occurs at the shallower stations (see
Appendix 18 for unfiltered temperature time histories), and the effects on the dissolved oxygen
concentrations of rapid growth of phytoplankton in the surface waters during the spring.

Calibration statistics for dissolved oxygen indicate the model predicts is well calibrated with a
normalized mean error of 5.4% (Table 22). Measures of the model’s ability to simulate temporal
and spatial variations in dissolved oxygen are also good with a coefficient of variation of 58.1%
and a Nash-Sutcliffe model efficiency of 55.0%. These numbers are similar to previous
modeling of dissolved oxygens done in other systems in North Carolina ((Bowen, Negusse et al.
2009).

Table 22. Statistical comparison of modeled vs observed DO concentration using all Jordan Lake
monitoring stations for the 2014-2016 time period (lower and upper model layers = 2 (or
bottom) and 24).
Calibration Statistic Value for
2014-2016 time period Units
Mean Error (predicted – observed) 0.42 mg/L
Normalized Mean Error 5.4% %
Root Mean Square Error 2.26 mg/L
Normalized Root Mean Square
Error 28.7% %
Mean Absolute Error 1.68 mg/L
Normalized Mean Absolute Error 21.4% %
Coefficient of determination (R2) 58.1% %
Number of Model/Data
Comparisons 1038 -
Nash-Sutcliffe Model Efficiency 0.550 -

71

5. MODEL EVALUATION

5.1. HYDROLOGIC ANALYSIS

As described in the previous section, the model was calibrated and the nutrient reduction
scenarios were analyzed using a twenty-six month time period from January 2014 to February
2016. This time period was chosen after a review of the available monitoring data. The chosen
time period was found to have the most extensive set of chlorophyll-a concentrations to support
the calibration of the water quality model. A long-term analysis of rainfall, streamflows, and
nutrient loading were performed to see how the model time period compares hydrologically with
the data over the past few decades. The time period chosen for analysis was 30 - 40 years to
include all of the original Jordan Lake model time period (2014-2018), depending upon data
availability. The analysis time period was chosen to be long enough to capture interannual
variability in hydrologic forcings and to see trends over the analysis time period. Rainfall
records came from the meteorological station at RDU airport. The total nitrogen and total
phosphorus loading time histories were taken from the Jordan Lake watershed model (Miller,
Karimi et al. 2019), and corresponding flow records from the USGS at the Haw River, Morgan
Creek, and New Hope Creek gages.

5.1.1. Annual Rainfall at RDU Airport
Hourly rainfall records available from the National Weather Service were collected for the RDU
Airport location for the years 1980 – 2018. The hourly rainfall values were summed for each
year. Yearly rainfall values varied from less than 35.0 inches in 2001 to more than sixty inches
in 2018 (Figure 38). The average rainfall over the 40-year time period was 43.7 inches with a
standard deviation of 6.8 inches. There was a noticeable upward trend in the data, and a
significant year-to-year variation. Rainfall from 1985-1995 was generally below the trend line,
although one year (1989) had a yearly rainfall of 54.2 inches. All three years of the model time
period (2014-2016) were above the trend line (Figure 38). Over the data collection time period
(1980-2018) the maximum rainfall occurred in 2018, with more than 60 inches of rain (Figure
38).
72
Figure 38. Total Annual Rainfall Measured at the RDU Meteorological Station, 1980-2018 (in). Years
2014 to 2016 are shown with a red oval.

5.1.2. Yearly Average Streamflows
The streamflow records for a similar time period to the rainfall record showed a different pattern
with regard to yearly variation. Whereas rainfalls trended upward over the time period
examined, streamflows decreased over the thirty-four-year time period analyzed (1983-2016)
(Figure 39). For this analysis, because of limited data availability, a slightly shorter time period
was analyzed, and two of the watersheds (Northeast Creek, White Oak Creek) were not used
because of data gaps during the 1983-2016 time period. The three watersheds analyzed do
represent over eighty percent of the Jordan Lake watershed (Miller, Karimi et al. 2019). There
were large variations in annual average flow from low values less than 20.0 m3/s in 1986, 1988,
and one very large value in 2003 that was almost twice the long-term average value. The
decrease in the cumulative flow as shown in the trend line was quite significant, decreasing from
y = 0.2681x -492.28
R² = 0.2047
30.0
35.0
40.0
45.0
50.0
55.0
60.0
65.0
1980 1985 1990 1995 2000 2005 2010 2015 2020
An
n
u
a
l

R
a
i
n
f
a
l
l

(
i
n
)
Year
73
just over 40 m3/s in the 1980’s to just over 30 m3/s in 2015(Figure 39). The three years modeled
(2014-2016) were slightly above the 30-year trend line.

Figure 39. Annual Average Cumulative Flow for Three Jordan Lake Watersheds (Haw River, Morgan
Creek, New Hope Creek), 1983-2016 (m3/s). Years 2014 to 2016 are shown with a red oval.

5.1.3. Annual Average Total Nitrogen and Total Phosphorus Loading
The three watersheds analyzed for streamflow (Haw River, Morgan Creek, New Hope Creek)
were also analyzed for annual nitrogen and phosphorus (total N & P) loading. Daily loading data
came from the Jordan Lake watershed model (Miller, Karimi et al. 2019). The three watershed
loading values were summed for each year from 1983 to 2018. Both the annual total N loading
(Figure 40) and the annual total P loading (Figure 41) showed downward trends, with the total P
loading decreasing from an average of 5x105 kg P/yr to 2x105 kg P/yr from 1983 to 2018.
Interestingly, the magnitude of decrease in total P loading is similar to that seen for streamflow.
For both N & P the loading was below the trend line for every year except two (2003, 2010) over
the fourteen years from 1999 to 2012. One year during the model time period (2014) had N and
P loading at or just below the trend line. The other two years (2015, 2016) were just above the
trend line for nitrogen and phosphorus loading (Figure 40, Figure 41). As was seen with the
rainfall data, for both N & P loading, the values were more than 50% above the corresponding
trend line values.
y = -0.3352x + 707.05
R² = 0.0638
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
1980 1985 1990 1995 2000 2005 2010 2015 2020
An
n
u
a
l

A
v
e
r
a
g
e

F
l
o
w

(
m
3/s
)
Year
74
Figure 40. Annual Cumulative Nitrogen Loading (kg N/yr) from Three Jordan Lake Watersheds (Haw
River, Morgan Creek, New Hope Creek), 1983-2018 (using data from Obenour Lab). Years
2014 to 2016 are shown with a red oval.

Figure 41. Annual Cumulative Phosphorus Loading (kg P/yr) from Three Jordan Lake Watersheds (Haw
River, Morgan Creek, New Hope Creek), 1983-2018 (using data from Obenour Lab). Years
2014 to 2016 are shown with a red oval.
y = -3104.1x + 8E+06
R² = 0.0028
0.0E+00
5.0E+05
1.0E+06
1.5E+06
2.0E+06
2.5E+06
3.0E+06
3.5E+06
4.0E+06
1980 1985 1990 1995 2000 2005 2010 2015 2020
An
n
u
a
l

N

L
o
a
d
i
n
g

(
k
g

N
/
y
r
)
Year
y = -9629.4x + 2E+07
R² = 0.3115
0.0E+00
1.0E+05
2.0E+05
3.0E+05
4.0E+05
5.0E+05
6.0E+05
7.0E+05
8.0E+05
9.0E+05
1.0E+06
1980 1985 1990 1995 2000 2005 2010 2015 2020
An
n
u
a
l

P

L
o
a
d
i
n
g

(
k
g

P
/
y
r
)
Year
75

5.1.4. Comparisons of Nitrate, Total Nitrogen, and Total Phosphorus
Concentration
The data from the watershed model was in the form of daily total N & P load estimates for five
inputs to Jordan Lake: Haw River, Morgan Creek, New Hope Creek, Northeast Creek, White
Oak Creek). For use as water quality inputs, these loadings were converted to “estimated”
concentrations based upon the corresponding streamflow for that watershed on that day. Those
estimated concentrations can be compared to the direct measurements of nitrogen or phosphorus
concentration measured in these same watersheds at the same time. The three watersheds shown
here were chosen because the Haw River is the main source of flow and nutrients into Jordan
Lake, New Hope Creek is a smaller input, and White Oak Creek is an even smaller input without
a wastewater treatment plant located before the entrance to Jordan Lake. This section compares
estimated concentrations from the watershed model to the measured values both as time history
comparisons and as direct comparisons of measured concentrations and the corresponding
estimated concentration for that day and that watershed.

5.1.4.1. Haw River
Time histories of model estimated and measured concentrations of nitrate (Figure 43), total
nitrogen (Figure 42) and total phosphorus (Figure 44) for the Haw River for the 2014 – 2016
model time period show both seasonal and shorter time scale variations. The estimated total P
time history varies much more significantly than either nitrate or total N. Nitrogen
concentrations are generally lower in the summer and higher in the winter. The measured values
follow these same trends, but the variability of measured concentration values is quite a bit
higher than the estimates from the watershed model. When measured concentrations of nitrate
(Figure 45), total N (Figure 46), and total P (Figure 47) are plotted against the corresponding
estimated concentration at the same time and place a similar pattern emerges. The two
concentrations are clearly correlated, but the variation in the measured values always exceeds the
estimated values from the watershed model.

Figure 42. Haw River Measured and Estimated Total Nitrogen Time History.
0
0.5
1
1.5
2
2.5
3
3.5
4
1/1/14 4/2/14 7/2/14 10/1/14 1/1/15 4/2/15 7/2/15 10/1/15 1/1/16 4/1/16 7/1/16 9/30/16 12/31/16
Co
n
c
e
n
t
r
a
t
i
o
n

(
m
g
/
L
)
Haw River Nitrate Concentration
Predicted Nitrate Measured Nitrate
76

Figure 43. Haw River Measured and Estimated Nitrate Time History.

Figure 44. Haw River Measured and Estimated Total Phosphorus Time History.

Figure 45. Haw River Measured Vs. Estimated Nitrate Concentration.

0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
1/1/14 4/2/14 7/2/14 10/1/14 1/1/15 4/2/15 7/2/15 10/1/15 1/1/16 4/1/16 7/1/16 9/30/16 12/31/16
Co
n
c
e
n
t
r
a
t
i
o
n

(
m
g
/
L
)
Haw River Total Nitrogen Concentration
Predicted TN Measured TN
0
0.1
0.2
0.3
0.4
0.5
0.6
1/1/14 4/2/14 7/2/14 10/1/14 1/1/15 4/2/15 7/2/15 10/1/15 1/1/16 4/1/16 7/1/16 9/30/16 12/31/16
Co
n
c
e
n
t
r
a
t
i
o
n

(
m
g
/
L
)
Haw River Total Phosphorus Concentration
Predicted TP Measured TP
0
0.5
1
1.5
2
2.5
0 0.5 1 1.5 2 2.5Pr
e
d
i
c
t
e
d

N
i
t
r
a
t
e

C
o
n
c
e
n
t
r
a
t
i
o
n

(
m
g
/
L
)
Measured Nitrate Concentration (mg/L)
Haw River Nitrate Concentration
measured v predicted
Nitrate
Measured=
Predicted
Linear (Nitrate)
77
Figure 46. Haw River Measured Vs. Estimated Total Nitrogen Concentration.

Figure 47. Haw River Measured Vs. Estimated Total Phosphorus Concentration.

0
0.5
1
1.5
2
2.5
3
0.5 1 1.5 2 2.5 3
Pr
e
d
i
c
t
e
d

T
N

C
o
n
c
e
n
t
r
a
t
i
o
n

(
m
g
/
L
)
Measured TN Concentration (mg/L)
Haw River Total Nitrogen
meas v pred
TN
Measured=
Predicted
Linear (TN)
0
0.05
0.1
0.15
0.2
0.25
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
Pr
e
d
i
c
t
e
d

T
P

C
o
n
c
e
n
t
r
a
t
i
o
n

(
m
g
/
L
)
Measured TP Concentration (mg/L)
Haw River Total Phosphorus
meas vs pred
TP
Measured=
Predicted
Linear (TP)
78

5.1.4.2. New Hope Creek

Time history comparisons of measured and watershed estimated nutrient concentrations for
nitrate, total N, and total P in New Hope Creek show some significant differences from those of
the Haw River. A seasonal pattern for nitrate (Figure 48) and total N (Figure 49) exists, but the
higher concentrations are seen in the summer and fall rather than the winter, and those
concentrations (4-8 mg/L) are higher than those seen in the Haw River (2-3 mg/L). Total P
concentrations in New Hope Creek (Figure 50) are much less variable than those of the Haw
River and are generally higher in concentration (0.2-0.4 mg/L vs. 0.1-0.2 mg/L).

Comparisons of measured vs. watershed estimated nutrient concentrations in New Hope Creek
look like the comparisons seen in the Haw River. Direct comparisons between measured and
estimated nitrate (Figure 51), total N (Figure 52), and total P (Figure 53) concentrations all
show correlations between the two corresponding concentrations, but with significantly more
variation in the measured values vs. the watershed model estimated concentrations. These trends
can be seen in both the New Hope Creek time history plots and the estimated vs. measured
concentration plots.

Figure 48. New Hope Creek Measured and Estimated Nitrate Time History.

Figure 49. New Hope Creek Measured and Estimated Total Nitrogen Time History.

0
2
4
6
8
10
12
1/1/14 4/2/14 7/2/14 10/1/14 1/1/15 4/2/15 7/2/15 10/1/15 1/1/16 4/1/16 7/1/16 9/30/16 12/31/16
Co
n
c
e
n
t
r
a
t
i
o
n

(
m
g
/
L
)
New Hope Creek Nitrate Concentration
Predicted Nitrate Measured Nitrate
0
2
4
6
8
10
12
1/1/14 4/2/14 7/2/14 10/1/14 1/1/15 4/2/15 7/2/15 10/1/15 1/1/16 4/1/16 7/1/16 9/30/16 12/31/16
Co
n
c
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n
t
r
a
t
i
o
n

(
m
g
/
L
)
New Hope Creek Total Nitrogen Concentration
Predicted TN Measured TN
79
Figure 50. New Hope Creek Measured and Estimated Total Phosphorus Time History.

Figure 51. New Hope Creek Measured Vs. Estimated Nitrate Concentration.
Figure 52. New Hope Creek Measured Vs. Estimated Total Nitrogen Concentration.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1/1/14 4/2/14 7/2/14 10/1/14 1/1/15 4/2/15 7/2/15 10/1/15 1/1/16 4/1/16 7/1/16 9/30/16 12/31/16
Co
n
c
e
n
t
r
a
t
i
o
n

(
m
g
/
L
)
New Hope Creek Total Phosphorus Concentration
Predicted TP Measured TP
0
1
2
3
4
5
6
7
8
0 2 4 6 8 10
Pr
e
d
i
c
t
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d

N
i
t
r
a
t
e

C
o
n
c
.

(
m
g
/
L
)
Measured Nitrate Conc. (mg/L)
New Hope Creek Nitrate Concentration
meas vs Predicted
Nitrate
Measured=
Predicted
Linear (Nitrate)
0
1
2
3
4
5
6
7
8
9
0 2 4 6 8 10 12
Pr
e
d
i
c
t
e
d

T
N

C
o
n
c
.

(
m
g
/
L
)
Measured TN Conc. (mg/L)
New Hope Creek Total Nitrogen
Predicted vs. Actual
TN
Measured=
Predicted
Linear (TN)
80

Figure 53. New Hope Creek Measured Vs. Estimated Total Phosphorus Concentration.

5.1.4.3. White Oak Creek

White Oak creek is the smallest of the three watersheds analyzed for this nutrient concentration
comparison, and the only one of the three without a wastewater treatment plant in the watershed.
It should also be noted that streamflows and concentrations for this watershed are used also to
estimate the inputs from the even smaller ungaged watersheds that drain directly to Jordan Lake
along the New Hope Creek arm of the lake (Bowen, Langley et al. 2019). The time history plots
for nitrate (Figure 54), total N (Figure 55), and total P (Figure 56) for White Oak Creek show
the highest degree of what appears to be event related variability in nutrient concentration.
Unlike the other two watersheds, a relatively small fraction of total N is nitrate. In the other two
watersheds, not only is most of the total N comprised of nitrate, but concentrations are
significantly higher than White Oak Creek (Haw River 1-2 mg/L, New Hope Creek 4-8 mg/L);
White Oak Creek total N is less than 1.2 mg/L (Figure 55) and the nitrate concentrations are
generally less than 0.2 mg/L (Figure 54). Like the other watersheds there is a seasonal
component to the nitrogen loading, but the highest concentrations seem to be in the spring and
summer months. Total P concentrations (Figure 56) have a less distinct seasonal variation than
total N, and a similar degree of event related variability.

Direct comparisons of measured vs. estimated concentrations of nitrate (Figure 57), total N
(Figure 58), and total P (Figure 59) in White Oak Creek look similar to the two watersheds,
with a higher degree of variability in the measured values and a positive correlation between the
estimated and corresponding measured concentrations. Total N comparisons (Figure 58)
showed a somewhat different pattern, with a roughly similar variation in the estimated
concentrations as compared to the measured total N concentrations.

0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Pr
e
d
i
c
t
e
d

T
P

C
o
n
c
.

(
m
g
/
L
)
Measured TP Conc. (mg/L)
New Hope Creek Total Phosphorus
measured vs predicted
TP
Measured=
Predicted
Linear (TP)
81
Figure 54. White Oak Creek Measured and Estimated Nitrate Time History.

Figure 55. White Oak Creek Measured and Estimated Total Nitrogen Time History.
Figure 56. White Oak Creek Measured Vs. Estimated Total Phosphorus Time History.

0
0.05
0.1
0.15
0.2
0.25
1/1/14 4/2/14 7/2/14 10/1/14 1/1/15 4/2/15 7/2/15 10/1/15 1/1/16 4/1/16 7/1/16 9/30/16 12/31/16
Co
n
c
e
n
t
r
a
t
i
o
n

(
m
g
/
L
)
White Oak Creek Nitrate Concentration
Predicted Nitrate Measured Nitrate
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1/1/14 4/2/14 7/2/14 10/1/14 1/1/15 4/2/15 7/2/15 10/1/15 1/1/16 4/1/16 7/1/16 9/30/16 12/31/16
Co
n
c
e
n
t
r
a
t
i
o
n

(
m
g
/
L
)
White Oak Creek Total Nitrogen Concentration
Predicted TN Measured TN
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
1/1/14 4/2/14 7/2/14 10/1/14 1/1/15 4/2/15 7/2/15 10/1/15 1/1/16 4/1/16 7/1/16 9/30/16 12/31/16
Co
n
c
e
n
t
r
a
t
i
o
n

(
m
g
/
L
)
White Oak Creek Total Phosphorus Concentration
Predicted TP Measured TP
82
Figure 57. White Oak Creek Measured Vs. Estimated Nitrate Concentration.

Figure 58. White Oak Creek Measured vs. Estimated Total Nitrogen Concentration.

0
0.05
0.1
0.15
0 0.05 0.1 0.15 0.2 0.25
Pr
e
d
i
c
t
e
d

N
i
t
r
a
t
e

C
o
n
c
.

(
m
g
/
L
)
Measured Nitrate Conc. (mg/L)
White Oak Creek Nitrate Concentration
meas v p
Nitrate
Measured=
Predicted
Linear (Nitrate)
0.4
0.5
0.6
0.7
0.8
0.9
1
0.3 0.4 0.5 0.6 0.7 0.8 0.9
Pr
e
d
i
c
t
e
d

T
N

C
o
n
c
.

(
m
g
/
L
)
Measured TN Conc. (mg/L)
White Oak Creek Total Nitrogen
Predicted vs. Actual
TN
Measured=
Predicted
Linear (TN)
83
Figure 59. White Oak Creek Measured Vs. Estimated Total Phosphorus Concentration.

5.2. Model Verification
The model verification was performed as part of the original development of the Jordan Lake
Water Quality Model (Bowen, Langley et al. 2019). No additional verification was conducted
with this updated version of the model. The results shown here come from the earlier work.
Model verification was performed using an eleven month time period (Jan 1, 2016 – December
31, 2016). The calibrated model was run using the parameter values determined using the two
other model time periods (2014 - 2015, 2017 – 2018). Statistical measures of calibration
performance were calculated in an identical fashion to that done during calibration using the
other two time periods. Statistical measures of calibration performance for water surface
elevation (not shown since there has been significant changes to the elevation model since 2019),
temperature (Table 23) and chlorophyll-a (Table 24) were found to be nearly identical for the
2016 verification time

0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.02 0.06 0.1 0.14 0.18
Pr
e
d
i
c
t
e
d

T
P

C
o
n
c
.

(
m
g
/
L
)
Measured TP Conc. (mg/L)
White Oak Creek Total Phosphorus
Predicted vs. Actual
TP
Measured=
Predicted
Linear (TP)
84
Table 23. Statistical comparison of modeled vs. observed water temperatures for three separate model time
periods (Bowen, Langley et al. 2019).
Parameter Time Period Units 2014-2015 2016 2017-2018
Mean Error (predicted – observed) 0.17 -0.3 -0.8 oC
Normalized Mean Error 1% -2% -4% %
Root Mean Square Error 2.11 1.56 2.07 oC
Normalized Root Mean Square Error 11% 9% 11% %
Correlation R2 94% 97% 96% %
Number of Model/Data Comparisons 1144 370 398 -
Model Efficiency 94% 97% 93% %

Table 24. Statistical comparison of modeled vs. observed chlorophyll-a concentration for three separate
model time periods (Bowen, Langley et al. 2019).
Parameter Time Period Units 2014-2015 2016 2017-2018
Mean Error (predicted – observed) 0.06 0.10 -0.03 log µg/L
Normalized Mean Error 4.1% 6.8% -1.9% %
Root Mean Square Error 0.41 0.40 0.27 log µg/L
Normalized Root Mean Square Error 29% 28% 17% %
Correlation R2 27% 26% 28% %
Number of Model/Data Comparisons 898 366 334 -

period as they were for the two time periods used for model calibration (2014-2015, 2017-2018),
which indicates that the calibrated model can adequately simulate conditions in years other than
the ones for which it was calibrated.

85
5.3. Analysis of Simulated Dye Releases
The age of water is unique to each water parcel that enters the Jordan Lake model domain.
Water age is defined as the time a water parcel has spent since entering the domain at one of its
boundaries. Inherent to the time scale of water age is the recognition of spatial heterogeneity:
parcels at different locations within a waterbody have different ages at any given time. Water
age as a function of time and location in Jordan Lake was calculated based upon the simulation
of a dye study release using the hydrodynamic model. Qi, Lu et al. (2016) describes this
approach in which a conservative tracer is discharged to the lake. Two advection-dispersion
equations for concentration are derived: one for the tracer and another for water age. The water
age at any specific location and time is defined as the ratio of water age concentration to tracer
concentration. Water age differs from flushing time (or retention time) in that flushing time is a
bulk parameter that quantifies the general exchange characteristics of a waterbody without
identifying their spatial distribution. On the other hand, water age can be combined with
constituent concentrations that also vary with location and time to develop a comprehensive
analysis of constituent fate and transport.

The distribution of water age within the model domain at any given simulation time represents
the transient effects of flow boundary locations (inflow and outflow), structures within the lake
(i.e., causeways), and water quality zones, which are defined by local flow conditions (water
depth and width) and water temperature. Water ages (Table 25) were found to most dependent
on the proximity of a given location to inflow boundaries and the time-dependent magnitude of
the lake inflows. Water age at station CPF055D is strongly influenced by inflow from the Haw
River as indicated by the significant decrease in water age corresponding to the high inflow
event occurring in late September/early October (Figure 60). Similarly, water age at CPF087B3
(Figure 63) is influenced by inflow from the New Hope arm of the lake including New Hope
Creek, which also contributed high flows during the same period (Figure 61). Water age at
CPF0880A (Figure 62) is likely influenced by inflows from a smaller tributary, Beaver Creek,
and the Haw River. Its response is similar to that as CPF055D with longer maximum water age.

Table 25. Estimates of Water Age at Selected DWR Monitoring Stations of Jordan Lake, NC for the May
2015-Oct 2015 Model Time Period.

Station
Water Age Range (days)
for 05/01/2015 to 10/31/2015
CPF055D 1-188
CPF086C 1-93
CPF086F 1-108
CPF087B3 1-170
CPF0880A 1-209

86

Figure 60. Water age model estimates (days) at station CPF055D for May 1 to Oct 31, 2015.

Figure 61. Inflows at USGS Gaging Stations 02096960 Haw River and 02097314 New Hope Creek.
\

10
100
1000
10000
100000
5/1/2015 6/1/2015 7/2/2015 8/2/2015 9/2/2015 10/3/2015 11/3/2015
in
f
l
o
w

(
c
f
s
)
Haw River & New Hope Creek Inflows
2096960 Haw R nr Bynum 2097314 New Hope Ck nr Blands
87
Figure 62. Water age model estimates (days) at station CPF0880A for May 1 to Oct 31, 2015.

Figure 63. Water age model estimates (days) at station CPF087B3 for May 1 to Oct 31, 2015.

88

Previous works cite 5 days and 418 days as the average hydraulic retention times for the Haw
River and the New Hope arm of Jordan Lake, respectively (DEHNR 1992). Loftis, Saunders et
al. (1976) reported travel time of less than one day for the Haw River inflow, and greater than a
physical test duration of 120 days for the New Hope inflow. Del Giudice, Aupperle et al. (2019)
reported flushing rates (inverse of water residence time) that ranged from 0.2-1.5 per month for
Segment 1 (furthest north), 0.1-0.5 per month for Segment 2 (north of U.S. 64), 0.1-0.5 per
month for Segment 3 (south of U.S. 64), and 1.0-5.0 for Segment 4 (furthest south) for the period
from 1983 to 2018. These times estimates reflect averaging over space and time, whereas the
water age estimates are specific for both location and time.

A second simulated dye release was done to reveal the extent to which inflow from the Haw
River mixes with waters in each of the four water quality regions of the lake (Table 26). Three
of the regions are in New Hope Creek arm of the lake (above causeways, between causeways,
below causeways), while the fourth region is the western arm of the lake along the old Haw
River bed. As expected, the contributions from the Haw decreased significantly moving uplake
(upstream) from the Haw River region through each region in the New Hope Creek arm of the
lake and eventually into to the Morgan Creek and New Hope arms of the lake (Table 26). At the
same time, the results indicated that the Haw contribution was still potentially measurable

Table 26. Time-average simulated dye concentrations at locations across four Jordan Lake regions.
Higher concentrations indicate a higher contribution from Haw River inflow. The average
concentration for a region is based upon all stations within that region.
Jordan Lake
Region Station
Time-Average Contribution
from Haw River Water (%)
2014-2015 2017-2018
Haw River CPF055C 100% 100%
CPF055D 100% 100%
CPF055E 100% 100%
Average 93.5% 93.1%
Above
Causeways CPF086C 0.0% 1.0%
CPF086D 0.8% 2.8%
CPF086F 1.0% 3.2%
Average 0.0% 1.2%
Between
Causeways CPF087B3 12.0% 20.1%
CPF087D 20.1% 30.0%
Average 16.0% 25.0%
Below
Causeways CPF0880A 59.2% 70.4%
Average 59.2% 70.4%

89
throughout each region of the lake, but makes up only a very small fraction of the input to the
upper and between causeways regions (< 2% and 25% Haw River water respectively). These
results are also consistent with the residence time model described previously, which showed
much longer residence times, and hence lower amounts of flushing in the New Hope Creek arm
of the lake.

90
6. PREDICTION OF WATER QUALITY CHANGES WITH REDUCED NUTRIENT
LOADING

This section presents the results of nutrient reduction scenarios run with the latest version of the
Jordan Lake model. Nutrient concentration reductions from 0% to 70% of the base case nitrogen
state variables (Table 7, state variables 11-15) and phosphorus state variables (Table 7, state
variables 7-10). All state variables for a particular nutrient (N or P) were reduced by an
equivalent amount. The nutrient reduction was implemented by simply multiplying the
appropriate base case state variable time history by the N fraction (1.0 to 0.3) and P fraction (1.0
to 0.3) for that case. No changes were made to the inflow time histories. The model predicted
chl-a concentration time series at the cells representing the eighteen Jordan Lake monitoring
stations (see Appendix 12 for more information on the stations) were taken for the full twenty-
six month model time period. Using this scheme, model predictions from total of 63 nutrient
reduction cases were run and compared to the base case model prediction value. The following
two sections provide the median predicted chlorophyll-a concentrations (section 6.1) and the
fraction of chlorophyll-a concentrations above 40 µg/L (section 6.2) for each of the 64 model
runs.

6.1. Median Predicted Chlorophyll-a Concentrations with Nutrient Reduction

Four separate station groupings were analyzed to determine the median chlorophyll-a
concentration over the entire model run (January 2014 – February 2016), using the model
prediction sampling scheme described above. The station groups were chosen to be
geographically distinct from one another, with stations grouped in such a way that the stations
within a group collectively represented conditions for that portion of the lake. Each station
group uses observations and predictions from multiple stations to increase the reliability of the
scenario results for that group. These four station groupings are as follows (Figure 64,
Appendix 12):

1. All eighteen Jordan lake monitoring stations (see Appendix 12 for list)
2. Haw River arm stations (CPF055C, CPF055C1, CPF055C2, CPF055C3, CPF055C4,
CPF055C5, CPF055C6, CPF055D, CPF055E)
3. Morgan and Upper New Hope Creek stations (CPF081A1B, CPF081A1C, CPF086C,
station 086D was not used because no water quality data were available for the 2014-
2016 time period)
4. Middle New Hope Arm stations (CPF086F, CPF087B3, CPF087D, CPF0880A)

The number of model predictions used to calculate either the median chlorophyll-a concentration
or the fraction of model predictions above the water quality criteria value varied depending on
the number of stations in the particular station grouping (Table 27), but in every case the
estimates were based on many thousands of model predictions.
91

Figure 64. Station Groupings for Analysis of Nutrient Reduction Scenarios

Table 27. Number of Model Predictions Analyzed for to Calculate Median Chlorophyll-a Concentration
and Fraction of Predictions Above Water Quality Criteria Value
Station Grouping Number of Model
Predictions Used in Analysis
All Jordan Lake Stations 54,036
Haw Arm Stations 27,018
Morgan and Upper New Hope Creek Stations 9,006
Middle New Hope Creek Arm Stations 12,008

Morgan & Upper
New Hope
Middle New
Hope
Haw Arm
92
To further increase the utility of the nutrient reduction scenarios results, chlorophyll-a to carbon
ratios for the two algal groups were adjusted for the station groupings that used a subset of total
number of stations. For these three station groups (Haw Arm, Morgan and Upper New
Hope Creeks, Middle New Hope Creek) the chlorophyll to carbon ratio were adjusted so that the
predicted 90th percentile chl-a value matched the corresponding observed value for the base case
model run. Chlorophyll-a concentrations were expected to decrease from this base case in each
of the 63 nutrient reduction scenarios. Manipulation of chlorophyll in this way is reasonable
since in the model chlorophyll-a concentration for an algal group is linearly related to that algal
group’s carbon concentration, and chlorophyll-adjustment does not affect any other water quality
state variable. The four station groupings had adjustment factors that were close to unity (Table
28), but sufficiently above or below 1.0 to affect the calculated nutrient reduction needed to
lower exceedances of 40 ug/L chl-a criteria below the targeted 10% value.

Table 28. Chl-a to Carbon Adjustment Factors for Each Station Group

Station Group Chl-a to C Adjustment
Factor
All Stations 1.00
Haw Arm 1.12
Morgan & Upper New Hope 0.89
Middle New Hope 0.80

As expected, reducing nitrogen and/or phosphorus loading reduced the model predicted median
chlorophyll-a concentrations in the “all station” grouping case. The base case (no nutrient load
reduction) had a median predicted chlorophyll-a concentration of 20.5 ug/L (Table 29). When
just phosphorus loading was reduced by 70%, the median chlorophyll-a concentration decreased
to 14.2 ug/L, a 30.7% reduction in median chlorophyll-a concentration. Reducing just nitrogen
by 70% reduced median chlorophyll-a concentration to 10.2 ug/L, a 50.2% reduction in median
chlorophyll-a concentration (Table 29), indicating that the lake as a whole is more sensitive to
nitrogen reduction as compared to phosphorus. Reducing both nutrients by 70% further reduced
the model predicted median chlorophyll-a concentration to 8.9 ug/L, a 56.6% reduction in
median chlorophyll-a concentration.
Median chlorophyll-a concentrations are generally lower in the Haw arm of Jordan Lake than in
the New Hope Creek arm of the lake and exhibit somewhat different sensitivities to N vs. P load
reductions. The base median chlorophyll-a concentration for the Haw arm stations is 16.1 ug/L.

93
Table 29. Median predicted Chl-a concentrations (µg/L) at all stations for nitrogen and phosphorus load
reductions from 0% to 70% for the 2014-2016 model period.

Station
Set: All Stations
Nitrogen Loading Reduction (%)
0% 10% 20% 30% 40% 50% 60% 70%
P loading 0% 20.5 19.7 18.8 17.8 16.5 14.8 12.8 10.2
reduction 10% 19.8 19.2 18.2 17.2 16.0 14.6 12.6 10.1
(%) 20% 19.0 18.4 17.7 16.6 15.6 14.2 12.4 10.0
30% 18.2 17.6 17.0 16.0 15.0 13.9 12.2 9.9
40% 17.3 16.8 16.2 15.4 14.4 13.3 11.9 9.8
50% 16.3 15.8 15.3 14.6 13.8 12.8 11.5 9.5
60% 15.3 14.8 14.3 13.7 13.0 12.1 11.0 9.3
70% 14.2 13.8 13.2 12.7 12.0 11.3 10.4 8.9

Color Scale
0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0

Reducing phosphorus or nitrogen alone by 70% reduces the corresponding median chlorophyll-a
concentration to 10.1 ug/L when just P is reduced (a 37.2% reduction) and is reduced to a similar
extent (to 10.3 ug/L, a 36.0% reduction) when just nitrogen is reduced by 70% (Table 30).
When both nutrients are reduced by 70% the median chlorophyll-a concentration in the Haw arm
of the lake is reduced by exactly 50% (from 16.1 ug/L to 8.0 ug/L) (Table 30). In general there

Table 30. Median predicted Chl-a concentrations (µg/L) at Haw arm stations for nitrogen and
phosphorus load reductions from 0% to 70% for the 2014-2016 model period.

Station
Set: Haw Stations
Nitrogen Loading Reduction (%)
0% 10% 20% 30% 40% 50% 60% 70%
P loading 0% 16.1 15.7 15.2 14.8 14.4 14.0 12.5 10.3
reduction 10% 15.4 15.0 14.5 14.2 13.8 13.4 12.2 10.2
(%) 20% 14.6 14.2 13.7 13.4 13.1 12.9 11.8 10.0
30% 13.9 13.4 13.0 12.6 12.3 12.2 11.4 9.8
40% 13.0 12.7 12.3 11.8 11.5 11.3 11.0 9.5
50% 12.2 11.8 11.5 11.0 10.6 10.5 10.4 9.1
60% 11.2 10.9 10.6 10.2 9.8 9.5 9.6 8.6
70% 10.1 9.9 9.6 9.3 8.9 8.6 8.6 8.0

Color Scale
0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0
94
are very similar results when either nitrogen or phosphorus is reduced for the Haw arm stations,
indicating a co-limitation of chlorophyll-a concentrations for this part of Jordan Lake.

When the full model time period is considered, the Morgan and Upper New Hope Creek stations
have the lowest chlorophyll-a concentrations in the lake. The median chlorophyll-a
concentration is 12.5 ug/L for the base case without any nutrient reduction (Table 31). This
median chlorophyll-a concentration is roughly 25% lower than the corresponding value for the
Haw arm of the lake. For this station grouping there was a striking difference in the sensitivity
of the chlorophyll-a concentrations to reductions in phosphorus vs. nitrogen. Chlorophyll-a
concentrations were found to be more sensitive to nitrogen loading reductions as compared to
phosphorus loading reductions (Table 31). Decreasing phosphorus loading only by 70%
reduced by median chlorophyll-a concentration by only 20.8% (from 12.5 ug/L to 9.9 ug/L),
whereas reducing nitrogen loading only by 70% reduced median chlorophyll-a by over 60%
(from 12.5 ug/L to 4.7 ug/L. Not surprisingly, due to the insensitivity of chlorophyll-a
concentrations to nitrogen load reductions, reducing both N & P loading by 70% produced little
additional reduction in median chlorophyll-a concentration, with a reduction from 4.7 ug/L to 4.6
ug/L (2.1% additional reduction) when both N and P were reduced by 70% (Table 31).

Table 31. Median predicted Chl-a concentrations (µg/L) at Morgan and Upper New Hope stations for
nitrogen and phosphorus load reductions from 0% to 70% for the 2014-2016 model period.

Station
Set:
Morgan &
Upper New
Hope
Nitrogen Loading Reduction (%)
0% 10% 20% 30% 40% 50% 60% 70%
P loading 0% 12.5 11.9 11.0 10.0 8.9 7.7 6.2 4.7
reduction 10% 12.2 11.6 10.8 10.0 8.8 7.7 6.2 4.7
(%) 20% 11.9 11.2 10.6 9.8 8.8 7.6 6.2 4.7
30% 11.6 11.0 10.4 9.5 8.7 7.6 6.2 4.7
40% 11.1 10.6 10.1 9.2 8.4 7.5 6.2 4.7
50% 10.7 10.3 9.7 9.0 8.2 7.4 6.2 4.7
60% 10.3 9.9 9.3 8.6 8.0 7.2 6.1 4.7
70% 9.9 9.5 8.9 8.3 7.7 6.9 5.9 4.6

Color Scale
0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0

The Middle New Hope Creek stations occupy a spot in Jordan Lake that is between the two other
station sets. It is downstream of the Morgan and Upper New Hope Creek station set, and just
upstream of the Haw River arm stations. As such it receives nutrient loadings from both regions
of the lake, and has the relatively long residence times of the New Hope Creek arm of the lake.
These stations were found to have the highest median chlorophyll-a concentrations. The base
95
case median chlorophyll-a concentration for this station group (25.2 ug/L) was significantly
higher than either the Haw arm (16.1 ug/L) or the Morgan and Upper New Hope Creek station
set (12.5 ug/L) (Table 32). In terms of response to nutrient load reduction, however, this station
group acts like the other New Hope Creek arm set of stations, with chlorophyll-a concentrations
that are relatively insensitive to phosphorus load reductions (Table 32). Reducing phosphorus
loading only by 70% reduces median chlorophyll-a by 15.9% (from 25.2 ug/L to 21.2 ug/L),
while reducing nitrogen loading only by 70% reduces median chlorophyll-a by 54.8% (from 25.2
ug/L to 11.4 ug/L). Reducing both N and P by 70% further reduces median chlorophyll-a
concentration by an additional 2.6% (from 11.4 ug/L to 11.1 ug/L) (Table 32).

Table 32. Median predicted Chl-a concentrations (µg/L) at the Middle New Hope stations for nitrogen
and phosphorus load reductions from 0% to 70% for the 2014-2016 model period.

Station
Set:
Middle New
Hope
Nitrogen Loading Reduction (%)
0% 10% 20% 30% 40% 50% 60% 70%
P loading 0% 25.2 24.2 22.9 21.3 19.6 17.0 14.2 11.4
reduction 10% 24.7 23.7 22.6 21.0 19.4 16.9 14.1 11.4
(%) 20% 24.2 23.3 22.2 20.7 19.1 16.8 14.1 11.4
30% 23.6 22.8 21.8 20.3 18.8 16.8 14.0 11.4
40% 23.0 22.3 21.3 19.9 18.6 16.6 14.0 11.3
50% 22.4 21.7 20.8 19.5 18.2 16.4 13.9 11.3
60% 21.9 21.2 20.3 19.1 17.8 16.2 13.7 11.2
70% 21.2 20.5 19.7 18.7 17.4 15.8 13.6 11.1

Color Scale
0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0

6.2. Fraction of Chlorophyll-a Concentrations Above 40 µg/L for Various Levels of Nutrient
Load Reduction

For each station grouping, the fraction of chlorophyll-a (chl-a) concentrations above the 40 ug/L
North Carolina water quality criteria value was determined. As for the median chl-a
concentrations, four station groupings (all stations, Haw arm stations, Morgan and Upper New
Hope stations, middle New Hope stations (Figure 64) were considered for a base case and 63
nutrient reduction cases that reduced nitrogen and/or phosphorus freshwater loading to the lake
by 0% to 70%. As mentioned earlier, adjustments to chl-a to carbon ratios were implemented so
that the fraction of model predictions that exceeded the 40 ug/L water quality criteria value
exactly match the fraction for the observed data for each station grouping. Those adjustment
96
values increased chlorophyll-a concentrations in the Haw arm station group, and decreased
chlorophyll-a concentration in the other two station groups. Adjustment factors were roughly
ten to twenty percent (Table 28). No adjustment was needed when all stations were considered
as the calibrated model exactly matched the observed 90% percentile chlorophyll-a concentration
(Figure 25).

The base case exceedance fraction of the 40 ug/L criteria when all stations were included in the
analysis was 0.26 (Table 33). As expected, nutrient load reduction produced lower fractions of
exceedance, all the way to an exceedance fraction of 0.04 when both nitrogen and phosphorus
were reduced by 70%. As seen with the median chlorophyll-a concentration values, the system
was somewhat more sensitive to nitrogen load reductions as compared to phosphorus load
reductions. As a result, even the highest level of phosphorus load reduction wasn’t enough to
lower the exceedance fraction below 0.10. Several combinations of load reduction lowered the
exceedance fraction to the target value of 0.10 (Table 33, cells with thick borders), with nitrogen
loading reductions between 50% and 70% and phosphorus loading reductions 0% and 50%.

Table 33. Fraction of model predicted Chl-a concentrations above 40 µg/L at all stations for nitrogen and
phosphorus load reductions from 0% to 70% for the 2014-2016 model period.

Station
Set: All Stations
Nitrogen Loading Reduction (%)
0% 10% 20% 30% 40% 50% 60% 70%
P loading 0% 0.26 0.24 0.23 0.21 0.19 0.17 0.13 0.08
reduction 10% 0.24 0.23 0.21 0.20 0.18 0.15 0.12 0.08
(%) 20% 0.23 0.21 0.19 0.18 0.16 0.13 0.11 0.07
30% 0.21 0.20 0.18 0.16 0.15 0.11 0.09 0.06
40% 0.20 0.19 0.17 0.16 0.14 0.11 0.08 0.05
50% 0.19 0.18 0.16 0.15 0.13 0.10 0.07 0.05
60% 0.18 0.17 0.15 0.14 0.12 0.10 0.07 0.04
70% 0.17 0.15 0.14 0.13 0.11 0.09 0.06 0.04

Color Scale
0.10 0.13 0.16 0.19 0.22 0.25 0.28 0.31 0.40

Not surprisingly, due to generally lower chl-a concentrations, the Haw arm station group had
lower exceedances of the 40 ug/L criteria value and required less nutrient load reduction to reach
the targeted exceedance of 0.10. The base case for the Haw arm stations had an exceedance
fraction of 0.21 (Table 34) as compared with the all station value of 0.26 (Table 33). Whereas a
97
50% nutrient load reduction of both nitrogen and phosphorus was needed for the all station case,
the target exceedance of 0.10 for the Haw arm stations could be reached with a phosphorus only
reduction of 40% and an P+N loading reductions of 40%+10%, or 30%+30% or 70%+0% (Table
34). Further nutrient load reductions below the minimum needed to reach the target value of
0.10 exceedance continued to reduce exceedances. The exceedance fraction reached a minimum
of 0.01 at the maximum load reduction case considered (70% N reduction, 70% P reduction)
(Table 34).

Table 34. Fraction of model predicted Chl-a concentrations above 40 µg/L at Haw Arm stations for
nitrogen and phosphorus load reductions from 0% to 70% for the 2014-2016 model period.

Station
Set: Haw Stations
Nitrogen Loading Reduction (%)
0% 10% 20% 30% 40% 50% 60% 70%
P loading 0% 0.21 0.20 0.18 0.17 0.16 0.16 0.17 0.10
reduction 10% 0.19 0.18 0.16 0.15 0.14 0.14 0.15 0.10
(%) 20% 0.16 0.15 0.14 0.13 0.12 0.12 0.12 0.09
30% 0.13 0.12 0.11 0.09 0.08 0.09 0.09 0.07
40% 0.10 0.10 0.08 0.07 0.06 0.05 0.05 0.05
50% 0.09 0.08 0.07 0.06 0.05 0.03 0.03 0.03
60% 0.08 0.08 0.06 0.05 0.04 0.03 0.02 0.02
70% 0.07 0.07 0.05 0.04 0.03 0.02 0.01 0.01

Color Scale
0.10 0.13 0.16 0.19 0.22 0.25 0.28 0.31 0.40

In contrast to the Haw arm stations, the Morgan and Upper New Hope Creek stations had the
highest exceedance fraction of the 40 ug/L criteria value (0.29) and required the highest level of
nutrient load reduction to reach the target exceedance fraction of 0.10 (Table 35). As seen
earlier with the median chlorophyll-a concentration, this station group had a higher degree of
sensitivity to nitrogen load reductions as compared to phosphorus load reductions. For instance,
reducing the P loading only by 70% reduced the exceedance fraction from 0.29 to 0.22, but
reducing N loading by the same amount (70%) lowered the exceedance fraction from 0.29 to
0.05. As a result, any level of P load reduction with an N load reduction of 70% was sufficient
to lower the exceedance fraction below 0.10 (Table 35). The minimum nitrogen load reduction
needed to achieve the target exceedance fraction was 60%, and for this case a phosphorus load
reduction of 50% was also required (Table 35).

98
Table 35. Fraction of model predicted Chl-a concentrations above 40 µg/L at Morgan and Upper New
Hope Creek stations for nitrogen and phosphorus load reductions from 0% to 70% for the
2014-2016 model period.

Station
Set:
Morgan &
Upper New
Hope
Nitrogen Loading Reduction (%)
0% 10% 20% 30% 40% 50% 60% 70%
P loading 0% 0.29 0.28 0.26 0.25 0.22 0.17 0.11 0.05
reduction 10% 0.29 0.27 0.26 0.24 0.22 0.16 0.11 0.05
(%) 20% 0.28 0.26 0.25 0.24 0.21 0.16 0.11 0.05
30% 0.27 0.25 0.24 0.23 0.21 0.16 0.11 0.05
40% 0.26 0.24 0.24 0.22 0.20 0.16 0.11 0.05
50% 0.24 0.23 0.23 0.21 0.19 0.15 0.10 0.05
60% 0.23 0.22 0.21 0.20 0.18 0.14 0.10 0.05
70% 0.22 0.21 0.20 0.19 0.17 0.13 0.09 0.05

Color Scale
0.10 0.13 0.16 0.19 0.22 0.25 0.28 0.31 0.40

The middle New Hope stations had exceedance fractions that were generally in between the two
adjoining station groups. The base case exceedance fraction of 0.25 for this station group (Table
36) was above the Haw arm station group value of 0.16 (Table 34) and below the Morgan and
Upper New Hope value of 0.29. The sensitivity to load reduction for this station group is similar
as seen when looking at median chlorophyll-a concentrations, and similar to what was seen for
the other New Hope Creek arm station group. In the middle New Hope stations, exceedance
fractions were much more sensitive to nitrogen load reductions than they were to phosphorus
load reductions. Reducing only the phosphorus loading by 70% reduced the exceedance fraction
from 0.25 to 0.15, but reducing only the nitrogen loading reduced the exceedance fraction from
0.25 to 0.06 (Table 36). As seen in other groups, several combinations of N+P load reduction
produced an exceedance fraction of 0.10 (P%+N% reduction = 70%+30%, 50%+40%,
10%+50%, 0%+~55%) (Table 36). As seen in the Haw arm stations, additional load reductions
continued to lower the exceedance fraction below 0.10. Reducing both N and P by 50%, 60%
and 70% reduced the exceedance fractions to 0.08, 0.05, and 0.02 (Table 36).

99

Table 36. Fraction of model predicted Chl-a concentrations above 40 µg/L at Middle New Hope stations
for nitrogen and phosphorus load reductions from 0% to 70% for the 2014-2016 model period.

Station
Set:
Middle New
Hope
Nitrogen Loading Reduction (%)
0% 10% 20% 30% 40% 50% 60% 70%
P loading 0% 0.25 0.23 0.21 0.18 0.13 0.11 0.09 0.06
reduction 10% 0.24 0.22 0.20 0.17 0.13 0.10 0.08 0.06
(%) 20% 0.23 0.21 0.19 0.17 0.12 0.10 0.08 0.05
30% 0.22 0.20 0.19 0.16 0.12 0.09 0.07 0.05
40% 0.21 0.19 0.18 0.15 0.11 0.09 0.07 0.04
50% 0.19 0.18 0.16 0.14 0.10 0.08 0.06 0.04
60% 0.17 0.16 0.14 0.12 0.08 0.06 0.05 0.03
70% 0.15 0.14 0.12 0.10 0.07 0.05 0.03 0.02

Color Scale
0.10 0.13 0.16 0.19 0.22 0.25 0.28 0.31 0.40

100

7. SUMMARY, DISCUSSION, AND CONCLUSIONS
The nutrient load reduction scenario analysis was designed to simulate the effects of a wide
range of possible reductions in both nitrogen and phosphorus loading. Considering this broad
range of possibilities with their resulting reductions in chlorophyll-a concentrations, a few
conclusions emerge. The first has to do with the magnitude of the decreases in chlorophyll-a
concentrations. Overall, the decreases in chlorophyll-a were generally less in magnitude than the
corresponding reductions in nutrient loading. For instance, a 50% N and P load reduction
produced a 35% decrease in chlorophyll-a concentrations for all stations (Table 29), a 35%
reduction in the Haw arm median chlorophyll value (Table 30), and a 35% reduction in the
middle New Hope Creek stations’ median chlorophyll concentration (Table 32). The Morgan
and Upper New Hope Creek arm stations were slightly more sensitive to load reduction,
however, with the 50% N and P reduction producing a 41% reduction in median chlorophyll-at
this station group (Table 31). The sensitivity of the New Hope arm of the lake to nitrogen load
reduction rather than phosphorus reduction was quite a surprising result that we believe bears
some additional investigation.

A second conclusion relates to the magnitude of the needed algal biomass reductions to meet
numeric water quality criteria. The analysis of 2014-2018 chlorophyll-a monitoring data in
Jordan Lake (Table 5) showed that a 44% biomass reduction would be needed to lower
chlorophyll-a concentrations enough so that Jordan Lake meets current numeric water quality
criteria for chlorophyll-a. This estimate turns out to be reasonably close to the conclusion based
upon the scenario analysis using the Jordan Lake water quality model, but only for the Haw Arm
of the lake. The analysis presented in this report showed that an average nutrient load reduction
of approximately 30-35% (e.g. 40% P + 20%N, or 30% P + 30% N, or 0%P + 70%N) was the
minimum needed to reduce the fraction of exceedances of the 40 ug/L chlorophyll-a criteria
below the target value of 0.10 (Table 34) for the Haw stations. For the entire lake, however,
higher nitrogen load reductions (>50%) are needed to meet water quality criteria (Table 33).
This can be seen as a consequence of the relatively higher chlorophyll concentrations in the New
Hope Creek arm of the lake, and the relative insensitivity of the chlorophyll-a concentrations to
phosphorus load reduction for the New Hope Creek arm. Taken together, these two results, one
based on the analysis of monitoring data, and the second based upon results from the model,
suggest that Jordan Lake needs significant load reductions in nitrogen over a period time to meet
water quality criteria. The results presented here are similar to our previous conclusions in
earlier modeling work (Bowen, Langley et al. 2019, Langley and Bowen 2020), but there are
significant differences seen in the model’s sensitivity to load reductions in nitrogen vs.
phosphorus in various parts of the lake. We attribute this to our current model doing a better job
of simulating bloom conditions across the lake and to a decision to adjust chlorophyll-a
conditions when necessary to guarantee that the 90th percentile model predicted chlorophyll-a
concentration matches the observed data. Given our calibration objective of matching the
chlorophyll CDF and the overall calibration performance of the model adds to our confidence in
the prediction of the needed load reduction.

101
Water quality calibration relied upon existing data for the most part. Unfortunately, there was
some missing information in the water quality monitoring dataset provided by NC DWR that
would have been useful for this modeling effort. In particular, there was no information
available in either the watershed data or the lake data on phosphorus fractionation. Datasets used
to support model efforts of this sort usually include orthophosphate measurements since this is
the bioavailable fraction taken up phytoplankton during photosynthesis. It should be noted that
some dissolved phosphorus and dissolved TKN data are available, but only for the 2017-2018
time period. This time period has been modeled previously (Bowen, Langley et al. 2019,
Langley and Bowen 2020), but because of its relative lack of chlorophyll data it was not modeled
in this study. Nitrogen fractionation into ammonia, Kjeldahl, and nitrate nitrogen was available
and was helpful in creating model input files and for calibration purposes. We believe that our
approach to estimate inorganic phosphorus using the nitrogen fractionation is a reasonable
approach, but we believe the model results might have been improved with actual measurements
of both inorganic and total phosphorus.

Using published criteria for model fitness based upon normalized mean error and normalized
RMSE (Systech Water Resources, Brown and Caldwell et al. 2018), the Jordan Lake model
presented here was found to be graded as “very good” for temperature, chlorophyll-a, and total
phosphorus. Although these measures were previously used for a watershed model, they are
considered to be also useful for evaluating lake model calibration performance. Each of these
state variables had normalized mean errors well below the 7% criteria for water temperature and
15% for water quality/nutrients (temperature = -2.7%, chl-a = -14%, TP = 4.6%) (Table 14,
Table 16, Table 21). The model also had acceptable fitness relative to the published goals for
normalized RMSE (50% for elevation, temperature, DO; 100% for other water quality) (Systech
Water Resources, Brown and Caldwell et al. 2018) for all hydrodynamic and water quality
variables (elevation = 0.3%, temp = 9.7%, chl = 72%, TKN = 54%, TN = 56%, TP = 75%, DO =
29%) except for two of the nitrogen species (normalized RMSE = 239% and 162% for ammonia
and nitrate+nitrite). Given the temporally dynamic nature of Jordan Lake, the hypereutrophic
water quality conditions, the extremely long residence times, the shallow water depths, and the
diversity of hydrologic conditions in the two arms of the lake, we believe this to be extremely
good performance for the model, and fully qualifies the model for use as a predictor of impact of
reduced nutrient loading to the lake.

A surprising challenge in this modeling effort was the simulation of water surface elevations.
Considerable effort was expended during the project on the estimation of the flow hydrograph
for the ungaged portion of the Jordan Lake watershed. The simple scaling approach based upon
watershed area for estimating watershed hydrographs produced unacceptably large errors in the
water surface elevation time histories at the dam. A review of previous modeling work on
Jordan Lake (Tetra Tech 2002, Tetra Tech 2003) and other recent modeling work using the
EFDC+ model for lake water quality simulation (Dynamic Solutions 2013, Michael Baker 2015)
indicated that the problems we experienced are not unique. The earlier Jordan Lake model and
the more recent Tenkiller reservoir model significantly improved the fit to observed water
surface elevations by increasing the outflows at the dam. We eventually chose to adjust
102
watershed inflows and the outflow hydrograph at the dam using a water balance estimation
approach. We adopted a judgement based approach on the limits to the adjustments that
incorporated a temporal smoothing of the adjustment flows and a limit on their magnitude.
These approaches were found to be sufficient to produce a good match to the observed water
surface elevations.

Another challenging aspect of the modeling effort related to the specification of benthic fluxes.
The original plan for this project to use both the prescriptive sediment flux approach and also use
the version of the Di Toro and Fitpatrick (Di Toro and Fitzpatrick 1993) sediment diagenesis
model that is available in this version of EFDC. The sediment diagenesis model had been used
for the Jordan Lake model, but it was prone to numerical instabilities and required very short
time steps that made for long model run times. This project included an effort to use the
diagenesis model, but eventually we settled on the use of the prescriptive approach only because
of ongoing problems with numerical instability. We believe the issues with the diagenesis model
relate to the special character of Jordan Lake. The lake is hypereutrophic, shallow, and subject
to rapid changes in water level that produce drying and wetting of a significant number of model
cells. For instance over our twenty-six month model time period we saw water surface elevation
variations of more than six meters (Figure 21). We undertook a long and eventually fruitless
attempt to use the diagenesis model (Craig 2018) for this study, but it proved difficult to
impossible to run, primarily due to numerical instability issues. To prevent numerical
instabilities, the maximum model time step was decreased to four seconds, rather than the 100
second values that could be used for a model using the standard EFDC specified spatially and
temporally varying benthic fluxes. Shortening the model time step by a factor of twenty
increased computer model run times by a similar amount so that a two year run took closer to
forty hours rather than two hours. This greatly complicated and lengthened model calibration
and scenario testing in cases using the predictive sediment model. To get the work done within
the available time, we therefore followed an approach that used a prescribed benthic flux. We
ended up not being able to do some model analyses that we had planned and have used in
previous work (Bowen and Harrigan 2017, Bowen and Harrigan 2018). We believe that all the
same factors that challenged our water quality calibration also made use of the diagenesis model
very challenging. We were satisfied with the calibration performance of the prescribed flux
model, but it did perhaps compromise our ability to model nitrate and ammonia, and did prevent
us from doing some of the analyses of system response time that we have done in the past.

103
8. REFERENCES
Abdelrhman, M. (2015). "Three-Dimensional Modeling of Hydrodynamics and Transport in
Narragansett Bay." Narragansett, Rhode Island: US Environmental Protection Agency, Office of
Research and Development, EPA.

Ambrose, R. B., et al. (1993). "The water quality analysis simulation program, WASP5, Part A:
Model documentation." Environmental Research Laboratory, US Environmental Protection
Agency, Athens, GA.

Bowen, J. and N. Harrigan (2018). "Water Quality Model Calibration via a Full-Factorial
Analysis of Algal Growth Kinetic Parameters." Journal of Marine Science and Engineering 6(4):
137.

Bowen, J. and N. B. Harrigan (2017). Comparing the impact of organic versus inorganic nitrogen
loading to the Neuse River Estuary with a mechanistic eutrophication model, NC WRRI.

Bowen, J. D., et al. (2022). Developing an Estimate of Needed Nutrient Load Reductions for
Jordan Lake Using a Three-Dimensional Water Quality Model. Charlotte, North Carolina, UNC
Charlotte: 74.

Bowen, J. D. and J. Hieronymus (2000). Neuse River Estuary modeling and monitoring project
stage 1: Predictions and uncertainty analysis of response to nutrient loading using a mechanistic
eutrophication model, NC Water Resources Research Institute: 124.

Bowen, J. D. and J. W. Hieronymus (2003). "A CE-QUAL-W2 model of Neuse Estuary for total
maximum daily load development." Journal of Water Resources Planning and Management
129(4): 283-294.

Bowen, J. D., et al. (2019). Jordan Lake Responses to Reduced Nutrient Loading: Results from a
New Three-Dimensional Mechanistic Water Quality Model. Chapel Hill, NC, UNC Policy
Collaboratory.

Bowen, J. D., et al. (2009). Development and Use of a Three-Dimensional Water Quality Model
to Predict Dissolved Oxygen Concentrations in the Lower Cape Fear River Estuary, North
Carolina. Charlotte, NC, University of North Carolina at Charlotte.

Chapra, S. C. (2008). Surface water-quality modeling, Waveland press.

Cole, T. M. and E. M. Buchak (1995). CE-QUAL-W2: A Two-Dimensional, Laterally Averaged,
Hydrodynamic and Water Quality Model, Version 2.0. User Manual, DTIC Document.
104

Cole, T. M. and S. A. Wells (2006). "CE-QUAL-W2: A two-dimensional, laterally averaged,
Hydrodynamic and Water Quality Model, Version 3.5."

Craig, P. M. (2018). User’s Manual for EFDC_Explorer8.4: A Pre/Post Processor for the
Environmental Fluid Dynamics Code. May, 2018, EFDC_Explorer8.4 Version 180509, Dynamic
Solutions- International (DSI), Seattle, WA 98020.

DEHNR, N. (1992). North Carolina lake assessment report, Report.

Del Giudice, D., et al. (2019). "Jordan Lake Reservoir Model Report." North Carolina Policy
Collaboratory.

Di Toro, D. M. and J. J. Fitzpatrick (1993). Chesapeake Bay sediment flux model, Hydroqual Inc
Mahwah NJ.

Duclaud, B. R. and J. D. Bowen (2007). Using Turbulence Model Results to Quantify Oxygen
Reaeration in an Estuary Dissolved Oxygen Model. Estuarine and Coastal Modeling (2007),
ASCE.

Dynamic Solutions, L. (2013). "Final Lake Thunderbird Report for Nutrient, Turbidity, and
Dissolved Oxygen TMDLs, prepared for Oklahoma Department of Environmental Quality,
Water Quality Division, Oklahoma City, OK." 306.

Falls Lake Technical Advisory Committee (2009). Falls Lake Nutrient Response Model Final
Report, prepared for Modeling & TMDL Unit, Division of Water Quality, North Carolina
Department of Environment and Natural Resources: 145.

Froelich, B., et al. (2013). "Mechanistic and statistical models of total< i> Vibrio</i> abundance
in the Neuse River Estuary." Water research 47(15): 5783-5793.

Georgia Department of Natural Resources-Environmental Protection Division (2017). Final
Total Maximum Daily Load Evaluation for Lake Lanier in the Chattahoochee River Basin for
Chlorophyll a, submitted to U.S. Environmental Protection Agency Region 4, Atlanta, GA: 130.

Hamrick, J. M. (1992). A three-dimensional environmental fluid dynamics computer code:
theoretical and computational aspects, Virginia Institute of Marine Science, College of William
and Mary.

105
Harrigan, N. and J. D. Bowen (2016). "Three for the Price of Two? A comparison of circulation
in the Neuse River Estuary predicted by a twoand three-dimensional model." Retrieved June 1,
2017, 2017, from https://www.watersmartinnovations.com/documents/poster_sessions/2016/P-
18.pdf.

Hieronymus, J. and J. D. Bowen (2004). Calibration and Verification of a Two-dimensional
Laterally Averaged Mechanistic Model of the Neuse River Estuary, Water Resources Research
Institute of the University of North Carolina.

Hirsch, R. M. and L. A. De Cicco (2015). User guide to Exploration and Graphics for RivEr
Trends (EGRET) and data retrieval: R packages for hydrologic data. Techniques and Methods 4-
A10. Reston, VA: 104.

Khangaonkar, T., et al. (2012). Puget Sound Dissolved Oxygen Modeling Study: Development
of an Intermediate Scale Water Quality Model, Washington State Department of Ecology
Publication No. 12-03-049, PNNL-20384 Rev 1, prepared for U.S. Department of Energy
Contract DE-AC05-76RL01830: 166.

Langley, W. and J. D. Bowen (2020). "Development and Use of a New Three-Dimensional
Mechanistic Water Quality Model of Jordan Lake to Predict Responses of Reduced Nutrient
Loading." A Report to the North Carolina Policy Collaboratory.

Lin, J. and J. Li (2011). "Nutrient response modeling in falls of the Neuse Reservoir."
Environmental Management 47(3): 398-409.

Loftis, B., et al. (1976). B. Everett Jordan Lake Water-quality Study, US Department of Defense,
Department of the Army, Corps of Engineers ….

Michael Baker, J., Inc., Aqua Terra Consultants, and Dynamic Solutions, LLC, (2015). Setup,
calibration, and validation for Illinois River Watershed Nutrient Model and Tenkiller Ferry Lake
EFDC Water Quality Model, prepared for the U.S. EPA, Region 6, Dallas, TX: 351.

Miller, J., et al. (2019). Jordan Lake Watershed Model Report, North Carolina Policy
Collaboratory.

NC Division of Water Resources (2017). Surface Water Quality Standards History Document:
Chlorophyll a. Raleigh, NC, NC Division of Water Resources: 2.

NC DWQ (2007). B. Everett Jordan Reservoir, North Carolina Phase I Total Maximum Daily
Load. Prepared by: NC Department of Environment and Natural Resources Division of Water
Quality, 1617 Mail Service Center Raleigh, NC 27699-1617. September 20, 2007.
106

NCDWR, N. C. D. o. W. R. (2018). 2018 303(d) LISTING AND DELISTING
METHODOLOGY, Approved by the North Carolina Environmental Management Commission
on March 8, 2018. N. C. D. o. E. Quality. Raleigh, NC.

Negusse, S. M. and J. D. Bowen (2009). Application of Three-Dimensional Hydrodynamic and
Water Quality Models to Study Water Hyacinth Infestation in Lake Nokoué, Benin. Estuarine
and Coastal Modeling (2009), ASCE.

Qi, H., et al. (2016). "Water age prediction and its potential impacts on water quality using a
hydrodynamic model for Poyang Lake, China." Environmental Science and Pollution Research
23(13): 13327-13341.

Redfield, A. C. (1958). "The biological control of chemical factors in the environment."
American scientist 46(3): 230A-221.

Rosenberry, D. O., et al. (2007). "Comparison of 15 evaporation methods applied to a small
mountain lake in the northeastern USA." Journal of hydrology 340(3-4): 149-166.

Systech Water Resources, et al. (2018). QUALITY ASSURANCE PROJECT PLAN for
The Upper Neuse River Basin Association Falls Lake and Watershed Modeling: 78
.

Tetra Tech (2002). Jordan Lake Nutrient Response Model. Prepared for the Jordan Lake Project
Partners by Tetra Tech, Inc., Research Triangle Park, NC. Nov. 13, 2002.

Tetra Tech (2003). B. Everett Jordan Reservoir Nutrient Response Model Enhancement.
Prepared for the North Carolina Division of Water Quality by Tetra Tech, Inc., Research
Triangle Park, NC. Final - September 2003.

Tetra Tech (2007). "The Environmental Fluid Dynamics Code." User Manual, US EPA Version
1: 231.

Tetra Tech, I. (2016). High Rock Lake Hydrodynamic and Nutrient Response Models, draft
report under EPA Contract (Sept 2012), report finalized by North Carolina Division of Water
Resources (Oct 2016): 94.

Tetra Tech Inc. (2003). B. Everett Jordan Lake Nutrient Response Model Enhancement.
Research Triangle Park, NC.
107

UNC Policy Collaboratory (2019). The University of North Carolina Jordan Lake Study,
Final Report to the North Carolina General Assembly, December 2019, The University of North
Carolina at Chapel Hill.

USACE (2019). "B. Everett Jordan - Cape Fear River Basin Data Retrieval." from
https://epec.saw.usace.army.mil/jord.htm.

Willey, J. D. and R. H. Kiefer (1993). "Atmospheric Deposition in Southeastern North Carolina:
Composition and Quantity." The Journal of the Elisha Mitchell Scientific Scociety 109(1): 1-19.

Zhang, Z., et al. (2015). "Integration of a benthic sediment diagenesis module into the two
dimensional hydrodynamic and water quality model–CE-QUAL-W2." Ecological modelling
297: 213-231.

108
Appendix 1. Creating Constituent Time Series at Inflow Model Boundaries

EFDC requires specification of the complete time history of all model constituents at each inflow
model boundary. For this Jordan Lake model, thirteen model boundaries were considered inflow
boundaries with a corresponding flow time history that was also specified in the QSER.INP file.
There were a total of fifteen model boundaries, two of which were outflow boundaries (Jordan
Lake Dam, Cary Water Treatment Plant specified as QSER7 and QSER13). More information
on the location of these fifteen model inputs and outputs can be found elsewhere (Bowen,
Langley et al. 2019, Langley and Bowen 2020) Five concentration time histories were used to
specify the thirteen inflowing model boundaries, as follows:

1. Haw River: QSER1
2. Morgan Creek: QSER2
3. New Hope Creek: QSER3
4. Northeast Creek: QSER4
5. White Oak Creek: QSER5
6. Ungaged watersheds discharging directly to Jordan Lake: QSER6, QSER8, QSER9,
QSER10, QSER11, QSER12, QSER14, QSER15 (used White Oak Creek concentrations
to estimate these concentrations)

There were eight ungaged watersheds that drained directly to Jordan Lake. One of these was in
the Haw River arm of the lake (QSER12). Three additional ungaged watersheds were in the
region above the Farrington Road causeway (QSER6, QSER14, QSER15) draining to Jordan
Lake from the west, north, and east. Two other ungaged watersheds (QSER10, QSER11)
drained to the Jordan Lake region below the Highway 64 causeway from the west and east. The
remaining two ungaged watersheds (QSER8, QSER9) drained to the region of Jordan Lake
between these two causeways from the west and east respectively.

Concentration time histories were developed on a daily basis for each model state variable. The
nutrient time histories used two separate pieces of information on nutrient loading and nutrient
fractionation. The nutrient loading data came from a WRTDS-based watershed model created
for the Jordan Lake (Miller, Karimi et al. 2019). The nutrient fractionation information came
from an analysis of water quality monitoring data collected by the North Carolina Department of
Environmental Quality (NC DEQ) in the Jordan Lake watershed at five locations (Haw River,
Morgan Creek, New Hope Creek Northeast Creek, White Oak Creek). The ungaged watersheds
used the White Oak Creek data to specify the concentration time history information.

A total of twenty-one concentration time history files (cwqsr1.inp to cwqsr21.inp), one for each
EFDC water quality state variable, were created for the full model time history (January 1, 2014
– December 31, 2018) at a daily time frequency. In each of these twenty-one files were all six
concentration time histories (the ungaged watersheds were treated as a sixth time history but
109
used White Oak creek data for the concentraitons) for that particular water quality state variable.
Additional information on the procedures for developing these time histories is provided below.

Daily data were available for only two constituents (total nitrogen, total phosphorus). These data
were estimated as a daily load by the WRTDS watershed model created for the Jordan Lake
(Miller, Karimi et al. 2019). Daily concentrations were calculated from the daily loads using the
daily flow for that watershed. The other three nitrogen state variable concentrations (ammonia,
nitrite+nitrate, organic nitrogen) were estimated on a daily basis by developing monthly
estimates of nitrogen fractionation. The fractionation estimates relied on the DWR watershed
data for a nineteen-year time period (2000-2018). Using all the available data, monthly average
values of total nitrogen, total phosphorus, ammonia fraction, nitrite+nitrate fraction, and organic
nitrogen fraction were calculated (Appendix 11). The nitrogen fractionation values were then
used to create daily estimates of ammonia, NOx, and organic nitrogen by multiplying the daily
TN value that came from the WRTDS model by the appropreiate nutrient fraction for that month
and that fraction (i.e., ammonia, NOx, organic) . In this way the WRTDS watershed model was
used together with the monitoring data to get the necessary daily nutrient time history.

Together with the calculated TN and TP concentration, the analysis described above provided
five time histories. A sixth parameter, dissolved oxygen, was also estimated on a daily
frequency for each of the five time histories using the available temperature data, with the
assumption that the inflowing waters were fully saturated with dissolved oxygen. Additional
information on how the remaining model state variables were estimated is provided after the
presentation of the nitrogen fractionation results.

There were some interesting features in the nutrient fractionation estimates for the five Jordan
Lake watersheds examined. The monthly nitrogen fractions were similar for four (Haw River,
Morgan Creek, New Hope Creek, Northeast Creek, Figures A1.1 – A1.4) of the watersheds, with
the White Oak Creek watershed (Figure A1.5) being the exception. These four watersheds were
found to have a nitrogen fractionation pattern that is typical for watersheds containing domestic
wastewater treatment plant inputs. For these watersheds, most of the nitrogen was in the nitrite +
nitrate fraction. The ammonia fraction had some seasonality, but was always less than 10%. The
organic fraction also showed some seasonality, with higher values in the spring months and
lower values in the fall months. The organic fraction in the Haw River (Figure A1- 2) was
generally higher than the three watersheds in the New Hope Creek arm of the lake (Figure A1- 2,
Figure A1- 3, Figure A1- 4). Total nitrogen concentrations were also lower for the Haw River
station as compared to the three stations in the New Hope Creek arm of the lake (data not
shown).

110

Figure A1- 1. Calculated Average Monthly Haw River Nitrogen Fractionation.

The one watershed without a wastewater treatment plant input, White Oak Creek (Figure A1- 5)
had a nitrogen fractionation that was significantly different than the other watersheds. In the
White Oak Creek watershed, the nitrogen was primarily in the organic fraction. Like the other
watersheds, the ammonia fraction was always less than 10% in all months (Figure A1- 5). There
was some seasonality in the nitrate + nitrite fraction, with highest values in the winter and spring
months. For all months the nitrate + nitrite fraction was less than 20%, which is significantly
less than seen in the other four stations (Figures A1.1 – A1.4).

0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1 2 3 4 5 6 7 8 9 10 11 12
Fr
a
c
t
i
o
n
Month
Haw River Nitrogen Fractionation
Am frac
NOX frac
Or Frac
111

Figure A1- 2. Calculated Average Monthly Morgan Creek Nitrogen Fractionation.

Figure A1- 3. Calculated Average Monthly New Hope Creek Nitrogen Fractionation.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1 2 3 4 5 6 7 8 9 10 11 12
Fr
a
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t
i
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Month
Morgan Creek Nitrogen Fractionation
Am frac
NOX frac
Or Frac
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1 2 3 4 5 6 7 8 9 10 11 12
Fr
a
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t
i
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n

Month
New Hope Creek Nitrogen Fractionation
Am frac
NOX frac
Or Frac
112
Figure A1- 4. Calculated Average Monthly Northeast Creek Nitrogen Fractionation.

Figure A1- 5. Calculated Average Monthly White Oak Creek Nitrogen Fractionation.

Monthly average total nitrogen and total phosphorus concentrations were also calculated for each
of the five watersheds using all available data in the Jordan Lake watershed from 2000 – 2018.
The four watersheds having domestic wastewater inputs (Haw River, Morgan Creek, Northeast
Creek, New Hope Creek) had significantly higher total P values than that for White Oak Creek,
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1 2 3 4 5 6 7 8 9 10 11 12
Fr
a
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t
i
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n
Month
Northeast Creek Nitrogen Fractionation
Am frac
NOX frac
Or Frac
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1 2 3 4 5 6 7 8 9 10 11 12
Fr
a
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t
i
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Month
White Oak Creek Nitrogen Fractionation
Am frac
NOX frac
Or Frac
113
which does not have a wastewater input (Figure A1- 6). Total phosphorus values were higher for
the three watersheds in New Hope Creek arm of the lake (Morgan Creek, New Hope Creek,
Northeast Creek). The total nitrogen concentrations show a similar pattern, with significantly
lower values in White Oak Creek than the other four watersheds, and somewhat lower values for
the Haw River than the other three watersheds in the New Hope Creek arm of the lake that
receive domestic wastewater inputs (data not shown).

Figure A1- 6. Monthly calculated total P concentrations (mg/L) for the five watersheds used to quantify
concentration time series for the Jordan Lake model.

The data analysis described produced daily time histories at the five locations for six
constituents:

1. Total Nitrogen (mg/L as N)
2. Total Phosphorus (mg/L as P)
3. Ammonia Nitrogen (mg/L as N)
4. Nitrite + Nitrate Nitrogen (mg/L as N)
5. Organic Nitrogen (mg/L as N)
6. Dissolved Oxygen (mg/L)

0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
1 2 3 4 5 6 7 8 9 10 11 12
TP

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.

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m
g
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L

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Haw
Morgan
New Hope
Northeast
White Oak
114
The remaining water quality constituents were estimated using the following assumptions:

• organic matter fractions for carbon, nitrogen, and phosphorus were assumed to be evenly
split between the labile particulate, refractory particulate, and dissolved constituents
• a background concentration of 0.01 mg/L was used for the three algal constituents
• the organic phosphorus concentration for all three constituents (labile particulate,
refractory particulate, dissolved) was calculated from the organic nitrogen fraction using
the Redfield N/P ratio
• the organic carbon concentration for all three constituents (labile particulate, refractory
particulate, dissolved) was calculated from the organic nitrogen fraction using the
Redfield N/C ratio
• soluble reactive phosphorus (total phosphate) was assumed to be equal to the total
phosphorus minus organic phosphorus
• the background ultimate carbonaceous oxygen demand was assumed to be 2.0 mg/L
• all concentrations were adjusted if necessary to be greater or equal to zero

Time history tables for all constituents (21 columns) were then calculated using matrix
multiplication from the six daily time histories (TN, TP, Ammonia N, NOx N, Organic N, DO),
using a 6x21 transformation matrix that was calculated using the assumptions shown above
(Figure A1- 7). The figure has been rotated to maximize the font size.
115

Figure A1- 7. Transformation Matrix Used to Develop the Concentration Time Histories for the Base
Case of the Jordan Lake Model (NB: COD background concentration has been updated to
be 0.0 mg/L).
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116
The transformation matrix approach was used to create six separate daily time histories for all
twenty-one EFDC water quality model constituents. These six sheets are found in the same .xlsx
file that contains the monitoring data time histories and the transformation matrix that creates
each state variable time history. For the updated Jordan Lake model the .xlsx file was called
1_Base_NewTONvsTOP.xlsx (a 14.4 MB .xlsx Excel spreadsheet with a last modification date
and time of September 27, 2021 at 3:35 PM).

A Matlab script (make_CWQSR.m, code below) was then used to take the data from the six
Excel sheets containing all 21 state variable time histories and then write the information into the
twenty-one files needed (cwqsr01.inp to cwqsr21.inp) needed by EFDC. The Matlab code for
this script is provided below. This function was also used as a part of the Matlab script
(RunN_P_ReductionScenarios) that was used to setup, run, and analyze the nutrient reduction
scenarios.

function make_CWQSR();
% make_PS_conc.m
% reads a spreadsheet w/ Point Source Concentration information
% and makes the concentration input files cwqrs##.inp files

% set the project name
% deleted curvy brace to fix Endnote bug
Project = 'Jordan Lake Model' ;

%set day 0 date (jday 0.0 is at 12:00 AM on this date)
jday0 = '01/01/2011';

% set the constituent names
constit = get_constit_names();

if ispc
slc = '\';
else
slc = '/';
end

% load the saved filename and path
load make_CWQSR

disp('choose a spreadsheet with the point source info');

[xlfile, xlpath] = uigetfile([xlpath,'*.xlsx'], 'Pick a spreadsheet w/ the
point source info');

disp(['Existing number of layers = ',int2str(num_lay)]);
opn = 'y';
while ~strcmp(opn,'y') & ~strcmp(opn,'n')
opn = input('Do you want to use the existing number of layers? (y or n):
','s');
117
end

if strcmp(opn,'n')
%set qser output filename
num_lay = input('Enter the number of layers: ');
end
% concentrations are uniform vertically
lyrs(1:num_lay) = 1.0;

save make_CWQSR xlpath xlfile num_lay

opn = 'y';
while ~strcmp(opn,'y') & ~strcmp(opn,'n')
opn = input('Do you want to use the list of point sources set in the
script? (y or n): ','s');
end
if strcmp(opn,'y')
%set the sheets to include
incl_sheet = {'PS 1, Haw R.';'PS 2, Morgan Ck.';'PS 3, New Hope Ck.';'PS
4, Northeast Ck.' ; ...
'PS 5, White Oak Ck.';'PS 6, Ungaged Watersheds'};
choose_sheets = false;
else
choose_sheets = true;
end

% now get the list of sheets in spreadsheet
[status,sheets] = xlsfinfo([xlpath,xlfile]);

if choose_sheets
disp('these are the sheets available, choose the number to select one');

for i = 1: length(sheets)
disp([int2str(i),' = ',char(sheets(i))]);
end

num_PS = input('How many point sources to include? ');
sheetnum(1:num_PS) = 0;
for i = 1:num_PS
while sheetnum(i) < 1 | sheetnum(i) > length(sheets)
sheetnum(i) = input('Choose a sheet number: ');
end
end
else
num_PS = size(incl_sheet,1);
% look for each point source in spreadsheet
for i = 1:num_PS
j = 1 ;
while ~strcmp(char(incl_sheet(i)),char(sheets(j))) & j<
size(sheets,2)
j = j+1;
end
if strcmp(char(incl_sheet(i)),char(sheets(j)))
sheetnum(i) = j;
118
else
disp([' cant find this point source in spreadsheet:
',char(incl_sheet(i))]);
disp(' fix and rerun')
return
end
end
end

% read first sheet to get the number and list of constituents
sheet = char(sheets(sheetnum(1)));
%read the spreadsheet data
[numg1,tex1] = xlsread([xlpath,xlfile],sheet);
num_con = size(tex1,2) - 1;
disp([int2str(num_con),' constituents in spreadsheet']);
if num_con ~= 21
disp(' number of constituents should be 21, fix and rerun');
return
end

% now read all the sheets
for i = 1:num_PS
%set the sheet to read
sheet = char(sheets(sheetnum(i)));
%read the spreadsheet data
[numg,tex] = xlsread([xlpath,xlfile],sheet);
num_rows(i) = size(numg,1); num_cols(i) = size(numg,2);
PS_vals(1:num_rows(i),1:num_cols(i),i)=numg;
disp(['Sheet=',sheet,', read ',int2str(size(numg,1)),' lines from
spreadsheet']);
end

% set the format for printing layer information
if num_lay == 8
l_form = '%6.3f %6.3f %6.3f %6.3f %6.3f %6.3f %6.3f %6.3f\r\n';
elseif num_lay == 16
l_form = '%5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f
%5.3f %5.3f %5.3f %5.3f %5.3f %5.3f\r\n';
elseif num_lay == 25
l_form =['%4.2f %4.2f %4.2f %4.2f %4.2f %4.2f %4.2f %4.2f %4.2f %4.2f
%4.2f %4.2f %4.2f %4.2f %4.2f %4.2f ', ...
'%4.2f %4.2f %4.2f %4.2f %4.2f %4.2f %4.2f %4.2f
%4.2f\r\n'];
else
disp('need a format for this number of layers, fix and rerun');
end
file_dir = [xlpath,xlfile(1:strfind(xlfile,'.')-1),slc];
if ~exist(file_dir)
mkdir(file_dir);
disp(['creating folder ',file_dir]);
end
% now make all the point source files
disp(' point source files being saved to specified folder');
for i= 1: num_con
fileout = sprintf('%scwqsr%2.2i.inp',file_dir,i);
%open the output filename
119
fout = fopen(fileout,'w');
% set the headers for this file
header1 = setheader(constit(i),project,jday0,xlfile);

%write the header at the top of the file
for r=1:size(header1,1)
fprintf(fout,'%s\r\n',char(header1(r)));
end
for j = 1: num_PS
% print the header for this point source and the layer information
fprintf(fout,' 1 %i 86400.0 0.0 1.0000 0.0 0
!%s\r\n',num_rows(j),char(sheets(sheetnum(j))));
fprintf(fout,l_form,lyrs);
% print the time history for this point source and this constituent
conc_data = [PS_vals(1:num_rows(j),1,j)' ; PS_vals(1:num_rows(j),1+i,j)'
];
fprintf(fout,'%9.4f %10.4f\r\n',conc_data);
end
fclose(fout);
end

disp(['finished creating ',int2str(num_con),' files']);
end

function constit = get_constit_names()

%set the constituent names for the first line of the header in cwqsr##.inp
constit = { ...
'C cwqsr01.inp, Cyanobacteria (mg/l as C)' ; ...
'C cwqsr02.inp, Diatoms (mg/l as C)' ; ...
'C cwqsr03.inp, Green Algae (mg/l as C)' ; ...
'C cwqsr04.inp, Refractory POC (mg/l)' ; ...
'C cwqsr05.inp, Labile POC (mg/l)' ; ...
'C cwqsr06.inp, Dis Org Carbon (mg/l)' ; ...
'C cwqsr07.inp, Ref Part Org Phosphorus (mg/l)' ; ...
'C cwqsr08.inp, Lab Part Org Phosphorus (mg/l)' ; ...
'C cwqsr09.inp, Dis Org Phosphorus (mg/l)' ; ...
'C cwqsr10.inp, Total Phosphate (mg/l)' ; ...
'C cwqsr11.inp, Ref Part Org Nitrogen (mg/l)' ; ...
'C cwqsr12.inp, Lab Part Org Nitrogen (mg/l)' ; ...
'C cwqsr13.inp, Dis Org Nitrogen (mg/l)' ; ...
'C cwqsr14.inp, Ammonia Nitrogen (mg/l)' ; ...
'C cwqsr15.inp, Nitrate Nitrogen (mg/l)' ; ...
'C cwqsr16.inp, Part Biogenic Silica (mg/l)' ; ...
'C cwqsr17.inp, Dis Available Silica (mg/l)' ; ...
'C cwqsr18.inp, Chemical Oxygen Demand (mg/l)' ; ...
'C cwqsr19.inp, Dissolved Oxygen (mg/l)' ; ...
'C cwqsr20.inp, Total Active Metal (mg/l)' ; ...
'C cwqsr21.inp, Fecal Coliform (MPN/100ml)'};

end

function header1 = setheader(constit,project,jday0,xlfile)
%SETHEADER sets the headers for the make_QSER script

120
% set last line of header
last_line = sprintf('C JDAY 0.0 is at midnight on %s',jday0);

%set the header at the top of the point source file
header1= {char(constit); ['C Project ID: ',char(project),', Concentration
Spreadsheet: ',xlfile] ; ...
'C'; ...
'C ISTYP MCSER(NS,8) TCCSER(NS,8) TACSER(NS,8) RMULADJ(NS,8)
ADDADJ(NS,8)'; ...
'C'; ...
'C if istyp.eq.1 then read depth weights and single value of CSER'; ...
'C'; ...
'C (WKQ(K),K=1,KC)'; ...
'C'; ...
'C TCSER(M,NS,8) CSER(M,NS,8) !(mcser(ns,8) pairs for ns=8,ncser(8)
series)'; ...
'C'; ...
'C else read a value of CSER for each layer'; ...
'C'; ...
'C TCSER(M,NS,8) (CSER(M,K,NS,8),K=1,KC) !(mcser(ns,8) pairs)'; last_line
};

end

121
Appendix 2. Observed vs. Model Predicted Temperature Time Histories at Each Jordan Lake
Monitoring Station for the 2014-2016 model time period.

Figure A2- 1. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C.
Figure A2- 2. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C1.
122
Figure A2- 3. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C2.
Figure A2- 4. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C3.
123
Figure A2- 5. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C4.
Figure A2- 6. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C5.
124
Figure A2- 7. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C6.
Figure A2- 8. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055D.
125
Figure A2- 9. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055E.
Figure A2- 10. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF081A1B.
126
Figure A2- 11. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF081A1C.
Figure A2- 12. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF086C.
127
Figure A2- 13. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF086CUPS.
Figure A2- 14. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF086D.
128
Figure A2- 15. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF086F.
Figure A2- 16. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF087B3.
129
Figure A2- 17. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF087D.
Figure A2- 18. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF0880A.

130
Appendix 3. Observed vs. Model Predicted Chlorophyll-a Time Histories at Each Jordan Lake
Monitoring Station during the 2014-2016 model time-period.

Figure A3- 1. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-a
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C.
Figure A3- 2. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-a
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C1.
131
Figure A3- 3. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-a
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C2.

Figure A3- 4. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-a
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C3.
132
Figure A3- 5. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-a
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C4.
Figure A3- 6. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-a
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C5.
133
Figure A3- 7. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-a
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C6.
Figure A3- 8. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-a
concentrations from Jan. 2014 to Feb. 2016 at station CPF055D.
134
Figure A3- 9. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-a
concentrations from Jan. 2014 to Feb. 2016 at station CPF055E.
Figure A3- 10. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-
a concentrations from Jan. 2014 to Feb. 2016 at station CPF081A1B.
135
Figure A3- 11. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-
a concentrations from Jan. 2014 to Feb. 2016 at station CPF081A1C.
Figure A3- 12. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-
a concentrations from Jan. 2014 to Feb. 2016 at station CPF086C.
136
Figure A3- 13. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-
a concentrations from Jan. 2014 to Feb. 2016 at station CPF086CUPS.
Figure A3- 14. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-
a concentrations from Jan. 2014 to Feb. 2016 at station CPF086D.
137
Figure A3- 15. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-
a concentrations from Jan. 2014 to Feb. 2016 at station CPF086F.
Figure A3- 16. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-
a concentrations from Jan. 2014 to Feb. 2016 at station CPF087B3.
138
Figure A3- 17. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-
a concentrations from Jan. 2014 to Feb. 2016 at station CPF087D.
Figure A3- 18. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) chl-
a concentrations from Jan. 2014 to Feb. 2016 at station CPF0880A.

139
Appendix 4. Observed vs. Model Predicted Nitrate + Nitrate (NOx) Time Histories at Each
Jordan Lake Monitoring Station during the 2014-2016 model time-period.
Figure A4- 1. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NOx
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C.
Figure A4- 2. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NOx
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C1.
140
Figure A4- 3. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NOx
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C2.
Figure A4- 4. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NOx
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C3.
141
Figure A4- 5. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NOx
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C4.
Figure A4- 6. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NOx
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C5.
142
Figure A4- 7. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NOx
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C6.
Figure A4- 8. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NOx
concentrations from Jan. 2014 to Feb. 2016 at station CPF055D.
143
Figure A4- 9. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NOx
concentrations from Jan. 2014 to Feb. 2016 at station CPF055E.
Figure A4- 10. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NOx concentrations from Jan. 2014 to Feb. 2016 at station CPF081A1B.
144
Figure A4- 11. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NOx concentrations from Jan. 2014 to Feb. 2016 at station CPF081A1C.
Figure A4- 12. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NOx concentrations from Jan. 2014 to Feb. 2016 at station CPF086C.
145
Figure A4- 13. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NOx concentrations from Jan. 2014 to Feb. 2016 at station CPF086CUPS.
Figure A4- 14. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NOx concentrations from Jan. 2014 to Feb. 2016 at station CPF086D.
146
Figure A4- 15. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NOx concentrations from Jan. 2014 to Feb. 2016 at station CPF086F.
Figure A4- 16. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NOx concentrations from Jan. 2014 to Feb. 2016 at station CPF087B3.
147
Figure A4- 17. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NOx concentrations from Jan. 2014 to Feb. 2016 at station CPF087D.
Figure A4- 18. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NOx concentrations from Jan. 2014 to Feb. 2016 at station CPF0880A.

148
Appendix 5. Observed vs. Model Predicted Ammonia (NH4+ + NH3 (aq)) Time Histories at
Each Jordan Lake Monitoring Station during the 2014-2016 model time-period.
Figure A5- 1. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NH4
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C.

Figure A5- 2. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NH4
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C1.

149
Figure A5- 3. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NH4
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C2.

Figure A5- 4. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NH4
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C3.

150
Figure A5- 5. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NH4
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C4.

Figure A5- 6. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NH4
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C5.

151
Figure A5- 7. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NH4
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C6.

Figure A5- 8. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NH4
concentrations from Jan. 2014 to Feb. 2016 at station CPF055D.

152
Figure A5- 9. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) NH4
concentrations from Jan. 2014 to Feb. 2016 at station CPF055E.

Figure A5- 10. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NH4 concentrations from Jan. 2014 to Feb. 2016 at station CPF081A1B.

153
Figure A5- 11. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NH4 concentrations from Jan. 2014 to Feb. 2016 at station CPF081A1C.

Figure A5- 12. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NH4 concentrations from Jan. 2014 to Feb. 2016 at station CPF086C.

154
Figure A5- 13. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NH4 concentrations from Jan. 2014 to Feb. 2016 at station CPF086CUPS.

Figure A5- 14. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NH4 concentrations from Jan. 2014 to Feb. 2016 at station CPF086D.

155
Figure A5- 15. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NH4 concentrations from Jan. 2014 to Feb. 2016 at station CPF086F.

Figure A5- 16. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NH4 concentrations from Jan. 2014 to Feb. 2016 at station CPF087B3.

156
Figure A5- 17. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NH4 concentrations from Jan. 2014 to Feb. 2016 at station CPF087D.

Figure A5- 18. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
NH4 concentrations from Jan. 2014 to Feb. 2016 at station CPF0880A.

157
Appendix 6. Observed vs. Model Predicted Total Kjeldahl Nitrogen (TKN) Time Histories at
Each Jordan Lake Monitoring Station during the 2014-2016 model time-period.
Figure A6- 1. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TKN
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C.

Figure A6- 2. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TKN
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C1.

158

Figure A6- 3. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TKN
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C2.

Figure A6- 4. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TKN
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C3.

159
Figure A6- 5. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TKN
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C4.

Figure A6- 6. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TKN
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C5.

160
Figure A6- 7. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TKN
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C6.

Figure A6- 8. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TKN
concentrations from Jan. 2014 to Feb. 2016 at station CPF055D.

161
Figure A6- 9. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TKN
concentrations from Jan. 2014 to Feb. 2016 at station CPF055E.

Figure A6- 10. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
TKN concentrations from Jan. 2014 to Feb. 2016 at station CPF081A1B.

162
Figure A6- 11. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
TKN concentrations from Jan. 2014 to Feb. 2016 at station CPF081A1C.

Figure A6- 12. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
TKN concentrations from Jan. 2014 to Feb. 2016 at station CPF086C.

163
Figure A6- 13. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
TKN concentrations from Jan. 2014 to Feb. 2016 at station CPF086CUPS.

Figure A6- 14. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
TKN concentrations from Jan. 2014 to Feb. 2016 at station CPF086D.

164
Figure A6- 15. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
TKN concentrations from Jan. 2014 to Feb. 2016 at station CPF086F.

Figure A6- 16. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
TKN concentrations from Jan. 2014 to Feb. 2016 at station CPF087B3.

165
Figure A6- 17. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
TKN concentrations from Jan. 2014 to Feb. 2016 at station CPF087D.

Figure A6- 18. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
TKN concentrations from Jan. 2014 to Feb. 2016 at station CPF0880A.

166
Appendix 7. Observed vs. Model Predicted Total Phosphorus (TP) Time Histories at Each
Jordan Lake Monitoring Station during the 2014-2016 model time-period.
Figure A7- 1. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C.

Figure A7- 2. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C1.
167

Figure A7- 3. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C2.

Figure A7- 4. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C3.

168

Figure A7- 5. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C4.

Figure A7- 6. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C5.

169

Figure A7- 7. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C6.

Figure A7- 8. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF055D.

170

Figure A7- 9. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF055E.

Figure A7- 10. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF081A1B.

171

Figure A7- 11. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF081A1C.

Figure A7- 12. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF086C.

172

Figure A7- 13. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF086CUPS.

Figure A7- 14. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF086D.

173

Figure A7- 15. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF086F.

Figure A7- 16. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF087B3.

174

Figure A7- 17. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF087D.

Figure A7- 18. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) TP
concentrations from Jan. 2014 to Feb. 2016 at station CPF0880A.

175
Appendix 8. Observed vs. Model Predicted Dissolved Oxygen (DO) Time Histories at Each
Jordan Lake Monitoring Station during the 2014-2016 model time-period.
Figure A8- 1. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C.

Figure A8- 2 Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C1.

176

Figure A8- 3. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C2.

Figure A8- 4. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C3.
177
Figure A8- 5. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C4.
Figure A8- 6. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C5.

178
Figure A8- 7. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF055C6.

Figure A8- 8. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF055D.

179

Figure A8- 9. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF055E.
Figure A8- 10. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF081A1B.

180
Figure A8- 11. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF081A1C.
Figure A8- 12. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF086C.

181
Figure A8- 13. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF086CUPS.
Figure A8- 14. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF086D.

182
Figure A8- 15. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF086F.

Figure A8- 16. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF087B3.

183
Figure A8- 17. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF087D.

Figure A8- 18. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines) DO
concentrations from Jan. 2014 to Feb. 2016 at station CPF0880A.

184
Appendix 9. Observed vs. Model Predicted Cumulative Distributions Functions (CDFs) for the
Seven Parameters in the Observed Data Set (Temperature, Chl-a, NOx, NH4,
TKN, TP, DO)
Figure A9- 1. Observed vs. Model Predicted Temperature Cumulative Distributions Functions (CDFs)
at Seventeen Jordan Lake Stations Over the 2014-2016 time period.

Figure A9- 2. Observed vs. Model Predicted Chl-a Cumulative Distributions Functions (CDFs) at
Seventeen Jordan Lake Stations Over the 2014-2016 time period.

185

Figure A9- 3. Observed vs. Model Predicted NOx Cumulative Distributions Functions (CDFs) at
Seventeen Jordan Lake Stations Over the 2014-2016 time period.

Figure A9- 4. Observed vs. Model Predicted Ammonia Cumulative Distributions Functions (CDFs) at
Seventeen Jordan Lake Stations Over the 2014-2016 time period.
186
Figure A9- 5. Observed vs. Model Predicted TKN Cumulative Distributions Functions (CDFs) at
Seventeen Jordan Lake Stations Over the 2014-2016 time period.

Figure A9- 6. Observed vs. Model Predicted TP Cumulative Distributions Functions (CDFs) at
Seventeen Jordan Lake Stations Over the 2014-2016 time period.

187
Figure A9- 7. Observed vs. Model Predicted DO Cumulative Distributions Functions (CDFs) at
Seventeen Jordan Lake Stations Over the 2014-2016 time period.

188
Appendix 10. Observed vs. Model Predicted Scatter Plots for the Seven Parameters in the
Observed Data Set (Temperature, Chl-a, NOx, NH4, TKN, TP, DO.
Figure A10- 1. Observed vs. Model Predicted Temperature Scatter Plot for Seventeen Jordan Lake
Stations Over the 2014-2016 time period.

Figure A10- 2. Observed vs. Model Predicted Chl-a Scatter Plot for Seventeen Jordan Lake Stations
Over the 2014-2016 time period.

189
Figure A10- 3. Observed vs. Model Predicted NOx Scatter Plot for Seventeen Jordan Lake Stations Over
the 2014-2016 time period.

Figure A10- 4. Observed vs. Model Predicted NH4 Scatter Plot for Seventeen Jordan Lake Stations Over
the 2014-2016 time period.

190
Figure A10- 5. Observed vs. Model Predicted TKN Scatter Plot for Seventeen Jordan Lake Stations Over
the 2014-2016 time period.

Figure A10- 6. Observed vs. Model Predicted TP Scatter Plot for Seventeen Jordan Lake Stations Over
the 2014-2016 time period.

191

Figure A10- 7. Observed vs. Model Predicted DO Scatter Plot for Seventeen Jordan Lake Stations Over
the 2014-2016 time period.

192
Appendix 11. Mean Monthly Nutrient Concentrations at Six Locations in the Jordan Lake
Watershed from an Analysis of Available Monitoring Data from 2000 – 2018.

Haw R.Morgan Ck.New Hope Ck.
Mo.
TN
(mg/L as
N)Am frac
NOX
frac Or Frac Mo.
TN
(mg/L as
N)Am frac
NOX
frac Or Frac Mo.
TN (mg/L
as N)
Am
frac
NOX
frac Or Frac
1 1.67 0.04 0.63 0.33 1 5.75 0.03 0.86 0.11 1 3.17 0.04 0.74 0.23
2 1.44 0.06 0.58 0.36 2 4.93 0.03 0.83 0.14 2 2.60 0.03 0.72 0.25
3 1.28 0.05 0.53 0.42 3 3.54 0.04 0.76 0.20 3 2.65 0.02 0.71 0.27
4 1.41 0.04 0.54 0.42 4 4.27 0.03 0.80 0.16 4 3.08 0.04 0.70 0.26
5 1.67 0.03 0.59 0.38 5 5.54 0.02 0.83 0.15 5 4.24 0.03 0.76 0.21
6 1.85 0.03 0.61 0.36 6 5.47 0.02 0.85 0.13 6 4.82 0.02 0.80 0.18
7 1.75 0.05 0.57 0.38 7 5.30 0.02 0.84 0.14 7 4.94 0.01 0.81 0.17
8 1.92 0.02 0.59 0.38 8 7.07 0.02 0.87 0.11 8 6.14 0.01 0.85 0.14
9 2.09 0.02 0.63 0.35 9 6.72 0.01 0.88 0.10 9 5.84 0.01 0.85 0.14
10 1.95 0.02 0.69 0.29 10 7.52 0.02 0.89 0.09 10 6.72 0.01 0.86 0.13
11 2.33 0.03 0.69 0.28 11 5.82 0.02 0.87 0.11 11 4.96 0.02 0.80 0.18
12 2.05 0.04 0.69 0.27 12 4.61 0.04 0.83 0.13 12 3.84 0.02 0.78 0.20
Northeast Ck.White Oak Ck.
Mo.
TN
(mg/L as
N)Am frac
NOX
frac Or Frac Mo.
TN
(mg/L as
N)Am frac
NOX
frac Or Frac
1 3.17 0.11 0.69 0.20 1 0.61 0.05 0.20 0.76
2 2.60 0.03 0.70 0.27 2 0.49 0.04 0.14 0.82
3 2.65 0.02 0.71 0.28 3 0.54 0.02 0.14 0.85
4 3.08 0.02 0.76 0.22 4 0.68 0.04 0.07 0.89
5 4.24 0.03 0.78 0.19 5 0.71 0.05 0.08 0.87
6 4.82 0.01 0.80 0.19 6 0.73 0.06 0.10 0.85
7 4.94 0.04 0.74 0.22 7 0.68 0.05 0.10 0.85
8 6.14 0.04 0.77 0.19 8 0.68 0.06 0.08 0.86
9 5.84 0.01 0.77 0.22 9 0.73 0.01 0.14 0.84
10 6.72 0.01 0.85 0.14 10 0.58 0.02 0.10 0.88
11 4.96 0.01 0.84 0.15 11 0.84 0.02 0.04 0.94
12 3.84 0.02 0.71 0.27 12 0.51 0.04 0.10 0.86
NB: no May values available, no sampling, used average of April and June
TP (mg/L as P)
Mo.Haw Morgan
New
Hope Northeast
White
Oak
1 0.11 0.19 0.24 0.32 0.07
2 0.12 0.21 0.26 0.26 0.06
3 0.10 0.17 0.35 0.26 0.08
4 0.13 0.18 0.25 0.29 0.08
5 0.14 0.27 0.32 0.41 0.08
6 0.16 0.21 0.34 0.40 0.08
7 0.17 0.19 0.31 0.41 0.06
8 0.17 0.32 0.37 0.44 0.08
9 0.15 0.32 0.25 0.32 0.15
10 0.15 0.25 0.42 0.36 0.05
11 0.13 0.17 0.43 0.30 0.16
12 0.12 0.15 0.32 0.24 0.05
193

Appendix 12. Jordan Lake Monitoring Stations Used for Model Calibration
Table A12- 1. Jordan Lake Monitoring Stations.
Station
No. Description
Station
Name Lat. Long.
407
cell I
407
cell J
1 Jordan Lake Dam Dam 35.6548 -79.0672 23 6
2
Jordan Lake above Stinking Creek
Near Pittsboro, NC CPF055C 35.6913 -79.0791
17 6
3
Jordan Lake in Haw River Bay Arm
Upstream CPF055C1 35.6988 -79.0820
15 6
4
Jordan Lake in Haw River Bay Arm
NE CPF055C2 35.6955 -79.0761
16 7
5
Jordan Lake in Haw River Bay Arm
NW CPF055C3 35.6932 -79.0830
16 6
6
Jordan Lake in Haw River Bay Arm
SE CPF055C4 35.6899 -79.0756
17 5
7
Jordan Lake in Haw River Bay Arm
SW CPF055C5 35.6867 -79.0841
17 8
8
Jordan Lake in Haw River Arm Bay
Downstream CPF055C6 35.6822 -79.0780
18 6
9
Jordan Lake in Middle of Haw River
Arm CPF055D 35.6725 -79.0772
20 6
10
Jordan Lake above Dam Near
Moncure, NC CPF055E 35.6600 -79.0700
22 6
11
Jordan Lake Downstream Crooked
Creek, New Hope Arm CPF081A1B 35.8365 -78.9763
18 42
12
Jordan Lake @ Mouth of New Hope
Creek CPF081A1C 35.8162 -78.9868
18 37
13
Jordan Lake @ Mouth of Morgan
Creek Near Farrington CPF086C 35.8215 -78.9974
15 35
14 Jordan Lake In Upstream CPF086CUPS 35.8382 -79.0014 11 34
15
Jordan Lake, Downstream Morgan,
New Hope Creek Arm CPF086D 35.8095 -78.9974
18 35
16 Jordan Lake Near Farrington, NC CPF086F 35.7970 -79.0108 18 31
17
Jordan Lake at Buoy #9 Near Merry
Oaks, NC CPF087B3 35.7652 -79.0260
18 25
18
Jordan Lake @ Mouth White Oak
Creek Nr Seaforth, NC CPF087D 35.7386 -79.0242
18 21
19
Jordan Lake Near Mouth Beaver
Creek Nr Merry Oaks, NC CPF0880A 35.6965 -79.0436
19 14
194

Figure A12- 1. Locations of 18 of the 19 Jordan Lake Monitoring Stations Sampled by the NC Division
of Water Resources and US Army Corps of Engineers (Haw arm station CPF055C2 not
shown). The 407 cell EFDC model grid is also shown.

195
Appendix 13. plot_ts_gui (v11) Users Manual

A time series plotting program for EFDC
For Mac and PC computers
written w/ MATLAB

by James D Bowen
[e-mail]

March 31, 2023

To start the program, do one of these: See following pages for more
information
1. Open the application (plot_ts_gui.exe) – for PC computers
2. Open the application (plot_ts_gui.app) – for Mac computers
3. Open matlab, go to the plot_ts_gui_v11 folder, enter “plot_ts_gui” at the command line (for
either mac or pc computers
196
Figure A13- 1. The plot_ts_gui user interface. Click on an element
for more information.

10
10.1
10.2

10.3

8
9
4
4.1

4.2

4.3

4.4

4.5

4.8 4.7
4.10
0

4.9
4.11
4.12
1
2
3
5
6
6.1
6.2
6.3
11
11.3
11.2

11.1

7 7.1
7.2
7.3

4.6

197

The plot_ts_gui User Interface – Summary of Items

1. Choose System to Plot
2. Julian Day 0 Date
3. Plot Layers (bot to top)
4. Set Plotting Options
4.1. Use month for x-axis tic (yes/no)
4.2. Set x-axis values? (yes/no)
4.3. Set y-axis values? (yes/no)
4.4. Save figures? (yes/no)
4.5. Make a cumulative freq. fig? (yes/no)
4.6. Make log plots? (yes/no)
4.7. Save DO sat or Travel Time to file? (yes/no)
4.8. 15-step filter preds? (button)
4.9. Stats Only? (button)
4.10. Plot data? (yes/no)
4.11. Save station fit stats? (yes/no)
4.12. Plot top/bot differences (yes/no)
5. Folder with Output Files
6. Observed Data Files and Sheets (1=Hydro, 2=WQ), in same directory
6.1. Choose folder for observed data spreadsheets
6.2. Select file and set sheet (file 1)
6.3. Select file and set sheet (file 2)
7. Choose One
7.1. Hydro
7.2. WQ
7.3. WQ, EE
8. Choose Constituent (Hydrodynamics, DO, NH4)
9. Choose Constituent (Water Quality)
10. Choose Stations to Plot
10.1. Plot All Stations
10.2. Station Range
10.3. Station List
11. Update Prefs / Make Plot / Quit
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The plot_ts_gui User Interface – Detailed Information on Each Item
1. Choose System to Plot back to GUI page
Choose the system under study to be plotted. The choices are Neuse River, Lake Nokoue (a
large tidal lagoon in Benin), the Lower Cape Fear River, Jordan Lake, and “other.” System
specific data on observed data stations and parameters have been setup for each of these systems
as a .mat file. There is a corresponding matlab script (make_CapeFearRiverData.m,
make_JordanLakeData.m, make_LakeNokoueData.m, make_NeuseRiverData.m,
make_OtherSystemData.m) that makes each of these .mat files. For the OtherSystemData.mat
file, all the variables are set to empty arrays (Figure A13- 2).

Figure A13- 2. The Matlab script (make_OtherSystemData.m) that defines the arrays used by
plot_ts_gui and saves it to a Matlab data (.mat) file.

Two different sorts of water quality programs can serve to generate the hydrodynamic and water
quality outputs for plotting. The plot_ts_gui program was originally written to read model
outputs from CE-QUAL-W2, a two-dimensional laterally-averaged water quality model (Cole
and Buchak 1995). The program’s original use was to display phytoplankton model predictions
199
in the Neuse River under various nutrient reduction scenarios (Bowen and Hieronymus 2000,
Bowen and Hieronymus 2003, Hieronymus and Bowen 2004). It was later modified to display
model predictions from the EPA version of EFDC (Tetra Tech 2007) and most recently modified
to display results from the EFDC+ version of the model (Craig 2018).
2. Julian Day 0 Date back to GUI page
EFDC uses Julian Day to specify the time. In EFDC the user is free to set the starting julian day
and the number of days of simulation. This information is set in the efdc.inp file. For comparing
model predictions to observed date it is necessary to tie the EFDC julian days to a calendar date.
This is done in plot_ts_gui by setting the calendar date corresponding to julian day 0 (jday = 0).
3. Plot Layers (bot to top) back to GUI page
Plot_ts_gui makes time history plots for two layers of EFDC or CE-QUAL-W2 model
predictions. Observed data may also be plotted if option 4.10 is set to “yes.” EFDC numbers its
layers from bottom (layer 1) to top (layer = number of layers). This horizontal and vertical grid
information is set in the efdc.inp file in cards C9, C9A, and C10. In plot_ts_gui the bottom layer
plotted is colored red and the upper layer is colored blue. The total number of layers must also
be set here (8 layers for Lower Cape Fear River, 25 layers for Jordan Lake). Model errors may
occur if the number of layers set in plot_ts_gui does not agree with the number of layers set in
the efdc.inp file.
EFDC+ has a layering scheme (sigma-zed or SGZ) that allows for a variable number of layers of
a constant thickness. If this option is chosen, there will be a variable number of layers for each
cell, depending on the local water depth. In EFDC+ this option is chosen in card C1A and other
SGZ options are set. The SGZ option was chosen for Jordan Lake. Plot_ts_gui will adjust the
bottom layer that is plotted when the SGZ layering scheme is used.
The EPA version of EFDC used for the Lower Cape Fear and Neuse River Estuary does not have
this feature. In this case a sigma vertical grid scheme is used whereby all cells have the same
number of layers that varying in thickness to give water depths that vary by cell. In this case
each plot will use the same layer in making the plot, but the depth that a particular layer
represents will vary cell by cell.
4. Set Plotting Options back to GUI page
A variety of options for setting the plot axes, and the information on what and how to plot are set
in this section.
4.1. Use month for x-axis tic (yes/no)
The x-axis can be plotted as numerical julian day values or as calendar values using
months, days, and years for the axis label. Setting this option to “yes” gives x-axis tic and
label values that are calendar date based (years/month/day) and adjusted based on the total
number of days plotted.

4.2. Set x-axis values? (yes/no) back to GUI page
The minimum and maximum jday or calendar date values can be set to plot either the full
time range of model predictions or a more narrow time window, depending on the option
200
setting. If the “set-axis values?” option is set, a pop-up window will appear that allows the
user to set the minimum and maximum values. These should be set as minimum and
maximum jday values, which can be displayed as calendar date values depending on the
setting for option 4.1. The pop-up window to set min and max x-axis value will continue to
appear until the option is set to “no.”

4.3. Set y-axis values? (yes/no)
The minimum and maximum y-axis values can also be specified in a fashion similar to that
of option 4.2. If the option is set to “yes” a pop-up window will appear for entering the
minimum and maximum y-axis values. These values will be saved and the pop-up will
continue to appear in subsequent plotting until the values are changed or the option is set to
“no.”

4.4. Save figures? (yes/no)
The plots displayed on the screen can be automatically saved to a folder (/figures) within
the folder where the output files reside if this option is set to “yes.” Each figure window is
saved with a unique identifying name as a .jpeg file.

4.5. Make a cumulative freq. fig? (yes/no) back to GUI page
Two cumulative distribution function (CDF) plots can be created if this option is set to
“yes.” One plot calculates the CDF from model predictions for the stations selected in
option 10. In this plot the corresponding observed data are also plotted as a CDF if
observed data are available and option 4.10 is set to “yes.” A second plot calculates and
plots a CDF of all model predictions that are available within the time window for the
stations selected.

4.6. Make log plots? (yes/no)
The y-axis can be plotted using a logarithmic scale (base 10) rather than a linear scale if
this option is set to “yes.” The y-axis labels will be adjusted to give the appropriate tic and
label values. Setting this option to “yes” when model predictions or observations are equal
to 0.0 may give an error.

4.7. Save DO sat or Travel Time to file? (yes/no) back to GUI page
Setting this option to “yes” will produce additional printout to a text file of either the
dissolved oxygen as a percentage of saturation or travel time. These two constituents are
options under the “Hydrodynamics, DO, NH4” set of constituents that are saved by EFDC
as text files according to options set in the efdc.inp file. This option was created for the
Lower Cape Fear River estuary project. It has not been used, and has not been tested for
the Jordan Lake model.

4.8. 15-step filter preds? (button) back to GUI page
Model predictions at a fixed location like at a monitoring station can be highly variable
when there are spatial gradients and back and forth water motions. This option was added
to smooth model predictions to improve the ability to visually discern the fit between the
observed data (shown as symbols) and the model predictions (shown as a line without
201
symbols). A 15-step moving average filter is applied to the model predictions only if this
selection is made.

4.9. Stats Only? (button)
If this button is turned on, then calibration statistics will be displayed, but none of the time
history plots will be displayed. This option is useful when the calibration statistics are
desired, but the plots are not. The cumulative distribution function (CDF) plot will be
plotted even if the time history plots are not shown if option 4.5 is set to “yes.”

4.10. Plot data? (yes/no) back to GUI page
The time history plots can include both model predictions and observed data if desired and
if data are available. Model predictions alone can be plotted by setting this option to “no.”
If this option is set to “yes,” the observed data folder and file and sheet information will be
used to set observed data time histories. Errors will may result if the option is set to “yes,”
but the information on the location of the observed data is not correct. It is recommended
to first set this option to “no” until the correct plotting of model predictions is obtained. At
that point the additional observed data information can be added and then this option can be
set to “yes.”

4.11. Save station fit stats? (yes/no)
If this option is set to “yes,” then a set of text files is saved with the calibration statistics for
each station with observed data. This option was created for the Lower Cape Fear River
model. It is unknown whether it works for the Jordan Lake model.

4.12. Plot top/bot differences (yes/no) back to GUI page
If this option is set to yes, then a derived constituent is created that is the difference
between the top and bottom values corresponding to the layers set in option 3. The time
history of the difference between top and bottom values is then plotted for each station
selected for the constituent selected. This option was created for use with the Neuse
Estuary model. It has not been tested and/or used for the Jordan Lake model.
5. Folder with Output Files back to GUI page
To plot model prediction it is necessary to identify the folder where the output files reside.
These output files may be text files or binary files or a combination of the two. Pushing the
“browse” buttons opens a browser that allows the user to select the folder where these output
files exist. An error will result or no plots will be made if a folder is selected that has no output
files.

Model predictions created by the EPA version of EFDC are text files (.OUT files of either a
hydrodynamic time series format or a water quality time series format) that are saved in the same
folder where the input (.INP) files can be found. This folder should be selected with the browser.

The output files of EFDC+ are both text based hydrodynamic time series files (.OUT files) plus
several binary files including a water quality output file (EE_WQ.OUT). This binary file must
be read by an auxiliary program (GetEFDC) before plotting. The output files are found in a
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subfolder (/#output) beneath the folder containing the input (.INP) files. The appropriate
/#output folder should be selected with the browser.
6. Observed Data Files and Sheets (1=Hydro, 2=WQ), in same directory
If observed data are to be plotted and compared to model predictions, then option 4.10 should be
set to “yes.” In this event, information from this section will be used to locate and identify the
observed data spreadsheets (Excel formatted .xls or .xlsx spreadsheets only). Two separate
spreadsheets (file 1 and file 2) can be used to specify the observed data. For instance, in the
Jordan Lake observed database, a different file structure is used to give the observed water
surface elevation data and the water quality data. In this instance a different file or a different
sheet should be specified in sections 6.2 and 6.3 below.
6.1. Choose folder for observed data spreadsheets back to GUI page
It is assumed that the observed data for a particular system reside in a single folder. The
user should select the browse button to open a folder browser. The folder containing the
observed data should be selected and will then be displayed in the top window to the left of
the “browse” button.
6.2. Select file and set sheet (file 1) back to GUI page
As described above, two separate Excel spreadsheets can be used to provide the observed
data that are compared to model predictions. Select the “Choose File 1” button to browse
within the folder select in section 1 for the first Excel file. The spreadsheet should be
either .xls or .xlsx file. The sheet to read within the spreadsheet is entered directly in the
smaller window to the immediate left of the “Choose file 1” button. If a sheet name is
entered that is not in the spreadsheet, the program will note the problem and will give a list
of appropriate sheet names that can then be entered. To speed up reading of the observed
data, the program will read the spreadsheet information and save it in the same folder to a
Matlab data file (.mat file). The filename for these .mat files appends the sheet name to the
.xlsx filename. Searching the data folder for these .mat files can be helpful in figuring
which of the sheets contain the desired observed data.
6.3. Select file and set sheet (file 2) back to GUI page
A second observed data spreadsheet can also be specified in a manner identical to file 1.
The “Choose file 2” button should be selected and then the file within the observed data
folder can be chosen. The sheet name should be entered directly. If a sheet name is entered
that is not in the spreadsheet, the program will note the problem and will give a list of
appropriate sheet names that can then be entered. The file to be used (1 or 2) is set
constituent by constituent and is specified in the system data file. The file and sheet values
for file 1 and file 2 can be the same.
7. Choose One back to GUI page
EFDC uses different output file types for hydrodynamic and water quality information. The
water quality output format for the EPA version of EFDC (Cape Fear Estuary and Neuse River
Estuary models) is different from EFDC+ (Jordan Lake model). The constituents available in
these output files also differs. The file to be read and constituent to be plotted is set in this
section.
203
7.1. Hydro back to GUI page
The hydrodynamic output information is saved by EFDC in text based .OUT files. There is
one file output for each constituent and each I, J location that is specified in the efdc.inp
file (e.g. SELTS19.OUT is the surface elevation time history of model predictions at
station 19). The header information of these output files contains the constituent and the I,
J being printed out. The set of constituents and stations to be output are both set in the
efdc.inp file. Certain water quality outputs (DO, DO percentage, ammonia, total organic
carbon) are also saved in this way. These are the constituent choices available:
• Temperature
• Salinity
• Water Surface Elevation
• Dye
• Shellfish Larvae
• Travel Time
• Dissolved Oxygen
• Ammonia
• E. Coli as Dye
• Total Coliforms as Dye
• Enterococcus as Dye
• Vibrio as Dye
• DO as percentage of Saturation
• External mode horizontal velocities
• External mode flows (need SEL & UVE files)
• Internal mode velocity, easterly
• Internal mode velocity, northerly
• Internal flow, easterly (need SEL & U3D files)
• Internal flow, northerly (need SEL & V3D files)
• Eddy Diffusivity
• Eddy Viscosity
• Total Suspended Solids
• External mode (U,V) transport

Some of these choices have been setup for special, system-specific uses of the hydrodynamic
model. For instance several constituents (E. coli as dye, total coliforms as dye, Enterococcus
as dye, Vibrio as dye) were created for a fate and transport model of pathogens in the Neuse
River Estuary (Froelich, Bowen et al. 2013) or were constituents used (e.g. eddy diffusivity
and viscosity) for a model using an alternate calculation of DO reaeration (Duclaud and Bowen
2007, Bowen, Negusse et al. 2009). In the Jordan Lake model temperature and water surface
elevation (one time series only per station) are the hydrodynamic constituents that are typically
chosen.
7.2. WQ back to GUI page
As described in previous sections, the water quality output format differs between the two
versions of EFDC (EPA version, EFDC+) and CE-QUAL-W2 that have been used for
204
various studies. Unless EFDC+ is used, the output file is text based. The following
constituents can be plotted:
• B-G Chlorophyll
• Diatom Chlorophyll
• Green Chlorophyll
• Refr. Partic. OC
• Labile Partic. OC
• Dissolved OC
• Refr. Partic. OP as P
• Labile Partic. OP as P
• Dissolved OP as P
• Orthophosphate as P
• Refr. Partic. ON as N
• Labile Partic. ON as N
• Dissolved ON as N
• Ammonia as N (NH4)
• Nitrate-Nitrite as N (NOx)
• Partic. Biogenic Silica as SI
• Bioavailable Silica as Si
• Chemical Oxygen Demand
• Dissolved Oxygen
• B-G Gross Growth Rate
• Diatom Gross Growth Rate
• Green Gross Growth Rate
• Velocity Reaeration
• Wind Reaeration
• Total Chlorophyll (chl-a)
• BOD5
• Total Phosphorus as P (TP)
• Total Kheldahl Nitrogen as N (TKN)

Some of the constituents are calculated by the model (e.g. algal growth rates, velocity and
wind reaeration) or are calculated by plot_ts_gui (chl-a, BOD5, TP, TKN) using additional
parameters that are read from the WQ3DWC.INP file.

This option should be selected if results from either CE-QUAL-W2 or the EPA version of
EFDC are to be plotted. There is a separate option available for plotting water quality
information from predictions using EFDC+ (see below). Model errors will occur if the
wrong option is chosen here.
205
7.3. WQ, EE back to GUI page
The user should select this option if water quality predictions from EFDC+ are to be
plotted. The water quality output file is binary and needs to be first read by a utility
program (GetEFDC) that reads a particular station and layer and then converts all water
quality model predictions to ASCII data. If necessary that program will be first be run to
fetch the binary data and subsample it for the stations desired. GetEFDC is called for each
layer that is in the output, so it may take several minutes to repeatedly call GetEFDC and
fetch and save the model predictions for the desired stations. When model predictions from
all layers are collected, the saved results are read from text files in a /Results folder and
then are saved to a matlab data file (WQWCTS.mat). A standalone program
make_WQWCTS.m has been created to do this step as part of batch process when EFDC is
run (see 0_RunEFDC.Bat on the PC or 0_RunEFDC.sh on the mac) so that it will not need
to be run later when viewing the results with plot_ts_gui.
8. Choose Constituent (Hydrodynamics, DO, NH4) back to GUI page
Use this selection element to choose one of the constituents listed in section 7.1. See that section
for more information on which constituents are available for a particular system.
9. Choose Constituent (Water Quality) back to GUI
page
Use this selection element to choose one of the constituents listed in section 7.2. Both the “WQ”
and “WQ, EE” options from sections 7.3 use the same constituent list.

10. Choose Stations to Plot back to GUI
page
10.1. Plot All Stations
Choosing this option plots all available stations in the folder set in option 5. The station list
(option 10.3) and station range (option 10.2) information are ignored if this option is
selected. For water quality constituents with the EFDC+ model, all I, J values for all
constituents are saved in the EE_WQ.OUT file. A subset of the stations are specified and
plotted in plot_ts_gui according to the selection chosen.
10.2. Station Range
A range of station numbers is plotted if this option is chosen as long as model predictions
are available for that particular station. The format for the output varies between the
hydrodynamic and water quality outputs of the model. The water quality output format
also varies between the EPA version of EFDC (used for the Lower Cape Fear model) and
the EFDC+ version (used for Jordan Lake).
For the EFDC+ based model (Jordan Lake), for the plotting of water quality information,
the I, J values corresponding to each water quality station number are set along with the
short and long versions of the station name in the system specific information that is for
each system (e.g. Neuse, Cape Fear, Lake Nokoue, Jordan, other). The EPA version of
EFDC sets the water quality station list in the wq3dwc.inp file and prints out the
information in a single text file WQWCTS.OUT. For both the EPA version and EFDC+
206
versions of EFDC, the hydrodynamic .OUT file numbers with corresponding I,J values are
set in the efdc.inp file. Within the station range selected all available model predictions
and corresponding observed data are plotted.
10.3. Station List
A specific set of specific station numbers can be plotted by choosing this option and by
inputting the set of stations to be plotted. Each station will be plotted if model predictions
are available. The figure window number will be set using the station number.
11. Run Control Buttons back to GUI page

11.1. Update Preferences
Pushing “Update Prefs” button saves the current selections in the GUI to the preferences
file. The user specific preference file is read when the program is first started. If no
preference file is found, then a default set of values is set and displayed. The user can then
set new values and save these preferences before plotting.
11.2. Make Plot
Pushing the “Make Plot” button saves then current preferences values and makes the plots
according to the selections made. Calibration statistics are displayed and information
related to this set of plots and fit statistics are saved to a file in the output folder called
ptsg_results.txt (Figure A13- 3).

11.3. Quit back to GUI page

Selecting this button saves the current preferences and closes the program.
207

Figure A13- 3. An example of the ptsg_results.txt file that is saved to the output folder each time
the "Make Plot" button is selected.

Model fit statistics produced by plot_ts_gui, most recent at bottom of file
-------------------------------------------------------
plot_ts_gui results, run time = 27-Mar-2023 11:06:43
Running plot_ts_gui.m on a Mac computer named jdb-macpro.local
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/
plot_ts_gui_v11
Constituent = Dissolved oxygen (mg/L)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a list of station numbers
First and last station (for plot range only) = 2 and 19
Station list (for station list only) = 9 16 18 19
Model predictions from time series .OUT files, e.g. temp,salinity,DO,NH4
Model predictions folder =
/Users/jdbowen/Desktop/Jordan_Lake_Model_Runs/March_2023_mac/
04431_10_ben_time_varying_PO4_1pt5SOD1pt4_180d/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_no_DOsat
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics
mean(pred-obs) = 1.68
ME_norm =0.265
RMSE = 3.59
MAE = 2.72
MAE_norm =0.430
RMSE_norm =0.567
r_squared =0.415
num data comparisons = 696
Nash-Sutcliffe Efficiency = 0.069
-------------------------------------------------------
plot_ts_gui results, run time = 27-Mar-2023 11:07:59
Running plot_ts_gui.m on a Mac computer named jdb-macpro.local
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
208
Appendix 14. Model input file EFDC.INP file for base case.
******************************************************************************
* *
* WELCOME TO THE ENVIRONMENTAL FLUID DYNAMICS COMPUTER CODE SERIES *
* ORIGINALLY DEVELOPED BY JOHN M. HAMRICK. *
* *
* THIS IS THE MASTER INPUT FILE EFDC.INP. *
* FOR EFDC+ VERSION DATED AFTER 2016-04-04 *
* VERSION ID AND DATE OF RELEASE CAN INFLUENCE INPUT FORMAT *
* DO NOT CHANGE WITHOUT CHECKING INPUT.F90 *
* For details on EFDC+ Theory see: *
* https://eemodelingsystem.atlassian.net/wiki/spaces/ETG/overview *
* *
* GENERATED WITH DYNAMIC SOLUTIONS-INTERNATIONAL'S EFDC_Explorer8.4 *
* EE8.4.4 Rel 181128 *
******************************************************************************
* PROJECT NAME: Jordan Lake
******************************************************************************
C1 RUN TITLE
* TEXT DESCRIPTION FOR THIS RUN
C1 TITLE
Qser8, w/ 407 cell grid, Rerun of base case Dec 12/27/22 restart, vary benflux, IWQBEN=2
(prescribed flux)
-------------------------------------------------------------------------------
C1A GRID CONFIGURATION AND TIME INTEGRATION MODE SELECTION
*
* IS2TIM: 0 THREE-TIME LEVEL INTEGRATION
* 1 TWO-TIME LEVEL INTEGRATION
*
* IGRIDH: NOT USED
*
* IGRIDV: 0 STANDARD SIGMA VERTICAL GRID OR SINGLE LAYER DEPTH AVERAGE
* 1 SIGMA-ZED (SGZ) VERTICAL LAYERING ALLOWING VARYING LAYERS FOR EACH CELL (DSI)
* 2 SIGMA-ZED (SGZ) VERTICAL GRID USING HORIZONTALLY UNIFORM LAYER THICKNESSES
(DSI)
* SGZMin: MINIMUM NUMBER OF LAYERS FOR SIGMA-ZED
* SGZHPDelta: TYPICAL RISE OF WATER ABOVE THE INITIAL CONDITIONS WHEN IGRIDV>0 (M)
*
C1A IS2TIM IGRIDH IGRIDV SGZMin SGZHPDelta
1 0 2 7 4
-------------------------------------------------------------------------------
C2 RESTART, GENERAL CONTROL AND DIAGNOSTIC SWITCHES
*
* ISRESTI: 1 FOR READING INITIAL CONDITIONS FROM FILE restart.inp
* -1 AS ABOVE BUT ADJUST FOR CHANGING BOTTOM ELEVATION
* 10 FOR READING IC'S FROM restart.inp WRITTEN BEFORE 8 SEPT 92
*
* ISRESTO: -1 FOR WRITING RESTART FILE restart.out AT END OF RUN
* N INTEGER.GE.0 FOR WRITING restart*.out EVERY N REF TIME PERIODS
* ISRESTR: 1 FOR WRITING RESIDUAL TRANSPORT FILE RESTRAN.OUT
* ISGREGOR: 0/1 NOT USE/USE DATE STAMPED RESTART FILES
* ICONTINUE: RUN CONTINUATION OPTION FOR EE LINKAGE FILES WHEN ISRESTI=1
* 0 NO RUN CONTINUATION - EFDC WRITES EE_*.OUT FILES AS USUAL
* 1 ACTIVATE RUN CONTINUATION - EE LINKAGE OUTPUT WILL BE APPENDED TO THE EXISTING
FILES
* ISLOG: 1 FOR WRITING LOG FILE EFDC.LOG
* IDUM: NOT USED
*
*
* ISDIVEX: 1 FOR WRITING EXTERNAL MODE DIVERGENCE TO SCREEN
* ISNEGH: 1 FOR SEARCHING FOR NEGATIVE DEPTHS AND WRITING TO SCREEN
* ISMMC: <0 FLAG TO GLOBALLY ACTIVATE WRITING EXTRA MODEL RESULTS LOG FILES
*
* ISBAL: 1 FOR ACTIVATING MASS, MOMENTUM AND ENERGY BALANCES AND
* WRITING RESULTS TO FILE bal.out
* IDUM: NOT USED
* ISHOW: >0 TO SHOW RUNTIME STATUS ON SCREEN, SEE INSTRUCTIONS FOR FILE SHOW.INP
*
C2 ISRESTI ISRESTO ISRESTR ISGREGOR ISLOG ISDIVEX ISNEGH ISMMC ISBAL ICONTINUE ISHOW
1 -1 0 0 1 0 2 -1 0 0 1
209
-------------------------------------------------------------------------------
C3 EXTERNAL MODE SOLUTION OPTION PARAMETERS AND SWITCHES
*
* RP: OVER RELAXATION PARAMETER
* RSQM: TARGET SQUARE RESIDUAL OF ITERATIVE SOLUTION SCHEME
* ITERM: MAXIMUN NUMBER OF ITERATIONS
* IRVEC: 0 CONJUGATE GRADIENT SOLUTION - NO SCALING
* 9 CONJUGATE GRADIENT SOLUTION - SCALE BY MINIMUM DIAGONAL
* 99 CONJUGATE GRADIENT SOLUTION - SCALE TO NORMAL FORM
*
*
* IATMP: 0 DO NOT USE ATMOSPHERIC PRESSURE IN THE CALPUV SOLUTION
* 1 USE ATMOSPHERIC PRESSURE IN THE CALPUV SOLUTION IF NASER > 1
* IWDRAG: 0 USE ORIGINAL EFDC WIND DRAG FORMULATION
* 1 USE ORIGINAL EFDC WIND DRAG FORMULATION WITH RELATIVE WATER VELOCITY CORRECTION
* 2 HERSBACH 2011, EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS (ECMWF)
* 3 USE SIMPLIFIED COARE 3.6 APPROACH AT NEUTRAL ATM AND RELATIVE WATER VELOCITY
CORRECTION
* DUMMY:
* ITERHPM: MAXIMUM ITERATIONS FOR STRONGLY NONLINEAR DRYING AND WETTING
* SCHEME (ISDRY=3 OR 4) ITERHPM.LE.4
* IDRYCK: ITERATIONS PER DRYING CHECK (ISDRY.GE.1) 2.LE.IDRYCK.LE.20
* ISDSOLV: 1 TO WRITE DIAGNOSTICS FILES FOR EXTERNAL MODE SOLVER
* FILT3TL: FILTER COEFFICIENT FOR 3 TIME LEVEL EXPLICIT ( 0.0625 )
*
C3 RP RSQM ITERM IRVEC IATMP IWDRAG DUMMY ITERHPM IDRYCK ISDSOLV FILT3TL
1.8 1E-09 200 9 0 0 0 3 20 0 .0625
-------------------------------------------------------------------------------
C4 LONGTERM MASS TRANSPORT INTEGRATION ONLY SWITCHES
*
* ISLTMT: NOT USED
* ISSSMMT: 0 WRITES MEAN MASS TRANSPORT TO RESTRAN.OUT AFTER EACH
* AVERAGING PERIOD (FOR WASP/ICM/RCA LINKAGE)
* 1 WRITES MEAN MASS TRANSPORT TO RESTRAN.OUT AFTER LAST
* AVERAGING PERIOD (FOR RESEARCH PURPOSES)
* 2 DISABLES MEAN MASS TRANSPORT FIELD CALCULATIONS & RESTRAN.OUT
* ISLTMTS: NOT USED
*
*
*
* ISIA: NOT USED
*
* RPIA: NOT USED
* RSQMIA: NOT USED
* ITRMIA: NOT USED
* ISAVEC: NOT USED
*
C4 ISLTMT ISSSMMT ISLTMTS ISIA RPIA RSQMIA ITRMIA ISAVEC
0 2 0 0 1.8 1E-10 0 0
-------------------------------------------------------------------------------
C5 MOMENTUM ADVEC AND HORIZ DIFF SWITCHES AND MISC SWITCHES
*
* ISCDMA: 1 FOR CENTRAL DIFFERENCE MOMENTUM ADVECTION (USED FOR 3TL ONLY)
* 0 FOR UPWIND DIFFERENCE MOMENTUM ADVECTION (USED FOR 3TL ONLY)
* 2 FOR EXPERIMENTAL UPWIND DIFF MOM ADV (FOR RESEARCH PURPOSES)
* ISHDMF: 1 TO ACTIVE HORIZONTAL MOMENTUM DIFFUSION
* 2 TO ACTIVE HORIZONTAL MOMENTUM DIFFUSION WITH WATER COLUMN DIFFUSION
* ISDISP: 1 CALCULATE MEAN HORIZONTAL SHEAR DISPERSION TENSOR OVER LAST MEAN MASS TRANSPORT
AVERAGING PERIOD
* ISWASP: 4 OR 5 TO WRITE FILES FOR WASP4 OR WASP5 MODEL LINKAGE, 17-WASP7HYDRO, 99 - CE-QUAL-
ICM
* ISDRY: 0 NO WETTING & DRYING OF SHALLOW AREAS
* 1 CONSTANT WETTING DEPTH SPECIFIED BY HWET ON CARD 11
* WITH NONLINEAR ITERATIONS SPECIFIED BY ITERHPM ON CARD C3
* 2 VARIABLE WETTING DEPTH CALCULATED INTERNALLY IN CODE
* WITH NONLINEAR ITERATIONS SPECIFIED BY ITERHPM ON CARD C3
* 11 SAME AS 1, WITHOUT NONLINEAR ITERATION
* -11 SAME AS 11 BUT WITH CELL MASKING
* 99 VARIABLE WETTING & DRYING USING CELL FACES
* -99 SAME AS 11 BUT WITH CELL MASKING
* ISQQ: 1 TO USE STANDARD TURBULENT INTENSITY ADVECTION SCHEME
210
* 2 RESEARCH TURBULENT INTENSITY ADVECTION SCHEME
* ISRLID: 1 TO RUN IN RIGID LID MODE (NO FREE SURFACE)
* ISVEG: 1 TO IMPLEMENT VEGETATION RESISTANCE
* 2 IMPLEMENT WITH DIAGNOSTICS TO FILE CBOT.LOG
* ISVEGL: 1 TO INCLUDE LAMINAR FLOW OPTION IN VEGETATION RESISTANCE
* ISITB: 1 FOR IMPLICIT BOTTOM & VEGETATION RESISTANCE IN EXTERNAL MODE
*
* IHMDSUB: 1 TO USE A SUBSET OF CELLS FOR HMD CALCULATIONS, MAPHMD.INP
* IINTPG: 0 ORIGINAL INTERNAL PRESSURE GRADIENT FORMULATION
* 1 JACOBIAN FORMULATION
* 2 FINITE VOLUME FORMULATION
*
*
C5 ISCDMA ISHDMF ISDISP ISWASP ISDRY ISQQ ISRLID ISVEG ISVEGL ISITB IHMDSUB
IINTPG
0 0 0 0 -99 1 0 0 0 0 0
0
-------------------------------------------------------------------------------
C6 DISSOLVED AND SUSPENDED CONSTITUENT TRANSPORT SWITCHES
* TURB INTENSITY=0,SAL=1,TEM=2,DYE=3,SFL=4,TOX=5,SED=6,SND=7,CWQ=8
*
* ISTRAN: 1 OR GREATER TO ACTIVATE TRANSPORT
* ISTOPT: NONZERO FOR TRANSPORT OPTIONS, SEE USERS MANUAL
* ISCDCA: 0 FOR STANDARD DONOR CELL UPWIND DIFFERENCE ADVECTION (3TL ONLY)
* 1 FOR CENTRAL DIFFERENCE ADVECTION FOR THREE TIME LEVEL STEPS (3TL ONLY)
* 2 FOR EXPERIMENTAL UPWIND DIFFERENCE ADVECTION (FOR RESEARCH) (3TL ONLY)
* ISADAC: 1 TO ACTIVATE ANTI-NUMERICAL DIFFUSION CORRECTION TO
* STANDARD DONOR CELL SCHEME
* ISFCT: 1 TO ADD FLUX LIMITING TO ANTI-NUMERICAL DIFFUSION CORRECTION
* ISPLIT: 1 TO OPERATOR SPLIT HORIZONTAL AND VERTICAL ADVECTION
* (FOR RESEARCH PURPOSES)
* ISADAH: 1 TO ACTIVATE ANTI-NUM DIFFUSION CORRECTION TO HORIZONTAL
* SPLIT ADVECTION STANDARD DONOR CELL SCHEME (FOR RESEARCH)
* ISADAV: 1 TO ACTIVATE ANTI-NUM DIFFUSION CORRECTION TO VERTICAL
* SPLIT ADVECTION STANDARD DONOR CELL SCHEME (FOR RESEARCH)
* ISCI: 1 TO READ CONCENTRATION FROM FILE restart.inp
* ISCO: 1 TO WRITE CONCENTRATION TO FILE restart.out
*
C6 ISTRAN ISTOPT ISCDCA ISADAC ISFCT ISPLIT ISADAH ISADAV ISCI ISCO
1 2 0 1 1 0 0 0 1 1 !TURB 0
0 0 0 1 1 0 0 0 0 0 !SAL 1
1 1 0 1 1 0 0 0 1 1 !TEM 2
1 1 0 1 1 0 0 0 0 1 !DYE 3
0 0 0 1 1 0 0 0 0 0 !SFL 4
0 0 0 1 1 0 0 0 0 0 !TOX 5
0 0 0 1 1 0 0 0 0 0 !SED 6
0 0 0 1 1 0 0 0 0 0 !SND 7
1 0 0 1 1 0 0 0 1 1 !CWQ 8
-------------------------------------------------------------------------------
C7 TIME-RELATED INTEGER PARAMETERS
*
* NTC: NUMBER OF REFERENCE TIME PERIODS IN RUN
* NTSPTC: NUMBER OF TIME STEPS PER REFERENCE TIME PERIOD
* NLTC: NUMBER OF LINEARIZED REFERENCE TIME PERIODS
* NLTC: NUMBER OF TRANSITION REF TIME PERIODS TO FULLY NONLINEAR
* NTCPP: NUMBER OF REFERENCE TIME PERIODS BETWEEN FULL PRINTED OUTPUT
* TO FILE EFDC.OUT
* NTSTBC: NUMBER OF TIME STEPS BETWEEN USING A TWO TIME LEVEL TRAPEZOIDAL
* CORRECTION TIME STEP, ** MASS BALANCE PRINT INTERVAL **
* NTCNB: NUMBER OF REFERENCE TIME PERIODS WITH NO BUOYANCY FORCING (NOT USED)
* NTCVB: NUMBER OF REF TIME PERIODS WITH VARIABLE BUOYANCY FORCING
* NTSMMT: NUMBER OF NUMBER OF TIME STEPS TO AVERAGE OVER TO OBTAIN
* MASS BALANCE RESIDUALS OR MEAN MASS TRANSPORT VARIABLES (e.g. WASP Linkage)
* NFLTMT: USE 1 (FOR RESEARCH PURPOSES)
* NDRYSTP: MIN NO. OF TIME STEPS A CELL REMAINS DRY AFTER INITIAL DRYING
* -NDRYSTP FOR ISDRY=-99 TO ACTIVATE WASTING WATER IN DRY CELLS
* NRAMPUP: NUMBER OF INITIAL LOOPS TO HOLD TIMESTEP CONSTANT FOR DYNAMIC TIME-STEPPING
* NUPSTEP: MINIMUM NUMBER OF ITERATIONS FOR EACH TIME STEP WHEN GROWING DTDYN
* full time is 780 days from JDAY 1096
C7 NTC NTSPTC NLTC NTTC NTCPP NTSTBC NTCNB NTCVB NTSMMT NFLTMT NDRYSTP
NRAMPUP NUPSTEP
211
750 172800 0 0 10 4 0 0 0 1 200
200 2
-------------------------------------------------------------------------------
C8 TIME-RELATED REAL PARAMETERS
*
* TCON: CONVERSION MULTIPLIER TO CHANGE TBEGIN TO SECONDS
* TBEGIN: TIME ORIGIN OF RUN
* TREF: REFERENCE TIME PERIOD IN sec (i.e. 44714.16S OR 86400S)
* CORIOLIS: CONSTANT CORIOLIS PARAMETER IN 1/sec =2*7.29E-5*SIN(LAT)
* ISCORV: 1 TO READ VARIABLE CORIOLIS COEFFICIENT FROM LXLY.INP FILE
* ISCCA: WRITE DIAGNOSTICS FOR MAX CORIOLIS-CURV ACCEL TO FILEEFDC.LOG
* ISCFL: 1 WRITE DIAGNOSTICS OF MAX THEORETICAL TIME STEP TO CFL.OUT
* GT 1 TIME STEP ONLY AT INTERVAL ISCFL FOR ENTIRE RUN
* ISCFLM: 1 TO MAP LOCATIONS OF MAX TIME STEPS OVER ENTIRE RUN
* DTSSFAC: DYNAMIC TIME STEPPING IF DTSSFAC > 0.0
* DTSSDHDT: DYNAMIC TIME STEPPING RATE OF DEPTH CHANGE FACTOR (USED WHEN > 0)
* DTMAX: MAXIMUM TIME STEP FOR DYNAMIC STEPPING (SECONDS)
*
C8 TCON TBEGIN TREF CORIOLIS ISCORV ISCCA ISCFL ISCFLM DTSSFAC
DTSSDHDT DTMAX
86400 1126 86400 8.49385E-05 0 1 1 1 .10
.10 120
-------------------------------------------------------------------------------
C9 SPACE-RELATED AND SMOOTHING PARAMETERS
*
* IC: NUMBER OF CELLS IN I DIRECTION
* JC: NUMBER OF CELLS IN J DIRECTION
* LC: NUMBER OF ACTIVE CELLS IN HORIZONTAL + 2
* LVC: NUMBER OF VARIABLE SIZE HORIZONTAL CELLS
* ISCO: 1 FOR CURVILINEAR-ORTHOGONAL GRID (LVC=LC-2)
* NDM: NUMBER OF DOMAINS FOR HORIZONTAL DOMAIN DECOMPOSITION
* ( NDM=1, FOR MODEL EXECUTION ON A SINGLE PROCESSOR SYSTEM OR
* NDM=MM*NCPUS, WHERE MM IS AN INTEGER AND NCPUS IS THE NUMBER
* OF AVAILABLE CPU'S FOR MODEL EXECUTION ON A PARALLEL MULTIPLE PROCESSOR SYSTEM )
* LDM: NUMBER OF WATER CELLS PER DOMAIN (LDM=(LC-2)/NDM, FOR MULTIPLE VECTOR PROCESSORS,
* LDM MUST BE AN INTEGER MULTIPLE OF THE VECTOR LENGTH OR
* STRIDE NVEC THUS CONSTRAINING LC-2 TO BE AN INTEGER MULTIPLE OF NVEC )
* ISMASK: 1 FOR MASKING WATER CELL TO LAND OR ADDING THIN BARRIERS
* USING INFORMATION IN FILE MASK.INP
* ISCONNECT: 1 FOR USER DEFINED N-S CONNECTION OF CELLS USING INFO IN FILE MAPPGNS.INP
* 2 FOR USER DEFINED E-W CONNECTION OF CELLS USING INFO IN FILE MAPPGEW.INP
* 3 FOR BOTH E-W AND N-S CONNECTIONS
* NSHMAX: NUMBER OF DEPTH SMOOTHING PASSES
* NSBMAX: NUMBER OF INITIAL SALINITY FIELD SMOOTHING PASSES
* WSMH: DEPTH SMOOTHING WEIGHT
* WSMB: SALINITY SMOOTHING WEIGHT
*
*
*
C9 IC JC LC LVC ISCO NDM LDM ISMASK CONNECT NSHMAX NSBMAX
WSMH WSMB
34 60 409 407 1 1 407 1 0 0 0
0.00000 0.00000
-------------------------------------------------------------------------------
C9A VERTICAL SPACE-RELATED PARAMETERS
* KC: NUMBER OF VERTICAL LAYERS
*
* KSIG: NOT USED
* ISETGVC: NOT USED
*
*
* SELVREF: NOT USED
* BELVREF: NOT USED
* ISGVCCK: NOT USED
*
*
C9A KC KSIG ISETGVC SELVREF BELVREF ISGVCCK
25 25 1 0.000 -1.000 0
-------------------------------------------------------------------------------
C10 LAYER THICKNESS IN VERTICAL
*
212
* K: LAYER NUMBER, K=1,KC
* DZC: DIMENSIONLESS LAYER THICKNESS (THICKNESSES MUST SUM TO 1.0)
*
*
*
C10 K DZC
1 0.0400000
2 0.0400000
3 0.0400000
4 0.0400000
5 0.0400000
6 0.0400000
7 0.0400000
8 0.0400000
9 0.0400000
10 0.0400000
11 0.0400000
12 0.0400000
13 0.0400000
14 0.0400000
15 0.0400000
16 0.0400000
17 0.0400000
18 0.0400000
19 0.0400000
20 0.0400000
21 0.0400000
22 0.0400000
23 0.0400000
24 0.0400000
25 0.0400000
-------------------------------------------------------------------------------
C11 GRID, ROUGHNESS AND DEPTH PARAMETERS
*
* DX: CARTESIAN CELL LENGTH IN X OR I DIRECTION
* DY: CARTESIAN CELL LENGTH IN Y OR J DIRECTION
* DXYCVT: MULTIPLY DX AND DY BY TO OBTAIN METERS
* IMDXDY: GREATER THAN 0 TO READ MODDXDY.INP FILE
* ZBRADJ: LOG BDRY LAYER CONST OR VARIABLE ROUGH HEIGHT ADJ IN METERS
* ZBRCVRT: LOG BDRY LAYER VARIABLE ROUGHNESS HEIGHT CONVERT TO METERS
* HMIN: MINIMUM DEPTH OF INPUTS DEPTHS IN METERS
* HADJ: ADJUSTMENT TO DEPTH FIELD IN METERS
* HCVRT: CONVERTS INPUT DEPTH FIELD TO METERS
* HDRY: DEPTH AT WHICH CELL OR FLOW FACE BECOMES DRY
* HWET: DEPTH AT WHICH WITHDRAWALS FROM CELL ARE TURNED OFF
* BELADJ: ADJUSTMENT TO BOTTOM BED ELEVATION FIELD IN METERS
* BELCVRT: CONVERTS INPUT BOTTOM BED ELEVATION FIELD TO METERS
*
C11 DX DY DXYCVT IMD ZBRADJ ZBRCVRT HMIN HADJ HCVRT HDRY HWET
BELADJ BELCVRT
1 1 1 0 0 1 .001 0 1 .05 .1
0 1
-------------------------------------------------------------------------------
C11A TWO-LAYER MOMENTUM FLUX AND CURVATURE ACCELERATION CORRECTION FACTORS
* (ONLY USED FOR 2 TIME LEVEL SOLUTION & ISDRY=0 PMC-Check to see if still true)
* ICK2COR: 0 NO CORRECTION
* ICK2COR: 1 CORRECTION USING CK2UUC,CK2VVC,CK2UVC FOR CURVATURE
* ICK2COR: 2 CORRECTION USING CK2FCX,CK2FCY FOR CURVATURE
* CK2UUM: CORRECTION FOR UU MOMENTUM FLUX
* CK2VVM: CORRECTION FOR UU MOMENTUM FLUX
* CK2UVM: CORRECTION FOR UU MOMENTUM FLUX
* CK2UUC: CORRECTION FOR UU CURVATURE ACCELERATION (NOT ACTIVE)
* CK2VVC: CORRECTION FOR VV CURVATURE ACCELERATION (NOT ACTIVE)
* CK2UVC: CORRECTION FOR UV CURVATURE ACCELERATION (NOT ACTIVE)
* CK2FCX: CORRECTION FOR X EQUATION CURVATURE ACCELERATION
* CK2FCY: CORRECTION FOR Y EQUATION CURVATURE ACCELERATION
*
C11A ICK2COR CK2UUM CK2VVM CK2UVM CK2UUC CK2VVC CK2UVC CK2FCX CK2FCY
0 .0825 .0825 .0825 .0825 .0825 .0825 .0825 .0825
-------------------------------------------------------------------------------
C11B CORNER CELL BOTTOM STRESS CORRECTION OPTIONS (2TL ONLY)
213
*
* ISCORTBC: 1 TO CORRECT BED STRESS AVERAGING TO CELL CENTERS IN CORNERS
* 2 TO USE SPATIALLY VARYING CORRECTION FOR CELLS IN CORNERC.INP
* ISCORTBCD: 1 WRITE DIAGNOSTICS EVERY NSPTC TIME STEPS (NOT USED)
* FSCORTBC: CORRECTION FACTOR, 0.0 GE FSCORTBC LE 1.0
* 1.0 = NO CORRECTION, 0.0 = MAXIMUM CORRECTION, 0.5 SUGGESTED
*
C11B ISCORTBC ISCORTBCD FSCORTBC
0 0 .5
-------------------------------------------------------------------------------
C12 TURBULENT DIFFUSION PARAMETERS
*
* AHO: CONSTANT HORIZONTAL MOMENTUM AND MASS DIFFUSIVITY m*m/s
* AHD: DIMESIONLESS HORIZONTAL MOMENTUM DIFFUSIVITY (ONLY FOR ISHDMF>0)
* AVO: BACKGROUND, CONSTANT OR EDDY (KINEMATIC) VISCOSITY m*m/s
* ABO: BACKGROUND, CONSTANT OR MOLECULAR DIFFUSIVITY m*m/s
* AVMX: MAXIMUM KINEMATIC EDDY VISCOSITY m*m/s (DS-INTL)
* ABMX: MAXIMUM EDDY DIFFUSIVITY m*m/s (DS-INTL)
* VISMUD: CONSTANT FLUID MUD VISCOSITY m*m/s
* AVCON: EQUALS ZERO FOR CONSTANT VERTICAL VISCOSITY AND DIFFUSIVITY
* WHICH ARE SET EQUAL TO AVO AND ABO, OTHERWISE SET TO 1.0
* ZBRWALL: SIDE WALL LOG LAW ROUGHNESS HEIGHT. USED WHEN HORIZONTAL
* MOMENTUM DIFFUSION IS ACTIVE AND AHO OR AHD ARE NONZERO
*
C12 AHO AHD AVO ABO AVMX ABMX VISMUD AVCON ZBRWALL
0 .1 .00001 .000001 1 1 .0001 1 .000002
-------------------------------------------------------------------------------
C12A TURBULENCE CLOSURE OPTIONS
*
* ISSTAB: 0 FOR GALPERIN et al. STABILITY FUNCTIONS IN CALAVBOLD (ISQQ=1)
* 1 FOR GALPERIN et al. STABILITY FUNCTIONS (ISQQ=1)
* 2 FOR KANTHA AND CLAYSON (1994) STABILITY FUNCTIONS (ISQQ=1)
* 3 FOR KANTHA (2003) STABILITY FUNCTIONS (ISQQ=1)
* (NOTE: OPTION SELECTED HERE OVERRIDES ISTOPT(0) ON C6)
* 4 VINCON-LEITE, ET.AL. (2014) APPROACH (ISQQ=1)
* ISSQL: 0 SETS QQ AND QQL STABILITY FUNCTIONS PROPORTIONAL TO
* MOMENTUM STABILITY FUNCTIONS (EXCEPT FOR ISSTAB=3)
* 1 SETS QQ AND QQL STABILITY FUNCTIONS TO CONSTANTS
* (FOR ISSTAB = 0,1,2) THIS OPTION NOT ACTIVE
* ISAVBMX: SET TO 1 TO ACTIVATE MAX VISCOSITY AND DIFFUSIVITY OF AVMX AND ABMX
* ISFAVB: SET TO 1 OR 2 TO AVG OR SQRT FILTER AVO AND AVB
* ISINWV: SET TO 2 TO WRITE EE_ARRAYS.OUT
* ISLLIM: 0 FOR NO LENGTH SCALE AND RIQMAX LIMITATIONS
* 1 LIMIT RIQMAX IN STABILITY FUNCTION ONLY
* 2 DIRECTLY LIMIT LENGTH SCALE AND LIMIT RIQMAX IN STABILITY FUNCTION
* IFPROX: 0 FOR NO WALL PROXIMITY FUNCTION
* 1 FOR PARABOLIC OVER DEPTH WALL PROXIMITY FUNCTION
* 2 FOR OPEN CHANNEL WALL PROXIMITY FUNCTION
* XYRATIO: LARGE ASPECT RATIOS, IF XYRATIO>1.1 AND >DX:DY THEN ZERO XY TERMS FMDUY AND
FMDVX (EFDC+)
* BC_EDGEFACTOR: BOUNDARY CELLS MOMENTUM CORRECTION FACTOR (0 TO 1)
*
C12A ISSTAB ISSQL ISAVBMX ISFAVB ISINWV ISLLIM IFPROX XYRATIO BC_EDGEFACTOR
2 0 0 2 1 2 0 0 0
-------------------------------------------------------------------------------
C13 TURBULENCE CLOSURE PARAMETERS
*
* VKC: VON KARMAN CONSTANT
* CTURB1: TURBULENT CONSTANT (UNIVERSAL)
* CTURB2: TURBULENT CONSTANT (UNIVERSAL)
* CTE1: TURBULENT CONSTANT (UNIVERSAL)
* CTE2: TURBULENT CONSTANT (UNIVERSAL)
* CTE3: TURBULENT CONSTANT (UNIVERSAL)
* CTE4: TURBULENCE CONSTANT E4 (SOMETIMES CALL E3) WALL FUNCTION IN Q*Q*L EQUATION
* CTE5: TURBULENCE CONSTANT E5 - 2ND OPEN CHANNEL WALL FUNCTION IN Q*Q*L EQUATION
* RIQMAX: MAXIMUM TURBULENT INTENSITY RICHARDSON NUMBER FOR STABLE CONDITIONS
* QQMIN: MINIMUM TURBULENT INTENSITY SQUARED
* QQLMIN: MINIMUM TURBULENT INTENSITY SQUARED * LENGTH-SCALE
* DMLMIN: MINIMUM DIMENSIONLESS LENGTH SCALE
*
214
C13 VKC CTURB1 CTURB2 CTE1 CTE2 CTE3 CTE4 CTE5 RIQMAX QQMIN QQLMIN
DMLMIN
.4 16.6 10.1 1.8 1 1.8 1.33 .25 .28 1E-08 1E-12
.0001
-------------------------------------------------------------------------------
C14 TIDAL & ATMOSPHERIC FORCING, GROUND WATER AND SUBGRID CHANNEL PARAMETERS
*
* MTIDE: NUMBER OF PERIOD (TIDAL) FORCING CONSTITUENTS
* NWSER: NUMBER OF WIND TIME SERIES (0 SETS WIND TO ZERO)
* NASER: NUMBER OF ATMOSPHERIC CONDITION TIME SERIES (0 SETS ALL ZERO)
* ISGWIT: 0 DISABLE GROUND WATER
* 1 TO ACTIVATE SOIL MOISTURE BALANCE WITH DRYING AND WETTING
* 2 TO ACTIVATE GROUNDWATER INTERACTION WITH BED AND WATER COLUMN (GWMAP & GWSER)
* 3 TO ZONED TEMPORALLY CONSTANT IN(+)/OUT(-) SEEPAGE RATE (M/S) (GWSEEP & GWMAP)
* ISCHAN: >0 ACTIVATE SUBGRID CHANNEL MODEL AND READ MODCHAN.INP
* ISWAVE: 1-FOR BOUNDARY LAYER IMPACTS ONLY (WAVEBL.INP),
* 2-FOR BOUNDARY LAYER & CURRENT IMPACTS (WVnnn.INP)
* 3-FOR INTERNALLY COMPUTED WIND WAVE BOUNDARY LAYER IMPACTS (DSI)
* 4-FOR INTERNALLY COMPUTED WIND WAVE BOUNDARY LAYER AND CURRENT IMPACTS (DSI)
* ITIDASM: 1 FOR TIDAL ELEVATION ASSIMILATION (NOT ACTIVE)
* ISPERC: 1 TO PERCOLATE OR ELIMINATE EXCESS WATER IN DRY CELLS
* ISBODYF: TO INCLUDE EXTERNAL MODE BODY FORCES FROM FBODY.INP
* 1 FOR UNIFORM OVER DEPTH, 2 FOR SURFACE LAYER ONLY
* ISPNHYDS: 1 FOR QUASI-NONHYDROSTATIC OPTION
*
C14 MTIDE NWSER NASER ISGWIT ISCHAN ISWAVE ITIDASM ISPERC ISBODYF ISPNHYDS
0 1 1 0 0 0 0 0 0 0
-------------------------------------------------------------------------------
C15 PERIODIC FORCING (TIDAL) CONSTITUENT SYMBOLS AND PERIODS
*
* SYMBOL: FORCING SYMBOL (CHARACTER VARIABLE) FOR TIDES, THE NOS SYMBOL
* PERIOD: FORCING PERIOD IN SECONDS
*
C15 SYMBOL PERIOD
-------------------------------------------------------------------------------
C16 SURFACE ELEVATION OR PRESSURE BOUNDARY CONDITION PARAMETERS
*
* NPBS: NUMBER OF SURFACE ELEVATION OR PRESSURE BOUNDARY CONDITIONS
* CELLS ON SOUTH OPEN BOUNDARIES
* NPBW: NUMBER OF SURFACE ELEVATION OR PRESSURE BOUNDARY CONDITIONS
* CELLS ON WEST OPEN BOUNDARIES
* NPBE: NUMBER OF SURFACE ELEVATION OR PRESSURE BOUNDARY CONDITIONS
* CELLS ON EAST OPEN BOUNDARIES
* NPBN: NUMBER OF SURFACE ELEVATION OR PRESSURE BOUNDARY CONDITIONS
* CELLS ON NORTH OPEN BOUNDARIES
* NPFOR: NUMBER OF HARMONIC FORCINGS
* NPFORT: FORCING TYPE, 0=CONSTANT, 1=LINEAR, 2= QUADRATIC VARIATION
* NPSER: NUMBER OF TIME SERIES FORCINGS
* PDGINIT: ADD THIS CONSTANT ADJUSTMENT GLOBALLY TO THE SURFACE ELEVATION
*
C16 NPBS NPBW NPBE NPBN NPFOR NPFORT NPSER PDGINIT
0 0 0 0 0 0 0 0
-------------------------------------------------------------------------------
C17 PERIODIC FORCING (TIDAL) SURF ELEV OR PRESSURE BOUNDARY COND. FORCINGS
*
* NPFOR: FORCING NUMBER
* SYMBOL: FORCING SYMBOL (FOR REFERENCE HERE ONLY)
* AMPLITUDE: AMPLITUDE IN M (PRESSURE DIVIDED BY RHO*G), NPFORT=0
* COSINE AMPLITUDE IN M, NPFORT.GE.1
* PHASE: FORCING PHASE RELATIVE TO TBEGIN IN SECONDS, NPFORT=0
* SINE AMPLITUDE IN M, NPFORT.GE.1
* NOTE: FOR NPFORT=0 SINGLE AMPLITUDE AND PHASE ARE READ, FOR NPFORT=1
* CONST AND LINEAR COS AND SIN AMPS ARE READ FOR EACH FORCING, FOR
* NPFORT=2, CONST, LINEAR, QUAD COS AND SIN AMPS ARE READ FOR EACH
* FOR EACH FORCING
*
C17 NPFOR SYMBOL AMPLITUDE PHASE
-------------------------------------------------------------------------------
C18 PERIODIC FORCING (TIDAL) SURF ELEV OR PRESSURE ON SOUTH OPEN BOUNDARIES
* IPBS: I CELL INDEX OF BOUNDARY CELL
* JPBS: J CELL INDEX OF BOUNDARY CELL
215
* ISPBS: 0 FOR ELEVATION SPECIFIED
* 1 FOR RADIATION-SEPARATION CONDITION, ZERO TANGENTIAL VELOCITY
* 2 FOR RADIATION-SEPARATION CONDITION, FREE TANGENTIAL VELOCITY
* 3 FOR ELEVATION SPECIFIED, FREE TANGENTIAL VELOCITY
* NPFORS: APPLY HARMONIC FORCING NUMBER NPFORS
* NPSERS: APPLY TIME SERIES FORCING NUMBER NPSERS
* NPSERS1: APPLY TIME SERIES FORCING NUMBER NPSERS1 FOR 2ND SERIES (NPFORT.GE.1)
* TPCOORDS: TANGENTIAL COORDINATE ALONG BOUNDARY (NPFORT.GE.1)
*
C18 IPBS JPBS ISPBS NPFORS NPSERS
-------------------------------------------------------------------------------
C19 PERIODIC FORCING (TIDAL) SURF ELEV OR PRESSURE ON WEST OPEN BOUNDARIES
*
* IPBW: SEE CARD 18
* JPBW:
* ISPBW:
* NPFORW:
* NPSERW:
* TPCOORDW:
*
C19 IPBW JPBW ISPBW NPFORW NPSERW
-------------------------------------------------------------------------------
C20 PERIODIC FORCING (TIDAL) SURF ELEV OR PRESSURE ON EAST OPEN BOUNDARIES
*
* IPBE: SEE CARD 18
* JPBE:
* ISPBE:
* NPFORE:
* NPSERE:
* TPCOORDE:
*
C20 IPBE JPBE ISPBE NPFORE NPSERE
-------------------------------------------------------------------------------
C21 PERIODIC FORCING (TIDAL) SURF ELEV OR PRESSURE ON NORTH OPEN BOUNDARIES
*
* IPBN: SEE CARD 18
* JPBN:
* ISPBN:
* NPFORN:
* NPSERN:
* TPCOORDN:
*
C21 IPBN JPBN ISPBN NPFORN NPSERN
-------------------------------------------------------------------------------
C22 SPECIFY NUM OF SEDIMENT AND TOXICS AND NUM OF CONCENTRATION TIME SERIES
*
* NTOX: NUMBER OF TOXIC CONTAMINANTS (DEFAULT = 1)
* NSED: NUMBER OF COHESIVE SEDIMENT SIZE CLASSES (DEFAULT = 1)
* NSND: NUMBER OF NON-COHESIVE SEDIMENT SIZE CLASSES (DEFAULT = 1)
* NCSER1: NUMBER OF SALINITY TIME SERIES
* NCSER2: NUMBER OF TEMPERATURE TIME SERIES
* NCSER3: NUMBER OF DYE CONCENTRATION TIME SERIES
* NCSER4: NUMBER OF SHELLFISH LARVAE CONCENTRATION TIME SERIES
* NCSER5: NUMBER OF TOXIC CONTAMINANT CONCENTRATION TIME SERIES
* EACH TIME SERIES MUST HAVE DATA FOR NTOX TOXICANTS
* NCSER6: NUMBER OF COHESIVE SEDIMENT CONCENTRATION TIME SERIES
* EACH TIME SERIES MUST HAVE DATA FOR NSED COHESIVE SEDIMENTS
* NCSER7: NUMBER OF NON-COHESIVE SEDIMENT CONCENTRATION TIME SERIES
* EACH TIME SERIES MUST HAVE DATA FOR NSND NON-COHESIVE SEDIMENTS
* ISSBAL: SET TO 1 FOR SEDIMENT MASS BALANCE
*
C22 NTOX NSED NSND NCSER1 NCSER2 NCSER3 NCSER4 NCSER5 NCSER6 NCSER7 ISSBAL
0 0 0 0 4 1 0 0 0 0 0
-------------------------------------------------------------------------------
C23 VELOCITY, VOLUME SOURCE/SINK, FLOW CONTROL, AND WITHDRAWAL/RETURN DATA
*
* NQSIJ: NUMBER OF CONSTANT AND/OR TIME SERIES SPECIFIED SOURCE/SINK
* LOCATIONS (RIVER INFLOWS,ETC) .
* NQJPIJ: NUMBER OF CONSTANT AND/OR TIME SERIES SPECIFIED SOURCE
* LOCATIONS TREATED AS JETS/PLUMES .
* NQSER: NUMBER OF VOLUME SOURCE/SINK TIME SERIES
216
* NQCTL: NUMBER OF PRESSURE CONTROLLED WITHDRAWAL/RETURN PAIRS
* NQCTLT: NUMBER OF PRESSURE CONTROLLED WITHDRAWAL/RETURN TABLES
* NHYDST: NUMBER OF HYDRAULIC STRUCTURE DEFINITIONS
* NQWR: NUMBER OF CONSTANT OR TIME SERIES SPECIFIED WITHDRAWAL/RETURN
* PAIRS
* NQWRSR: NUMBER OF TIME SERIES SPECIFYING WITHDRAWAL,RETURN AND
* CONCENTRATION RISE SERIES
* ISDIQ: SET TO 1 TO WRITE DIAGNOSTIC FILE, DIAQ.OUT
* NQCTLSER: NUMBER OF GATE OPENING TIME-SERIES FOR HYDRAULIC STRUCTURE CONTROL
* NQCRULES: NUMBER OF OPERATIONAL RULES FOR HYDRAULIC STRUCTURE CONTROL
*
C23 NQSIJ NQJPIJ NQSER NQCTL NQCTLT NHYDST NQWR NQWRSR ISDIQ NQCTLSER NQCRULES
15 0 8 0 0 0 0 0 1 0 0
-------------------------------------------------------------------------------
C24 VOLUMETRIC SOURCE/SINK LOCATIONS, MAGNITUDES, AND CONCENTRATION SERIES
*
* IQS: I CELL INDEX OF VOLUME SOURCE/SINK
* JQS: J CELL INDEX OF VOLUME SOURCE/SINK
* QSSE: CONSTANT INFLOW/OUTFLOW RATE IN (m^3/s)
* NQSMUL: MULTIPLIER SWITCH FOR CONSTANT AND TIME SERIES VOL S/S
* = 0 MULT BY 1. FOR NORMAL IN/OUTFLOW (L*L*L/T)
* = 1 MULT BY DY FOR LATERAL IN/OUTFLOW (L*L/T) ON U FACE
* = 2 MULT BY DX FOR LATERAL IN/OUTFLOW (L*L/T) ON V FACE
* = 3 MULT BY DX+DY FOR LATERAL IN/OUTFLOW (L*L/T) ON U&V FACES
* NQSMF: IF NON ZERO ACCOUNT FOR VOL S/S MOMENTUM FLUX (NEGATIVE VALUES REVERSE FLOW
DIRECTION)
* = 1 MOMENTUM FLUX ON WEST U FACE
* = 2 MOMENTUM FLUX ON SOUTH V FACE
* = 3 MOMENTUM FLUX ON EAST U FACE
* = 4 MOMENTUM FLUX ON NORTH V FACE
* IQSERQ: ID NUMBER OF ASSOCIATED VOLUME FLOW TIME SERIES
* ICSER1: ID NUMBER OF ASSOCIATED SALINITY TIME SERIES
* ICSER2: ID NUMBER OF ASSOCIATED TEMPERATURE TIME SERIES
* ICSER3: ID NUMBER OF ASSOCIATED DYE CONC TIME SERIES
* ICSER4: ID NUMBER OF ASSOCIATED SHELL FISH LARVAE RELEASE TIME SERIES
* ICSER5: ID NUMBER OF ASSOCIATED TOXIC CONTAMINANT CONC TIME SERIES
* ICSER6: ID NUMBER OF ASSOCIATED COHESIVE SEDIMENT CONC TIME SERIES
* ICSER7: ID NUMBER OF ASSOCIATED NON-COHESIVE SED CONC TIME SERIES
* QWIDTH: WIDTH OF THE DISCHARGE FOR FOR MOMENTUM FLUX (M) (NQSMF /= 0)
* QSFACTOR: FRACTION OF TIME SERIES FLOW NQSERQ ASSIGNED TO THIS CELL
*
C24 IQS JQS QSSE NQSMUL NQSMF IQSERQ ICSER1 ICSER2 ICSER3 ICSER4 ICSER5
ICSER6 ICSER7 QWIDTH QSFACTOR ! ID
24 24 0.0000E+00 0 0 5 0 4 1 0 0
0 0 0.0000 1.0000E+00 ! 5, White Oak. Crk.
16 30 0.0000E+00 0 0 6 0 4 1 0 0
0 0 0.0000 5.7265E-02 ! 6, Ungaged, Seg 4, west
13 35 0.0000E+00 0 0 2 0 2 1 0 0
0 0 0.0000 1.0000E+00 ! 2, Morgan Ck. at lake
20 25 0.0000E+00 0 0 6 0 4 1 0 0
0 0 0.0000 7.2650E-02 ! 9, Ungaged Seg 3, east
17 45 0.0000E+00 0 0 6 0 1 1 0 0
0 0 0.0000 3.3618E-02 ! 14, Ungaged Seg 4, east
18 44 0.0000E+00 0 0 6 0 1 1 0 0
0 0 0.0000 1.8034E-01 ! 14, Ungaged Seg 4, east
23 6 0.0000E+00 0 0 7 0 0 0 0 0
0 0 0.0000 1.0000E+00 ! 7, Dam Out
16 46 0.0000E+00 0 0 3 0 3 1 0 0
0 0 0.0000 1.0000E+00 ! 3, New Hope
21 40 0.0000E+00 0 0 4 0 4 1 0 0
0 0 0.0000 1.0000E+00 ! 4, Northeast
14 25 0.0000E+00 0 0 6 0 4 1 0 0
0 0 0.0000 1.0969E-01 ! 8, Ungaged Seg 3, west
12 17 0.0000E+00 0 0 6 0 4 1 0 0
0 0 0.0000 4.4729E-02 ! 10, Ungaged Seg 2, west
24 20 0.0000E+00 0 0 6 0 4 1 0 0
0 0 0.0000 2.3419E-01 ! 11, Ungaged Seg 2, east
17 5 0.0000E+00 0 0 6 0 4 1 0 0
0 0 0.0000 2.6752E-01 ! 12, Ungaged Seg 1
19 20 0.0000E+00 0 0 8 0 0 0 0 0
0 0 0.0000 1.0000E+00 ! 13, Cary withdrawal
217
13 6 0.0000E+00 0 0 1 0 1 1 0 0
0 0 0.0000 1.0000E+00 ! 1, Haw River at Bynum
-------------------------------------------------------------------------------
C25 TIME CONSTANT INFLOW CONCENTRATIONS FOR TIME CONSTANT VOLUMETRIC SOURCES
*
* SAL: SALT CONCENTRATION CORRESPONDING TO INFLOW ABOVE
* TEM: TEMPERATURE CORRESPONDING TO INFLOW ABOVE
* DYE: DYE CONCENTRATION CORRESPONDING TO INFLOW ABOVE
* SFL: SHELL FISH LARVAE CONCENTRATION CORRESPONDING TO INFLOW ABOVE
* TOX: NTOX TOXIC CONTAMINANT CONCENTRATIONS CORRESPONDING TO
* INFLOW ABOVE WRITTEN AS TOXC(N), N=1,NTOX A SINGLE DEFAULT
* VALUE IS REQUIRED EVEN IF TOXIC TRANSPORT IS NOT ACTIVE
*
C25 SAL TEM DYE SFL ! ID
0 30 0 0 ! 5, White Oak. Crk.
0 30 0 0 ! 6, Ungaged, Seg 4, west
0 30 0 0 ! 2, Morgan Ck. at lake
0 30 0 0 ! 9, Ungaged Seg 3, east
0 30 0 0 ! 14, Ungaged Seg 4, east
0 30 0 0 ! 14, Ungaged Seg 4, east
0 30 0 0 ! 7, Dam Out
0 30 0 0 ! 3, New Hope
0 30 0 0 ! 4, Northeast
0 30 0 0 ! 8, Ungaged Seg 3, west
0 30 0 0 ! 10, Ungaged Seg 2, west
0 30 0 0 ! 11, Ungaged Seg 2, east
0 30 0 0 ! 12, Ungaged Seg 1
0 30 0 0 ! 13, Cary withdrawal
0 30 0 0 ! 1, Haw River at Bynum
-------------------------------------------------------------------------------
C26 TIME CONSTANT INFLOW CONCENTRATIONS FOR TIME CONSTANT VOLUMETRIC SOURCES
*
* SED: NSED COHESIVE SEDIMENT CONCENTRATIONS CORRESPONDING TO
* INFLOW ABOVE WRITTEN AS SEDC(N), N=1,NSED. I.E., THE FIRST
* NSED VALUES ARE COHESIVE A SINGLE DEFAULT VALUE IS REQUIRED
* EVEN IF COHESIVE SEDIMENT TRANSPORT IS INACTIVE
* SND: NSND NON-COHESIVE SEDIMENT CONCENTRATIONS CORRESPONDING TO
* INFLOW ABOVE WRITTEN AS SND(N), N=1,NSND. I.E., THE LAST
* NSND VALUES ARE NON-COHESIVE. A SINGLE DEFAULT VALUE IS
* REQUIRED EVEN IF NON-COHESIVE SEDIMENT TRANSPORT IS INACTIVE
*
C26 SED1 SND1
! 5, White Oak. Crk.
! 6, Ungaged, Seg 4, west
! 2, Morgan Ck. at lake
! 9, Ungaged Seg 3, east
! 14, Ungaged Seg 4, east
! 14, Ungaged Seg 4, east
! 7, Dam Out
! 3, New Hope
! 4, Northeast
! 8, Ungaged Seg 3, west
! 10, Ungaged Seg 2, west
! 11, Ungaged Seg 2, east
! 12, Ungaged Seg 1
! 13, Cary withdrawal
! 1, Haw River at Bynum
-------------------------------------------------------------------------------
C27 JET/PLUME SOURCE LOCATIONS, GEOMETRY AND ENTRAINMENT PARAMETERS
*
* ID: ID COUNTER FOR JET/PLUME
* ICAL: 0 BYPASS, 1 ACTIVE (NORMAL - TOTAL LAYER FLOW AT DIFFUSER), 2 - W/R (USE W/R SERIES)
* IQJP: I CELL INDEX OF JET/PLUME
* JQJP: J CELL INDEX OF JET/PLUME
* KQJP: K CELL INDEX OF JET/PLUME (DEFAULT, QJET=0 OR JET COMP DIVERGES)
* NPORT: NUMBER OF IDENTICAL PORTS IN THIS CELL
* XJET: LOCAL EAST JET LOCATION RELATIVE TO DISCHARGE CELL CENTER (m) (NOT USED)
* YJET: LOCAL NORTH JET LOCATION RELATIVE TO DISCHARGE CELL CENTER (m)(NOT USED)
* ZJET: ELEVATION OF DISCHARGE (m)
* PHJET: VERTICAL JET ANGLE POSITIVE FROM HORIZONTAL (DEGREES)
* THJET: HORIZONTAL JET ANGLE POS COUNTER CLOCKWISE FROM EAST (DEGREES)
218
* DJET: DIAMETER OF DISCHARGE PORT (m)
* CFRD: ADJUSTMENT FACTOR FOR FROUDE NUMBER
* DJPER: ENTRAINMENT ERROR CRITERIA
*
C27 ID ICAL IQJP JQJP KQJP NPORT XJET YJET ZJET PHJET THJET
DJET CFRD DJPER !ID
-------------------------------------------------------------------------------
C28 JET/PLUME SOLUTION CONTROL AND OUTPUT CONTROL PARAMETERS
*
* ID: ID COUNTER FOR JET/PLUME
* NJEL: MAXIMUM NUMBER OF ELEMENTS ALONG JET/PLUME LENGTH
* NJPMX: MAXIMUM NUMBER OF ITERATIONS
* ISENT: 0 USE MAXIMUM OF SHEAR AND FORCED ENTRAINMENT
* 1 USE SUM OF SHEAR AND FORCED ENTRAINMENT
* ISTJP: 0 STOP AT SPECIFIED NUMBER OF ELEMENTS
* 1 STOP WHEN CENTERLINE PENETRATES BOTTOM OR SURFACE
* 2 STOP WITH BOUNDARY PENETRATES BOTTOM OR SURFACE
* NUDJP: FREQUENCY FOR UPDATING JET/PLUME (NUMBER OF TIME STEPS)
* IOJP: 1 FOR FULL ASCII, 2 FOR COMPACT ASCII OUTPUT AT EACH UPDATE
* 3 FOR FULL AND COMPACT ASCII OUTPUT, 4 FOR BINARY OUTPUT
* IPJP: NUMBER OF SPATIAL PRINT/SAVE POINT IN VERTICAL
* ISDJP: 1 WRITE DIAGNOSTICS TO JPLOG__.OUT
* IUPJP: I INDEX OF UPSTREAM WITHDRAWAL CELL IF ICAL=2
* JUPJP: J INDEX OF UPSTREAM WITHDRAWAL CELL IF ICAL=2
* KUPJP: K INDEX OF UPSTREAM WITHDRAWAL CELL IF ICAL=2
*
C28 ID NJEL NJPMX ISENT ISTJP NUDJP IOJP IPJP ISDJP IUPJP JUPJP
KUPJP ! ID
-------------------------------------------------------------------------------
C29 JET/PLUME SOURCE PARAMETERS AND DISCHARGE/CONCENTRATION SERIES IDS
*
* ID: ID COUNTER FOR JET/PLUME
* QQJP: CONSTANT JET/PLUME FLOW RATE IN (m^/s)
* FOR ICAL = 1 OR 2 (FOR SINGLE PORT)
* NQSERJP: ID NUMBER OF ASSOCIATED VOLUME FLOW TIME SERIES
* NQWRSERJP: ID NUMBER OF ASSOCIATED WITHDRAWAL-RETURN TIME SERIES (ICAL=2)
* ICSER1: ID NUMBER OF ASSOCIATED SALINITY TIME SERIES
* ICSER2: ID NUMBER OF ASSOCIATED TEMPERATURE TIME SERIES
* ICSER3: ID NUMBER OF ASSOCIATED DYE CONC TIME SERIES
* ICSER4: ID NUMBER OF ASSOCIATED SHELL FISH LARVAE RELEASE TIME SERIES
* ICSER5: ID NUMBER OF ASSOCIATED TOXIC CONTAMINANT CONC TIME SERIES
* ICSER6: ID NUMBER OF ASSOCIATED COHESIVE SEDIMENT CONC TIME SERIES
* ICSER7: ID NUMBER OF ASSOCIATED NON-COHESIVE SED CONC TIME SERIES
*
C29 ID QQJP NQSERJP NQWRSERJP ICSER1 ICSER2 ICSER3 ICSER4 ICSER5 ICSER6 ICSER7 ! ID
-------------------------------------------------------------------------------
C30 TIME CONSTANT INFLOW CONCENTRATIONS FOR TIME CONSTANT JET/PLUME SOURCES
*
* SAL: SALT CONCENTRATION CORRESPONDING TO INFLOW ABOVE
* TEM: TEMPERATURE CORRESPONDING TO INFLOW ABOVE
* DYE: DYE CONCENTRATION CORRESPONDING TO INFLOW ABOVE
* SFL: SHELL FISH LARVAE CONCENTRATION CORRESPONDING TO INFLOW ABOVE
* TOX: NTOX TOXIC CONTAMINANT CONCENTRATIONS CORRESPONDING TO
* INFLOW ABOVE WRITTEN AS TOXC(N), N=1,NTOX A SINGLE DEFAULT
* VALUE IS REQUIRED EVEN IF TOXIC TRANSPORT IS NOT ACTIVE
*
C30 SAL TEM DYE SFL ! ID
-------------------------------------------------------------------------------
C31 TIME CONSTANT INFLOW CONCENTRATIONS FOR TIME CONSTANT JET/PLUME SOURCES
*
* SED: NSED COHESIVE SEDIMENT CONCENTRATIONS CORRESPONDING TO
* INFLOW ABOVE WRITTEN AS SEDC(N), N=1,NSED. I.E., THE FIRST
* NSED VALUES ARE COHESIVE A SINGLE DEFAULT VALUE IS REQUIRED
* EVEN IF COHESIVE SEDIMENT TRANSPORT IS INACTIVE
* SND: NSND NON-COHESIVE SEDIMENT CONCENTRATIONS CORRESPONDING TO
* INFLOW ABOVE WRITTEN AS SND(N), N=1,NSND. I.E., THE LAST
* NSND VALUES ARE NON-COHESIVE. A SINGLE DEFAULT VALUE IS
* REQUIRED EVEN IF NON-COHESIVE SEDIMENT TRANSPORT IS INACTIVE
*
C31 SED1 SND1 ! ID
-------------------------------------------------------------------------------
219
C32 SURFACE ELEV OR PRESSURE DEPENDENT FLOW INFORMATION
*
* IQCTLU: I INDEX OF UPSTREAM OR WITHDRAWAL CELL
* JQCTLU: J INDEX OF UPSTREAM OR WITHDRAWAL CELL
* IQCTLD: I INDEX OF DOWNSTREAM OR RETURN CELL
* JQCTLD: J INDEX OF DOWNSTREAM OR RETURN CELL
* NQCTYP: FLOW CONTROL TYPE
* = -2 FLOW AS FUNCTION OF UPSTREAM ELEVATION RATING CURVE OF A GROUP OF CELLS
* = -1 FLOW AS FUNCTION OF UPSTREAM DEPTH (STAGE RATING CURVE)
* = 0 FLOW AS FUNCTION OF ELEVATION OR PRESSURE DIFFERENCE TABLE
* = 1 SAME AS 0 WITH ACCELERATING FLOW (E.G. TIDAL INLET)
* = 2 FLOW DERIVED FROM UPSTREAM AND DOWNSTREAM WS ELEVATIONS
* = 3 LOWER CHORD OPTION USING UPSTREAM DEPTH WHEN WSEL > BQCLCE
* = 4 LOWER CHORD OPTION USING ELEVATION DIFFERENCE WHEN WSEL > BQCLCE
* = 5 CULVERT
* = 6 SLUICE GATE
* = 7 WEIR
* = 8 ORIFICE
* = 9 FLOATING SKIMMER WALL (NOT AVAILABLE)
* = 10 SUBMERGED WEIR (NOT AVAILABLE)
* NQCTLQ: ID NUMBER OF CONTROL CHARACTERIZATION TABLE
* NQCMUL: MULTIPLIER SWITCH FOR FLOWS FROM UPSTREAM CELL
* = 0 MULT BY 1. FOR CONTROL TABLE IN (L*L*L/T)
* = 1 MULT BY DY FOR CONTROL TABLE IN (L*L/T) ON U FACE
* = 2 MULT BY DX FOR CONTROL TABLE IN (L*L/T) ON V FACE
* = 3 MULT BY DX+DY FOR CONTROL TABLE IN (L*L/T) ON U&V FACES
* HQCTLU: OFFSET FOR UPSTREAM HEAD (m)
* SET TO CELL'S BOTTOM ELEVATION TO USE ELEVATION INSTEAD OF DEPTH FOR NQCTYP = -1
or 3
* HQCTLD: OFFSET FOR DOWNSTREAM HEAD (m)
* QTCLMU: MULTIPLIER TO SPLIT THE TOTAL QCTL RATING TABLE INTO CELL SPECIFIC FLOWS [ONLY
USED IF NQCTYP = -2]
* QTCLGRP: NUMBER IDENTIFIER TO ASSOCIATE PHYSICALLY BASED FLOW GROUPS [ONLY
USED IF NQCTYP = -2]
* BQCLCE: LOWER CHORD ELEVATION (m) [ONLY USED IF NQCTYP = 3 OR
4]
* NQCMINS: MINIMUM NUMBER OF STEPS REQUIRED ABOVE LOWER CHORD [ONLY USED IF NQCTYP = 3 OR
4]
*
* *** LOOKUP TABLE HEAD DETERMINATION (HUP & HDW) FOR LOW CHORD
* *** NQCTYP = 3: HUP = HP(LU) + HCTLUA(NCTLT) + HQCTLU(NCTL)
* *** NQCTYP = 4: HUP = HP(LU) + BELV(LU) + HCTLUA(NCTLT) + HQCTLU(NCTL)
* *** NQCTYP = 4: HDW = HP(LD) + BELV(LD) + HCTLDA(NCTLT) + HQCTLD(NCTL)
*
*HS_FACTOR: DISCHARGE DISTRIBUTION FACTOR (ONLY USED FOR NQCTYP>4)
*HS_NTIMES: NUMBER OF TIMES HYDRAULIC STRUCTURE DEFINITION CHANGES (IN DEVELOPMENT)
*HS_TRANSITION: NUMBER OF SECONDS TO TRANSITION FROM TIME (T) TO TIME (T+1) (IN DEVELOPMENT)
*
C32 IQCTLU JQCTLU IQCTLD JQCTLD NQCTYP NQCTLQ NQCMUL HQCTLU HQCTLD QTCLMU QTCLGRP
BQCLCE NQCMINS FACTOR NTIMES TRANSIT
-------------------------------------------------------------------------------
C33 FLOW WITHDRAWAL, HEAT OR MATERIAL ADDITION, AND RETURN DATA
*
* IWRU: I INDEX OF UPSTREAM OR WITHDRAWAL CELL
* JWRU: J INDEX OF UPSTREAM OR WITHDRAWAL CELL
* KWRU: K INDEX OF UPSTREAM OR WITHDRAWAL LAYER
* IWRD: I INDEX OF DOWNSTREAM OR RETURN CELL
* JWRD: J INDEX OF DOWNSTREAM OR RETURN CELL
* KWRD: J INDEX OF DOWNSTREAM OR RETURN LAYER
* QWRE: CONSTANT VOLUME FLOW RATE FROM WITHDRAWAL TO RETURN
* NQWRSERQ: ID NUMBER OF ASSOCIATED VOLUME WITHDRAWAL-RETURN FLOW AND
* CONCENTRATION RISE TIME SERIES
* NQWRMFU: IF NON ZERO ACCOUNT FOR WITHDRAWAL FLOW MOMENTUM FLUX
* = 1 MOMENTUM FLUX ON WEST U FACE
* = 2 MOMENTUM FLUX ON SOUTH V FACE
* = 3 MOMENTUM FLUX ON EAST U FACE
* = 4 MOMENTUM FLUX ON NORTH V FACE
* NQWRMFD: IF NON ZERO ACCOUNT FOR RETURN FLOW MOMENTUM FLUX
* = 1 MOMENTUM FLUX ON WEST U FACE
* = 2 MOMENTUM FLUX ON SOUTH V FACE
* = 3 MOMENTUM FLUX ON EAST U FACE
220
* = 4 MOMENTUM FLUX ON NORTH V FACE
* BQWRMFU: UPSTREAM MOMENTUM FLUX WIDTH (m)
* BQWRMFD: DOWNSTREAM MOMENTUM FLUX WIDTH (m)
* ANGWRMFD: ANGLE FOR HORIZONTAL FOR RETURN FLOW MOMENTUM FLUX
*
C33 IWRU JWRU KWRU IWRD JWRD KWRD QWRE NQW_RQ NQWR_U NQWR_D BQWR_U
BQWR_D ANG_D
-------------------------------------------------------------------------------
C34 TIME CONSTANT WITHDRAWAL AND RETURN CONCENTRATION RISES
*
* SAL: SALINITY RISE
* TEM: TEMPERATURE RISE
* DYE: DYE CONCENTRATION RISE
* SFL: SHELLFISH LARVAE CONCENTRATION RISE
* TOX#: NTOX TOXIC CONTAMINANT CONCENTRATION RISES
*
C34 SALT TEMP DYEC SFLC TOX1
-------------------------------------------------------------------------------
C35 TIME CONSTANT WITHDRAWAL AND RETURN CONCENTRATION RISES
*
* SED#: NSEDC COHESIVE SEDIMENT CONCENTRATION RISE
* SND#: NSEDN NON-COHESIVE SEDIMENT CONCENTRATION RISE
*
C35 SED1 SND1
-------------------------------------------------------------------------------
C36 SEDIMENT INITIALIZATION AND WATER COLUMN/BED REPRESENTATION OPTIONS
* DATA REQUIRED IF ISTRAN(6) OR ISTRAN(7) <> 0
*
* ISEDINT: 0 FOR CONSTANT INITIAL CONDITIONS
* 1 FOR SPATIALLY VARIABLE WATER COLUMN INITIAL CONDITIONS
* FROM SEDW.INP AND SNDW.INP
* 2 FOR SPATIALLY VARIABLE BED INITIAL CONDITIONS
* FROM SEDB.INP AND SNDB.INP
* 3 FOR SPATIALLY VARIABLE WATER COL AND BED INITIAL CONDITIONS
* ISEDBINT: 0 FOR SPATIALLY VARYING BED INITIAL CONDITIONS IN MASS/AREA
* 1 FOR SPATIALLY VARYING BED INITIAL CONDITIONS IN MASS FRACTION
* OF TOTAL SEDIMENT MASS (REQUIRES BED LAYER THICKNESS
* FILE BEDLAY.INP)
* NSEDFLUME: 0 USE THE SEDIMENT TRANSPORT FUNCTIONS IN EFDC MAIN CODE
* 98 USE THE SEDZLJ SEDIMENT TRANSPORT SUB-MODEL
* 99 USE THE SEDZLJ SEDIMENT TRANSPORT SUB-MODEL WITH TOXICS
*
* ISMUD: 1 INCLUDE COHESIVE FLUID MUD VISCOUS EFFECTS USING EFDC
* FUNCTION CSEDVIS(SEDT)
* ISNDWC: NOT USED
*
*
* ISEDVW: 0 FOR CONSTANT OR SIMPLE CONCENTRATION DEPENDENT
* COHESIVE SEDIMENT SETTLING VELOCITY
* >1 CONCENTRATION AND/OR SHEAR/TURBULENCE DEPENDENT COHESIVE
* SEDIMENT SETTLING VELOCITY. VALUE INDICATES OPTION TO BE USED
* IN EFDC FUNCTION CSEDSET(SED,SHEAR,ISEDVWC)
* 1 HUANG AND MEHTA - LAKE OKEECHOBEE
* 2 SHRESTHA AND ORLOB - FOR KRONES SAN FRANCISCO BAY DATA
* 3 ZIEGLER AND NESBIT - FRESH WATER
* 98 LICK FLOCCULATION
* 99 LICK FLOCCULATION WITH FLOC DIAMETER ADVECTION
* ISNDVW: 0 USE CONSTANT SPECIFIED NON-COHESIVE SED SETTLING VELOCITIES
* OR CALCULATE FOR CLASS DIAMETER IF SPECIFIED VALUE IS NEG
* >1 FOLLOW OPTION 0 PROCEDURE BUT APPLY HINDERED SETTLING
* CORRECTION. VALUE INDICATES OPTION TO BE USED WITH EFDC
* FUNCTION CSNDSET(SND,SDEN,ISNDVW) VALUE OF ISNDVW INDICATES
* EXPONENTIAL IN CORRECT (1-SDEN(NS)*SND(NS)**ISNDVW
* KB: MAXIMUM NUMBER OF BED LAYERS (EXCLUDING ACTIVE LAYER)
* ISDTXBUG: 1 TO ACTIVATE SEDIMENT AND TOXICS DIAGNOSTICS
*
C36 ISEDINT ISEDBINT NSEDFLUME ISMUD ISNDWC ISEDVW ISNDVW KB ISDTXBUG
-------------------------------------------------------------------------------
C36A SEDIMENT INITIALIZATION AND WATER COLUMN/BED REPRESENTATION OPTIONS
* DATA REQUIRED EVEN IF ISTRAN(6) AND ISTRAN(7) ARE 0
*
221
* ISBEDSTR: 0 USE HYDRODYNAMIC MODEL STRESS FOR SEDIMENT TRANSPORT
* 1 SEPARATE GRAIN STRESS FROM TOTAL IN COHESIVE AND NON-COHESIVE COMPONENTS
* 2 SEPARATE GRAIN STRESS FROM TOTAL APPLY TO COHESIVE AND NON-COHESIVE SEDS
* 3 USE INDEPENDENT LOG LAW ROUGHNESS HEIGHT FOR SEDIMENT TRANSPORT
* READ FROM FILE SEDROUGH.INP
* 4 SEPARATE GRAIN STRESS FROM TOTAL USING COHESIVE/NON-COHESIVE WEIGHTED
* ROUGHNESS AND LOG LAW RESISTANCE (IMPLEMENTED 5/31/05)
* 5 SEPARATE GRAIN STRESS FROM TOTAL USING COHESIVE/NON-COHESIVE WEIGHTED
* ROUGHNESS AND POWER LAW RESISTANCE (IMPLEMENTED 5/31/05)
* ISBSDIAM: 0 USE D50 DIAMETER FOR NON-COHESIVE ROUGHNESS
* 1 USE 2*D50 FOR NON-COHESIVE ROUGHNESS
* 2 USE D90 FOR NON-COHESIVE ROUGHNESS
* 3 USE 2*D90 FOR NON-COHESIVE ROUGHNESS
* ISBSDFUF: 1 CORRECT GRAIN STRESS PARTITIONING FOR NON-UNIFORM FLOW EFFECTS
* DO NOT USE FOR ISBEDSTR = 4 AND 5
* COEFTSBL: COEFFICIENT SPECIFYING THE HYDRODYNAMIC SMOOTHNESS OF
* TURBULENT BOUNDARY LAYER OVER COHESIVE BED IN TERMS OF
* EQUIVALENT GRAIN SIZE FOR COHESIVE GRAIN STRESS
* CALCULATION, FULLY SMOOTH = 4, FULLY ROUGH = 100.
* NOT USED FOR ISBEDSTR = 4 AND 5
* VISMUDST: KINEMATIC VISCOSITY TO USE IN DETERMINING COHESIVE GRAIN STRESS
* ISBKERO: 1 FOR BANK EROSION SPECIFIED BY EXTERNAL TIME SERIES
* 2 FOR BANK EROSION INTERNALLY CALCULATED BY STABILITY ANALYSIS (Not Active)
*
C36A ISBEDSTR ISBSDIAM ISBSDFUF COEFTSBL VISMUDST ISBKERO
-------------------------------------------------------------------------------
C36B SEDIMENT INITIALIZATION AND WATER COLUMN/BED REPRESENTATION OPTIONS
* DATA REQUIRED EVEN IF ISTRAN(6) AND ISTRAN(7) ARE 0
*
* ISEDAL: NOT USED
* ISNDAL: 1 TO ACTIVATE NON-COHESIVE ARMORING EFFECTS (GARCIA & PARKER)
* 2 SAME AS 1 WITH ACTIVE-PARENT LAYER FORMULATION
* IALTYP: 0 CONSTANT THICKNESS ARMORING LAYER
* 1 CONSTANT TOTAL SEDIMENT MASS ARMORING LAYER
* IALSTUP: 1 CREATE ARMORING LAYER FROM INITIAL TOP LAYER AT START UP
* ISEDEFF: 1 MODIFY NON-COHESIVE RESUSPENSION TO ACCOUNT FOR COHESIVE EFFECTS
* USING MULTIPLICATION FACTOR: EXP(-COEHEFF*FRACTION COHESIVE)
* 2 MODIFY NON-COHESIVE CRITICAL STRESS TO ACCOUNT FOR COHESIVE EFFECTS
* USING MULT FACTOR: 1+(COEHEFF2-1)*(1-EXP(-COEHEFF*FRACTION COHESIVE))
* HBEDAL: ACTIVE ARMORING LAYER THICKNESS
* COEHEFF: COHESIVE EFFECTS COEFFICIENT
* COEHEFF2: COHESIVE EFFECTS COEFFICIENT
*
C36B ISEDAL ISNDAL IALTYP IALSTUP ISEDEFF HBEDAL COEHEFF COEHEFF2
-------------------------------------------------------------------------------
C37 BED MECHANICAL PROPERTIES PARAMETER SET 1
* DATA REQUIRED IF NSED>0, EVEN IF ISTRAN(6) = 0
*
* SEDSTEP: SEDIMENT BED INTERACTION TIME STEP (SECONDS)
*SETSTART: START TIME FOR BED/WATER COLUMN INTERACTION (DAYS)
* IBMECH: 0 TIME INVARIANT CONSTANT BED MECHANICAL PROPERTIES (UNIFORM BED ONLY)
* 1 SIMPLE CONSOLIDATION CALCULATION WITH CONSTANT COEFFICIENTS
* 2 SIMPLE CONSOLIDATION WITH VARIABLE COEFFICIENTS DETERMINED
* EFDC FUNCTIONS CSEDCON1,2,3(IBMECH)
* 3 COMPLEX CONSOLIDATION WITH VARIABLE COEFFICIENTS DETERMINED
* EFDC FUNCTIONS CSEDCON1,2,3(IBMECH). IBMECH > 0 SETS THE
* C38 PARAMETER ISEDBINT=1 AND REQUIRES INITIAL CONDITIONS
* FILES BEDLAY.INP, BEDBDN.INP AND BEDDDN.IN
* 9 TYPE OF CONSOLIDATION VARIES BY CELL WITH IBMECH FOR EACH
* DEFINED IN INPUT FILE CONSOLMAP.INP
* IMORPH: 0 CONSTANT BED MORPHOLOGY (IBMECH=0, ONLY)
* 1 ACTIVE BED MORPHOLOGY: NO WATER ENTRAIN/EXPULSION EFFECTS
* 2 ACTIVE BED MORPHOLOGY: WITH WATER ENTRAIN/EXPULSION EFFECTS
* HBEDMAX: TOP BED LAYER THICKNESS (m) AT WHICH NEW LAYER IS ADDED OR IF
* KBT(I,J)=KB, NEW LAYER ADDED AND LOWEST TWO LAYERS COMBINED
* BEDPORC: CONSTANT BED POROSITY (IBMECH=0, OR NSED=0)
* ALSO USED AS POROSITY OF DEPOSITION NON-COHESIVE SEDIMENT
* SEDMDMX: MAXIMUM FLUID MUD COHESIVE SEDIMENT CONCENTRATION (MG/L)
* SEDMDMN: MINIMUM FLUID MUD COHESIVE SEDIMENT CONCENTRATION (MG/L)
* SEDVDRD: VOID RATIO OF DEPOSITING COHESIVE SEDIMENT
* SEDVDRM: MINIMUM COHESIVE SEDIMENT BED VOID RATIO (IBMECH > 0)
222
* SEDVDRT: BED CONSOLIDATION RATE CONSTANT (sec) (IBMECH = 1,2), EXP(-DELT/SEDVDRT)
* > 0 CONSOLIDATE OVER TIME TO SEDVDRM
* = 0 CONSOLIDATE INSTANTANEOUSLY TO SEDVDRM (0.0>=SEDVDRT<=0.0001)
* < 0 CONSOLIDATE TO INITIAL VOID RATIOS
*
C37 SEDSTEP SEDSTART IBMECH IMORPH HBEDMAX BEDPORC SEDMDMX SEDMDMN SEDVDRD SEDVDRM SEDVRDT
-------------------------------------------------------------------------------
C38 BED MECHANICAL PROPERTIES PARAMETER SET 2
* DATA REQUIRED IF NSED>0, EVEN IF ISTRAN(6) = 0
*
* IBMECHK: 0 FOR HYDRAULIC CONDUCTIVITY, K, FUNCTION K=KO*EXP((E-EO)/EK)
* 1 FOR HYD COND/(1+VOID RATIO),K', FUNCTION K'=KO'*EXP((E-EO)/EK)
* BMECH1: REFERENCE EFFECTIVE STRESS/WATER SPECIFIC WEIGHT, SEO (m)
* IF BMECH1<0 USE INTERNAL FUNCTION, BMECH1,BMECH2,BMECH3 NOT USED
* BMECH2: REFERENCE VOID RATIO FOR EFFECTIVE STRESS FUNCTION, EO
* BMECH3: VOID RATIO RATE TERM ES IN SE=SEO*EXP(-(E-EO)/ES)
* BMECH4: REFERENCE HYDRAULIC CONDUCTIVITY, KO (m/s)
* IF BMECH4<0 USE INTERNAL FUNCTION, BMECH1,BMECH2,BMECH3 NOT USED
* BMECH5: REFERENCE VOID RATIO FOR HYDRAULIC CONDUCTIVITY, EO
* BMECH6: VOID RATIO RATE TERM EK IN (K OR K')=(KO OR KO')*EXP((E-EO)/EK)
*
C38 IBMECHK BMECH1 BMECH2 BMECH3 BMECH4 BMECH5 BMECH6
-------------------------------------------------------------------------------
C39 COHESIVE SEDIMENT PARAMETER SET 1 REPEAT DATA LINE NSED TIMES
* DATA REQUIRED IF NSED>0, EVEN IF ISTRAN(6) = 0
*
* SEDO: CONSTANT INITIAL COHESIVE SEDIMENT CONC IN WATER COLUMN
* (MG/LITER=GM/M^3)
* SEDBO: CONSTANT INITIAL COHESIVE SEDIMENT IN BED PER UNIT AREA
* (GM/SQ METER) IE 1CM THICKNESS BED WITH SSG=2.5 AND
* N=.6,.5 GIVES SEDBO 1.E4, 1.25E4
* SDEN: SEDIMENT SPEC VOLUME (IE 1/2.25E6 M^3/GM)
* SSG: SEDIMENT SPECIFIC GRAVITY
* WSEDO: CONSTANT OR REFERENCE SEDIMENT SETTLING VELOCITY
* IN FORMULA WSED=WSEDO*( (SED/SEDSN)**SEXP )
* SEDSN: (NOT USED)
* SEXP: (NOT USED)
* TAUD: BOUNDARY STRESS BELOW WHICH DEPOSITION TAKES PLACE ACCORDING
* TO (TAUD-TAU)/TAUD
* ISEDSCOR: 1 TO CORRECT BOTTOM LAYER CONCENTRATION TO NEAR BED CONCENTRATION
* ISPROBDEP: 0 KRONE PROBABILITY OF DEPOSITION USING COHESIVE GRAIN STRESS
* 1 KRONE PROBABILITY OF DEPOSITION USING TOTAL BED STRESS
* 2 PARTHENIADES PROBABILITY OF DEPOSITION USING COHESIVE GRAIN STRESS
* 3 PARTHENIADES PROBABILITY OF DEPOSITION USING TOTAL BED STRESS
*
C39 SEDO SEDBO SDEN SSG WSEDO SEDSN SEXP TAUD ISEDSCOR ISPROBDEP
-------------------------------------------------------------------------------
C40 COHESIVE SEDIMENT PARAMETER SET 2 REPEAT DATA LINE NSED TIMES
* DATA REQUIRED IF NSED>0, EVEN IF ISTRAN(6) = 0
*
* IWRSP: 0 USE RESUSPENSION RATE AND CRITICAL STRESS BASED ON PARAMETERS
* ON THIS DATA LINE
* >0 USE BED PROPERTIES DEPENDEDNT RESUSPENSION RATE AND CRITICAL
* STRESS GIVEN BY EFDC FUNCTIONS CSEDRESS,CSEDTAUS,CSEDTAUB
* FUNCTION ARGUMENTS ARE (BDENBED,IWRSP)
* 1 HWANG AND MEHTA - LAKE OKEECHOBEE
* 2 HAMRICK'S MODIFICATION OF SANFORD AND MAA
* 3 SAME AS 2 EXCEPT VOID RATIO OF COHESIVE SEDIMENT FRACTION IS USED
* 4 SEDFLUME WITHOUT CRITICAL STRESS
* 5 SEDFLUME WITH CRITICAL STRESS
* >= 99 SITE SPECIFIC
* IWRSPB:0 NO BULK EROSION
* 1 USE BULK EROSION CRITICAL STRESS AND RATE IN FUNCTIONS
* CSEDTAUB AND CSEDRESSB
* WRSPO: REF SURFACE EROSION RATE IN FORMULA
* WRSP=WRSP0*( ((TAU-TAUR)/TAUN)**TEXP ) (gm/m^2/sec)
* TAUR: BOUNDARY STRESS ABOVE WHICH SURFACE EROSION OCCURS (m/s)**2
* TAUN: (NOT USED, TAUN=TAUR SET IN CODE)
* TEXP: EXPONENT OF WRSP=WRSP0*( ((TAU-TAUR)/TAUN)**TEXP )
* VDRRSPO: REFERENCE VOID RATIO FOR CRITICAL STRESS AND RESUSPENSION RATE
* IWRSP=2,3
223
* COSEDHID: COHESIVE SEDIMENT RESUSPENSION HIDING FACTOR TO REDUCE COHESIVE
* RESUSPENSION BY FACTOR = (COHESIVE FRACTION OF SEDIMENT)**COSEDHID
*
C40 IWRSP IWRSPB WRSPO TAUR TAUN TEXP VDRRSPO COSEDHID
-------------------------------------------------------------------------------
C41 NON-COHESIVE SEDIMENT PARAMETER SET 1 REPEAT DATA LINE NSND TIMES
* DATA REQUIRED IF NSND>0, EVEN IF ISTRAN(7) = 0
*
* SNDO: CONSTANT INITIAL NON-COHESIVE SEDIMENT CONC IN WATER COLUMN
* (MG/LITER=GM/M^3)
* SNDBO: CONSTANT INITIAL NON-COHESIVE SEDIMENT IN BED PER UNIT AREA
* (GM/SQ METER) IE 1CM THICKNESS BED WITH SSG=2.5 AND
* N=.6,.5 GIVES SNDBO 1.E4, 1.25E4
* SDEN: SEDIMENT SPEC VOLUME (IE 1/2.65E6 M^3/GM)
* SSG: SEDIMENT SPECIFIC GRAVITY
* SNDDIA: REPRESENTATIVE DIAMETER OF SEDIMENT CLASS (m)
* WSNDO: CONSTANT OR REFERENCE SEDIMENT SETTLING VELOCITY
* WSNDO < 0, SETTLING VELOCITY INTERNALLY COMPUTED
* SNDN: (NOT USED)
* SEXP: (NOT USED)
* TAUD: (NOT USED)
* ISNDSCOR: (NOT USED)
*
C41 SNDO SNDBO SDEN SSG SNDDIA WSNDO SNDN SEXP TAUD ISNDSCOR
-------------------------------------------------------------------------------
C42 NON-COHESIVE SEDIMENT PARAMETER SET 2 REPEAT DATA LINE NSND TIMES
* DATA REQUIRED IF NSND>0, EVEN IF ISTRAN(7) = 0
*
* ISNDEQ: 0 USER SPECIFIED SPATIALLY AND TEMPORALLY CONSTANT EQUILIBRIUM CONCENTRATION
* ISNDEQ: >1 CALCULATE ABOVE BED REFERENCE NON-COHESIVE SEDIMENT
* EQUILIBRIUM CONCENTRATION USING EFDC FUNCTION
* CSNDEQC(SNDDIA,SSG,WS,TAUR,TAUB,SIGPHI,SNDDMX,IOTP)
* WHICH IMPLEMENT FORMULATIONS OF
* 1 GARCIA AND PARKER
* 2 SMITH AND MCLEAN
* 3 VAN RIJN
* 4 SEDFLUME WITHOUT CRITICAL STRESS
* 5 SEDFLUME WITH CRITICAL STRESS
* ISBDLD: 0 BED LOAD PHI FUNCTION IS CONSTANT, SBDLDP
* 1 VAN RIJN PHI FUNCTION
* 2 MODIFIED ENGULAND-HANSEN
* 3 WU, WANG, AND JIA
* 4 (NOT USED)
* 5 (NOT USED)
* TAUR: EQUILIBRIUM CONCENTRATION (g/m**3)
* TAUN: Not Used
* TCSHIELDS: Not Used
* ISLTAUC: Not Used
* IBLTAUC: 1 TO IMPLEMENT BEDLOAD ONLY WHEN STRESS EXCEEDS TAUC FOR EACH GRAINSIZE
* 2 TO IMPLEMENT BEDLOAD ONLY WHEN STRESS EXCEEDS TAUCD50
* 3 TO USE TAUC FOR NONUNIFORM BEDS, THESE APPLY ONLY TO BED LOAD
* FORMULAS NOT EXPLICITLY CONTAINING CRITICAL SHIELDS STRESS SUCH AS E-H
* IROUSE: 0 USE TOTAL STRESS FOR CALCULATING ROUSE NUMBER
* 1 USE GRAIN STRESS FOR ROUSE NUMBER
* ISNDM1: 0 SET BOTH BEDLOAD AND SUSPENDED LOAD FRACTIONS TO 1.0
* 1 SET BEDLOAD FRACTION TO 1. USE BINARY RELATIONSHIP FOR SUSPENDED
* 2 SET BEDLOAD FRACTION TO 1, USE LINEAR RELATIONSHIP FOR SUSPENDED
* 3 USE BINARY RELATIONSHIP FOR BEDLOAD AND SUSPENDED LOAD
* 4 USE LINEAR RELATIONSHIP FOR BEDLOAD AND SUSPENDED LOAD
* ISNDM2: 0 USE TOTAL SHEAR VELOCITY IN USTAR/WSET RATIO
* 1 USE GRAIN SHEAR VELOCITY IN USTAR/WSET RATIO
* RSNDM: VALUE OF USTAR/WSET FOR BINARY SWITCH BETWEEN BEDLOAD AND SUSPENDED LOAD
*
C42 ISNDEQ ISBDLD TAUR TAUN TCSHIELDS ISLTAUC IBLTAUC IROUSE ISNDM1 ISNDM2 RSNDM
-------------------------------------------------------------------------------
C42A NON-COHESIVE SEDIMENT PARAMETER SET 3 (BED LOAD FORMULA PARAMETERS)
* DATA REQUIRED IF NSND>0, EVEN IF ISTRAN(7) = 0
*
* ISBDLDBC: 0 DISABLE BEDLOAD
* 1 ACTIVATE BEDLOAD OPTION. USES SEDBLBC.INP TO SPECIFY CELLS
* SBDLDA: ALPHA EXPONENTIAL FOR BED LOAD FORMULA
224
* SBDLDB: BETA EXPONENTIAL FOR BED LOAD FORMULA
* SBDLDG1: GAMMA1 CONSTANT FOR BED LOAD FORMULA
* SBDLDG2: GAMMA2 CONSTANT FOR BED LOAD FORMULA
* SBDLDG3: GAMMA3 CONSTANT FOR BED LOAD FORMULA
* SBDLDG4: GAMMA4 CONSTANT FOR BED LOAD FORMULA
* SBDLDP: CONSTANT PHI FOR BED LOAD FORMULA
* ISBLFUC: BED LOAD FACE FLUX , 0 FOR DOWN WIND PROJECTION,1 FOR DOWN WIND
* WITH CORNER CORRECTION,2 FOR CENTERED AVERAGING
* BLBSNT: ADVERSE BED SLOPE (POSITIVE VALUE) ACROSS A CELL FACE ABOVE
* WHICH NO BED LOAD TRANSPORT CAN OCCUR. NOT ACTIVE FOR BLBSNT=0.0
*
C42a IBEDLD SBDLDA SBDLDB SBDLDG1 SBDLDG2 SBDLDG3 SBDLDG4 SBDLDP ISBLFUC BLBSNT
-------------------------------------------------------------------------------
C43A TOXIC CONTAMINANT INITIAL CONDITIONS
* USER MAY CHANGE ORDER OF MAGNITUDE OF WATER AND SED PHASE TOXIC CONCENTRATIONS
* AND PARTITION COEFFICIENTS ON C44 - C46 BUT MUST BE CONSISTENT UNITS
*
* NTOXN: TOXIC CONTAMINANT NUMBER ID
* ITXINT: 0 FOR SPATIALLY CONSTANT WATER COL AND BED INITIAL CONDITIONS
* 1 FOR SPATIALLY VARIABLE WATER COLUMN INITIAL CONDITIONS
* 2 FOR SPATIALLY VARIABLE BED INITIAL CONDITIONS
* 3 FOR SPATIALLY VARIABLE WATER COL AND BED INITIAL CONDITION
* ITXBDUT: SET TO 0 FOR INITIAL BED GIVEN BY TOTAL TOXIC CONCENTRATION (mg/m^3)
* SET TO 1 FOR INITIAL BED GIVEN BY TOTAL SEDIMENT NORMALIZED CONCENTRATION (mg/kg)
* TOXINTW: INIT WATER COLUMN TOT TOXIC VARIABLE CONCENTRATION (ug/l)
* TOXINTB: INIT SED BED TOXIC CONCENTRATION. SEE ITXBDUT FOR UNITS
*
C43A NTOXN ITXINT ITXBDUT TOXINTW TOXINTB COMMENTS
-------------------------------------------------------------------------------
C43B TOXIC KINETIC OPTION FLAGS
*
* NTOXN: TOXIC CONTAMINANT NUMBER ID
* ITOXKIN(1): 0 DO NOT USE BULK DECAY
* : 1 USE BULK DECAY FOR WATER COLUMN AND SEDIMENT
* ITOXKIN(2): 0 DO NOT USE BIODEGRADATION
* : 1 USE BIODEGRADATION FOR WATER COLUMN AND SEDIMENT
* ITOXKIN(3): 0 DO NOT USE VOLATILIZATION
* : 1 USE VOLATILIZATION FOR RIVER AND LAKE CONDITIONS. LAKE USES O'CONNOR
* : 2 USE VOLATILIZATION FOR RIVER AND LAKE CONDITIONS. LAKE USES MACKAY & YEUN
* ITOXKIN(4): 0 DO NOT USE PHOTOLYSIS (NOT IMPLEMENTED)
* : 1 USE PHOTOLYSIS FOR WATER COLUMN (NOT IMPLEMENTED)
* ITOXKIN(5): 0 DO NOT USE HYDROLYSIS (NOT IMPLEMENTED)
* : 1 USE HYDROLYSIS FOR WATER COLUMN (NOT IMPLEMENTED)
* ITOXKIN(6): 0 DO NOT USE DAUGHTER PRODUCTS (NOT IMPLEMENTED)
* : 1 USE DAUGHTER PRODUCTS (NOT IMPLEMENTED)
*
C43B NTOXN KIN(1) KIN(2) KIN(3) KIN(4) KIN(5) KIN(6) COMMENTS
-------------------------------------------------------------------------------
C43C TOXIC TIME STEPS AND VOLATILIZATION SWITCHES
*
* TOXSTEPW: TIME STEP IN SECONDS FOR TOXIC KINETICS IN WATER COLUMN AND BED
* TOXSTEPB: TIME STEP IN SECONDS FOR TOXIC BED PROCESSES OF DIFFUSION AND MIXING
* TOX_VEL_MAX: VELOCITY SWITCH FOR VOLATILIZATION APPROACH: LAKE < TOX_VEL_MAX > RIVER
* TOX_DEP_MAX: DEPTH SWITCH FOR VOLATILIZATION APPROACH: LAKE > TOX_DEP_MAX < RIVER
* ITOXTEMP: TEMPERATURE OVERRIDE IF ISTRAN(2)=0
* 1 - CONSTANT TEMPERATURE = TOXTEMP
* >1 - TIME VARYING TEMPERATURE SERIES FROM TSER(ITOXTEMP-1)
* TOXTEMP: CONSTANT TEMPERATURE FOR TOXICS CALCULATIONS (DEG C)
*
C43C STEPW STEPB VEL_MAX DEP_MAX ITOXTEMP TOXTEMP
0 0 0 0 0 0
-------------------------------------------------------------------------------
C43D TOXIC BULK DECAY AND BIODEGRADATION PARAMETERS
*
* NTOXN: TOXIC CONTAMINANT NUMBER ID
* TOX_BLK_KW: BULK DECAY RATE IN THE WATER COLUMN (1/SECOND)
* TOX_BLK_KB: BULK DECAY RATE IN THE SEDIMENT BED (1/SECOND)
* TOX_BLK_MXD: MAXIMUM DEPTH OF BULK DECAY IN THE SEDIMENT BED (METERS)
* TOX_BIO_KW: BIODEGRADATION RATE IN THE WATER COLUMN (1/SECOND)
* TOX_BIO_KB: BIODEGRADATION RATE IN THE SEDIMENT BED (1/SECOND)
* TOX_BIO_MXD: MAXIMUM DEPTH OF BIODEGRADATION IN THE SEDIMENT BED (METERS)
225
* TOX_BIO_Q10W: Q10 TEMPERATURE ADJUSTMENT COEFFICIENT FOR WATER COLUMN
* BIODEGRADATION (dimensionless)
* TOX_BIO_Q10B: Q10 TEMPERATURE ADJUSTMENT COEFFICIENT FOR SEDIMENT BED
* BIODEGRADATION (dimensionless)
* TOX_BIO_TW: REFERENCE TEMPERATURE FOR BIODEGRADATION IN WATER COLUMN (DEG C)
* COEFF = TOX_BIO_KW(NT)*TOX_BIO_Q10W(NT)^((TEM(L,K)-TOX_BIO_TB(NT))/10)
* TOX_BIO_TB: REFERENCE TEMPERATURE FOR BIODEGRADATION IN SEDIMENT BED (DEG C)
* COEFF = TOX_BIO_KB(NT)*TOX_BIO_Q10B(NT)^((TEMB(L)-TOX_BIO_TB(NT))/10)
*
C43D NTOXN BLK_KW BLK_KB BLK_MXD BIO_KW BIO_KB BIO_MXD Q10W Q10B BIO_TW BIO_TW
COMMENTS
-------------------------------------------------------------------------------
C43E TOXIC VOLATILIZATION PARAMETERS
*
* NTOXN: TOXIC CONTAMINANT NUMBER ID
* TOX_MW: MOLECULAR WEIGHT (G/MOLE)
* TOX_HE: HENRY'S LAW COEFFICIENT FOR THE TOXIC (ATM-M3/MOLE)
* TOX_KV_TCOEFF: MASS TRANSFER TEMPERATURE COEFFICIENT (DIMENSIONLESS)
* TOX_KV_TCOEFF**(TEM(L,KC)-20)
* TOX_ATM: ATMOSPHERIC CONCENTRATION OF TOXIC (micro G/L)
* TOX_VOL_ADJ: ADJUSTMENT FACTOR (DIMENSIONLESS)
*
C43E NTOXN TOX_MW TOX_HE TCOEFF ATM VOL_ADJ COMMENTS
-------------------------------------------------------------------------------
C44 TOXIC SORPTION OPTION, DIFFUSION AND MIXING
*
* NTOXN: TOXIC CONTAMINANT NUMBER ID (1 LINE OF DATA BY DEFAULT)
* ISTOC: 0 INORGANIC SOLIDS BASED PARTITIONING ONLY (Kd APPROACH)
* 1 FOR DISS AND PART ORGANIC CARBON SORPTION, POC IS SPECIFIED
* 2 FOR DISS ORGANIC CARBON SORPTION AND POC FRACTIONALLY
* DISTRIBUTED TO INORGANIC SEDIMENT CLASSES
* 3 FOR NO DISS ORGANIC CARBON SORPTION AND POC FRACTIONALLY
* DISTRIBUTED TO INORGANIC SEDIMENT CLASSES
* DIFTOX: DIFFUSION COEFF FOR TOXICANT IN SED BED PORE WATER (m^2/s)
* DIFTOXS: DIFFUSION COEFF FOR TOXICANT BETWEEN WATER COLUMN AND
* PORE WATER IN TOP LAYER OF THE BED(m^2/s)
* > 0.0 INTERPRET AS DIFFUSION COEFFICIENT (m^2/s)
* < 0.0 INTERPRET AS FLUX VELOCITY (m/s)
* PDIFTOX: PARTICLE MIXING DIFFUSION COEFF FOR TOXICANT IN SED BED (m^2/s)
* (if negative use zonal files PARTMIX.INP and PMXMAP.INP)
* DPDIFTOX: DEPTH IN BED OVER WHICH PARTICLE MIXING IS ACTIVE (m)
*
C44 NTOXN ISTOC DIFTOX DIFTOXS PDIFTOX DPDIFTOX
-------------------------------------------------------------------------------
C45 TOXIC CONTAMINANT SEDIMENT INTERACTION PARAMETERS
*
*
* NTOXC: TOXIC CONTAMINANT NUMBER ID. NSEDC+NSEDN LINES OF DATA
* FOR EACH TOXIC CONTAMINANT (DEFAULT = 2)
* NSEDN/NSNDN: FIRST NSED LINES COHESIVE, NEXT NSND LINES NON-COHESIVE.
* REPEATED FOR EACH CONTAMINANT
* ITXPARW: O FOR NORMAL WC PARTITIONING
* 1 FOR SOLIDS DEPENDENT WC PARTITIONING TOXPAR=PARO*(CSED**CONPAR)
* TOXPARW: WATER COLUMN PARO (ITXPARW=1) OR EQUIL TOX CON PART COEFF BETWEEN
* EACH TOXIC IN WATER AND ASSOCIATED SEDIMENT PHASES (LITERS/MG)
* CONPARW: EXPONENT IN TOXPAR=PARO*(CSED**CONPARW) IF ITXPARW=1
* ITXPARB: Not Used
* TOXPARB: SEDIMENT BED PARO (ITXPARB=1) OR EQUIL TOX CON PART COEFF BETWEEN
* EACH TOXIC IN WATER AND ASSOCIATED SEDIMENT PHASES (LITERS/MG)
* CONPARB: Not Used
* 1 0.8770 -0.943 0.025
C45 NTOXN NSEDN ITXPARW TOXPARW CONPARW ITXPARB TOXPARB CONPARB
-------------------------------------------------------------------------------
C45A TOXIC CONTAMINANT NON-SEDIMENT BASED ORGANIC CARBON (OC) INTERACTION PARAMETERS
*
* ISTDOCW: 0 CONSTANT DOC IN WATER COLUMN OF STDOCWC (DEFAULT=0.)
* 1 TIME CONSTANT, SPATIALLY VARYING DOC IN WATER COLUMN FROM docw.inp
* ISTPOCW: 0 CONSTANT POC IN WATER COLUMN OF STPOCWC (DEFAULT=0.)
* 1 TIME CONSTANT, SPATIALLY VARYING POC IN WATER COLUMN FROM pocw.inp
* 2 TIME CONSTANT, FPOC IN WATER COLUMN, SEE C45C
* 3 TIME CONSTANT, SPATIALLY VARYING FPOC IN WATER COLUMN FORM fpocw.inp
226
* 4 FUNCTIONAL SPECIFICATION OF TIME AND SPATIALLY VARYING
* FPOC IN WATER COLUMN
* ISTDOCB: 0 CONSTANT DOC IN BED OF STDOCBC (DEFAULT=0.)
* 1 TIME CONSTANT, SPATIALLY VARYING DOC IN BED FROM docb.inp
* ISTPOCB: 0 CONSTANT POC IN BED OF STPOCBC (DEFAULT=0.)
* 1 TIME CONSTANT, SPATIALLY VARYING POC IN BED FROM pocb.inp
* 2 TIME CONSTANT, FPOC IN BED, SEE C45D
* 3 TIME CONSTANT, SPATIALLY VARYING FPOC IN BED FROM fpocb.inp
* 4 FUNCTIONAL SPECIFICATION OF TIME AND SPATIALLY VARYING
* FPOC IN BED, REQUIRES CODE MODIFICATION FOR EACH APPLICATION (ADVANCED)
* STDOCWC: CONSTANT WATER COLUMN DOC (ISTDOCW=0)
* STPOCWC: CONSTANT WATER COLUMN POC (ISTPOCW=0)
* STDOCBC: CONSTANT BED DOC (ISTDOCB=0)
* STPOCBC: CONSTANT BED POC (ISTPOCB=0)
*
C45A ISTDOCW ISTPOCW ISTDOCB ISTPOCB STDOCWC STPOCWC STDOCBC STPOCBC
-------------------------------------------------------------------------------
C45B TOXIC CONTAMINANT NON-SEDIMENT BASED ORGANIC CARBON (OC) INTERACTION PARAMETERS
*
*
* NTOXC: TOXIC CONTAMINANT NUMBER ID. FOR EACH TOXIC CONTAMINANT
* NOC : FIRST LINE FOR DISSOLVED ORGANIC CARBON (DOC)
* SECOND LINE FOR PARTICULATE ORGANIC CARBON (POC)
* REPEATED FOR EACH CONTAMINANT
* ITXPARWC: O FOR NORMAL WC PARTITIONING
* 1 FOR SOLIDS DEPENDENT WC PARTITIONING TOXPAR=PARO*(CSED**CONPAR)
* TOXPARWC: WATER COLUMN PARO (ITXPARW=1) OR EQUIL TOX CON PART COEFF BETWEEN
* EACH TOXIC IN WATER AND ASSOCIATED SEDIMENT PHASES (liters/mg)
* CONPARWC: EXPONENT IN TOXPAR=PARO*(CSED**CONPARW) IF ITXPARW=1
* ITXPARBC: Not Used
* TOXPARBC: SEDIMENT BED PARO (ITXPARB=1) OR EQUIL TOX CON PART COEFF BETWEEN
* EACH TOXIC IN WATER AND ASSOCIATED SEDIMENT PHASES (liters/mg)
* CONPARBC: Not Used
* 1 0.8770 -0.943 0.025
C45B NTOXN NOC ITXPARWC TOXPARWC CONPARWC ITXPARBC TOXPARBC CONPARBC *CARBON*
-------------------------------------------------------------------------------
C45C TOXIC CONTAMINANT POC FRACTIONAL DISTRIBUTIONS IN WATER COLUMN
* 1 LINE OF DATA REQUIRED EVEN IT ISTRAN(5) IS 0. DATA USED WHEN
* ISTOC(NT)=1 OR 2
*
* NTOXN: TOXIC CONTAMINANT NUMBER ID. NSEDC+NSEDN 1 LINE OF DATA
* FOR EACH TOXIC CONTAMINANT (DEFAULT = 2)
* FPOCSED1-NSED: FRACTION OF OC ASSOCIATED WITH SED CLASSES 1,NSED
* FPOCSND1-NSND: FRACTION OF OC ASSOCIATED WITH SND CLASSES 1,NSND
*
C45C NTOXN
-------------------------------------------------------------------------------
C45D TOXIC CONTAMINANT POC FRACTIONAL DISTRIBUTIONS IN SEDIMENT BED
* 1 LINE OF DATA REQUIRED EVEN IT ISTRAN(5) IS 0. DATA USED WHEN
* ISTOC(NT)=1 OR 2
*
* NTOXN: TOXIC CONTAMINANT NUMBER ID. NSEDC+NSEDN 1 LINE OF DATA
* FOR EACH TOXIC CONTAMINANT (DEFAULT = 2)
* FPOCSED1-NSED: FRACTION OF OC ASSOCIATED WITH SED CLASSES 1,NSED
* FPOCSND1-NSND: FRACTION OF OC ASSOCIATED WITH SND CLASSES 1,NSND
*
C45D NTOXN
-------------------------------------------------------------------------------
C46 BUOYANCY, TEMPERATURE, DYE DATA AND CONCENTRATION BC DATA
*
* BSC: BUOYANCY INFLUENCE COEFFICIENT 0 TO 1, BSC=1. FOR REAL PHYSICS
* TEMO: REFERENCE, INITIAL, EQUILIBRIUM AND/OR ISOTHERMAL TEMP IN DEG C
* HEQT: EQUILIBRIUM TEMPERATURE TRANSFER COEFFICIENT M/sec
* ISBEDTEMI: 0 READ INITIAL BED TEMPERATURE FROM TEMPB.INP
* 1 INITIALIZE AT START OF COLD RUN
* KBH: NOT USED
* RKDYE: FIRST ORDER DECAY RATE FOR DYE VARIABLE IN 1/sec
* NCBS: NUMBER OF CONCENTRATION BOUNDARY CONDITIONS ON SOUTH OPEN
* BOUNDARIES
* NCBW: NUMBER OF CONCENTRATION BOUNDARY CONDITIONS ON WEST OPEN
* BOUNDARIES
227
* NCBE: NUMBER OF CONCENTRATION BOUNDARY CONDITIONS ON EAST OPEN
* BOUNDARIES
* NCBN: NUMBER OF CONCENTRATION BOUNDARY CONDITIONS ON NORTH OPEN
* BOUNDARIES
*
C46 BSC TEMO HEQT ISBEDTEMI KBH RKDYE NCBS NCBW NCBE NCBN
1.00 -12.55 1.000E-06 0 0 0.000E+00 0 0 0 0
-------------------------------------------------------------------------------
C46A ICE EFFECTS
C
ISICE: 0 ICE IMPACTS NOT SIMULATED. AUTOMATICALLY LIMITS ASER.INP DRY BULB TO > 0.0
1 READ ICE THICKNESS FROM FILE ISER.INP (LEGACY ICECOVER.INP)
2 SPECIFIED ON/OFF DATES FOR ICE (ENTIRE MODEL)
3 CALCULATION COUPLED WITH HEAT MODEL
4 CALCULATION COUPLED WITH HEAT MODEL AND FRAZIL TRANSPORT
NISER: NUMBER OF ICE TIME SERIES FOR ISICE=1
TEMPICE: WATER TEMPERATURE AT WATER ICE INTERFACE FOR ISICE <= 2
CDICE: DRAG COEFFICIENT BETWEEN ICE/WATER (DEFAULT = 0.001)
ICETHMX: MAXIMUM ICE COVER THICKNESS FOR ISICE>2, METERS
RICETHK0: ICE THICKNESS FOR ISICE=2 (CONSTANT, METERS)
C
C46A ISICE NISER TEMPICE CDICE ICETHMX RICETHK0
0 0 .1 .001 1 0
-------------------------------------------------------------------------------
C46C ATMOSPHERIC LOCATION AND WIND FUNCTION COEFFICIENTS
*
* SOLAR_LNG = LONGITUDE TO BE USED TO COMPUTE SOLAR RADIATION (Decimal degree)
* SOLAR_LAT = LATITUDE TO BE USED TO COMPUTE SOLAR RADIATION (Decimal degree)
* COMPUTESR = OVERRIDE SOLAR RADIATION IN ASER.INP WITH COMPUTED [.TRUE/.FALSE.]
* USESHADE = USE CELL SPECIFIC SHADE VALUES USING SHADE.INP [.TRUE/.FALSE.]
* IEVAP = EVAPORATION OPTION FOR WATER FLUX ONLY (ALWAYS USED FOR HEAT EXCHANGE)
* 0 - DO NOT INCLUDE IN WATER BUDGET
* 1 - USE SPECIFIED EVAP FROM ASER.INP
* 2 - COMPUTE EVAP USING ORIGINAL EFDC EQUATION
* 3-10 - COMPUTE USING WIND FUNCTION USING WINDFA, WINDFB, WINDFC
* 11 - COMPUTE EVAP USING RYAN-HARLEMAN
* 12 - COMPUTE EVAP USING ARIFIN ET AL. (2016)
* WINDFA = WIND FUNCTION FACTOR A FUNCTION = A + B*WIND2M + C*WIND2m^2
* WINDFB = WIND FUNCTION FACTOR B UNITS: W/M^2/millibar
* WINDFC = WIND FUNCTION FACTOR C
C46C SOLAR_LNG SOLAR_LAT COMPUTESR USESHADE IEVAP WINDFA WINDFB WINDFC
-79.03200 35.73200 .false. .true. 1 0.00000 0.00000 0.00000
-------------------------------------------------------------------------------
C46D ATMOSPHERIC PARAMETERS
*
* IASWRAD = DISTRIBUTE SW SOL RAD OVER WATER COL AND INTO BED, =1 ALL TO SURF LAYER (LEGACY
FULL HEAT)
* REVC = 1000*EVAPORATIVE TRANSFER COEF, REVC<0 USE WIND SPD DEPD DRAG COEF
* RCHC = 1000*CONVECTIVE HEAT TRANSFER COEF, REVC<0 USE WIND SPD DEPD DRAG COEF
* ISVHEAT = FLAG TO INDICATE USE OF SPATIALLY VARYING SURFACE HEAT EXCHANGE COEFFICIENTS REVC
& RCHC
* 0 - USE CONSTANTS FROM REVC/RCHC, 1 - USE VARYING FROM SVHTFACT.INP
* SWRATNF = FAST SCALE SOLAR SW RADIATION ATTENUATION COEFFICIENT, 1/M (LEGACY FULL HEAT)
* SWRATNS = SLOW SCALE SOLAR SW RADIATION ATTENUATION COEFFICIENT, 1/M (LEGACY FULL HEAT)
* FSWRATF = FRACTION OF SOLSR SW RADIATION ATTENUATED FAST, 0<FSWRATF<1 (LEGACY FULL HEAT)
* DABEDT = DEPTH OR THICKNESS OF ACTIVE BED TEMPERATURE LAYER, METERS, <0 FOR SPATIALLY
VARIABLE BED T AND THICK USING TEMPB.INP
* TBEDIT = INITIAL BED TEMPERATURE, DEG C <0 TO ALLOW BED TEM TO
CHANGE WITH TIME
* HTBED1 = CONVECTIVE HEAT COEFFICIENT BETWEEN BED AND BOTTOM WATER LAYER, NO DIM
* HTBED2 = HEAT TRANSFER COEFFICIENT BETWEEN BED AND BOTTOM WATER LAYER, W/M2/DEGC
* WQKEB = BACKGROUND LIGHT EXTINCTION, 1/M (DSI)
* WQKETSS = LIGHT EXTINCTION FOR TSS, 1/M PER G/M^3 (DSI)
* old values for C46D, but new values for HTBED, WQKEB, and WQKETSS
C46D IASWRAD REVC RCHC ISVHEAT SWRATNF SWRATNS FSWRATF DABEDT TBEDIT HTBED1 HTBED2
WQKEB WQKETSS
0 -.9 -1.5 0 2.5 1 .7 10 6 3.3E-05 2.0E-07
1E-09 .0052
-------------------------------------------------------------------------------
C47 LOCATION OF CONC BC'S ON SOUTH BOUNDARIES
*
228
* ICBS: I CELL INDEX
* JCBS: J CELL INDEX
* NTSCRS: NUMBER OF TIME STEPS TO RECOVER SPECIFIED VALUES ON CHANGE
* TO INFLOW FROM OUTFLOW
* NSSERS: SOUTH BOUNDARY CELL SALINITY TIME SERIES ID NUMBER
* NTSERS: SOUTH BOUNDARY CELL TEMPERATURE TIME SERIES ID NUMBER
* NDSERS: SOUTH BOUNDARY CELL DYE CONC TIME SERIES ID NUMBER
* NSFSERS: SOUTH BOUNDARY CELL SHELLFISH LARVAE TIME SERIES ID NUMBER
* NTXSERS: SOUTH BOUNDARY CELL TOXIC CONTAMINANT CONC TIME SERIES ID NUM.
* NSDSERS: SOUTH BOUNDARY CELL COHESIVE SED CONC TIME SERIES ID NUMBER
* NSNSERS: SOUTH BOUNDARY CELL NON-COHESIVE SED CONC TIME SERIES ID NUMBER
C
C47 IBBS JBBS NTSCRS NSSERS NTSERS NDSERS NSFSERS NTXSERS NSDSERS NSNSERS
-------------------------------------------------------------------------------
C48 TIME CONSTANT BOTTOM CONC ON SOUTH CONC BOUNDARIES
*
* SAL: ULTIMATE INFLOWING BOTTOM LAYER SALINITY
* TEM: ULTIMATE INFLOWING BOTTOM LAYER TEMPERATURE
* DYE: ULTIMATE INFLOWING BOTTOM LAYER DYE CONCENTRATION
* SFL: ULTIMATE INFLOWING BOTTOM LAYER SHELLFISH LARVAE CONCENTRATION
* TOX: NTOX ULTIMATE INFLOWING BOTTOM LAYER TOXIC CONTAMINANT
* CONCENTRATIONS NTOX VALUES TOX(N), N=1,NTOX
*
C48 SAL TEM DYE SFL
-------------------------------------------------------------------------------
C49 TIME CONSTANT BOTTOM CONC ON SOUTH CONC BOUNDARIES
*
* SED: NSED ULTIMATE INFLOWING BOTTOM LAYER COHESIVE SEDIMENT
* CONCENTRATIONS FIRST NSED VALUES SED(N), N=1,NSND
* SND: NSND ULTIMATE INFLOWING BOTTOM LAYER NON-COHESIVE SEDIMENT
* CONCENTRATIONS LAST NSND VALUES SND(N), N=1,NSND
*
C49 SED1 SND1
------------------------------------------------------------------------------
C50 TIME CONSTANT SURFACE CONC ON SOUTH CONC BOUNDARIES
*
* SAL: ULTIMATE INFLOWING SURFACE LAYER SALINITY
* TEM: ULTIMATE INFLOWING SURFACE LAYER TEMPERATURE
* DYE: ULTIMATE INFLOWING SURFACE LAYER DYE CONCENTRATION
* SFL: ULTIMATE INFLOWING SURFACE LAYER SHELLFISH LARVAE CONCENTRATION
* TOX: NTOX ULTIMATE INFLOWING SURFACE LAYER TOXIC CONTAMINANT
* CONCENTRATIONS NTOX VALUES TOX(N), N=1,NTOX
*
C50 SAL TEM DYE SFL
-------------------------------------------------------------------------------
C51 TIME CONSTANT SURFACE CONC ON SOUTH CONC BOUNDARIES
*
* SED: NSED ULTIMATE INFLOWING SURFACE LAYER COHESIVE SEDIMENT
* CONCENTRATIONS FIRST NSED VALUES SED(N), N=1,NSND
* SND: NSND ULTIMATE INFLOWING SURFACE LAYER NON-COHESIVE SEDIMENT
* CONCENTRATIONS LAST NSND VALUES SND(N), N=1,NSND
*
C51 SED1 SND1
-------------------------------------------------------------------------------
C52 LOCATION OF CONC BC'S ON WEST BOUNDARIES AND SERIES IDENTIFIERS
*
* ICBW: I CELL INDEX
* JCBW: J CELL INDEX
* NTSCRW: NUMBER OF TIME STEPS TO RECOVER SPECIFIED VALUES ON CHANGE
* TO INFLOW FROM OUTFLOW
* NSSERW: WEST BOUNDARY CELL SALINITY TIME SERIES ID NUMBER
* NTSERW: WEST BOUNDARY CELL TEMPERATURE TIME SERIES ID NUMBER
* NDSERW: WEST BOUNDARY CELL DYE CONC TIME SERIES ID NUMBER
* NSFSERW: WEST BOUNDARY CELL SHELLFISH LARVAE TIME SERIES ID NUMBER
* NTXSERW: WEST BOUNDARY CELL TOXIC CONTAMINANT CONC TIME SERIES ID NUM.
* NSDSERW: WEST BOUNDARY CELL COHESIVE SED CONC TIME SERIES ID NUMBER
* NSNSERW: WEST BOUNDARY CELL NON-COHESIVE SED CONC TIME SERIES ID NUMBER
*
C52 IBBW JBBW NTSCRW NSSERW NTSERW NDSERW NSFSERW NTXSERW NSDSERW NSNSERW
-------------------------------------------------------------------------------
C53 TIME CONSTANT BOTTOM CONC ON WEST CONC BOUNDARIES
229
*
* SAL: ULTIMATE INFLOWING BOTTOM LAYER SALINITY
* TEM: ULTIMATE INFLOWING BOTTOM LAYER TEMPERATURE
* DYE: ULTIMATE INFLOWING BOTTOM LAYER DYE CONCENTRATION
* SFL: ULTIMATE INFLOWING BOTTOM LAYER SHELLFISH LARVAE CONCENTRATION
* TOX: NTOX ULTIMATE INFLOWING BOTTOM LAYER TOXIC CONTAMINANT
* CONCENTRATIONS NTOX VALUES TOX(N), N=1,NTOX
*
C53 SAL TEM DYE SFL
-------------------------------------------------------------------------------
C54 TIME CONSTANT BOTTOM CONC ON WEST CONC BOUNDARIES
*
* SED: NSED ULTIMATE INFLOWING BOTTOM LAYER COHESIVE SEDIMENT
* CONCENTRATIONS FIRST NSED VALUES SED(N), N=1,NSND
* SND: NSND ULTIMATE INFLOWING BOTTOM LAYER NON-COHESIVE SEDIMENT
* CONCENTRATIONS LAST NSND VALUES SND(N), N=1,NSND
*
C54 SED1 SND1
-------------------------------------------------------------------------------
C55 TIME CONSTANT SURFACE CONC ON WEST CONC BOUNDARIES
*
* SAL: ULTIMATE INFLOWING SURFACE LAYER SALINITY
* TEM: ULTIMATE INFLOWING SURFACE LAYER TEMPERATURE
* DYE: ULTIMATE INFLOWING SURFACE LAYER DYE CONCENTRATION
* SFL: ULTIMATE INFLOWING SURFACE LAYER SHELLFISH LARVAE CONCENTRATION
* TOX: NTOX ULTIMATE INFLOWING SURFACE LAYER TOXIC CONTAMINANT
* CONCENTRATIONS NTOX VALUES TOX(N), N=1,NTOX
*
C55 SAL TEM DYE SFL
-------------------------------------------------------------------------------
C56 TIME CONSTANT SURFACE CONC ON WEST CONC BOUNDARIES
*
* SED: NSED ULTIMATE INFLOWING SURFACE LAYER COHESIVE SEDIMENT
* CONCENTRATIONS FIRST NSED VALUES SED(N), N=1,NSND
* SND: NSND ULTIMATE INFLOWING SURFACE LAYER NON-COHESIVE SEDIMENT
* CONCENTRATIONS LAST NSND VALUES SND(N), N=1,NSND
*
C56 SED1 SND1
-------------------------------------------------------------------------------
C57 LOCATION OF CONC BC'S ON EAST BOUNDARIES AND SERIES IDENTIFIERS
*
* ICBE: I CELL INDEX
* JCBE: J CELL INDEX
* NTSCRE: NUMBER OF TIME STEPS TO RECOVER SPECIFIED VALUES ON CHANGE
* TO INFLOW FROM OUTFLOW
* NSSERE: EAST BOUNDARY CELL SALINITY TIME SERIES ID NUMBER
* NTSERE: EAST BOUNDARY CELL TEMPERATURE TIME SERIES ID NUMBER
* NDSERE: EAST BOUNDARY CELL DYE CONC TIME SERIES ID NUMBER
* NSFSERE: EAST BOUNDARY CELL SHELLFISH LARVAE TIME SERIES ID NUMBER
* NTXSERE: EAST BOUNDARY CELL TOXIC CONTAMINANT CONC TIME SERIES ID NUM.
* NSDSERE: EAST BOUNDARY CELL COHESIVE SED CONC TIME SERIES ID NUMBER
* NSNSERE: EAST BOUNDARY CELL NON-COHESIVE SED CONC TIME SERIES ID NUMBER
*
C57 IBBE JBBE NTSCRE NSSERE NTSERE NDSERE NSFSERE NTXSERE NSDSERE NSNSERE
-------------------------------------------------------------------------------
C58 TIME CONSTANT BOTTOM CONC ON EAST CONC BOUNDARIES
*
* SAL: ULTIMATE INFLOWING BOTTOM LAYER SALINITY
* TEM: ULTIMATE INFLOWING BOTTOM LAYER TEMPERATURE
* DYE: ULTIMATE INFLOWING BOTTOM LAYER DYE CONCENTRATION
* SFL: ULTIMATE INFLOWING BOTTOM LAYER SHELLFISH LARVAE CONCENTRATION
* TOX: NTOX ULTIMATE INFLOWING BOTTOM LAYER TOXIC CONTAMINANT
* CONCENTRATIONS NTOX VALUES TOX(N), N=1,NTOX
*
C58 SAL TEM DYE SFL
-------------------------------------------------------------------------------
C59 TIME CONSTANT BOTTOM CONC ON EAST CONC BOUNDARIES
*
* SED: NSED ULTIMATE INFLOWING BOTTOM LAYER COHESIVE SEDIMENT
* CONCENTRATIONS FIRST NSED VALUES SED(N), N=1,NSND
* SND: NSND ULTIMATE INFLOWING BOTTOM LAYER NON-COHESIVE SEDIMENT
230
* CONCENTRATIONS LAST NSND VALUES SND(N), N=1,NSND
*
C59 SED1 SND1
-------------------------------------------------------------------------------
C60 TIME CONSTANT SURFACE CONC ON EAST CONC BOUNDARIES
*
* SAL: ULTIMATE INFLOWING SURFACE LAYER SALINITY
* TEM: ULTIMATE INFLOWING SURFACE LAYER TEMPERATURE
* DYE: ULTIMATE INFLOWING SURFACE LAYER DYE CONCENTRATION
* SFL: ULTIMATE INFLOWING SURFACE LAYER SHELLFISH LARVAE CONCENTRATION
* TOX: NTOX ULTIMATE INFLOWING SURFACE LAYER TOXIC CONTAMINANT
* CONCENTRATIONS NTOX VALUES TOX(N), N=1,NTOX
*
C60 SAL TEM DYE SFL
-------------------------------------------------------------------------------
C61 TIME CONSTANT SURFACE CONC ON EAST CONC BOUNDARIES
*
* SED: NSED ULTIMATE INFLOWING SURFACE LAYER COHESIVE SEDIMENT
* CONCENTRATIONS FIRST NSED VALUES SED(N), N=1,NSND
* SND: NSND ULTIMATE INFLOWING SURFACE LAYER NON-COHESIVE SEDIMENT
* CONCENTRATIONS LAST NSND VALUES SND(N), N=1,NSND
*
C61 SED1 SND1
-------------------------------------------------------------------------------
C62 LOCATION OF CONC BC'S ON NORTH BOUNDARIES AND SERIES IDENTIFIERS
*
* ICBN: I CELL INDEX
* JCBN: J CELL INDEX
* NTSCRN: NUMBER OF TIME STEPS TO RECOVER SPECIFIED VALUES ON CHANGE
* TO INFLOW FROM OUTFLOW
* NSSERN: NORTH BOUNDARY CELL SALINITY TIME SERIES ID NUMBER
* NTSERN: NORTH BOUNDARY CELL TEMPERATURE TIME SERIES ID NUMBER
* NDSERN: NORTH BOUNDARY CELL DYE CONC TIME SERIES ID NUMBER
* NSFSERN: NORTH BOUNDARY CELL SHELLFISH LARVAE TIME SERIES ID NUMBER
* NTXSERN: NORTH BOUNDARY CELL TOXIC CONTAMINANT CONC TIME SERIES ID NUM.
* NSDSERN: NORTH BOUNDARY CELL COHESIVE SED CONC TIME SERIES ID NUMBER
* NSNSERN: NORTH BOUNDARY CELL NON-COHESIVE SED CONC TIME SERIES ID NUMBER
*
C62 IBBN JBBN NTSCRN NSSERN NTSERN NDSERN NSFSERN NTXSERN NSDSERN NSNSERN
-------------------------------------------------------------------------------
C63 TIME CONSTANT BOTTOM CONC ON NORTH CONC BOUNDARIES
*
* SAL: ULTIMATE INFLOWING BOTTOM LAYER SALINITY
* TEM: ULTIMATE INFLOWING BOTTOM LAYER TEMPERATURE
* DYE: ULTIMATE INFLOWING BOTTOM LAYER DYE CONCENTRATION
* SFL: ULTIMATE INFLOWING BOTTOM LAYER SHELLFISH LARVAE CONCENTRATION
* TOX: NTOX ULTIMATE INFLOWING BOTTOM LAYER TOXIC CONTAMINANT
* CONCENTRATIONS NTOX VALUES TOX(N), N=1,NTOX
*
C63 SAL TEM DYE SFL
-------------------------------------------------------------------------------
C64 TIME CONSTANT BOTTOM CONC ON NORTH CONC BOUNDARIES
*
* SED: NSED ULTIMATE INFLOWING BOTTOM LAYER COHESIVE SEDIMENT
* CONCENTRATIONS FIRST NSED VALUES SED(N), N=1,NSND
* SND: NSND ULTIMATE INFLOWING BOTTOM LAYER NON-COHESIVE SEDIMENT
* CONCENTRATIONS LAST NSND VALUES SND(N), N=1,NSND
*
C64 SED1 SND1
-------------------------------------------------------------------------------
C65 TIME CONSTANT SURFACE CONC ON NORTH CONC BOUNDARIES
*
* SAL: ULTIMATE INFLOWING SURFACE LAYER SALINITY
* TEM: ULTIMATE INFLOWING SURFACE LAYER TEMPERATURE
* DYE: ULTIMATE INFLOWING SURFACE LAYER DYE CONCENTRATION
* SFL: ULTIMATE INFLOWING SURFACE LAYER SHELLFISH LARVAE CONCENTRATION
* TOX: NTOX ULTIMATE INFLOWING SURFACE LAYER TOXIC CONTAMINANT
* CONCENTRATIONS NTOX VALUES TOX(N), N=1,NTOX
*
C65 SAL TEM DYE SFL
-------------------------------------------------------------------------------
231
C66 TIME CONSTANT SURFACE CONC ON NORTH CONC BOUNDARIES
*
* SED: NSED ULTIMATE INFLOWING SURFACE LAYER COHESIVE SEDIMENT
* CONCENTRATIONS FIRST NSED VALUES SED(N), N=1,NSND
* SND: NSND ULTIMATE INFLOWING SURFACE LAYER NON-COHESIVE SEDIMENT
* CONCENTRATIONS LAST NSND VALUES SND(N), N=1,NSND
*
C66 SED1 SND1
-------------------------------------------------------------------------------
C66A CONCENTRATION DATA ASSIMILATION
*
* NLCDA: NUMBER OF HORIZONTAL LOCATIONS FOR DATA ASSIMILATION
* TSCDA: WEIGHTING FACTOR, 0 to 1, 1 = FULL ASSIMILATION
* ISCDA: 1 FOR CONCENTRATION DATA ASSIMILATION VALUES (NC=1,7)
*
C66A NLCDA TSCDA ISCDA
0 0 0 0 0 0 0 0 0
-------------------------------------------------------------------------------
C66B CONCENTRATION DATA ASSIMILATION
*
* ITPCDA: 0 ASSIMILATE DATA FROM TIME SERIES
* 1 ASSIMILATE DATA FROM ANOTHER CELL IN GRID
* ICDA: I INDEX OF CELL ASSIMILATING DATA
* JCDA: J INDEX OF CELL ASSIMILATING DATA
* ICCDA: I INDEX OF CELL PROVIDING DATA, ITPCDA=1
* JCCDA: J INDEX OF CELL PROVIDING DATA, ITPCDA=1
* NCSERA: ID OF TIME SERIES PROVIDING DATA
*
C66B ITPCDA ICDA JCDA ICCDA JCCDA NS NT ND NSF NTX NSD
NSN
-------------------------------------------------------------------------------
C67 DRIFTER DATA (FIRST 4 PARAMETER FOR SUB DRIFTER, SECOND 6 FOR SUB LAGRANGIAN)
*
* ISPD: 1 TO ACTIVE SIMULTANEOUS RELEASE AND LAGRANGIAN TRANSPORT OF
* NEUTRALLY BUOYANT PARTICLE DRIFTERS AT LOCATIONS INPUT ON C68
* 2 TO ACTIVATE DS-INTERNATIONAL'S LPT DRIFTER COMPUTATIONS (DRIFTER.INP)
* NPD: NUMBER OF PARTICLE DRIFTERS
* NPDRT: TIME STEP AT WHICH PARTICLES ARE RELEASED
* NWPD: NUMBER OF TIME STEPS BETWEEN WRITING TO TRACKING FILE
* DRIFTER.OUT
* ISLRPD: 1 TO ACTIVATE CALCULATION OF LAGRANGIAN MEAN VELOCITY OVER TIME
* INTERVAL TREF AND SPATIAL INTERVAL ILRPD1<I<ILRPD2,
* JLRPD1<J<JLRPD2, 1<K<KC, WITH MLRPDRT RELEASES. ANY AVERAGE
* OVER ALL RELEASE TIMES IS ALSO CALCULATED
* 2 SAME BUT USES A HIGHER ORDER TRAJECTORY INTEGRATION
* ILRPD1 WEST BOUNDARY OF REGION
* ILRPD2 EAST BOUNDARY OF REGION
* JLRPD1 NORTH BOUNDARY OF REGION
* JLRPD2 SOUTH BOUNDARY OF REGION
* MLRPDRT NUMBER OF RELEASE TIMES
* IPLRPD 1,2,3 WRITE FILES TO PLOT ALL,EVEN,ODD HORIZ LAG VEL VECTORS
*
C67 ISPD NPD NPDRT NWPD ISLRPD ILRPD1 ILRPD2 JLRPD1 JLRPD2 MLRPDRT IPLRPD
0 0 0 0 0 0 0 0 0 0 0
-------------------------------------------------------------------------------
C68 INITIAL DRIFTER POSITIONS (FOR USE WITH SUB DRIFTER)
*
* RI: I CELL INDEX IN WHICH PARTICLE IS RELEASED IN
* RJ: J CELL INDEX IN WHICH PARTICLE IS RELEASED IN
* RK: K CELL INDEX IN WHICH PARTICLE IS RELEASED IN
*
C68 RI RJ RK
-------------------------------------------------------------------------------
C69 CONSTANTS FOR CARTESIAN GRID CELL CENTER LONGITUDE AND LATITUDE
*
* CDLON1: 6 CONSTANTS TO GIVE CELL CENTER LAT AND LON OR OTHER
* CDLON2: COORDINATES FOR CARTESIAN GRIDS USING THE FORMULAS
* CDLON3: DLON(L)=CDLON1+(CDLON2*FLOAT(I)+CDLON3)/60.
* CDLAT1: DLAT(L)=CDLAT1+(CDLAT2*FLOAT(J)+CDLAT3)/60.
* CDLAT2:
* CDLAT3:
232
*
C69 CDLON1 CDLON2 CDLON3 CDLAT1 CDLAT2 CDLAT3
0 0 0 0 0 0
-------------------------------------------------------------------------------
C70 CONTROLS FOR WRITING ASCII OR BINARY DUMP FILES
*
* ISDUMP: GREATER THAN 0 TO ACTIVATE
* 1 SCALED ASCII INTEGER (0<VAL<65535)
* 2 SCALED 16BIT BINARY INTEGER (0<VAL<65535) OR (-32768<VAL<32767)
* 3 UNSCALED ASCII FLOATING POINT
* 4 UNSCALED BINARY FLOATING POINT
* ISADMP: GREATER THAN 0 TO APPEND EXISTING DUMP FILES
* NSDUMP: NUMBER OF TIME STEPS BETWEEN DUMPS
* TSDUMP: STARTING TIME FOR DUMPS - DAYS (NO DUMPS BEFORE THIS TIME)
* TEDUMP: ENDING TIME FOR DUMPS - DAYS (NO DUMPS AFTER THIS TIME)
* ISDMPP: GREATER THAN 0 FOR WATER SURFACE ELEVATION DUMP
* ISDMPU: GREATER THAN 0 FOR HORIZONTAL VELOCITY DUMP
* ISDMPW: GREATER THAN 0 FOR VERTICAL VELOCITY DUMP
* ISDMPT: GREATER THAN 0 FOR TRANSPORTED VARIABLE DUMPS
* IADJDMP: 0 FOR SCALED BINARY INTEGERS (0<VAL<65535)
* -32768 FOR SCALED BINARY INTEGERS (-32768<VAL<32767)
*
C70 ISDUMP ISADMP NSDUMP TSDUMP TEDUMP ISDMPP ISDMPU ISDMPW ISDMPT IADJDMP
0 0 0 0 0 0 0 0 0 -32768
-------------------------------------------------------------------------------
C71 CONTROLS FOR HORIZONTAL PLANE SCALAR FIELD CONTOURING - RESIDUAL ONLY
*
* ISSPH: NOT USED
*
* NPSPH: NOT USED
* ISRSPH: 1 TO WRITE FILE FOR RESIDUAL SCALAR VARIABLE IN HORIZONTAL PLANE
*
* ISPHXY: 0 DOES NOT WRITE I,J,X,Y IN ***CNH.OUT AND R***CNH.OUT FILES (RESIDUAL ONLY)
* 1 WRITES I,J ONLY IN ***CNH.OUT AND R***CNH.OUT FILES (RESIDUAL ONLY)
* 2 WRITES I,J,X,Y IN ***CNH.OUT AND R***CNH.OUT FILES (RESIDUAL ONLY)
*
* DATA LINE REPEATS 7 TIMES FOR SAL,TEM,DYE,SFL,TOX,SED,SND
*
C71 ISSPH NPSPH ISRSPH ISPHXY
0 1 0 3 !SAL
0 1 0 3 !TEM
0 1 0 3 !DYE
1 2 0 3 !SFL
0 1 0 3 !TOX
0 1 0 3 !SED
0 1 0 3 !SND
-------------------------------------------------------------------------------
C71A CONTROLS FOR HORIZONTAL PLANE SEDIMENT BED PROPERTIES CONTOURING
*
* ISBPH: NOT USED
*
* ISBEXP: 0 >0 EXPLORER BINARY FORMAT, OUTPUT FREQUENCY
* NPBPH: NOT USED
* ISRBPH: NOT USED
* ISBBDN: NOT USED
* ISBLAY: NOT USED
* ISBPOR: NOT USED
* SBSED: NOT USED
*
*
* ISBSED: NOT USED
*
*
* ISBVDR: NOT USED
* ISBARD: NOT USED
*
*
C71A ISBPH ISBEXP NPBPH ISRBPH ISBBDN ISBLAY ISBPOR ISBSED ISBSND ISBVDR ISBARD
0 0 0 0 0 0 0 0 0 0 0
-------------------------------------------------------------------------------
C71B FOOD CHAIN MODEL OUTPUT CONTROL
233
*
* ISFDCH: 1 TO WRITE OUTPUT FOR HOUSATONIC RIVER FOOD CHAIN MODEL
* NFDCHZ: NUMBER OF SPATIAL ZONES
* HBFDCH: AVERAGING DEPTH FOR TOP PORTION OF BED (METERS)
* TFCAVG: TIME AVERAGING INTERVAL FOR FOOD CHAIN OUTPUT (SECONDS)
*
C71B ISFDCH NFDCHZ HBFDCH TFCAVG
0 0 0 0
-------------------------------------------------------------------------------
C72 CONTROLS FOR EFDC_EXPLORER LINKAGE AND SURFACE ELEVATION RESIDUAL OUTPUT
*
* ISPPH: 1 TO WRITE FILE FOR EFDC_EXPLORER LINKAGE
* 2 WRITE ONLY FOR THE FIRST AND LAST REFERENCE TIME PERIOD
* 100 TO ACTIVATE THE HIGH FREQUENCY DOMAIN OUTPUT READING SNAPSHOT.INP
* NPPPH: NUMBER OF WRITES PER REFERENCE TIME PERIOD
* ISRPPH: 1 TO WRITE FILE FOR RESIDUAL SURFACE ELEVATION CONTOURNG IN
* HORIZONTAL PLANE
* IPPHXY: NOT USED
*
*
*
C72 ISPPH NPPPH ISRPPH IPPHXY
1 2 0 3
-------------------------------------------------------------------------------
C73 CONTROLS FOR HORIZONTAL PLANE RESIDUAL VELOCITY VECTOR PLOTTING
*
* ISVPH: NOT USED
* NOT USED
* NPVPH: NOT USED
* ISRVPH: 1 TO WRITE FILE FOR RESIDUAL VELOCITY PLOTTING
* HORIZONTAL PLANE
* IVPHXY: NOT USED
* NOT USED
* NOT USED
* NOT USED
*
C73 ISVPH NPVPH ISRVPH IVPHXY
1 2 0 3
-------------------------------------------------------------------------------
C74 NOT USED
*
* ISECSPV: NOT USED
*
* NPSPV: NOT USED
* ISSPV: NOT USED
*
* ISRSPV: NOT USED
* ISHPLTV: NOT USED
*
*
*
C74 ISECSPV NPSPV ISSPV ISRSPV ISHPLTV
0 0 0 0 0 !SAL
0 0 0 0 0 !TEM
0 0 0 0 0 !DYE
0 0 0 0 0 !SFL
0 0 0 0 0 !TOX
0 0 0 0 0 !SED
0 0 0 0 0 !SND
-------------------------------------------------------------------------------
C75 NOT USED
*
* ISECSPV: NOT USED
* NIJSPV: NOT USED
* SEC ID: NOT USED
*
C75 ISECSPV NIJSPV SEC ID
-------------------------------------------------------------------------------
C76 NOT USED
*
* ISECSPV: NOT USED
234
* ISPV: NOT USED
* JSPV: NOT USED
*
C76 ISECSPV ISPV JSPV
-------------------------------------------------------------------------------
C77 NOT USED
*
* ISECVPV: NOT USED
*
* NPVPV: NOT USED
* ISVPV: NOT USED
*
* ISRSPV: NOT USED
*
C77 ISECVPV NPVPV ISVPV ISRSPV
0 1 0 0
-------------------------------------------------------------------------------
C78 NOT USED
*
* ISCEVPV: NOT USED
* NIJVPV: NOT USED
* ANGVPV: NOT USED
* SEC ID: NOT USED
*
C78 ISECVPV NIJVPV ANGVPV SEC ID
-------------------------------------------------------------------------------
C79 NOT USED
*
* ISECVPV: NOT USED
* IVPV: NOT USED
* JVPV: NOT USED
*
C79 ISECVPV IVPV JVPV
-------------------------------------------------------------------------------
C80 CONTROLS FOR 3D FIELD OUTPUT
*
* IS3DO: 1 TO WRITE TO 3D ASCII INTEGER FORMAT FILES, JS3DVAR.LE.2 SEE|
* 1 TO WRITE TO 3D ASCII FLOAT POINT FORMAT FILES, JS3DVAR.EQ.3 C57|
* 2 TO WRITE TO 3D CHARACTER ARRAY FORMAT FILES (NOT ACTIVE)
* 3 TO WRITE TO 3D HDF IMAGE FORMAT FILES (NOT ACTIVE)
* 4 TO WRITE TO 3D HDF FLOATING POINT FORMAT FILES (NOT ACTIVE)
* ISR3DO: SAME AS IS3DO EXCEPT FOR RESIDUAL VARIABLES
* NP3DO: NUMBER OF WRITES PER LAST REF TIME PERIOD FOR INST VARIABLES
* KPC: NUMBER OF UNSTRETCHED PHYSICAL VERTICAL LAYERS
* NWGG: IF NWGG IS GREATER THAN ZERO, NWGG DEFINES THE NUMBER OF !2877|
* WATER CELLS IN CARTESIAN 3D GRAPHICS GRID OVERLAY OF THE
* CURVILINEAR GRID. FOR NWGG>0 AND EFDC RUNS ON A CURVILINEAR
* GRID, I3DMI,I3DMA,J3DMI,J3DMA REFER TO CELL INDICES ON THE
* ON THE CARTESIAN GRAPHICS GRID OVERLAY DEFINED BY FILE
* GCELL.INP. THE FILE GCELL.INP IS NOT USED BY EFDC, BUT BY
* THE COMPANION GRID GENERATION CODE GEFDC.F. INFORMATION
* DEFINING THE OVERLAY IS READ BY EFDC.F FROM THE FILE
* GCELLMP.INP. IF NWGG EQUALS 0, I3DMI,I3DMA,J3DMI,J3DMA REFER
* TO INDICES ON THE EFDC GRID DEFINED BY CELL.INP.
* ACTIVATION OF THE REWRITE OPTION I3DRW=1 WRITES TO THE FULL
* GRID DEFINED BY CELL.INP AS IF CELL.INP DEFINES A CARTESIAN
* GRID. IF NWGG EQ 0 AND THE EFDC COMP GRID IS CO, THE REWRITE
* OPTION IS NOT RECOMMENDED AND A POST PROCESSOR SHOULD BE USED
* TO TRANSFER THE SHORT FORM, I3DRW=0, OUTPUT TO AN APPROPRIATE
* FORMAT FOR VISUALIZATION. CONTACT DEVELOPER FOR MORE DETAILS
* I3DMI: MINIMUM OR BEGINNING I INDEX FOR 3D ARRAY OUTPUT
* I3DMA: MAXIMUM OR ENDING I INDEX FOR 3D ARRAY OUTPUT
* J3DMI: MINIMUM OR BEGINNING J INDEX FOR 3D ARRAY OUTPUT
* J3DMA: MAXIMUM OR ENDING J INDEX FOR 3D ARRAY OUTPUT
* I3DRW: 0 FILES WRITTEN FOR ACTIVE CO WATER CELLS ONLY
* 1 REWRITE FILES TO CORRECT ORIENTATION DEFINED BY GCELL.INP
* AND GCELLMP.INP FOR CO WITH NWGG.GT.O OR BY CELL.INP IF THE
* COMPUTATIONAL GRID IS CARTESIAN AND NWGG.EQ.0
* SELVMAX: MAXIMUM SURFACE ELEVATION FOR UNSTRETCHING (ABOVE MAX SELV )
* BELVMIN: MINIMUM BOTTOM ELEVATION FOR UNSTRETCHING (BELOW MIN BELV)
*
235
C80 IS3DO ISR3DO NP3DO KPC NWGG I3DMI I3DMA J3DMI J3DMA I3DRW SELVMAX
BELVMIN
0 0 0 1 0 1 1 1 1 0 1000 -
1000
-------------------------------------------------------------------------------
C81 OUTPUT ACTIVATION AND SCALES FOR 3D FIELD OUTPUT
*
* VARIABLE: DUMMY VARIABLE ID (DO NOT CHANGE ORDER)
* IS3(VARID): 1 TO ACTIVATE THIS VARIABLE
* JS3(VARID): 0 FOR NO SCALING OF THIS VARIABLE
* 1 FOR AUTO SCALING OF THIS VARIABLE OVER RANGE 0<VAL<255
* AUTO SCALES FOR EACH FRAME OUTPUT IN FILES OUT3D.DIA AND
* ROUT3D.DIA OUTPUT IN I4 FORMAT
* 2 FOR SCALING SPECIFIED IN NEXT TWO COLUMNS WITH OUTPUT
* DEFINED OVER RANGE 0<VAL<255 AND WRITTEN IN I4 FORMAT
* 3 FOR MULTIPLIER SCALING BY MAX SCALE VALUE WITH OUTPUT
* WRITTEN IN F7.2 FORMAT (IS3DO AND ISR3DO MUST BE 1)
*
C81 VARIABLE IS3D JS3D SMAX SMIN
'U VEL' 0 0 0 0
'V VEL' 0 0 0 0
'W VEL' 0 0 0 0
'SAL' 0 0 0 0
'TEMP' 0 0 0 0
'DYE' 0 0 0 0
'SED' 0 0 0 0
'SND' 0 0 0 0
'TOX' 0 0 0 0
-------------------------------------------------------------------------------
C82 INPLACE HARMONIC ANALYSIS PARAMETERS
*
* ISLSHA: 1 FOR IN PLACE LEAST SQUARES HARMONIC ANALYSIS
* MLLSHA: NUMBER OF LOCATIONS FOR LSHA
* NTCLSHA: LENGTH OF LSHA IN INTEGER NUMBER OF REFERENCE TIME PERIODS
* ISLSTR: 1 FOR TREND REMOVAL
* ISHTA : 1 FOR SINGLE TREF PERIOD SURFACE ELEV ANALYSIS
* 90
C82 ISLSHA MLLSHA NTCLSHA ISLSTR ISHTA
0 0 0 0 0
-------------------------------------------------------------------------------
C83 HARMONIC ANALYSIS LOCATIONS AND SWITCHES
*
* ILLSHA: I CELL INDEX
* JLLSHA: J CELL INDEX
* LSHAP: 1 FOR ANALYSIS OF SURFACE ELEVATION
* LSHAB: 1 FOR ANALYSIS OF SALINITY
* LSHAUE: 1 FOR ANALYSIS OF EXTERNAL MODE HORIZONTAL VELOCITY
* LSHAU: 1 FOR ANALYSIS OF HORIZONTAL VELOCITY IN EVERY LAYER
* CLSL: LOCATION AS A CHARACTER VARIABLE
*
C83 ILLSHA JLLSHA LSHAP LSHAB LSHAUE LSHAU CLSL
-------------------------------------------------------------------------------
C84 CONTROLS FOR WRITING TO TIME SERIES FILES
*
* ISTMSR: 1 OR 2 TO WRITE TIME SERIES OF SURF ELEV, VELOCITY, NET
* INTERNAL AND EXTERNAL MODE VOLUME SOURCE-SINKS, AND
* CONCENTRATION VARIABLES, 2 APPENDS EXISTING TIME SERIES FILES
* MLTMSR: NUMBER HORIZONTAL LOCATIONS TO WRITE TIME SERIES OF SURF ELEV,
* VELOCITY, AND CONCENTRATION VARIABLES
* NBTMSR: TIME STEP TO BEGIN WRITING TO TIME SERIES FILES (Inactive)
* NSTMSR: TIME STEP TO STOP WRITING TO TIME SERIES FILES (Inactive)
* NWTMSR: NUMBER OF TIME STEPS TO SKIP BETWEEN OUTPUT
* NTSSTSP: NUMBER OF TIME SERIES START-STOP SCENARIOS, 1 OR GREATER
* TCTMSR: UNIT CONVERSION FOR TIME SERIES TIME. FOR SECONDS, MINUTES,
* HOURS,DAYS USE 1.0, 60.0, 3600.0, 86400.0 RESPECTIVELY
*
*
C84 ISTMSR MLTMSR NBTMSR NSTMSR NWTMSR NTSSTSP TCTMSR
1 19 1 999999999 86400 1 86400
-------------------------------------------------------------------------------
C85 CONTROLS FOR WRITING TO TIME SERIES FILES
236
*
* ITSSS: START-STOP SCENARIO NUMBER 1.GE.ISSS.LE.NTSSTSP
* MTSSTSP: NUMBER OF STOP-START PAIRS FOR SCENARIO ISSS
*
C85 ITSSS MTSSTSP
1 1
-------------------------------------------------------------------------------
C86 CONTROLS FOR WRITING TO TIME SERIES FILES
*
* ITSSS: START-STOP SCENARIO NUMBER 1.GE.ISSS.LE.NTSSTSP
* MTSSS: NUMBER OF STOP-START PAIRS FOR SCENARIO ISSS
* TSSTRT: STARTING TIME FOR SCENARIO ITSSS, SAVE INTERVAL MTSSS
* TSSTOP: STOPPING TIME FOR SCENARIO ITSSS, SAVE INTERVAL MTSSS
* -1000.
C86 ISSS MTSSS TSSTRT TSSTOP COMMENT
1 1 1096 99999
-------------------------------------------------------------------------------
C87 CONTROLS FOR WRITING TO TIME SERIES FILES
*
* ILTS: I CELL INDEX
* JLTS: J CELL INDEX
* NTSSSS: WRITE SCENARIO FOR THIS LOCATION
* MTSP: 1 FOR TIME SERIES OF SURFACE ELEVATION
* MTSC: 1 FOR TIME SERIES OF TRANSPORTED CONCENTRATION VARIABLES
* MTSA: 1 FOR TIME SERIES OF EDDY VISCOSITY AND DIFFUSIVITY
* MTSUE: 1 FOR TIME SERIES OF EXTERNAL MODE HORIZONTAL VELOCITY
* MTSUT: 1 FOR TIME SERIES OF EXTERNAL MODE HORIZONTAL TRANSPORT
* MTSU: 1 FOR TIME SERIES OF HORIZONTAL VELOCITY IN EVERY LAYER
* MTSQE: 1 FOR TIME SERIES OF NET EXTERNAL MODE VOLUME SOURCE/SINK
* MTSQ: 1 FOR TIME SERIES OF NET EXTERNAL MODE VOLUME SOURCE/SINK
* CLTS: LOCATION AS A CHARACTER VARIABLE
*
C87 ILTS JLTS NTSSSS MTSP MTSC MTSA MTSUE MTSUT MTSU MTSQE MTSQ
CLTS
23 6 1 1 1 0 0 0 0 0 0
'Dam'
17 6 1 1 1 0 0 0 0 0 0
'CPF055C'
15 6 1 1 1 0 0 0 0 0 0
'CPF055C1'
16 7 1 1 1 0 0 0 0 0 0
'CPF055C2'
16 6 1 1 1 1 1 0 0 0 0
'CPF055C3'
17 7 1 1 1 0 0 0 0 0 0
'CPF055C4'
17 8 1 1 1 0 0 0 0 0 0
'CPF055C5'
18 6 1 1 1 0 0 0 0 0 0
'CPF055C6'
20 6 1 1 1 1 1 0 0 0 0
'CPF055D'
22 6 1 1 1 0 0 0 0 0 0
'CPF055E'
18 42 1 1 1 0 0 0 0 0 0
'CPF081A1B'
18 37 1 1 1 0 0 0 0 0 0
'CPF081A1C'
15 35 1 1 1 1 1 0 0 0 0
'CPF086C'
11 34 1 1 1 0 0 0 0 0 0
'CPF086CUPS'
18 35 1 1 1 0 0 0 0 0 0
'CPF086D'
18 31 1 1 1 0 0 0 0 0 0
'CPF086F'
18 25 1 1 1 1 1 0 0 0 0
'CPF087B3'
18 21 1 1 1 0 0 0 0 0 0
'CPF087D'
237
19 14 1 1 1 1 1 0 0 0 0
'CPF0880A'
-------------------------------------------------------------------------------
C88 High frequency output for specific locations and times
*
* HFREOUT: 1 use high frequency dates for output
* 0 specific output option is not used
*
C88 HFREOUT
0
-------------------------------------------------------------------------------
C89 NOT USED
*
* MMDVSFP: NOT USED
* DMSFP: NOT USED
*
C89 MMDVSFP DMVSFP
-------------------------------------------------------------------------------
C90 NOT USED
*
* MMLVSFP: NOT USED
* TIMVSFP: NOT USED
* IVSFP: NOT USED
* JVSFP: NOT USED
*
C90 MMLVSFP TIMVSFP IVSFP JVSFP
-------------------------------------------------------------------------------
C91 OPTIONS FOR GENERATION OF NETCDF FILE(S)
*
* NCDFOUT: OPTION FOR NETCDF EXPORT
* =1 GENERATE NETCDF FILE NC
* =0 NO GENERATION
* DEFLEV: LEVEL OF COMPRESSION OF NETCDF FILE FROM 0 TO 9
* ROTA: =1 ROTATING 2D VELOCITY FIELD TO THE TRUE EAST AND TRUE NORTH
* =0 NO ROTATION TO TRUE EAST AND TRUE NORTH
* UTMZ: UTM ZONE
* >0 FOR NORTHERN HEMISPHERE; <0 FOR SOUTHERN HEMISPHERE
* BASEDATE: YYYY-MM-DD (NO BLANK)
* BASETIME: HH:MM:SS (NO BLANK)
* PROJ: PROJECT NAME IS A STRING OF MAXIMUM LENGTH 20
* WITHOUT ANY BLANKS
*
C91 NCDFOUT DEFLEV ROTA BLK UTMZ HREST BASEDATE BASETIME PROJ
1 1 1 -999 17 24 2011-01-01 00:00:00 JordanLake
-------------------------------------------------------------------------------
C91A OPTIONS FOR NETCDF OUTPUT
*
* TYPE File creation option
* =1: Single file
* =0: Multiple daily files
* BEGIN Start julian day of writing netcdf file
* END End julian day of writing netcdf file
C91A TYPE BEGIN END
0 0 0
-------------------------------------------------------------------------------
C91B OPTIONS FOR NETCDF OUTPUT
*
* ISNCDF(I) OPTION FOR OUTPUT, I=1:12
* = 0:NO
* = 1:YES
*
* 1 2 3 4 5 6 7 8 9 10 11
12
C91B SAL TEM DYE SLF TOX SED SND WQL LPT SHR WIN
WAV
0 0 0 0 0 0 0 0 0 0 0
0

End of Input File EFDC.INP
238
Appendix 15. Model input file WQ3DWC.INP file for base case.

# <-- set first character to '#' to use extended annotation in this file
C
C GENERATED BY DYNAMIC SOLUTIONS-INTL'S EFDC_EXPLORER: EE8.4.4 Rel 181128
C-----------------------------------------------------------------------------
C01 MAIN TITLE CARDS
C
C01 THREE TITLE CARDS FOLLOW:
' Jordan Lake, '
' BG/GRN algae '
' '
C-----------------------------------------------------------------------------
C02 I/O CONTROL VARIABLE CARD
C
C ONE TITLE CARD FOLLOWS:
$$ C02 I/O control variables $$
C
C IWQLVL = Kinetic Complexity Level
C 1 - Wasp5 Level Kinetics (Single Organic Carbon, Phosphorous, And
C Nitrogen Classes + Reactive DOC/CBOD Variable)
C 2 - Intermediate Level Kinetics (Total Refractory And Total Labile
C Organic Carbon, Phosphorous, And Nitrogen Classes
C + Reactive DOC/CBOD)
C 3 - In base model the same as Level 2
C 4 - Extended CE-QUAL-ICM (4 Organic Carbon, Phosphorous, And
C Nitrogen Classes + Reactive DOC/CBOD Variable)
C NWQV = Number of water quality water column variables
C NWQZ = Max. number of spatial zones having varying WQ settling
C NWQPS = Max. number of water quality point source locations
C NWQTD = Number of data points in the temperature lookup table
C NWQTS = Max. number of water quality time-series output locations
C NTSWQV = Max. number of water quality time-series output variables
C NSMG = Max. Number of sediment diagenesis model groups (Hardwired, Must=3)
C NSMZ = Max. number of sediment model spatial variation zones
C NTSSMV = Max. number of sediment model time-series output variables
C NSMTS = Not used
C WQKDPT = Kinetic Update Time Step (seconds). Used to Allow for Variable Delta T's.
C ISRPEM = 1 To Activate Rooted Plant And Epiphyte Algae Sub-Model (RPEM)
C
C02 IWQLVL NWQV NWQZ NWQPS NWQTD NWQTS NTSWQV NSMG NSMZ NTSSMV NSMTS
WQKDPT ISRPEM
1 21 1 15 400 0 0 3 1 0 0
3600 0
C-----------------------------------------------------------------------------
C02A KINETIC BYPASS FLAGS
C ONLY USED IF IWQLVL=1 (ISTRWQ(NV),NV=1,NWQV)
C ISTRWQ = 0 - Skip Kinetics
C 1 - Compute Kinetics
C ! 1) CHC - cyanobacteria
C ! 2) CHG - diatom algae
C ! 3) CHD - green algae
C ! 4) ROC - refractory particulate organic carbon
C ! 5) LOC - labile particulate organic carbon
C ! 6) DOC - dissolved organic carbon
C ! 7) ROP - refractory particulate organic phosphorus
C ! 8) LOP - labile particulate organic phosphorus
C ! 9) DOP - dissolved organic phosphorus
C ! 10) P4D - total phosphate
C ! 11) RON - refractory particulate organic nitrogen
C ! 12) LON - labile particulate organic nitrogen
C ! 13) DON - dissolved organic nitrogen
C ! 14) NHX - ammonia nitrogen
C ! 15) NOX - nitrate nitrogen
C ! 16) SUU - particulate biogenic silica
C ! 17) SAA - dissolved available silica
C ! 18) COD - chemical oxygen demand
C ! 19) DOX - dissolved oxygen
C ! 20) TAM - total active metal
C ! 21) FCB - fecal coliform bacteria
239
C ! MAC - macroalgae/periphyton (Not included in ISTRWQ)
C02A 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0
C-----------------------------------------------------------------------------
C03
C IWQDT = Not Used
C IWQM = Not Used
C IWQBEN = benthic flux model switch (0=spec/const; 1=predictive flux, 2=specified)
C IWQSI = switch to activate silica state variables (0=off; 1=activated)
C IWQFCB = switch to activate fecal coliform bacteria (0=off; 1=activated)
C IWQSRP = switch for sediment sorption (1=TAM sorption; 2=sediment sorption)
C IWQSTOX = cyanobacteria salinity toxicity switch (0=no toxicity; 1=toxicity)
C IWQKA = reaeration option switch
C = 0, constant reaeration (WQKRO), no wind reaeration
C = 1, constant reaeration (WQKRO) plus wind reaeration
C = 2, use O'Connor-Dobbins (1958) formula
C = 3, use Owens & Gibbs (1964) formula
C = 4, use Owens & Gibbs Modifed formula (see code)
C IWQVLIM = Switch for limiting macroalgae/periphyton growth rate due to vegetative cover
C = 0, No growth rate limit
C = 1, Limit macroalgae/periphyton growth, Michaelis-Menton Eq
C = 2, Limit macroalgae/periphyton growth, 5-Param Logistic Function
C When IDNOTRVA>0 shade factor PSHADE(L) will be readfrom file MACALGMP.INP
C
C03 IWQDT IWQM IWQBEN IWQSI IWQFCB IWQSRP IWQSTOX IWQKA IWQVLIM
0 1 2 0 0 0 0 1 0
C-----------------------------------------------------------------------------
C04
C IWQZ = number of zones for spatially varying WQ parameters
C IWQNC = switch to save negative concentrations to WQ3DNC.LOG (0=OFF; 1=ON, 2-ZERO)
C IWQRST = switch to save WQ restart data to WQWCRST.OUT (0=OFF; 1=ON)
C NDMWQ = number of horizontal spatial domains for decomposition calc. (=1)
C LDMWQ = number of horizontal cells in the WQ computational domain (=LC-2)
C NDDOAVG = no longer used
C NDLTAVG = Light Extinction Analysis/Diagnostics
C IDNOTRVA = ID number of macroalgae/periphyton water quality variable
C
C04 IWQZ IWQNC IWQRST NDMWQ LDMWQ NDDOAVG NDLTAVG IDNOTRVA
1 1 1 1 406 0 0 0
C-----------------------------------------------------------------------------
C05
C IWQICI = initial condition switch
C 0=spatially constant initial conditions (card C44)
C 1=read initial condition file ICIFN (see card C51)
C 2=read initial conditions from restart file WQWCRST.INP
C IWQAGR = algae growth kinetics switch
C 0=use constant kinetics on card C45
C 1=read spatial/time-varying kinetics from file AGRFN (card C51)
C IWQSTL = settling velocity switch
C 0=use spatially/temporally constant settling velocities (card C46)
C 1=use spatial/time-varying settling vel. from STLFN (card C51)
C IWQSUN = solar radiation switch
C 0=use constant solar radiation (I0) and FD from card C10
C 1=use daily average solar rad. and FD from file SUNDAY.INP
C 2=use hourly solar rad. from ASER.INP file
C 3=Compute Daily Averages of solar rad. and FD From ASER.INP
C IWQPSL = point source load switch
C 0=use constant point source loads
C 1=use time-variable point source loads from file WQPSL.INP
C 2=use time-variable point source CONCENTRATIONS from CSQSR##.INP files
C IWQNPL = 0=use time constant atmospheric dry deposition
C isDIURDO = switch for saving diurnal D.O. data
C 0=do not save diurnal D.O. data to file
C 1=save diurnal D.O. data to binary file WQDIURDO.BIN
C if WQDIURDO.BIN already exists, delete it
C 2=save diurnal D.O. data to binary file WQDIURDO.BIN
C if WQDIURDO.BIN already exists, append to it
C WQDIUDT = time interval for writing to diurnal D.O. file (hours)
C IWQKIN = switch for using spatially-varying kinetic rate constants
C 0=do not use spatially-varying kinetics
C 1=use spatially-varying kinetics in file KINETICS.INP
240
C Only applies to IWQKA, KRO, KTR, REAC, KDC, KDCALGm, KHRm
C DOPTm, KCD, and KHCOD.
C
C05 IWQICI IWQAGR IWQSTL IWQSUN IWQPSL IWQNPL isDIURDO WQDIUDT IWQKIN
2 0 0 3 2 0 0 0 0
C-----------------------------------------------------------------------------
C06
C IWQTS = number of time-series locations to output to ASCII file WQWCTS.OUT
C TWQTSB = beginning time for recording time-series data (Julian Day)
C TWQTSE = ending time for recording time-series data (Julian Day)
C WQTSDT = write interval (hours), also averaging interval for binary files
C use 24.0 hours for daily averages (solar day)
C use 24.8412 hours to average over the M2 tide period (lunar day)
C isWQAVG = switch to save WQ averages to binary file WQWCAVG.BIN
C 0=OFF; 1=ON, overwrite existing file; 2=ON, append to existing file
C isWQMIN = switch to save WQ minimums to binary file WQWCMIN.BIN
C 0=OFF; 1=ON, overwrite existing file; 2=ON, append to existing file
C isWQMAX = switch to save WQ minimums to binary file WQWCMAX.BIN
C 0=OFF; 1=ON, overwrite existing file; 2=ON, append to existing file
C isCOMP = switch to save DO components to file WQDOCOMP.BIN
C 0=OFF; 1=ON, overwrite existing file; 2=ON, append to existing file
C
C06 IWQTS TWQTSB TWQTSE WQTSDT isWQAVG isWQMIN isWQMAX isCOMP
0 0 0 0 0 0 0 0
C-----------------------------------------------------------------------------
C07 TIME-SERIES WRITE CONTROLS
C ! 1) CHC – Chlorophyll-a (cyanobacteria)
C ! 2) CHG – Chlorophyll-a (diatom algae)
C ! 3) CHD – Chlorophyll-a (green algae)
C ! 4) ROC - refractory particulate organic carbon
C ! 5) LOC - labile particulate organic carbon
C ! 6) DOC - dissolved organic carbon
C ! 7) ROP - refractory particulate organic phosphorus
C ! 8) LOP - labile particulate organic phosphorus
C ! 9) DOP - dissolved organic phosphorus
C ! 10) P4D - total phosphate
C ! 11) RON - refractory particulate organic nitrogen
C ! 12) LON - labile particulate organic nitrogen
C ! 13) DON - dissolved organic nitrogen
C ! 14) NHX - ammonia nitrogen
C ! 15) NOX - nitrate nitrogen
C ! 16) SUU - particulate biogenic silica
C ! 17) SAA - dissolved available silica
C ! 18) COD - chemical oxygen demand
C ! 19) DOX - dissolved oxygen
C ! 20) TAM - total active metal
C ! 21) FCB - fecal coliform bacteria
C ! 22) MAC - macroalgae/periphyton
C
C07 Two title cards follow:
$ I J CHC CHG CHD ROC LOC DOC ROP LOP DOP P4D RON LON DON
$ NHX NOX SUU SAA COD DOX TAM FCB MAC
C-----------------------------------------------------------------------------
C08 ONE TITLE CARD FOLLOWS:
$$ C08 constant parameters for ALGAE (see Table 3-1) $$
C
C KHNc = nitrogen half-saturation for cyanobacteria (mg/L)
C KHNd = nitrogen half-saturation for algae diatoms (mg/L)
C KHNg = nitrogen half-saturation for algae greens algae (mg/L)
C KHNm = nitrogen half-saturation for macroalgae (mg/L)
C KHPc = phosphorus half-saturation for cyanobacteria (mg/L)
C KHPd = phosphorus half-saturation for algae diatoms (mg/L)
C KHPg = phosphorus half-saturation for algae greens algae (mg/L)
C KHPm = phosphorus half-saturation for macroalgae (mg/L)
C KHS = silica half-saturation for algae diatoms (mg/L)
C STOX = salinity at which microsystis growth is halved for cyanobacteria
C
C08 KHNc KHNd KHNg KHNm KHPc KHPd KHPg KHPm KHS STOX WQKHCO2C
KHCO2D KHCO2G KHCO2M
.06 .01 .01 .01 .008 .001 .001 .001 .05 1
C-----------------------------------------------------------------------------
241
C09 constant parameters for ALGAE (see Table 3-1)
C KeTSS = light extinction for total suspended solids (1/m per g/m^3)
C KeCHL = light extinction for total suspended chlorophyll (1/m per mg/m^3)
C Note: if KeCHL is negative, the Riley (1956) formula is used to
C compute the extinction coefficient due to chlorophyll:
C KeCHL = 0.054*CHL^0.6667 + 0.0088*CHL
C where CHL = total chloryphyll concentration (ug/L)
C KeCHLE = chlorophyll exponent for light extinction, Default = 1
C KePOC = light extinction due to particular organic matter (using POC) (1/m per g/m^3)
C KeDOC = light extinction for dissolved organic carbon (1/m per g/m^3)
C CChlc = carbon-to-chlorophyll ratio for cyanobacteria (mg C / ug Chl)
C CChld = carbon-to-chlorophyll ratio for algae diatoms (mg C / ug Chl)
C CChlg = carbon-to-chlorophyll ratio for algae greens (mg C / ug Chl)
C CChlm = carbon-to-chlorophyll ratio for macroalgae (mg C / ug Chl)
C DOPTc = optimal depth (m) for cyanobacteria growth
C DOPTd = optimal depth (m) for algae diatoms growth
C DOPTg = optimal depth (m) for algae greens growth
C DOPTm = optimal depth (m) for macroalgae growth
C
C09 KeTSS KeChl KeChl KePOC KeDOC CChlc CChld CChlg CChlm DOPTc DOPTd
DOPTg DOPTm
.0052 .007 1.30 .008 .016 .0156 .0237 .025 .25 1.0 1.0
0.5 1.0
C-----------------------------------------------------------------------------
C10 constant parameters for ALGAE (see Table 3-1)
C I0 = initial solar radiation (Langley/day) at water surface
C IsMIN = minimum optimum solar radiation (Langley/day)
C FD = fraction of day that is daylight
C CIa = weighting factor for solar radiation at current day
C CIb = weighting factor for solar radiation at (-1) days
C CIc = weighting factor for solar radiation at (-2) days
C CIm = not used
C Rea = global reaeration adjustment factor
C PARadj = solar radiation multiplied by this factor to get the
C photoactive available radiation (PAR) for algae growth
C SIceAdj = fraction of solar radiation transmitted through the ice (NOT USED)
C
C10 I0 IsMIN FD CIa CIb CIc CIm Rea PARadj SIceAdj
0 20 .5 .7 .2 .1 .7 1 .43 0
C-----------------------------------------------------------------------------
C11 constant parameters for ALGAE (see Table 3-1)
C TMc1 = lower optimal temperature for cyanobacteria growth (degC)
C TMc2 = upper optimal temperature for cyanobacteria growth (degC)
C TMd1 = lower optimal temperature for algae diatoms growth (degC)
C TMd2 = upper optimal temperature for algae diatoms growth (degC)
C TMg1 = lower optimal temperature for algae greens growth (degC)
C TMg2 = upper optimal temperature for algae greens growth (degC)
C TMm1 = lower optimal temperature for macroalgae growth (degC)
C TMm2 = upper optimal temperature for macroalgae growth (degC)
C TMp1 = lower optimal temperature for diatom predation (degC)
C TMp2 = upper optimal temperature for diatom predation (degC)
C
C11 TMc1 TMc2 TMd1 TMd2 TMg1 TMg2 TMm1 TMm2 TMp1 TMp2
30 39 5 17 15 26 20 24 20 30
C-----------------------------------------------------------------------------
C12 constant parameters for ALGAE (see Table 3-1)
C KTG1c = suboptimal temperature effect coef. for cyanobacteria growth
C KTG2c = superoptimal temperature effect coef. for cyanobacteria growth
C KTG1d = suboptimal temperature effect coef. for algae diatoms growth
C KTG2d = superoptimal temperature effect coef. for algae diatoms growth
C KTG1g = suboptimal temperature effect coef. for algae greens growth
C KTG2g = superoptimal temperature effect coef. for algae greens growth
C KTG1m = suboptimal temperature effect coef. for macroalgae growth
C KTG2m = superoptimal temperature effect coef. for macroalgae growth
C KTG1p = suboptimal temperature effect coef. for diatom predation growth
C KTG2p = superoptimal temperature effect coef. for diatom predation growth
C
C12 KTG1c KTG2c KTG1d KTG2d KTG1g KTG2g KTG1m KTG2m KTG1p KTG2p
.006 .006 .0025 .012 .01 .01 .0001 .0001 .02 .0001
C-----------------------------------------------------------------------------
C13 constant parameters for ALGAE (see Table 3-1)
242
C TRc = reference temperature for cyanobacteria metabolism (degC)
C TRd = reference temperature for algae diatoms metabolism (degC)
C TRg = reference temperature for algae greens metabolism (degC)
C TRm = reference temperature for macroalgae metabolism (degC)
C KTBc = temperature effect coef. for cyanobacteria metabolism
C KTBd = temperature effect coef. for algae diatoms metabolism
C KTBg = temperature effect coef. for algae greens metabolism
C KTBm = temperature effect coef. for macroalgae metabolism
C
C13 TRc TRd TRg TRm KTBc KTBd KTBg KTBm
20 20 20 20 .069 .069 .069 .069
C-----------------------------------------------------------------------------
C14 ONE TITLE CARD FOLLOWS:
$$ C14 constant parameters for CARBON (see Table 3-2) $$
C
C FCRP = carbon distribution coef. for algae predation: refractory POC
C FCLP = carbon distribution coef. for algae predation: labile POC
C FCDP = carbon distribution coef. for algae predation: DOC
C FCDc = carbon distribution coef. for cyanobacteria metabolism
C FCDd = carbon distribution coef. for algae diatoms metabolism
C FCDg = carbon distribution coef. for algae greens metabolism
C KHRc = half-sat. constant (gO2/m^3) for cyanobacteria DOC excretion
C KHRd = half-sat. constant (gO2/m^3) for algae diatoms DOC excretion
C KHRg = half-sat. constant (gO2/m^3) for algae greens DOC excretion
C Note: FCRP + FCLP + FCDP = 1.0
C
C14 FCRP FCLP FCDP FCDc FCDd FCDg KHRc KHRd KHRg
.28 .12 .6 1 1 1 .5 .5 .5
C-----------------------------------------------------------------------------
C15 ONE TITLE CARD FOLLOWS:
$$ C15 constant parameters for CARBON (macroalgae)
C
C FCRPm = carbon distribution coef. for macroalgae predation: refractory POC
C FCRPm = carbon distribution coef. for macroalgae predation: labile POC
C FCRPm = carbon distribution coef. for macroalgae predation: DOC
C FCDm = carbon distribution coef. for macroalgae metabolism
C KHRm = half-sat. constant (gO2/m^3) for macroalgae DOC excretion
C Note: FCRPm + FCLPm + FCDPm = 1.0
C
C15 FCRPm FCLPm FCDPm FCDm KHRm
0 0 0 0 0
C-----------------------------------------------------------------------------
C16 constant parameters for CARBON (see Table 3-2)
C KRC = minimum hydrolysis rate (1/day) of refractory POC
C KLC = minimum hydrolysis rate (1/day) of labile POC
C KDC = minimum hydrolysis rate (1/day) of DOC
C KRCalg = constant relating refractory POC hydrolysis rate to total Chl-a
C KLCalg = constant relating labile POC hydrolysis rate to total Chl-a
C KDCalg = constant relating DOC hydrolysis rate to total Chl-a
C KDCalgm = constant relating DOC hydrolysis rate to macroalgae
C
C16 KRC KLC KDC KRCalg KLCalg KDCalg KDCalgm
.0025 .0375 .005 0 0 0 0
C-----------------------------------------------------------------------------
C17 constant parameters for CARBON (see Table 3-2)
C TRHDR = reference temperature for hydrolysis (degC)
C TRMNL = reference temperature for mineralization (degC)
C KTHDR = temperature effect constant for hydrolysis
C KTMNL = temperature effect constant for mineralization
C KHORDO = oxic respiration half-sat. constant for D.O. (gO2/m^3)
C KHDNN = half-sat. constant for denitrification (gN/m^3)
C AANOX = ratio of denitrification rate to oxic DOC respiration rate
C
C17 TRHDR TRMNL KTHDR KTMNL KHORDO KHDNN AANOX
20 20 .069 .069 0.1 0.1 5
C-----------------------------------------------------------------------------
C18 ONE TITLE CARD FOLLOWS:
$$ C18 constant parameters for PHOSPHORUS (see Table 3-3) $$
C
C FPRP = phos. distribution coef. for algae predation: refractory POP
C FPLP = phos. distribution coef. for algae predation: labile POP
243
C FPDP = phos. distribution coef. for algae predation: DOP
C FPIP = phos. distribution coef. for algae predation: Inorganic P
C FPRc = phos. distribution coef. of RPOP for cyanobacteria metabolism
C FPRd = phos. distribution coef. of RPOP for algae diatoms metabolism
C FPRg = phos. distribution coef. of RPOP for algae greens metabolism
C FPLc = phos. distribution coef. of LPOP for cyanobacteria metabolism
C FPLd = phos. distribution coef. of LPOP for algae diatoms metabolism
C FPLg = phos. distribution coef. of LPOP for algae greens metabolism
C Note, the following must sum to 1.0:
C FPRP + FPLP + FPDP + FPIP = 1.0
C FPRc + FPLc + FPDc + FPIc = 1.0
C FPRd + FPLd + FPDd + FPId = 1.0
C FPRg + FPLg + FPDg + FPIg = 1.0
C
C18 FPRP FPLP FPDP FPIP | FPRc FPRd FPRg | FPLc FPLd FPLg
.51 .19 .2 .1 0 0 0 0 0 0
C-----------------------------------------------------------------------------
C19 ONE TITLE CARD FOLLOWS:
$$ C19 constant parameters for PHOSPHORUS (macroalgae)
C
C FPRPM = phos. distribution coef. for macroalgae predation: RPOP
C FPLPM = phos. distribution coef. for macroalgae predation: LPOP
C FPDPM = phos. distribution coef. for macroalgae predation: DOP
C FPIPM = phos. distribution coef. for macroalgae predation: Inorganic P
C FPRm = phos. distribution coef. of RPOP for macroalgae metabolism
C FPLm = phos. distribution coef. of LPOP for macroalgae metabolism
C APCM = factor to modify APC for macroalgae
C Note, the following must sum to 1.0:
C FPRPM + FPLPM + FPDPM + FPIPM = 1.0
C FPRm + FPLm + FPDm + FPIm = 1.0
C
C19 FPRPM FPLPM FPDPM FPIPM FPRm FPLm APCM
0 0 0 0 0 0 0
C-----------------------------------------------------------------------------
C20 constant parameters for PHOSPHORUS (see Table 3-3)
C FPDc = phosphorus distribution coef. of DOP for cyanobacteria metabolism
C FPDd = phosphorus distribution coef. of DOP for algae diatoms metabolism
C FPDg = phosphorus distribution coef. of DOP for algae greens metabolism
C FPDm = phosphorus distribution coef. of DOP for macroalgae metabolism
C FPIc = phosphorus distribution coef. of P4T for cyanobacteria metabolism
C FPId = phosphorus distribution coef. of P4T for algae diatoms metabolism
C FPIg = phosphorus distribution coef. of P4T for algae greens metabolism
C FPIm = phosphorus distribution coef. of P4T for macroalgae metabolism
C KPO4p = partition coefficient for sorbed/dissolved PO4
C
C Notes, the following must sum to 1.0:
C FPRc + FPLc + FPDc + FPIc = 1.0
C FPRd + FPLd + FPDd + FPId = 1.0
C FPRg + FPLg + FPDg + FPIg = 1.0
C
C20 FPDc FPDd FPDg FPDm | FPIc FPId FPIg FPIm | KPO4p
1 1 1 1 0 0 0 0 .01
C-----------------------------------------------------------------------------
C21 constant parameters for PHOSPHORUS (see Table 3-3)
C KRP = minimum hydrolysis rate (1/day) of RPOP
C KLP = minimum hydrolysis rate (1/day) of LPOP
C KDP = minimum hydrolysis rate (1/day) of DOP
C KRPalg = constant relating hydrolysis rate of RPOP to algae
C KLPalg = constant relating hydrolysis rate of LPOP to algae
C KDPalg = constant relating hydrolysis rate of DOP to algae
C CPprm1 = constant used in determining algae Phos-to-Carbon ratio
C CPprm2 = constant used in determining algae Phos-to-Carbon ratio
C CPprm3 = constant used in determining algae Phos-to-Carbon ratio
C
C21 KRP KLP KDP KRPalg KLPalg KDPalg CPprm1 CPprm2 CPprm3
.005 .075 .1 0 0 .2 38 85 200
C-----------------------------------------------------------------------------
C22 ONE TITLE CARD FOLLOWS:
$$ C22 constant parameters for NITROGEN (see Table 3-4) $$
C
C FNRP = nitrogen distribution coef. for algae predation: RPON
244
C FNLP = nitrogen distribution coef. for algae predation: LPON
C FNDP = nitrogen distribution coef. for algae predation: DON
C FNIP = nitrogen distribution coef. for algae predation: Inorganic N
C FNRc = nitrogen distribution coef. of RPON for cyanobacteria metabolism
C FNRd = nitrogen distribution coef. of RPON for algae diatoms metabolism
C FNRg = nitrogen distribution coef. of RPON for algae greens metabolism
C FNLc = nitrogen distribution coef. of LPON for cyanobacteria metabolism
C FNLd = nitrogen distribution coef. of LPON for algae diatoms metabolism
C FNLg = nitrogen distribution coef. of LPON for algae greens metabolism
C
C22 FNRP FNLP FNDP FNIP FNRc FNRd FNRg FNLc FNLd FNLg
.28 .12 .35 .25 .075 .075 .075 .075 .075 .075
C-----------------------------------------------------------------------------
C23 ONE TITLE CARD FOLLOWS:
$$ C23 constant parameters for NITROGEN (macroalgae)
C
C FNRPM = nitrogen distribution coef. for marcoalgae predation: RPON
C FNLPM = nitrogen distribution coef. for marcoalgae predation: LPON
C FNDPM = nitrogen distribution coef. for marcoalgae predation: DON
C FNIPM = nitrogen distribution coef. for marcoalgae predation: Inorganic N
C FNRm = nitrogen distribution coef. of RPON for macroalgae metabolism
C FNLm = nitrogen distribution coef. of LPON for macroalgae metabolism
C
C23 FNRPM FNLPM FNDPM FNIPM FNRm FNLm
0 0 0 0 0 0
C-----------------------------------------------------------------------------
C24 constant parameters for NITROGEN (see Table 3-4)
C FNDc = nitrogen distribution coef. of DON for cyanobacteria metabolism
C FNDd = nitrogen distribution coef. of DON for algae diatoms metabolism
C FNDg = nitrogen distribution coef. of DON for algae greens metabolism
C FNDm = nitrogen distribution coef. of DON for macroalgae metabolism
C FNIc = nitrogen distribution coef. of DIN for cyanobacteria metabolism
C FNId = nitrogen distribution coef. of DIN for algae diatoms metabolism
C FNIg = nitrogen distribution coef. of DIN for algae greens metabolism
C FNIm = nitrogen distribution coef. of DIN for macroalgae metabolism
C ANCc = nitrogen-to-carbon ratio for cyanobacteria
C ANCd = nitrogen-to-carbon ratio for algae diatoms
C ANCg = nitrogen-to-carbon ratio for algae greens
C ANCm = nitrogen-to-carbon ratio for macroalgae
C Note: FNRx + FNLx + FNDx + FNIx = 1.0
C
C24 FNDc FNDd FNDg FNDm FNIc FNId FNIg FNIm ANCc ANCd ANCg
ANCm
.25 .25 .25 1 .6 .6 .6 0 .167 .100 .125
.088
C-----------------------------------------------------------------------------
C25 constant parameters for NITROGEN (see Table 3-4)
C ANDC = mass NO3 reduces per DOC oxidized (gN/gC)
C rNitM = maximum nitrification rate (/day)
C KHNitDO = nitrification half-sat. constant for D.O.
C KHNitN = nitrification half-sat. constant for NH4
C TNit = reference temperature for nitrification (degC)
C KNit1 = suboptimal temperature effect constant for nitrification
C Knit2 = superoptimal temperature effect constant for nitrification
C
C25 ANDC rNitM KHNitDO KHNitN TNit KNit1 KNit2
.933 .125 1 0.5 21 .045 .045
C-----------------------------------------------------------------------------
C26 constant parameters for NITROGEN (see Table 3-4)
C KRN = minimum hydrolysis rate (1/day) of RPON
C KLN = minimum hydrolysis rate (1/day) of LPON
C KDN = minimum hydrolysis rate (1/day) of DON
C KRNalg = constant relating hydrolysis rate of RPON to algae
C KLNalg = constant relating hydrolysis rate of LPON to algae
C KDNalg = constant relating hydrolysis rate of DON to algae
C
C26 KRN KLN KDN KRNalg KLNalg KDNalg
.005 .1 .03 0 0 0
C-----------------------------------------------------------------------------
C27 ONE TITLE CARD FOLLOWS:
$$ C27 constant parameters for SILICA (see Table 3-5) $$
245
C
C FSPP = silica distribution coef. for diatom predation
C FSIP = silica distribution coef. for diatom predation
C FSPd = silica distribution coef. for diatom metabolism
C FSId = silica distribution coef. for diatom metabolism
C ASCd = silica-to-carbon ratio for algae diatoms
C KSAp = partition coef. for sorbed/dissolved SA
C KSU = dissolution rate (1/day) of particulate silica (PSi)
C TRSUA = reference temperature (degC) for PSi dissolution
C KTSUA = temperature effect on PSi dissolution
C
C27 FSPP FSIP FSPd FSId ASCd KSAp KSU TRSUA KTSUA
.5 .5 .5 .5 .8 0 .03 20 .092
C-----------------------------------------------------------------------------
C28 ONE TITLE CARD FOLLOWS:
$$ C28 constant parameters for COD & DO (see Table 3-6) $$
C
C AOCR = stoichiometric algae oxygen-to-carbon ratio (gO2/gC)
C AONT = stoichiometric algae oxygen=to-nitrate ratio (gO2/gN)
C KRO = reaeration constant (3.933 for OConnor-Dobbins; 5.32 for Owen-Gibbs)
C KTR = temperature rate constant for reaeration
C KHCOD = oxygen half-saturation constant for COD decay (mg/L O2)
C KCD = COD decay rate (per day)
C TRCOD = reference temperature for COD decay (degC)
C KTCOD = temperature rate constant for COD decay
C AOCRpm = macroalgae photosynthesis oxygen-to-carbon ratio
C AOCRrm = macroalgae respiration oxygen-to-carbon ratio
C
C28 AOCR AONT KRO KTR KHCOD KCD TRCOD KTCOD AOCRpm AOCRrm
2.67 4.33 3.933 1.024 1.5 0 20 .041 0 0
C-----------------------------------------------------------------------------
C29 ONE TITLE CARD FOLLOWS:
$$ C29 constant parameters for TAM & FCB (see Table 3-7) $$
C
C KHbmf = D.O. concentration where TAM release is half the anoxic rate
C BFTAM = anoxic release rate of TAM (mol/m2/day)
C Ttam = reference temperature for TAM release (degC)
C Ktam = temperature effect constant for TAM release
C TAMdmx = TAM solubility at anoxic conditions (mol/m^3)
C Kdotam = constant relating TAM solubility to D.O.
C KFCB = first-order fecal coliform bacteria decay rate (1/day)
C TFCB = temperature effect constant for KFCB decay rate
C
C29 KHbmf BFTAM Ttam Ktam TAMdmx Kdotam KFCB TFCB
.5 .1 16 .2 .015 1 .25 1.07
C-----------------------------------------------------------------------------
C30 SIX TITLE CARDS FOLLOW:
$$ C30 CONCENTRATION TIME SERIES DATA FOR OPEN BOUNDARIES $$
$$ NUMBER OF TIME SERIES FOR EACH STATE VARIABLE
$ C C C R L D R L D P R L D N N S S C D T F
$ H H H O O O O O O 4 O O O H O U A O O A C
$ C G D C C C P P P D N N N X X U A D X M B
$
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
C-----------------------------------------------------------------------------
C31 ONE TITLE CARD FOLLOWS:
$$ C31 parameters for OPEN BDRY CONDITIONS $$
C
C NWQOBS = number of WQ open boundary cells on SOUTH boundary
C NWQOBW = number of WQ open boundary cells on WEST boundary
C NWQOBE = number of WQ open boundary cells on EAST boundary
C NWQOBN = number of WQ open boundary cells on NORTH boundary
C
C31 NWQOBS NWQOBW NWQOBE NWQOBN
0 0 0 0
C-----------------------------------------------------------------------------
C32 SIX TITLE CARDS FOLLOW:
$$ C32 SOUTH OPEN BOUNDARY $$
$$ TIME SERIES ID'S FOR EACH STATE VARIABLE
$ I J C C C R L D R L D P R L D N N S S C D T F
$ H H H O O O O O O 4 O O O H O U A O O A C
246
$ C G D C C C P P P D N N N X X U A D X M B
$
C-----------------------------------------------------------------------------
C33 FIVE TITLE CARDS FOLLOW:
$$ C33 SOUTH SOUTH OPEN BOUNDARY $$
$$ CONSTANT BOTTOM CONCENTRATION BC'S
$ I J CHC CHD CHG ROC LOC DOC
$ ROP LOP DOP P4D RON LON DON
$ NHX NOX SUU SAA COD DOX TAM FCB
C-----------------------------------------------------------------------------
C34 FIVE TITLE CARDS FOLLOW:
$$ C34 SOUTH SOUTH OPEN BOUNDARY $$
$$ CONSTANT SURFACE CONCENTRATION BC'S
$ I J CHC CHD CHG ROC LOC DOC
$ ROP LOP DOP P4D RON LON DON
$ NHX NOX SUU SAA COD DOX TAM FCB
C-----------------------------------------------------------------------------
C35 SIX TITLE CARDS FOLLOW:
$$ C35 WEST OPEN BOUNDARY $$
$$ TIME SERIES ID'S FOR EACH STATE VARIABLE
$ I J C C C R L D R L D P R L D N N S S C D T F
$ H H H O O O O O O 4 O O O H O U A O O A C
$ C G D C C C P P P D N N N X X U A D X M B
$
C-----------------------------------------------------------------------------
C36 FIVE TITLE CARDS FOLLOW:
$$ C36 WEST SOUTH OPEN BOUNDARY $$
$$ CONSTANT BOTTOM CONCENTRATION BC'S
$ I J CHC CHD CHG ROC LOC DOC
$ ROP LOP DOP P4D RON LON DON
$ NHX NOX SUU SAA COD DOX TAM FCB
C-----------------------------------------------------------------------------
C37 FIVE TITLE CARDS FOLLOW:
$$ C37 WEST SOUTH OPEN BOUNDARY $$
$$ CONSTANT SURFACE CONCENTRATION BC'S
$ I J CHC CHD CHG ROC LOC DOC
$ ROP LOP DOP P4D RON LON DON
$ NHX NOX SUU SAA COD DOX TAM FCB
C-----------------------------------------------------------------------------
C38 SIX TITLE CARDS FOLLOW:
$$ C38 EAST OPEN BOUNDARY $$
$$ TIME SERIES ID'S FOR EACH STATE VARIABLE
$ I J C C C R L D R L D P R L D N N S S C D T F
$ H H H O O O O O O 4 O O O H O U A O O A C
$ C G D C C C P P P D N N N X X U A D X M B
$
C-----------------------------------------------------------------------------
C39 FIVE TITLE CARDS FOLLOW:
$$ C39 EAST SOUTH OPEN BOUNDARY $$
$$ CONSTANT BOTTOM CONCENTRATION BC'S
$ I J CHC CHD CHG ROC LOC DOC
$ ROP LOP DOP P4D RON LON DON
$ NHX NOX SUU SAA COD DOX TAM FCB
C-----------------------------------------------------------------------------
C40 FIVE TITLE CARDS FOLLOW:
$$ C40 EAST SOUTH OPEN BOUNDARY $$
$$ CONSTANT SURFACE CONCENTRATION BC'S
$ I J CHC CHD CHG ROC LOC DOC
$ ROP LOP DOP P4D RON LON DON
$ NHX NOX SUU SAA COD DOX TAM FCB
C-----------------------------------------------------------------------------
C41 SIX TITLE CARDS FOLLOW:
$$ C41 NORTH OPEN BOUNDARY $$
$$ TIME SERIES ID'S FOR EACH STATE VARIABLE
$ I J C C C R L D R L D P R L D N N S S C D T F
$ H H H O O O O O O 4 O O O H O U A O O A C
$ C G D C C C P P P D N N N X X U A D X M B
$
C-----------------------------------------------------------------------------
C42 FIVE TITLE CARDS FOLLOW:
$$ C42 NORTH SOUTH OPEN BOUNDARY $$
247
$$ CONSTANT BOTTOM CONCENTRATION BC'S
$ I J CHC CHD CHG ROC LOC DOC
$ ROP LOP DOP P4D RON LON DON
$ NHX NOX SUU SAA COD DOX TAM FCB
C-----------------------------------------------------------------------------
C43 FIVE TITLE CARDS FOLLOW:
$$ C43 NORTH SOUTH OPEN BOUNDARY $$
$$ CONSTANT SURFACE CONCENTRATION BC'S
$ I J CHC CHD CHG ROC LOC DOC
$ ROP LOP DOP P4D RON LON DON
$ NHX NOX SUU SAA COD DOX TAM FCB
C-----------------------------------------------------------------------------
C44 ONE TITLE CARD FOLLOWS:
$$ C44 constant ICs (g/m^3): TAM(mol/m^3), FCB(MPN/100mL) $$
C
C Definitions:
C ! 1) CHC - cyanobacteria
C ! 2) CHD - diatom algae
C ! 3) CHG - green algae
C ! 4) ROC - refractory particulate organic carbon
C ! 5) LOC - labile particulate organic carbon
C ! 6) DOC - dissolved organic carbon
C ! 7) ROP - refractory particulate organic phosphorus
C ! 8) LOP - labile particulate organic phosphorus
C ! 9) DOP - dissolved organic phosphorus
C ! 10) P4D - total phosphate
C ! 11) RON - refractory particulate organic nitrogen
C ! 12) LON - labile particulate organic nitrogen
C ! 13) DON - dissolved organic nitrogen
C ! 14) NHX - ammonia nitrogen
C ! 15) NOX - nitrate nitrogen
C ! 16) SUU - particulate biogenic silica
C ! 17) SAA - dissolved available silica
C ! 18) COD - chemical oxygen demand
C ! 19) DOX - dissolved oxygen
C ! 20) TAM - total active metal
C ! 21) FCB - fecal coliform bacteria
C ! 22) MAC - macroalgae/periphyton
C Bmin - minimum macroalgae/periphyton biomass
C
C CHC CHD CHG ROC LOC DOC
C ROP LOP DOP P4D RON LON DON
C NHX NOX SUU SAA COD DOX TAM FCB MAC BMin
0.1755 0.3315 0.143 0.1 0.02 0.1
0.001 0.001 0.001 0.003 0.02 0.02 0.02
0.01 0.1 1.5 1.5 1 9 0 0 0 0
C-----------------------------------------------------------------------------
C45 ONE TITLE CARD FOLLOWS:
$$ C45 spatially/temporally constant ALGAL PARAMETERS (/d except Keb in /m) $$
C
C PMc = max. growth rate for cyanobacteria (1/day)
C PMd = max. growth rate for algae diatoms (1/day)
C PMg = max. growth rate for algae greens (1/day)
C PMm = max. growth rate for macroalgae (1/day)
C BMRc = basal metabolism rate for cyanobacteria (1/day)
C BMRd = basal metabolism rate for algae diatoms (1/day)
C BMRg = basal metabolism rate for algae greens (1/day)
C BMRm = basal metabolism rate for macroalgae (1/day)
C PRRc = predation rate on cyanobacteria (1/day)
C PRRd = predation rate on algae diatoms (1/day)
C PRRg = predation rate on algae greens (1/day)
C PRRm = predation rate on macroalgae (1/day)
C Keb = background light extinction coefficient (1/m)
C
C45 PMc PMd PMg PMm BMRc BMRd BMRg BMRm PRRc PRRd PRRg
PRRm Keb
1.40 0.8 0.0 0.0 .002 .025 0.015 0 .03 .05 .02
.0 .9
C-----------------------------------------------------------------------------
C46 ONE TITLE CARD FOLLOWS:
$$ C46 spatially/temporally constant SETTLING VELOCITIES (m/d) $$
248
C46
C WSc = settling velocity for cyanobacteria (m/day)
C WSd = settling velocity for algae diatoms (m/day)
C WSg = settling velocity for algae greens (m/day)
C WSrp = settling velocity for refractory POM (m/day)
C WSlp = settling velocity for labile POM (m/day)
C WSs = settling velocity for particles sorbed to TAM (m/day)
C WSM = settling velocity for macroalgae (m/day = 0.0)
C REAC = reaeration adjustment factor
C
C46 WSc WSd WSg WSrp WSlp WSs WSM REAC
.1 .2 .3 .5 .5 .04 0 1
C-----------------------------------------------------------------------------
C47 ONE TITLE CARD FOLLOWS:
$$ C47 constant benthic flux rates (g/m^2/d) $$
C
C FPO4 = benthic flux rate of phosphate
C FNH4 = benthic flux rate of ammonia nitrogen
C FNO3 = benthic flux rate of nitrite+nitrite nitrogen
C FSAD = benthic flux rate of silica
C FCOD = benthic flux rate of chemical oxygen demand
C SOD = sediment oxygen demand rate
C STEMFAC = SOD temperature factor, use 1.0 to ignore temperature effects
C
C47 FPO4 FNH4 FNO3 FSAD FCOD SOD STEMFAC
0.015 0.10 -0.05 0.0 0.0 -0.5 1
C-----------------------------------------------------------------------------
C48 ONE TITLE CARD FOLLOWS:
$$ C48 Constant WQ LoadingsC
C IWQPS = number of point sources
C NPSTMSR = number of point source mass loading time series
C
C Following the this line and the header enter the I J and NSR (series ID).
C You must also enter then enter a mass flow rate (PSQ, m^3/s) followed by.
C Constant loadings (Conc, mg/l) for each constituents 1-19. TAM (kmol/d), FCB(MPN/100ml)
C Enter a 0.0 to skip constant loadings.
C
C48 IWQPS NPSTMSR
15 0
C I J K N PSQ CHC CHD CHG ROC LOC DOC
C S ROP LOP DOP P4D RON LON DON
C R NHX NOX SUU SAA COD DOX TAM FCB
24 24 0 5 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
16 30 0 6 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
13 35 0 2 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
20 25 0 6 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
17 45 0 6 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
18 44 0 6 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
23 6 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
16 46 0 3 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
21 40 0 4 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
14 25 0 6 0 0 0 0 0 0 0
0 0 0 0 0 0 0
249
0 0 0 0 0 0 0 0
12 17 0 6 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
24 20 0 6 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
17 5 0 6 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
19 20 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
13 6 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
C-----------------------------------------------------------------------------
C49 FOUR TITLE CARDS FOLLOW:
$$ C49 Constant Dry Atmospheric Deposition (g/m2/day; MPN/m2/day)
$ DSQ CHC CHD CHG ROC LOC DOC
$ ROP LOP DOP P4D RON LON DON
$ NHX NOX SUU SAA COD DOX TAM FCB
0 0 0 0 0 0 0
0 0 0 7.565E-05 0 0 0
3.655E-04 1.479E-03 0 0 0 0 0 0
C-----------------------------------------------------------------------------
C50 FOUR TITLE CARDS FOLLOW:
$$ C50 Wet Atmospheric Deposition concentrations (mg/L, TAM-MOLES/L, FCB-MPN/100ml)
$ C HC CHD CHG ROC LOC DOC
$ ROP LOP DOP P4D RON LON DON
$ NHX NOX SUU SAA COD DOX TAM FCB
0 0 0 0 0 0
0 0 0 0.012 0 0 0
0.198 0.087 0 0 0 8 0 0
C-----------------------------------------------------------------------------
C51 ONE TITLE CARD FOLLOWS:
$$ C51 File names for spatially/temporally varying parameters:$$
Restart file for end spatial distribution = WQWCRST.out
File for initial conditions (ICIFN) = wqwcrst.inp
File for algal growth, resp, pred (AGRFN) = NONE
File for settling of algae, POM (STLFN) = NONE
Input file for SUNDAY.INP (HARDWIRED) = NONE
Input file for benthic fluxes (BENFN) = benflux.inp
Input file for point source input (PSLFN) = CWQSRXX.INP
Orig for NPS/atm input (NPLFN)**NOT USED = NONE
Diagnostic file-negative conc. (NCOFN) = wq3dnc.log

End of Model Input File WQ3DWC.INP

250
Appendix 16. Model input file BENFLUX.INP file for base case.

# Time varying benthic fluxes, by Jim Bowen, see benflux.xlsx, Zone 1 is New Hope, Zone 2 is Haw
# PO4 NH4 NOx SAD COD SOD see wqbenmap.xlsx for zones, PO4 = 2.0
# IZ assume min values on 10% of max 11/1-5/1 and max on 8/1
2
1065
1 0.00175 0.00058 -0.073 0.0 0.0 -1.22
2 0.00087 0.00029 -0.036 0.0 0.0 -0.61
1096
1 0.00158 0.00053 -0.066 0.0 0.0 -1.10
2 0.00079 0.00026 -0.033 0.0 0.0 -0.55
1155
1 0.00128 0.00043 -0.053 0.0 0.0 -0.90
2 0.00064 0.00021 -0.027 0.0 0.0 -0.45
1339
1 0.00554 0.00185 -0.231 0.0 0.0 -3.88
2 0.00277 0.00092 -0.115 0.0 0.0 -1.94
1430
1 0.00175 0.00058 -0.073 0.0 0.0 -1.22
2 0.00087 0.00029 -0.036 0.0 0.0 -0.61
1520
1 0.00128 0.00043 -0.053 0.0 0.0 -0.90
2 0.00064 0.00021 -0.027 0.0 0.0 -0.45
1704
1 0.00554 0.00185 -0.231 0.0 0.0 -3.88
2 0.00277 0.00092 -0.115 0.0 0.0 -1.94
1795
1 0.00175 0.00058 -0.073 0.0 0.0 -1.22
2 0.00087 0.00029 -0.036 0.0 0.0 -0.61
1826
1 0.00158 0.00053 -0.066 0.0 0.0 -1.10
2 0.00079 0.00026 -0.033 0.0 0.0 -0.55
1886
1 0.00128 0.00043 -0.053 0.0 0.0 -0.90
2 0.00064 0.00021 -0.027 0.0 0.0 -0.45

End of Model Input File BENFLUX.INP

251
Appendix 17. plot_ts_gui results output file ptsg_results.txt for base case.

ptsg_results.txt from base case run output using plot_ts_gui_v11
-------------------------------------------------------

plot_ts_gui results, run time = 09-Sep-2023 16:55:55
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = Temperature (deg C)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a range of stations
First and last station (for plot range only) = 2 and 19
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Model predictions from time series .OUT files, e.g. temp,salinity,DO,NH4
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_W_DA
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = -0.54
ME_norm =-0.027
RMSE = 1.91
MAE = 1.42
MAE_norm =0.072
RMSE_norm =0.097
r_squared =0.952
num data comparisons = 1075
Nash-Sutcliffe Efficiency = 0.947

-------------------------------------------------------

plot_ts_gui results, run time = 09-Sep-2023 16:56:56
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = Water Surface Elevation (m)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a range of stations
252
First and last station (for plot range only) = 1 and 1
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Model predictions from time series .OUT files, e.g. temp,salinity,DO,NH4
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_W_DA
Using file 1
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = -0.02
ME_norm =-0.000
RMSE = 0.23
MAE = 0.16
MAE_norm =0.002
RMSE_norm =0.003
r_squared =0.957
num data comparisons = 749
Nash-Sutcliffe Efficiency = 0.935

-------------------------------------------------------

plot_ts_gui results, run time = 09-Sep-2023 16:57:33
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = Water Surface Elevation (m)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a range of stations
First and last station (for plot range only) = 1 and 1
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Model predictions from time series .OUT files, e.g. temp,salinity,DO,NH4
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_W_DA
Using file 1
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = -0.02
ME_norm =-0.000
RMSE = 0.23
MAE = 0.16
MAE_norm =0.002
RMSE_norm =0.003
r_squared =0.957
num data comparisons = 749
Nash-Sutcliffe Efficiency = 0.935
-------------------------------------------------------

plot_ts_gui results, run time = 11-Sep-2023 08:19:58
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = Total Chl-a (ug/L)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 20 and 24
Plotting a range of stations
253
First and last station (for plot range only) = 2 and 19
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Water quality model predictions from EFDC plus file EE_WQ.OUt, using GetEFDC utility
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_W_DA
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = -5.37
ME_norm =-0.142
RMSE = 27.22
MAE = 18.85
MAE_norm =0.499
RMSE_norm =0.721
r_squared =0.283
num data comparisons = 584
Nash-Sutcliffe Efficiency = -0.184

-------------------------------------------------------

plot_ts_gui results, run time = 11-Sep-2023 08:43:42
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = Temperature (deg C)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a range of stations
First and last station (for plot range only) = 2 and 19
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Model predictions from time series .OUT files, e.g. temp,salinity,DO,NH4
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_W_DA
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = -0.54
ME_norm =-0.027
RMSE = 1.91
MAE = 1.42
MAE_norm =0.072
RMSE_norm =0.097
r_squared =0.952
num data comparisons = 1075
Nash-Sutcliffe Efficiency = 0.947

-------------------------------------------------------

plot_ts_gui results, run time = 11-Sep-2023 08:44:29
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = Total Chl-a (ug/L)
System = Jordan Lake
254
Number layers = 25, first and second layers plotted = 20 and 24
Plotting a range of stations
First and last station (for plot range only) = 2 and 19
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Water quality model predictions from EFDC plus file EE_WQ.OUt, using GetEFDC utility
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_W_DA
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = -5.37
ME_norm =-0.142
RMSE = 27.22
MAE = 18.85
MAE_norm =0.499
RMSE_norm =0.721
r_squared =0.283
num data comparisons = 584
Nash-Sutcliffe Efficiency = -0.184

-------------------------------------------------------

plot_ts_gui results, run time = 11-Sep-2023 08:46:36
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = NO3+NO2 as N (mg/L)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a range of stations
First and last station (for plot range only) = 2 and 19
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Water quality model predictions from EFDC plus file EE_WQ.OUt, using GetEFDC utility
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_W_DA
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = 0.14
ME_norm =0.577
RMSE = 0.39
MAE = 0.24
MAE_norm =1.002
RMSE_norm =1.622
r_squared =0.115
num data comparisons = 563
Nash-Sutcliffe Efficiency = -1.042

-------------------------------------------------------

plot_ts_gui results, run time = 11-Sep-2023 08:47:30
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = NO3+NO2 as N (mg/L)
255
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a range of stations
First and last station (for plot range only) = 2 and 19
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Water quality model predictions from EFDC plus file EE_WQ.OUt, using GetEFDC utility
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_W_DA
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = 0.14
ME_norm =0.577
RMSE = 0.39
MAE = 0.24
MAE_norm =1.002
RMSE_norm =1.622
r_squared =0.115
num data comparisons = 563
Nash-Sutcliffe Efficiency = -1.042

-------------------------------------------------------

plot_ts_gui results, run time = 11-Sep-2023 08:48:12
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = NO3+NO2 as N (mg/L)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a range of stations
First and last station (for plot range only) = 2 and 19
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Water quality model predictions from EFDC plus file EE_WQ.OUt, using GetEFDC utility
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_W_DA
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = 0.14
ME_norm =0.577
RMSE = 0.39
MAE = 0.24
MAE_norm =1.002
RMSE_norm =1.622
r_squared =0.115
num data comparisons = 563
Nash-Sutcliffe Efficiency = -1.042

-------------------------------------------------------

plot_ts_gui results, run time = 11-Sep-2023 08:48:44
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
256
Constituent = NH4 as N (mg/L)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a range of stations
First and last station (for plot range only) = 2 and 19
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Water quality model predictions from EFDC plus file EE_WQ.OUt, using GetEFDC utility
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_W_DA
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = 0.07
ME_norm =1.646
RMSE = 0.11
MAE = 0.09
MAE_norm =1.972
RMSE_norm =2.387
r_squared =0.009
num data comparisons = 563
Nash-Sutcliffe Efficiency = -3.865

-------------------------------------------------------

plot_ts_gui results, run time = 11-Sep-2023 08:50:56
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = TKN as N (mg/L)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a range of stations
First and last station (for plot range only) = 2 and 19
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Water quality model predictions from EFDC plus file EE_WQ.OUt, using GetEFDC utility
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_W_DA
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = -0.40
ME_norm =-0.461
RMSE = 0.47
MAE = 0.41
MAE_norm =0.475
RMSE_norm =0.540
r_squared =0.026
num data comparisons = 587
Nash-Sutcliffe Efficiency = -4.487

-------------------------------------------------------

plot_ts_gui results, run time = 11-Sep-2023 08:51:42
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
257
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = Total P as P (mg/L)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a range of stations
First and last station (for plot range only) = 2 and 19
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Water quality model predictions from EFDC plus file EE_WQ.OUt, using GetEFDC utility
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_W_DA
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = -0.00
ME_norm =-0.046
RMSE = 0.06
MAE = 0.04
MAE_norm =0.556
RMSE_norm =0.755
r_squared =0.126
num data comparisons = 572
Nash-Sutcliffe Efficiency = -1.379

-------------------------------------------------------

plot_ts_gui results, run time = 11-Sep-2023 09:48:58
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = Diss. Oxygen (mg/L)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a range of stations
First and last station (for plot range only) = 2 and 19
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Water quality model predictions from EFDC plus file EE_WQ.OUt, using GetEFDC utility
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_no_DOsat
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = 0.42
ME_norm =0.054
RMSE = 2.26
MAE = 1.68
MAE_norm =0.214
RMSE_norm =0.287
r_squared =0.581
num data comparisons = 1038
Nash-Sutcliffe Efficiency = 0.550

-------------------------------------------------------

plot_ts_gui results, run time = 11-Sep-2023 09:50:45
258
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = Diss. Oxygen (mg/L)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a range of stations
First and last station (for plot range only) = 2 and 19
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Water quality model predictions from EFDC plus file EE_WQ.OUt, using GetEFDC utility
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_no_DOsat
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = 0.42
ME_norm =0.054
RMSE = 2.26
MAE = 1.68
MAE_norm =0.214
RMSE_norm =0.287
r_squared =0.581
num data comparisons = 1038
Nash-Sutcliffe Efficiency = 0.550

-------------------------------------------------------

plot_ts_gui results, run time = 11-Sep-2023 10:07:34
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = Diss. Oxygen (mg/L)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting all stations
First and last station (for plot range only) = 20 and 20
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Water quality model predictions from EFDC plus file EE_WQ.OUt, using GetEFDC utility
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_no_DOsat
Using file 2
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = 0.42
ME_norm =0.054
RMSE = 2.26
MAE = 1.68
MAE_norm =0.214
RMSE_norm =0.287
r_squared =0.581
num data comparisons = 1038
Nash-Sutcliffe Efficiency = 0.550

-------------------------------------------------------

259
plot_ts_gui results, run time = 13-Sep-2023 03:41:12
Running plot_ts_gui.m on a Mac computer named jb-macmini-m1.lan
plot_ts_gui folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Matlab_Scripts/plot_ts_gui/plot_ts_gui_v11
Constituent = Water Surface Elevation (m)
System = Jordan Lake
Number layers = 25, first and second layers plotted = 2 and 24
Plotting a range of stations
First and last station (for plot range only) = 1 and 1
Station list (for station list only) = 2 3 4 5 6 8 12 13 16 17 18 19
Model predictions from time series .OUT files, e.g. temp,salinity,DO,NH4
Model predictions folder = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/New_Model_Runs/July_2023/final_w_otherhigh_r2/18_
baseANCc_pt167_CPprm1_38_best_final/#output
Observations folder,file 1 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 1 = JordanLakeDamElevations.xlsx, sheet = Data
Observations folder,file 2 = /Users/jdbowen/Dropbox (UNC
Charlotte)/Projects_Research/Jordan_lake_models/Monitoring_Data/CalibrationData/
Data file 2 = JordanWaterQualitySince2014.xlsx, sheet = Data_no_DOsat
Using file 1
Julian day 0 date = 01-Jan-2011
printing cumulative statistics

mean(pred-obs) = -0.02
ME_norm =-0.000
RMSE = 0.23
MAE = 0.16
MAE_norm =0.002
RMSE_norm =0.003
r_squared =0.957
num data comparisons = 749
Nash-Sutcliffe Efficiency = 0.935

End of ptsg_results.txt

260
Appendix 18. Observed vs. Model Predicted Temperature Time Histories at Each Jordan Lake
Monitoring Station for the 2014-2016 model time period without the 15-step time
filter applied to model predictions (layers 2 or bottom and 24).
Figure A18-1. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C
Figure A18-2. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C1.

261
Figure A18-3. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C2.
Figure A18-4. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C3.
262
Figure A18-5. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C4.
Figure A18-6. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C5.
263
Figure A18-7. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055C6.
Figure A18-8. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055D.
264
Figure A18-9. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF055E.
Figure A18-10. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF081A1B.
265
Figure A18-11. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF081A1C.
Figure A18-12. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF086C.
266
Figure A18-13. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF086CUPS.
Figure A18-14. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF086D.
267
Figure A18-15. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF086F.
Figure A18-16. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF087B3.
268
Figure A18-17. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF087D.
Figure A18- 18. Top and bottom (blue and red), observed (symbols) and model predicted (solid lines)
temperature from Jan. 2014 to Feb. 2016 at station CPF0880A.