Final Neuse River Watershed Simulation Plan

respec.com

SIMULATION PLAN FOR THE
NEUSE RIVER WATERSHED MODEL
REPORT RSI-3348

PREPARED BY
Cindie M. Kirby
Paul Hummel
Seth J. Kenner
RESPEC
3824 Jet Drive
Rapid City, South Dakota 57703
PREPARED FOR
North Carolina Department of Environmental Quality
Division of Water Resources
512 N. Salisbury Street
Raleigh, North Carolina 27604
JULY 2023
Project Number W0392.22001.002

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TABLE OF CONTENTS
1.0 INTRODUCTION ............................................................................................................................................................... 1
1.1 PROJECT OVERVIEW ............................................................................................................................................................................ 1
1.2 PROBLEM STATEMENT AND BACKGROUND ................................................................................................................................. 2
2.0 DATA COLLECTION AND DEVELOPMENT ...................................................................................................................... 6
2.1 PRECIPITATION ...................................................................................................................................................................................... 6
2.2 EVAPOTRANSPIRATION AND OTHER METEOROLOGICAL DATA ............................................................................................. 8
2.3 STREAMFLOW DATA ............................................................................................................................................................................. 8
2.4 WATER QUALITY DATA ......................................................................................................................................................................... 10
2.5 POINT SOURCES .................................................................................................................................................................................... 12
2.6 ATMOSPHERIC DEPOSITION .............................................................................................................................................................. 14
2.7 OTHER DATA ........................................................................................................................................................................................... 15
2.7.1 Diversions and Withdrawals ................................................................................................................................................. 15
2.7.2 Irrigation.................................................................................................................................................................................... 17
3.0 SEGMENTATION AND CHARACTERIZATION .................................................................................................................. 18
3.1 DRAINAGE AREAS .................................................................................................................................................................................. 18
3.2 CHANNEL SEGMENTATION AND CHARACTERIZATION .............................................................................................................. 18
3.2.1 Reach Properties and Lake Selection ............................................................................................................................... 18
3.2.2 Numbering Scheme ............................................................................................................................................................... 19
3.2.3 F-Table Development ............................................................................................................................................................ 19
3.2.3.1 Lake F-Tables ............................................................................................................................................................ 19
3.2.3.2 Stream F-Tables ....................................................................................................................................................... 21
3.3 LAND SEGMENTATION ......................................................................................................................................................................... 21
3.3.1 Elevation ................................................................................................................................................................................... 22
3.3.2 Land Use ................................................................................................................................................................................... 22
3.3.2.1 Pervious and Impervious Land Classification ................................................................................................... 22
3.3.2.2 Septic Systems ......................................................................................................................................................... 24
4.0 CALIBRATION AND VALIDATION .................................................................................................................................... 26
4.1 CALIBRATION AND VALIDATION TIME PERIODS........................................................................................................................... 26
4.2 HYDROLOGY CALIBRATION AND VALIDATION PROCEDURES AND COMPARISONS ......................................................... 26
4.3 WATER QUALITY CALIBRATION ......................................................................................................................................................... 30
4.4 SENSITIVITY ANALYSIS ........................................................................................................................................................................ 32
5.0 DATA MANAGEMENT....................................................................................................................................................... 33
6.0 REFERENCES .................................................................................................................................................................... 34

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LIST OF TABLES
Table Page
1-1 Land-Cover Distribution ................................................................................................................................................................................ 4
2-1 U.S. Geological Streamflow Stations and Data Availability During the Model Calibration Time Period
(January 1, 2004–December 31, 2022).................................................................................................................................................... 9
2-2 Pollutants That Will Be Calculated From the Point Sources ................................................................................................................. 13
2-3 Atmospheric Deposition Site Summary..................................................................................................................................................... 15
3-1 General Description of Hydrologic Soil Groups and Makeup of Watershed Areas ........................................................................ 23
4-1 General Calibration and Validation Targets or Tolerances for HSPF Applications ........................................................................ 29
4-2 Flow Calibration Criteria From Expert System for Calibration of HSPF ............................................................................................. 29
4-2 General Calibration Targets or Tolerances for HSPF Applications ..................................................................................................... 32

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LIST OF FIGURES
FIGURE Page
1-1 Neuse River Watershed Model Study Area ............................................................................................................................................... 3
2-1 Example Calibration Figure to Evaluate the Snowfall and Snow Depth Simulation ....................................................................... 7
2-2 U.S. Geological Survey Flow-Monitoring Station Locations ................................................................................................................. 11
2-3 Atmospheric Deposition Monitoring Station Locations ........................................................................................................................ 16
3-1 Land-Use Category Aggregation ................................................................................................................................................................ 23
4-1 Average Annual Precipitation for Modeled Watershed Areas from PRISM for Years 1990 to 2022 ......................................... 26
4-2 R and R2 Value Ranges for Model Performance ...................................................................................................................................... 28

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1.0 INTRODUCTION
1.1 PROJECT OVERVIEW
The Simulation Plan for the Neuse River Watershed Model was developed to meet the requirements of
SL 2020-18 Section 15(c) and calculate nutrient transport factors for the Neuse River Watershed with
the watershed model and Scenario Application Manager. This watershed model will have a nitrogen-
based focus and will also include phosphorus.

The Neuse Nutrient Strategy (https://www.deq.nc.gov/about/divisions/water-resources/water-
planning/nonpoint-source-planning/neuse-nutrient-strategy), implemented in 1997, addresses this
problem by regulating major nutrient pollution sources throughout the Neuse River Watershed,
including wastewater, urban stormwater, and agriculture. The strategy has succeeded partly by
stemming additional nutrient loading during rapid population growth; however, the Neuse River Estuary
remains impaired.

The Neuse Nutrient Strategy is not supported by a calibrated watershed model, a standard tool for the
development and evaluation of more modern nutrient strategies. A watershed model informs regulatory
development and management decisions in many important ways, including by giving users a refined
understanding of the influence of geography or various regulatory sectors on estuarine algal blooms.
This project uses a contractor with oversight and support by the North Carolina Department of
Environmental Quality (NC DEQ) Division of Water Resources (NC DWR) to develop a watershed model
that meets agency standards for regulatory use and support in the Neuse River Watershed. The model
will be a core product the NC DWR staff, stakeholders, and Environmental Management Commission
rely on in their continual refinement of the Neuse Nutrient Strategy rules.

This project will support long-overdue regulatory innovation that can drive systemic water quality
improvements in the Neuse River Estuary. Recreation, property enhancement, recreational and
commercial fishing, and greenways are some of the advantages of nutrient management. Excessive
nutrient inputs can negatively influence the estuarine ecosystem and the communities that benefit from
them. Conversely, these ecosystems and communities realize improvements from managing nutrient
inputs.

A watershed model is a critical scientific tool in structuring regulatory programs to achieve these
broad-based environmental benefits, and several regulatory challenges or initiatives will be
well -informed by the development of the Neuse River Watershed model. The Phase II Total Maximum
Daily Load (TMDL), published in 2001 (https://www.deq.nc.gov/water-quality/planning/bpu/neuse/
neuse-tn-tmdl-ii/download), identified a specific need for watershed modeling. A dynamic watershed
modeling approach is the most efficient means of obtaining detailed information on nonpoint-source
and stormwater nutrient loads across the watershed. The dynamic watershed modeling approach will
also help users better understand the impact of point sources. Directly measuring nutrient loads at the
spatial and temporal scales of the watershed model would be impossible. Simplified watershed yield
models provide annual nutrient loads; however, these models lack the temporal variability of loads,
which is important for understanding episodic events or predicting loads under different climatic
conditions.

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The project team recommends the U.S. Environmental Protection Agency’s (EPA’s) Hydrological
Simulation Program – FORTRAN (HSPF) [Bicknell et al., 2005 as the dynamic watershed model of choice
for the Neuse River Watershed Model. Details of the model selection are presented in the model
selection memorandum [Kenner, 2023]. Hydrologic Simulation Program-Fortran (HSPF) has been widely
used throughout the United States to analyze water hydrology and quality in support of developing
implementation plans based on attaining environmental goals. This complex and dynamic model can
address soil, groundwater and surface-water processes, storm events, and impacts from point and
nonpoint sources of pollution. The primary modeled parameters are nitrogen, phosphorus, suspended
sediments, and streamflow. The model continues to be supported by the EPA and U.S. Geological
Survey (USGS). The Neuse River Watershed Model study area is shown in Figure 1-1.
1.2 PROBLEM STATEMENT AND BACKGROUND
RESPEC Company, LLC (RESPEC) proposes a project approach that will achieve the NC DWR’s project
goals and objectives. The primary goal of this effort is to meet the requirements of Session Law (SL)
2020-18 Section 15(c) by developing nitrogen delivery factors for the Neuse River Watershed. The
project will produce a calibrated watershed model for developing and evaluating modern nutrient
strategies. As directed by SL 2020-18, the priority of this effort is to determine delivery factors for
point-source discharges and nutrient offset credits. RESPEC will use the calibrated and validated
watershed model to rigorously estimate the proportion of end-of-pipe nutrient loading from wastewater
sources that reach the Neuse River Estuary. The delivery factors also play a key role in the availability
and cost of nutrient trades between wastewater facilities and from wastewater facilities to watershed
treatment best management practices; therefore, the watershed model we develop will provide a
rigorous and unbiased approach to estimating relative nutrient contributions from the vast array of
nonpoint nutrient sources throughout the watershed. The current Neuse Nutrient Strategy seeks to
reduce nonpoint-source nutrients from cropland agriculture and new development while providing
important protection by preserving riparian buffers. The watershed model can confirm existing nitrogen
trading schemes and identify other nonpoint nutrient sources and their relative impacts on nutrient
loading to the Neuse River Estuary. Understanding these sources offers opportunities to develop new
trading strategies.

The HSPF model is a comprehensive watershed model of hydrology and water quality that includes
land-surface and subsurface hydrologic and water quality processes that are linked and closely
integrated with corresponding stream and reservoir processes [Donigian et al., 2018]. HSPF is
considered a premier, high-level model among those currently available for comprehensive watershed
assessments and has experienced widespread usage and acceptance since its initial release in 1980,
as demonstrated through hundreds of applications across the United States and abroad. HSPF is jointly
supported and maintained by the EPA and USGS. HSPF is also the primary watershed model in the
EPA BASINS modeling system and has been incorporated into the U.S. Army Corps of Engineers
(USACE) Watershed Modeling System. This widespread usage and support have helped to ensure the
continued code availability and maintenance for more than two decades despite varying federal
priorities and budget restrictions.

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Figure 1-1. Neuse River Watershed Model Study Area.

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The Neuse River Watershed comprises four Hydrologic Unit Code 8 (HUC8) areas: the Upper, Middle,
and Lower Neuse basins and the Contentnea Creek basin (Figure 1-1). These areas total approximately
6,062 square miles. The HSPF model being developed will characterize nutrient input from the
Neuse River below Falls Lake, with a boundary condition established at the lake’s outlet using the USGS
gage at that location (USGS 02087183, NEUSE RIV AT SR 2000 NR FALLS). The drainage area for this
gage is 771 square miles, leaving a total area of 5,291 square miles in the model. Model results will be
generated for this area; however, calibration will only be completed for the areas that are not tidally
influenced. In coordination with NC DEQ, the model’s simulation period has been established as 2003 to
2022, with calibration beginning in 2004 after a warm-up period. Details of the model’s spatial and
temporal scope are provided in the ensuing chapters.

Table 1-1 shows the total area, average slope, and land cover, based on the 2019 National Land-Cover
Database (NLCD) (https://www.mrlc.gov), of the project area (not including Falls Lake). The watershed
has very diverse land use, with approximately one quarter crops, one quarter wetlands, one quarter
forest, and one quarter other categories.
Table 1-1. Land-Cover Distribution
Watershed Neuse River
Area (acres) 3,129,357
Average Slope (%) 2.7
Land Cover of Area (%)
Open Water 1
Developed, Open Space 7
Developed, Low Intensity 5
Developed, Medium Intensity 2
Developed, High Intensity 1
Barren Land 0
Deciduous Forest 2
Evergreen Forest 14
Mixed Forest 6
Shrub/Scrub 2
Herbaceous 3
Hay/Pasture 3
Cultivated Crops 27
Woody Wetlands 24
Emergent Herbaceous Wetlands 3
The scope of work entails the following five major tasks:
/ Compile and Preprocess Data and Information to Support Model Development
/ Develop a Watershed Model of the Neuse River Watershed

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/ Apply Model to Establish Load Estimates
/ Develop Scenario Application Manager (SAM)
/ Deliver Model, SAM, and Documentation
This report presents the simulation plan for developing hydrology and water quality model applications
using HSPF for the Neuse River Watershed. This simulation plan presents the initial planned approach
for constructing and calibrating the model applications with an emphasis on identifying and describing
data requirements, sources, and availability. This plan will be revised after review comments are
received, ongoing discussions with the participating agencies are completed, and the additional data
needed to support the modeling effort are reviewed. This study plan, therefore, will be revised on an
ongoing basis and will ultimately become part of the final report.

The major steps in the model application development process consist of the following:
1. Collecting and developing time-series data
2. Characterizing and segmenting the watershed
3. Calibrating and validating the model
These three steps are discussed in detail in the following sections. This report consists of seven
chapters, including this introduction. Chapter 2.0 describes the collection and development of the
hydrologic, meteorological, and other data needed for the simulations. Chapter 3.0 discusses the other
types of spatial data needed for the segmentation and characterization of the watersheds. The
calibration and validation process for the model is described in Chapter 4.0. Chapter 5.0 describes data
management, and Chapter 6.0 includes references cited herein.

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2.0 DATA COLLECTION AND DEVELOPMENT
Hydrology and water quality simulation within the Neuse River Watershed requires the following types
of time-series data:
/ Precipitation
/ Potential evapotranspiration
/ Other meteorological data (e.g., air temperature, wind, solar radiation, dewpoint, and
cloud cover)
/ Streamflow
/ Water quality observations
/ Point sources
/ Atmospheric deposition
/ Other data (e.g., irrigation, diversions, and withdrawals)
This section discusses the availability, selection, and processing methods of these time-series data for
use in the watershed modeling. The quality assurance/quality control (QA/QC) and data management
procedures are provided in Chapter 5.0. Only meteorological data are required to run the HSPF model;
however, streamflow measurements and water quality observations are used to calibrate and validate
the model. Other data types (e.g., point sources, atmospheric deposition, and diversions) help define
the watershed’s inflow, outflow, and water quality. All the time-series data for the model will be placed
into a Watershed Data Management (WDM) file, which is a binary database format originally developed
to efficiently store large datasets used by HSPF and other models.
2.1 PRECIPITATION
The HSPF model requires complete (i.e., no missing records) precipitation time-series data at an hourly
timestep and with adequate spatial coverage and density across the model domains. Precipitation is
the critical forcing function for all the watershed models because it drives the hydrologic cycle and
provides the foundation for transport mechanisms that move pollutants from the land to the waterbody,
where the pollutant impacts are imposed.

The primary sources of long-term precipitation and other meteorological inputs for these watershed
models include gridded data from the North American Land Data Assimilation System (NLDAS) and
Parameter Elevation Regressions on Independent Slopes Model (PRISM). These data products are
complete and available from 1979 to present (within the last few weeks of the download date). Because
these data are gridded, they allow for easy extraction and aerial averaging over each hydrozone (i.e., an
aggregation of subwatersheds that receive the same meteorological inputs) using scripted processes
while providing efficient and consistent time-series extension.

The NLDAS is a 12-kilometer (km) by 12-km dataset that provides hourly meteorological data. PRISM is
a 4-km by 4-km dataset that provides daily precipitation totals, which are computed by combining a
dense network of station data with radar measurement estimates that are interpolated based on a
climate-elevation regression for each digital elevation model (DEM). Daily PRISM data will be used for

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the modeling because these data provide a finer spatial resolution and generally a better fit to point
precipitation data. The daily values will be disaggregated to an hourly timestep using the NLDAS data.
The hourly NLDAS precipitation will also be loaded into the WDM file to provide another option to test
during calibration. Specific stations are not associated with the gridded meteorological data.
Precipitation data for the modeling period (January 2003 through December 2022) will be downloaded
online from NLDAS (https://ldas.gsfc.nasa.gov/nldas/nldas-get-data) and PRISM
(https://prism.oregonstate.edu/).

The Neuse River Watershed typically gets less than 5 inches of snow per year. Because some snow falls
occasionally, snow will be represented but will likely not be a significant portion of the calibration. Snow
depth (i.e., snow on the ground) data are used to calibrate the snow accumulation and melt processes
when the snow section of the model is active. These data are also used with mean and maximum winter-
air temperatures to assess whether to activate the snow simulation capability within the watershed
model. For the Neuse River Watershed, the snow depth (in inches) and snowfall (in inches) data are
available through the National Climatic Data Center Global Historical Climatology Network stations
[Menne et al., 2022] (ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/). The snow depth data will be used
during the hydrology calibration in multiple locations throughout the project area to ensure that snow
processes are accurately represented. Graphs similar to that shown in Figure 2-1 will be developed for
plotting snowfall, snow depth, and air temperature.

Figure 2-1. Example Calibration Figure to Evaluate the Snowfall and Snow Depth Simulation.
Snowfall and Depth for 398652 PERLND 552
Sno
wfal
l (in)

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2.2 EVAPOTRANSPIRATION AND OTHER METEOROLOGICAL DATA
In addition to precipitation, evaporation data are needed to drive the water-balance calculations in
HSPF. Other meteorological time series are often required in temperate climates where snow
accumulation and melt are significant components of the hydrologic cycle and water balance. These
time series, such as air temperature (ATEM), solar radiation (SOLR), dewpoint temperature (DEWP), wind
speed (WIND), and cloud cover (CLOU), are often required if soil and/or water temperatures are
simulated. Water temperature is subsequently used to adjust rate coefficients in most water quality
processes, and other time series are used in selected calculations (e.g., solar radiation affecting algal
growth).

The NLDAS dataset (https://ldas.gsfc.nasa.gov/nldas/) provides hourly ATEM, SOLR, and WIND
parameters that will be directly applied to the meteorological time series with a conversion to the units
needed for HSPF. The remaining meteorological constituents of CLOU, DEWP, and potential
evapotranspiration (PEVT) are not directly available from the NLDAS dataset and require additional
computations for this model.

CLOU will be estimated for this model by SOLR data provided from the NLDAS database by using a
parabolic equation [Thompson, 1976]. Two options for DEWP will be computed from a series of
calculations that stem from the NLDAS specific humidity. The first option uses the specific humidity and
ATEM to calculate the relative humidity [World Meteorological Organization, 2014]. Relative humidity will
then be applied with ATEM to the August-Roche-Magnus approximation of the Clausius-Clapeyron
equation [Stull, 2017] to calculate DEWP. The second option calculates a mixing ratio using specific
humidity, and that mixing ratio is used with atmospheric pressure to estimate vapor pressure. DEWP is
then calculated using the Clausius-Clapeyron equation [Stull, 2017]. Both options for DEWP will be
assessed during calibration.

Hourly PEVT estimates are included in the NLDAS dataset generated using a modified Penman [1948]
energy-balance method; however, the NLDAS estimates of PEVT are included only for legacy
compatibility with input requirements of the Sacramento Soil Moisture Accounting Model
(http://hydromad.catchment.org/man/sacramento.html). The NLDAS PEVT estimates do not
incorporate subsequent corrections to NLDAS estimates of energy forcing and have been found to
overestimate evapotranspiration (ET) in other modeling efforts. Hourly PEVT will be represented by a
computed Penman pan evaporation based on the Penman [1948] formula and the method of
Kohler et al. [1955]. The necessary variables to compute the Penman pan evaporation are daily SOLR,
DEWP, ATEM, and wind travel. Because two options for DEWP will be calculated, two options for PEVT
will be calculated and assessed during calibration.
2.3 STREAMFLOW DATA
Flow data are needed for calibrating and validating the watershed models to ensure that the hydrologic
behavior of the watersheds, along with the transport of sediment and water quality constituents, is
reproduced. Table 2-1 lists the continuous, observed streamflow data available in the Neuse River
Watershed, with corresponding recording periods and the percent of missing data during the model
calibration period (January 2004 through December 2022). The locations of the flow-monitoring sites

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Table 2-1. U.S. Geological Streamflow Stations and Data Availability During the Model Calibration Time Period
(January 1, 2004–December 31, 2022) (Page 1 of 2)
Station
I.D.
Station
Name
Start
Date
End
Date
Missing
(%)
02084909 SEVENMILE CREEK NR EFLAND, NC 06/24/1981 10/21/2004 95.7
02085000 ENO RIVER AT HILLSBOROUGH, NC 10/01/1927 01/30/2023 0
02085070 ENO RIVER NEAR DURHAM, NC 09/01/1963 01/30/2023 0.5
0208521324 LITTLE RIVER AT SR1461 NEAR ORANGE FACTORY, NC 09/30/1987 01/30/2023 0
0208524090 MOUNTAIN CREEK AT SR1617 NR BAHAMA, NC 10/01/1994 01/30/2023 0.1
0208524975 LITTLE R BL LITTLE R TRIB AT FAIRNTOSH, NC 10/24/1995 01/30/2023 0.1
02085500 FLAT RIVER AT BAHAMA, NC 08/01/1925 01/30/2023 0
02086500 FLAT RIVER AT DAM NEAR BAHAMA, NC 09/01/1927 01/30/2023 0
0208650112 FLAT RIVER TRIB NR WILLARDVILLE, NC 03/01/1988 09/29/2012 54
02086624 KNAP OF REEDS CREEK NEAR BUTNER, NC 10/01/1982 01/30/2023 10.7
0208675010 ELLERBE CREEK AT CLUB BOULEVARD AT DURHAM, NC 08/01/2008 01/30/2023 24.2
02086849 ELLERBE CREEK NEAR GORMAN, NC 10/01/1982 01/30/2023 10.7
02087183 NEUSE RIVER NEAR FALLS, NC 06/26/1970 01/30/2023 0
0208726005 CRABTREE CR AT EBENEZER CHURCH RD NR RALEIGH, NC 12/01/1987 01/30/2023 0
02087275 CRABTREE CREEK AT HWY 70 AT RALEIGH, NC 06/01/1997 01/30/2023 0
02087324 CRABTREE CREEK AT US 1 AT RALEIGH, NC 06/01/1990 01/30/2023 0
0208732534 PIGEON HOUSE CR AT CAMERON VILLAGE AT RALEIGH, NC 08/19/1987 01/30/2023 0
0208732885 MARSH CREEK NEAR NEW HOPE, NC 01/01/1984 01/30/2023 0
02087337 WALNUT CREEK AT BUCK JONES ROAD AT RALEIGH, NC 07/31/2018 01/31/2023 76.7
0208734210 WALNUT CREEK AT TRAILWOOD DRIVE AT RALEIGH, NC 08/08/2018 01/31/2023 76.8
0208734795 WALNUT CREEK AT SOUTH WILMINGTON ST AT RALEIGH NC 08/08/2018 01/31/2023 76.8
0208735012 ROCKY BRANCH BELOW PULLEN ROAD AT RALEIGH, NC 06/26/1992 01/30/2023 0
02087359 WALNUT CREEK AT SUNNYBROOK DRIVE NR RALEIGH, NC 05/01/1996 01/30/2023 0
0208739674 NEUSE R TRIB AT NRWWTP (CMP SITE) NR AUBURN, NC 05/09/2007 05/29/2008 94.4
0208739678 NEUSE R TRIB AT NRWWTP (CENTRAL STE) NR AUBURN, NC 06/07/2007 05/28/2008 94.9
0208741400 NEUSE R TRIB AT NRWWTP (EASTERN STE) NR AUBURN, NC 05/09/2007 05/28/2008 94.4
02087500 NEUSE RIVER NEAR CLAYTON, NC 08/01/1927 01/30/2023 0
02087580 SWIFT CREEK NEAR APEX, NC 03/01/2002 01/30/2023 0.3
0208758850 SWIFT CREEK NEAR MCCULLARS CROSSROADS, NC 12/01/1987 01/30/2023 0
0208762750 UNNAMED TRIB TO SWIFT CR NR YATES MILL POND, NC 05/01/2002 01/05/2011 89.6
0208762755 UNNM TRIB TO SWIFT CR AT NCSU RSRCH UNIT, RALEIGH 10/03/2009 01/05/2011 93.4
0208773375 SWIFT CREEK AT SR1555 NEAR CLAYTON, NC 10/01/2008 01/30/2023 25
02088000 MIDDLE CREEK NEAR CLAYTON, NC 01/01/2004 12/31/2022 0.1
02088383 LITTLE RIVER NEAR ZEBULON, NC 10/01/2008 12/31/2022 25.3
02088500 LITTLE RIVER NEAR PRINCETON, NC 01/01/2004 12/31/2022 0

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Table 2-1. U.S. Geological Survey Stations and Data Availability During the Model Calibration Time Period
(January 1, 2004–December 31, 2022) (Page 2 of 2)
Station
I.D.
Station
Name
Start
Date
End
Date
Missing
(%)
02089000 NEUSE RIVER NEAR GOLDSBORO, NC 01/01/2004 12/31/2022 0
0208925200 BEAR CREEK AT MAYS STORE, NC 01/01/2004 11/29/2015 37.3
02089500 NEUSE RIVER AT KINSTON, NC 01/01/2004 12/31/2022 0
02090380 CONTENTNEA CREEK NEAR LUCAMA, NC 01/01/2004 12/31/2022 0
02090504 CONTENTNEA CREEK NR BLACK CREEK, NC 03/12/2021 12/31/2022 90.5
02091000 NAHUNTA SWAMP NEAR SHINE, NC 01/01/2004 12/31/2022 0
02091500 CONTENTNEA CREEK AT HOOKERTON, NC 01/01/2004 12/31/2022 0
0209173150 UNNAMED TRIB TO SANDY RUN AT SR1335 NR LIZZIE, NC 05/03/2006 02/17/2009 86.7
0209173190 UNNAMED TRIB TO SANDY RUN NEAR LIZZIE, NC 01/01/2004 02/18/2009 73.9
02091736 MIDDLE SWAMP NEAR FARMVILLE, NC 01/01/2004 03/14/2005 93.7
02091814 NEUSE RIVER NEAR FORT BARNWELL, NC 01/01/2004 10/24/2022 1
0209205053 SWIFT CREEK AT HWY 43 NR STREETS FERRY, NC 01/01/2004 06/30/2008 76.5
02092500 TRENT RIVER NEAR TRENTON, NC 01/01/2004 12/31/2022 0
are illustrated in Figure 2-2. As noted in Section 1.2, the Neuse River Watershed Model will be
developed to characterize nutrient inputs for the area below Falls Lake, thus requiring a boundary
condition to be established at the lake’s outlet. To quantify the Falls Lake boundary condition,
streamflow and water quality data were downloaded from USGS gage 02087183 (NEUSE RIV AT SR
2000 NR FALLS).

Flow data were downloaded from the USGS National Water Information System (NWIS)
(https://waterdata.usgs.gov/nwis). All continuous streamflow data in the watershed will be included in
the calibration; however, noncontinuous streamflow data are not as valuable for calibration purposes.
As part of the model calibrations, the data will be plotted with a simulated flow.
2.4 WATER QUALITY DATA
Water quality data are used primarily for model calibration and validation and to help quantify source
contributions and boundary conditions. The specific constituents to be modeled in this study include all
of the constituents needed for modeling nutrients. The following conventional constituents are
modeled whenever nutrients are the purpose of a modeling effort:
/ Total suspended solids (TSS)
/ Water temperature
/ Dissolved oxygen (DO)
/ Carbonaceous biochemical oxygen demand ultimate (CBODu) (i.e., total CBOD)
/ Nitrite-nitrate (NO2/NO3)

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Figure 2-2. U.S. Geological Survey Flow-Monitoring Station Locations.

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/ Total ammonia (NH3/NH4)
/ Total Kjeldahl Nitrogen (TKN)
/ Total nitrogen (TN)
/ Orthophosphate (PO4)
/ Total phosphorus (TP)
/ Phytoplankton as chlorophyll a
/ Benthic chlorophyll a
Water quality data were collected from two sources:
/ USGS NWIS
/ NC DWR
USGS NWIS water quality data were downloaded from the National Water Quality Monitoring Council
Water Quality Portal (https://www.waterqualitydata.us/). NC DEQ provided data through 2021 from the
North Carolina Ambient Monitoring System and Coalition Monitoring Program. These data will play a
key role in the water quality calibration.

For boundary condition, the Neuse River Near Falls, NC (USGS 02087183) gage has ample flow, and the
NC DEQ water quality site Neuse Riv at SR 2000 nr Falls (J1890000) has ample water quality data to
develop boundary condition loads with the FLUX software (https://water.usgs.gov/software/loadest/)
for most parameters. Water quality parameters available at the J1890000 station include total
ammonia, nitrate/nitrite, total Kjeldahl nitrogen, total phosphorus, total suspended solids, water
temperature, and dissolved oxygen. Ratios of parameters throughout the watershed may be used for
parameters not measured at the model boundary.

Water quality data sources include the following:
/ USGS from Water Quality Portal (https://www.waterqualitydata.us/)
/ NC DEQ-provided water quality data
2.5 POINT SOURCES
Point-source data throughout the Neuse River Watershed were provided by the North Carolina
Department of Environment and Natural Resources (NC DWR). Discharging point sources with
applicable data will be represented in the HSPF model applications. Applicable parameters available at
the facilities include flow, 5-day biochemical oxygen demand (BOD5), ammonia nitrogen, nitrate and
nitrite nitrogen, Kjeldahl nitrogen, total phosphorous, total organic phosphorus, total suspended
sediment, dissolved oxygen, and temperature.

The data, which were provided as daily data with values available approximately once a month
(sometimes more or less), will be used to develop a filled daily time series following a set of rules and
assumptions. Sites will be assumed as discharging every day of every month unless otherwise noted.

Months with missing data can be filled in using the average of similar months (e.g., if January 2021 is
missing data, the average of all of the other January data will be used to fill the month). Dates were

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provided that represent when a permit was rescinded or operation ceased. In those instances, no flow
will be represented after the date provided. It will be assumed that sites were operating before data
were available because of the light nature of historical point-source data.

Applicable parameters for the discharging facilities generally include CBOD5, ammonia nitrogen,
Kjeldahl nitrogen, nitrate-nitrite nitrogen, DO, TP, TSS, and temperature. HSPF requires more input
parameters than facilities typically provide or sample. A complete list of the required HSPF parameters
is provided in Table 2-2.
Table 2-2. Pollutants That Will Be Calculated From the Point Sources
Pollutant
Name
Pollutant
Description
Daily Model
Input Units
Flow Effluent Flow Acre-Foot
Heat Heat Energy of the Effluent BTU
TSS Total Suspended Solids Tons
DO Dissolved Oxygen Pounds
NO3-N Nitrate as Nitrogen Pounds
NO2-N Nitrite as Nitrogen Pounds
NH4-N Total Ammonia as Nitrogen Pounds
ORN Refractory Organic Nitrogen Pounds
PO4-P Orthophosphorus as Phosphorus Pounds
ORP Refractory Organic Phosphorus Pounds
CBODu Ultimate Carbonaceous Organic Demand Pounds
ORC Organic Carbon Pounds
BTU = British thermal unit
When facilities do not sample or report all the parameters listed, a dataset could be derived using a
surrogate facility estimated with nutrient speciation factors or by setting a constant concentration,
depending on the missing constituent. The assumptions that will be used for estimating missing
parameters (provided in the following paragraph) have been applied to more than 50 HPSF model
applications spanning several states and have been widely accepted by modelers, watershed
managers, and point-source permitters.

If dissolved oxygen or 5-day biochemical oxygen demand data are missing, concentrations of 8 and
1 milligrams per liter (mg/L) will be assumed, respectively. If nitrate-nitrite data are missing, a combined
concentration of 2 mg/L will be assumed for non-wastewater facilities, and a combined concentration
of 7 mg/L will be assumed for wastewater facilities. The combined nitrate-nitrite concentration will be
partitioned into nitrate and nitrite based on other facilities with similar available data. If ammonia data
are missing, a concentration of 2 mg/L will be assumed. Facilities without total phosphorus data will be
assumed to have a total phosphorus concentration of 0.1 to 0.8 mg/L, depending on if the
orthophosphate calculations result in a negative value; and 60 to 75 percent of the total phosphorus will
be assumed as orthophosphate. Total phosphorus that is associated with biochemical oxygen demand

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in HSPF is 0.7 percent, and the remainder of the total phosphorus that is not orthophosphate or
associated with biochemical oxygen demand will be assumed as organic. Similarly, total nitrogen that is
associated with biochemical oxygen demand in HSPF is 4.3 percent, and total nitrogen that is not
nitrate, nitrite, or ammonia will be assumed as organic nitrogen. If total suspended solids data are
missing, a concentration of 1 mg/L will be assumed, and all the total suspended solids concentrations
will be split into 40 percent silt and 60 percent clay at each facility. Organic carbon will be assumed as
13 percent of the biochemical oxygen demand concentration.

Besides temperature, concentrations of all the available constituents, including ultimate biochemical
oxygen demand that will be converted from 5-day biochemical oxygen by using Equation 2-1 [Chapra,
1997], will be converted from mg/L to loads in pounds per day (lb/day) (i.e., concentration × flow ×
conversion factor; conversion factor = 8.34). Temperature will be converted from degrees Fahrenheit
(°F) to a heat load in BTUs per day (i.e., temperature × flow × conversion factor; conversion factor =
8,339,145).
−=−1
5
(5)1o k
yLe (2-1)
where:

=
=
=
u
55
1
CBOD
CBOD
0.10(minimum value after primary treatment)
oL
y
k

Estimated daily time series will be imported into a WDM file, and loads will be applied to the
corresponding stream in the external sources block of the user control input (UCI) file.
2.6 ATMOSPHERIC DEPOSITION
Atmospheric deposition of nutrients is commonly included in watershed modeling efforts that focus on
eutrophication issues. Nitrate and ammonium atmospheric depositions will be explicitly represented as
a daily time series in the HSPF model applications. Wet atmospheric deposition data were downloaded
from the National Atmospheric Deposition Program (NADP) (http://nadp.slh.wisc.edu/), and dry
atmospheric deposition data will be downloaded from the EPA’s Clean Air Status and Trends Network
(CASTNet) (https://www.epa.gov/castnet/). The nearest sites and corresponding recording periods are
summarized in Table 2-3 and the locations are shown in Figure 2-3. Wet and dry atmospheric
depositions will be applied evenly and directly to the waterbodies and land throughout the watersheds.

The original dry deposition data are supplied at a weekly timestep as a particulate flux kilogram per
hectare (kg/ha). The weekly data will be divided by seven to transform the data into a daily time series.
The wet deposition is also supplied at a weekly timestep, but, in rare cases, sampling periods range
from 1 to 8 days. Wet deposition data will not need to be divided by the number of days in the sampling
period because wet deposition units are in concentration (mg/L) form. The concentration will instead be
assigned to each day of the sampling period. In the model, the wet deposition data are multiplied by the
precipitation amount to calculate the nutrient load. After being transformed to daily time-series data,
the missing dry and wet deposition data will be filled in using interpolation when fewer than 14 missing

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days have occurred between samples and by using monthly mean values when more than 14
missing days have occurred between values. The data will be converted to elemental concentrations
and fluxes using multiplication factors from the UCI file (i.e., data are still nitrate and ammonia (not as N).
The multiplication factors are used to convert the filled data into the units required by HSPF. The
nitrogen deposition is applied as a time series to each segment, and the wash-off rates are mainly
driven by precipitation intensity and calibration parameters.
Table 2-3. Atmospheric Deposition Site Summary
Site
I.D. Name State Type Start
Date
End
Date
NC03 Lewiston NC Wet 10/31/1978 Present
NC41 Finley Farm NC Wet 10/3/1978 Present
NC35 Clinton Crops Research Station NC Wet 10/24/1978 Present
NC29 Hofmann Forest NC Wet 7/2/2002 Present
NC06 Beaufort NC Wet 1/26/1999 Present
BFT142 Beaufort NC Dry 12/28/1993 Present
CND125 Candor NC Dry 9/20/1990 Present
Continuous wet and dry atmospheric phosphorus deposition data are not monitored through the
NADP or CASTNet. Because of the lack of temporal data, an annual average value of total phosphorus
deposition obtained from regional studies will be dispersed using the MONTH-DATA block in HSPF.
Values of total phosphorus atmospheric deposition fluxes from the Chesapeake Bay area will be used
for the model and range from 0.037 kilogram per hectare per year (kg/ha/yr) to 0.082 kg/ha/yr [Yang et
al., 1996; Hu et al., 1998; Koelliker et al., 2004]. A midpoint value of 0.060 kg/ha/yr will initially be set, with
higher values occurring in the summer and lower values occurring in the winter [Yang et al., 1996]. The
total flux and monthly distribution may be adjusted as part of the calibration process.
2.7 OTHER DATA
Additionally, ideal items to represent in the model application include groundwater and surface-water
withdrawals, irrigation, and diversions information; these items would be represented using time-series
data. If available, time-series data and/or estimates will be provided and processed to be included in the
model. If time-series data are not available at a subwatershed or smaller level, estimations can be
derived as described in the following sections.
2.7.1 Diversions and Withdrawals
Time-series data for the surface-water and groundwater withdrawals were supplied by NC DWR. These
values are available on a monthly basis and will be applied as a monthly value to represent stream
diversion and withdrawals. Total surface-water withdrawals will be assigned to the reach they occur in
and will be removed from the flow in each applicable reach. Individual withdrawals may be reduced as a
part of the calibration process. Simulated withdrawals were also provided from the Oasis Model.
Withdrawal data from NC DWR will be prioritized over Oasis simulated values.

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Figure 2-3. Atmospheric Deposition Monitoring Station Locations.

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2.7.2 Irrigation
Irrigation in the Neuse River Watershed occurs on cropland, golf courses, and turf. Surface water and
groundwater withdrawals for agricultural uses were supplied by NC DWR. As appropriate, these
withdrawals can be applied to cropland in the hydrozone as precipitation if needed.

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3.0 SEGMENTATION AND CHARACTERIZATION
This section describes the methods proposed for the development of subwatershed, reach, and
land-cover segments for the Neuse River HSPF model application. The segmentation and
characterization define water travel from the various land uses within each subwatershed to each
reach segment.
3.1 DRAINAGE AREAS
Appropriate resolution for the subwatershed areas will be defined by the needs of North Carolina.
Subwatersheds will be small enough to represent impaired reaches and lakes, as well as monitoring
locations. The National Hydrography Dataset Plus Version 2 (NHDPlusV2) layers will be used to
delineate watersheds where needed (https://www.nhdplus.com/NHDPlus/NHDPlusV2_data.php). Breaks
will be made at HUC12 outlets, impairment outlets (impaired constituents), and monitoring locations. In
most cases, significant point sources should be upstream of major monitoring and calibration locations
within a subwatershed. The NHDPlusV2 dataset is a national, geospatial, surface-water framework that
includes elevation, flow accumulation, and flow-direction grids. To delineate areas, batch points will be
created in GIS at the desired breakpoints, and the ArcPro Watershed tools will be used with the
NHDPlusV2.
3.2 CHANNEL SEGMENTATION AND CHARACTERIZATION
The river channel network is the major pathway by which sediment and contaminants are transported
from the watershed to each waterway; therefore, accurate representation or characterization of the
channel system in the watershed for the model application is important. The river-reach segmentation
considers river travel time, riverbed slope continuity, cross-section and morphologic changes, entry
points of major tributaries, sampling locations, and impairment status.

The channel characteristics are needed to define routing and stage-discharge behavior; bed
composition for sediment, carbon, and nutrients; and bed/water-column interactions related to
temperature, benthic oxygen demand, nutrient fluxes, and benthic algal mass. Because channel
characteristics need to be defined spatially throughout the stream system, information from as many
sites as possible will be used to define channel characteristics.
3.2.1 REACH PROPERTIES AND LAKE SELECTION
The NHDPlus High Resolution flowline layer ((https://www.usgs.gov/core-science-systems/ngp/
national-hydrography/nhdplus-high-resolution) will be used to create the primary reach network. The
primary reach layer was edited as needed by using the DEM and an imagery basemap. The North
Carolina assessed streams and lakes (NC DWR Server – https://services2.arcgis.com/
kCu40SDxsCGcuUWO/ArcGIS/rest/services) will also be used as needed. The lakes selected to be
explicitly modeled will be chosen based on impairment status, lake size, data availability, and location in
the watershed. To be explicitly modeled, a lake was either impaired for a modeled parameter or was
greater than 200 acres and connected to a modeled reach.

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Reach length and slope are required to determine the physically based parameters in the model
application and develop function tables (F-tables). These values will be calculated using ArcGIS for all
non-lake reaches. Lakes that are modeled explicitly will be assumed to have an outflow; however, this
assumption can be easily changed during calibration if any of the modeled lakes are determined as
landlocked. Slope was derived from the USGS 10-meter (m) by 10-m, three-dimensional (3D) Elevation
Program grid.
3.2.2 NUMBERING SCHEME
This section describes the numbering scheme that will be used for the watershed drainage network.
Reach identifications (I.D.s) are limited to one to three numerical digits in HSPF. Main-stem reaches
occur along the main stem of each HUC8 watershed and will be given a reach I.D. that ends in zero
(i.e., 0). These reaches will be assigned an odd-tens digit (i.e., middle number) if they represent a stream
segment (e.g., 110, 130, 150, and 190 in the schematic) and an even-tens digit if they represent a lake
(e.g., 120 and 160 in the schematic). Tributaries will be assigned an odd reach I.D. for the ones digit (i.e.,
end number) if they represent a reach (e.g., 141, 143, and 153 in the schematic) and an even number if
they represent a reservoir (e.g., 142 in the schematic). The tens digit of the tributary reach I.D.s will
correspond with the downstream, main-stem reach I.D. (e.g., 111 and 113 flow into 120). Reach I.D.s for
subwatersheds and reaches will be numbered in order beginning with lower numbers upstream and
ending with higher numbers downstream. If the next logical downstream, main-stem reach I.D. is not
used, the downstream reach will be given the next largest main-stem reach I.D. For example, if a reach
downstream of a main-stem reach with a reach I.D. of 170 and five tributary reaches (i.e., 171, 173, 175,
179, and 181) flow into the next downstream, main-stem reach, then that next main-stem reach would
need to haven a reach I.D. of 190. Each subwatershed will typically only contain one waterbody (i.e.,
reach or lake) and will be given the corresponding reach I.D.
3.2.3 F-TABLE DEVELOPMENT
This section describes the development of F-tables, which are required by the HSPF model to route
water through each modeled reach (i.e., lake or stream). An F-table summarizes the hydraulic and
geometric properties of a reach and is used to specify functional relationships among surface area,
volume, and discharge at a given depth.
3.2.3.1 LAKE F-TABLES
Data for lake F-table calculations include surface area and volume at various water elevations (depths)
and overflow information. When available, surface area, volume, depth, spillway length, height above sill,
and lake runout elevation data will be used for F-table development. Dam information was from the
National Inventory of Dams (https://nid.sec.usace.army.mil/ords/f?p=105:1). Because these data are
often unavailable, the F-tables will be based on the average values where data are missing, which is
sufficient for the purposes of this model. If additional data become available, the data will be
incorporated into the existing model application. The equations that will be used to calculate flows from
lakes at different water elevations, as well as any assumptions made, are discussed in this section. For
simplicity and because of the lack of overflow data, the equation of discharge for overflow spillways will
be used to calculate discharge from lakes (Equation 3-1). Because of the project scale, coefficient
correction factors for overflow calculations will not be used, and side contractions of the overflow and
approach velocity have been disregarded, which allows for using the equation in its simplest form.

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=1.5
eQ C L H (3-1)
where:

= Discharge (cubic feet per second[cfs])
= Water depth above weir (head, feet [ft])
= Effective length of crest (ft)
= Variable coefficient of discharge.
e
Q
H
L
C

The total head ()H used in the equation will be calculated at variable water levels as the difference
between the water-surface and outlet elevations. The outlet will be assumed at the maximum recorded
depth (if available) or maximum contour depth. An effective length of the crest ()eL can be derived from
a spillway length. When a spillway length is not available, the mean length of all of the available sites will
be assumed. At lake depths below the outlet, eL will be set equal to the spillway length. At lake depths
above the outlet, eL varies as a function of depth and will be increased assuming a 0.02 floodplain slope
at each end of the crest. The variable coefficient of discharge ()C will be calculated using an empirical
relationship derived by plotting x-y points along a basic-discharge coefficient curve for a vertical-faced
section with atmospheric pressure on the crest from the U.S. Bureau of Reclamation [1987]
(Equation 3-2):
=  +
0.1528 3.8327
d
PC In H (3-2)
where:
= Crest height (ft)
= Head (ft).
P
H

The crest height ()P will be assumed as the height above the sill (if available). The head ()H will vary
with the water surface and will be calculated as described in the previous paragraph. When the height
above the sill is unavailable, the mean value from all of the available sites will be assumed.

After the available data are collected and combined, an F-table will be developed for each lake by
calculating the surface area, volume, and discharge over a range of depths. F-tables for lakes will be
developed using the calculated surface area, volume, and depth relations. For these lakes, the volume
and surface area at incremental depths will be estimated using conical geometry and assuming a flat
bottom for an inner circle with half of the radius of the maximum surface area. The highest contour (if
available) or maximum depth will be assumed as the outlet. Depths will be added incrementally above
the outlet until the F-table discharge exceeds the maximum observed discharge levels. The surface
area and volume above the outlet will be calculated using conical geometry with an initial floodplain
slope of 0.01. The discharge at each height above the outlet will be calculated using Equations 3-1
and 3-2. The discharge values of depths at or below the outlet will be zero. The initial value of the
floodplain slope is arbitrary and can easily be adjusted during the calibration process.

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3.2.3.2 STREAM F-TABLES
Data requirements for stream F-table development include cross-section and discharge
measurements. Cross-section measurements have been obtained from the North Carolina Flood Risk
Information System HEC-RAS models (https://fris.nc.gov/fris/Home.aspx?ST=NC), where available;
m-ain-stem reaches for which HEC-RAS crosssection data are unavailable will be assigned a
representative cross section using best engineering judgment. Representative main-stem cross
sections will be assigned based on the nearest available downstream main-stem cross section because
a cross-section area generally increases from upstream to downstream. Tributary reaches for which
cross-section data are unavailable will be assigned a representative tributary cross section based on
the proximity to an available cross section and similar drainage area. After each reach is assigned the
most appropriate cross section based on the location and drainage area, the discharge will be
calculated for each reach by using length, slope, and cross-section data with the Manning’s equation
shown in Equation 3-3. The channel slope (S ) for each reach will be calculated by dividing the difference
between the maximum and minimum elevations by the reach length.
=   
2
2
3
11.486 Q A R Sn (3-3)
where:
2
= Discharge (cfs)
= Manning’s roughness coefficient
= Cross-section area (squared feet [ft ])
= Hydraulic radius (ft)
= Channel slope
Q
n
A
R
S

Manning’s roughness coefficients ()n have also been obtained from the North Carolina Flood Risk
Information System. The values for the floodplain slope, channel slope, Manning’s roughness
coefficient, and horizontal bank extension length will be set based on local topography and by using
best engineering judgment, and the values can easily be adjusted during the calibration process. An
F-table will be developed for each reach by calculating the surface area, volume, and discharge over a
range of depths. The cross section can be extended 1,000 feet horizontally beyond each bank to allow
the F-table to handle large storm flows. The floodplain slope will be assumed as 0.05. The volume and
surface area will be calculated with the cross sections and stream segment lengths. The data used to
calculate the elevation and slope for the model include the USGS 3D Elevation Program
(https://www.usgs.gov/core-science-systems/ngp/3dep).
3.3 LAND SEGMENTATION
Land-use, or land-cover, data is a critical factor in modeling watersheds because these data provide the
detailed characterization of the potential pollutant sources entering the reaches as nonpoint-source
contributions. Land-use distribution also has a major determining impact on the hydrologic response of
the watershed.

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This section describes how the proposed Pervious Land Segment (PERLND) and Impervious Land
Segment (IMPLND) module-use categories were selected for explicit representation in the model
application. The PERLND and IMPLND blocks of the UCI file contain most of the parameters that
describe the way that water flows over and through the watershed. The objective of this task, therefore,
will be to separate the watershed into unique land segments by using physical watershed
characteristics to effectively represent the variability of hydrologic and water quality responses in the
watershed. The primary watershed characteristics selected for the PERLND and IMPLND categorization
include drainage patterns, meteorological variability, land cover, and soil properties. The municipal
separate storm sewer system (MS4) areas will also be represented because of their link to permitting
and water management. The NC DEQ has provided links to use for the MS4 areas in North Carolina.
Final model land-cover characteristics will be selected based on the significance of their influence on
the hydrologic processes and water quality constituents of interest, as well as the quality and
availability of spatial data associated with the characteristics.
3.3.1 ELEVATION
Topography provides elevation and slope values for the project area that are important to setting up
HSPF because these values are needed for characterizing the landscape and land areas of the
watershed. The flow accumulation and direction derived from elevation raster data are used to
delineate subwatersheds. Average elevations and slopes are also calculated for each model
subwatershed.

The delineated subwatershed models are linked to the pervious or impervious lands that drain to the
subwatersheds in the schematic block of the UCI file. Aggregating the subwatersheds into hydrozones
based on meteorological variability will provide initial boundaries for the PERLNDs and IMPLNDs and
allow for accurately representing the hydrologic processes while reducing computational demands.
The procedures for determining the PERLND and IMPLND categories within each hydrozone are
described in the following paragraphs. The 3D Elevation Program from the USGS has 10-m by 10-m
elevation data across the United States available for download at (https://www.usgs.gov/core-science-
systems/ngp/3dep). These 3D elevation data will be used to calculate the slope information for this
model application.
3.3.2 LAND USE
The NLCD 2019 land-cover layer (https://www.mrlc.gov) will be the primary layer used for the model
land cover. Land covers will be aggregated/reclassified into a set of model land covers that will be used
to develop the PERLND and IMPLND classifications within each subwatershed and hydrozone.
Aggregated land-cover categories will be used to define the movement of water through the system
(i.e., infiltration, surface runoff, and water losses from evaporation or transpiration) that is significantly
affected by the land cover and its associated characteristics.
3.3.2.1 PERVIOUS AND IMPERVIOUS LAND CLASSIFICATION
The number of operations (e.g., PERLND, IMPLND, RCHRES, PLTGEN, and COPY) allowed in one HSPF
model application is limited; therefore, the categories represented in each state land-cover layer will be
aggregated into relatively homogeneous model categories. Cropland will be separated using the
National Agricultural Statistics Services Crop Data Layer (2021), as well as tile drainage status. Tile
drainage status will be estimated as cropland with a slope of less than 4 percent calculated using USGS

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3D elevation data (https://www.usgs.gov/core-science-systems/ngp/3dep) and hydrologic soil group
(HSG) of A/D, B/D, C/D, or D from the Soil Survey Geographic Database (SSURGO,
https://www.nrcs.usda.gov) Natural Resources Conservation Service (NRCS) Dataset [2022]. Figure 3-1
shows the general reclassification schemes for converting the land-cover classes to the model land-
cover classes, and HSG categories are described in Table 3-1. HSG distributions by subwatershed can
also be used as a basis for model parameterization related to infiltration and soil-moisture capacity
values in the models, and the erodibility factor for each PERLND can be used to parameterize the
erodibility factor of soils in the watershed. HSG percentages in each Neuse River Watershed project
area are shown in Table 3-1.

Figure 3-1. Land-Use Category Aggregation.
Table 3-1. General Description of Hydrologic Soil Groups and Makeup of Watershed Areas
Hydrologic
Soil Group
Abbreviated
Description % Area
A Sand; sandy loams with high-infiltration rates; well-drained soils with high transmission 20
B Silt loam or loam soils with moderate infiltration; moderately drained 14
C Sandy, clay loams; low-infiltration rates that impede water transmission 10
D Heavy soils, clay loams, silty, clay; low-infiltration rates that impede water transmission 8
AD A-Group soil, if drained 18
BD B-Group soil, if drained 20
CD C-Group soil, if drained 8
Unclassified No classification determined 3

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Feedlots located in the project area were identified using a layer downloaded from the NC DEQ Feature
Server (https://services2.arcgis.com/kCu40SDxsCGcuUWO/ArcGIS/rest/services/Animal_Feed_
Operation_Permits_(View)/FeatureServer/0). Because this layer did not include poultry, poultry numbers
were estimated by county with Census of Agriculture data. The feedlot data will be used to estimate
fertilizer application to inform the calibration process throughout the watershed. Manure from feedlots
will be assumed to have spread into the subwatersheds in which the feedlots are located within and
adjacent to.

The effective impervious area (EIA) is important to accurately represent in watershed models because
of the EIA’s impact on the hydrologic processes that occur in urban environments. The term “effective”
implies that the impervious region is directly connected to a local hydraulic conveyance system
(e.g., gutter, curb drain, storm sewer, open channel, or river) and the resulting overland flow will not run
onto pervious areas and, therefore, will not have the opportunity to infiltrate along the respective
overland flow path before reaching a stream or waterbody. The average percent impervious on each
developed model category will be calculated using the NLCD 2019 impervious layer. The data represent
the total impervious area percentage, which will be used to determine the percent EIA by using
Equation 3-4 from Sutherland [2000] to represent areas that are mostly storm sewered, with curb and
gutter, and with residential rooftops connected to the MS4.
()=1.5EIA 0.1 TIA ,TIA 1 (3-4)
Polluted stormwater runoff is commonly transported through MS4s before being discharged into local
waterbodies. Certain MS4s are required to obtain National Pollutant Discharge Elimination System
(NPDES) permits and develop stormwater management programs that describe stormwater control
practices that will be implemented following permit requirements to minimize the discharge of
pollutants from the storm sewer system [NPDES, 2023]. Representing regulated MS4s in the watershed
in the HSPF model applications is important. GIS layers of the MS4 areas (i.e., polygons) were
downloaded from the North Carolina Department of Transportation (NCDOT)
(https://www.nconemap.gov/datasets/NCDOT::ncdot-city-boundaries/about) and NC DEQ
(https://ncdenr.maps.arcgis.com/home/item.html?id=968f0ac608e44662bdb9a2d56350e605). MS4
areas will be represented in the model application schematic by using a separate mass link so that flow
from those areas can be identified as separate from flow that originates in non-MS4 areas.
3.3.2.2 SEPTIC SYSTEMS
A septic system falls under the category of on-site wastewater treatment systems (OWTS). OWTS are
used by many households in the Neuse River Watershed. North Carolina has polygons that represent
areas that are sewered (https://services.nconemap.gov/secure/rest/services/
NC1Map_Water_Sewer_2004/MapServer/2). Blockpop points that fall outside of the sewered areas
provide the populations from the 2010 United States Census (https://www.census.gov/programs-
surveys/decennial-census/data.html) that will be assumed to be on septic systems. OWTS are generally
responsible for some pollutant loads to either the groundwater or tributaries. OWTS will be represented
in the model application as a constant load and assumed to discharge at 50 gallons per person per day.
The loading rates will initially be set at 40.4, 10.58, and 2.5 pounds per person per day for 5-day
biochemical oxygen demand, total nitrogen, and total phosphorus, respectively [EPA,1980 and 1993].
The 5-day biochemical oxygen demand loads will be converted to carbonaceous biochemical oxygen

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demand by using a factor of 1.2 for untreated waste [Thomann and Mueller, 1987]. Initial attenuation
(i.e., pass-through) factors that represent septic-system efficiency will be set at 0.60, 0.77, and 0.14 for
5-day biochemical oxygen demand, total nitrogen, and total phosphorus, respectively [EPA,1980 and
1993; Vaudrey et al., 2016]. Soil attenuation will be represented as a function of simulated groundwater
flow with less pass-through of pollutant loads occurring at lower flows, assuming more soil residence
time results in greater pollutant degradation and transformation (e.g., denitrification). Initial failure rates
and loading estimates will be source from the Chesapeake Assessment Scenario Tool (CAST;
https://cast.chesapeakebay.net/About) from the southernmost Virginia area.

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4.0 CALIBRATION AND VALIDATION
4.1 CALIBRATION AND VALIDATION TIME PERIODS
Time-period selection for model calibration and validation depends on numerous factors, including the
availability of data for model operations, land-use data for model setup, climate variability, and
observed data for model-data comparisons. The principal time-series data that are needed for
hydrologic and water quality calibration (i.e., meteorological, point-source, atmospheric deposition,
observed flow, and water quality observations) indicate that a long-term simulation is possible at
numerous streamflow gages throughout the Neuse River Watershed. Partial record periods, while not
ideal, can still be used for consistency checks as part of the calibration and validation process.

The date ranges for the calibration and validation periods include mixed wet and dry periods, as shown
in Figure 4-1. Based on this consideration, the overall modeling period for hydrology and water quality is
from 2003–2022, with 2003 being a “warm up” period and calibration beginning in 2004. Validation will
likely be performed on the five most wet years and the five most dry years.

Figure 4-1. Average Annual Precipitation for Modeled Watershed Areas from PRISM for Years 1990 to 2022.
4.2 HYDROLOGY CALIBRATION AND VALIDATION PROCEDURES AND COMPARISONS
The Neuse River model application will be calibrated through an iterative process of making parameter
changes, running the model, producing comparisons of simulated and observed values, and
interpreting the results. This process will first occur for the hydrology portions of the model, followed by
the water quality portions. The procedures have been well established during the past 35 years, as
described in the application guide for HSPF [Donigian et al., 1984] and summarized by Donigian [2002].
The hydrology calibration process is greatly facilitated by using scripted processes in MATLAB.

Calibrating HSPF to represent the hydrology of the Neuse River Watershed is an iterative trial-and-error
process. Simulated results are compared with recorded data for the entire calibration period, including
wet and dry conditions, to observe how well the simulation represents the hydrologic response under
various climatic conditions. By iteratively adjusting specific calibration-parameter values within
Driest
Wettest Modeling Period

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accepted and physically based ranges, the simulation results are changed until an acceptable
comparison of simulation and recorded data is achieved [EPA, 2000].

The standard HSPF hydrologic calibration is divided into four phases:
1. Establish an annual water balance. This phase consists of comparing the total annual simulated
and observed flows (in inches). The annual water balance is primarily governed by the input of
rainfall and evaporation and the parameters for the lower zone nominal storage (LZSN), lower
zone evapotranspiration parameter (LZETP), and infiltration index (INFILT).
2. Adjust low-flow/high-flow distribution. This step is generally performed by adjusting the
groundwater or baseflow because the distribution between high and low flow is the easiest to
identify in low-flow periods. Mean daily flow conditions are used, and the primary parameters
involved are the INFILT, groundwater recession (AGWRC), and baseflow evapotranspiration
index (BASETP).
3. Adjust storm-flow/hydrograph shape. The storm flow, which is compared in the form of short,
timestep (1-hour) hydrographs, is largely composed of surface runoff and interflow.
Adjustments are made with the upper zone storage (UZSN), interflow parameter (INTFW),
interflow recession (IRC), and overland flow parameters (length of the overland flow plane
[LSUR], Manning’s n [NSUR], and slope of the overland flow plane [SLSUR]). INFILT can also be
used for minor adjustments.
4. Make seasonal adjustments. Differences in the simulated and observed total flow over each
month and season are compared to see if runoff needs to be shifted from one month or season
to another. These adjustments are generally accomplished by using seasonal (monthly
variable) values for the parameters of vegetal interception (CEPSC), LZETP, and UZSN.
Adjustments to variable groundwater recession (KVARY) and BASETP are also used.
The procedures and parameter adjustments involved in these phases are more completely described in
Donigian et al. [1984] and the HSPF hydrologic calibration expert system (HSPEXP) [Lumb et al., 1994;
Duda et al., 2019]. The same model-data comparisons will be performed for the calibration and
validation periods. The specific comparisons of simulated and observed values include:
/ Annual and monthly runoff volumes (inches)
/ Daily time series of flow (cubic feet per second [cfs])
/ Storm-event periods (e.g., hourly values) (cfs)
/ Flow-frequency (flow-duration) curves (cfs)
The water-balance components (input and simulated) are also reviewed. This effort involves displaying
model results for individual land uses, as well as the entire watershed, for the following water-balance
components:
/ Precipitation
/ Total runoff (sum of the following components):
» Overland flow
» Interflow
» Baseflow
/ PEVT

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/ Total actual ET (sum of the following components):
» Interception ET
» Upper zone ET
» Lower zone ET
» Baseflow ET
» Active-groundwater ET
/ Deep-groundwater recharge/losses
Although observed values are not available for every water-balance component listed above, the
average annual values must be consistent with expected values for the region, as impacted by the
individual land-use categories. This consistency (or reality) check is separate, with data independent of
the modeling (except for precipitation) to ensure that land-use categories and the overall water balance
reflect the local conditions.

The value ranges of the correlation coefficient (R) and R2 for assessing the model performance for daily
and monthly flows are provided in Figure 4-2. The figure shows the range of values that may be
appropriate for judging how well the model is performing based on the daily and monthly simulation
results. As shown in Figure 4-2, the ranges for daily values are lower to reflect the difficulties in exactly
duplicating the timing of flows given the uncertainties in the timing of model inputs, mainly precipitation.
The general calibration and validation tolerances or targets that have been provided to model users as
a part of HSPF training workshops over the past 20 years (e.g., Donigian [2000]) are listed in Table 4-1.
The values in Table 4-1 attempt to provide general guidance in terms of the percent mean errors, or
differences between simulated and observed values, so that users can determine what level of
agreement or accuracy (i.e., very good, good, or fair) can be expected from the model applications. The
target level of accuracy for this project will correspond in Table 4-1 to “Good” or “Very Good” results at
more downstream main-stem calibration sites and “Fair” at more upstream tributary sites. Accuracy
targets are highly dependent on the amount and quality of available data, and consequently, the targets
will be finalized after the data gaps are analyzed.

Figure 4-2. R and R2 Value Ranges for Model Performance.
The caveats in the Table 4-1 notes indicate that the tolerance ranges should be applied to mean values
and that individual events or observations may show larger differences and still be acceptable. The
level of agreement to be expected also depends on numerous site- and application-specific conditions,
including the data quality, purpose of the study, available resources, and available alternative
assessment procedures that could meet the study objectives.

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Table 4-1. General Calibration and Validation Targets or Tolerances for HSPF
Applications [Donigian, 2000]
Calibration
Parameter
Difference Between Simulated and Recorded Values
(%)
Very Good Good Fair
Hydrology/Flow < 10 10–15 15–25
Stipulations:
/ Storm peaks may differ more than monthly and annual values
/ Quality detail of input and calibration data
/ Purpose of model application
/ Availability of alternative assessment procedures
/ Resource availability (i.e., time, money, and personnel)
For any watershed modeling effort, the level of expected agreement is tempered by the complexities of
the hydrologic system, quality of the available precipitation and flow data, and available information to
characterize the watershed and quantify the human impacts on water-related activities. These
tolerances are applied to comparisons of simulated and observed mean flows, annual runoff volumes,
mean monthly and seasonal runoff volumes, and daily flow-duration curves. Larger deviations would be
expected for individual storm events and flood peaks in both space and time. The values shown in
Figure 4-2 were primarily derived from HSPF experience and past efforts on model performance
criteria; however, the values do reflect common tolerances accepted by many modeling professionals.
To provide a robust weight of statistical evidence, additional metrics, such as coefficient of model fit
efficiency, root mean square error, seasonal differences, and low and high flow efficiency, will be
reported. Additional evaluation criteria to be considered at primary gages are shown in Table 4-2.
Table 4-2. Flow Calibration Criteria From Expert System for
Calibration of HSPF [Lumb et al., 1994]
Prediction
Error
Percent Difference
Criteria
(%)
Error in Total Volume ±10
Error in Volume of 50% Lowest Flows ±10
Error in Volume of 10% Highest Flows ±15
Seasonal Volume Error (Summer) ±30
Seasonal Volume Error (Fall) ±30
Seasonal Volume Error (Winter) ±30
Seasonal Volume Error (Spring) ±30
Given the uncertain state of the art in model performance criteria, inherent errors in input and observed
data, and approximate nature of model formulations, absolute criteria for watershed model acceptance
or rejection are not generally considered appropriate by most modeling professionals. Most
decision-makers, however, want a definitive answer to the question, “Is the model good enough for this
evaluation?” Consequently, for the Neuse River modeling effort, the targets and tolerance ranges for

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daily flows are proposed to correspond, at a minimum, to a fair to good agreement, and the ranges for
monthly flows should correspond to good to very good agreement for calibration and validation at the
primary calibration flow gages. Secondary calibration flow gages should ideally correspond to a fair
agreement. Poor to fair ranges will be allowed for the tertiary sites because these sites are on smaller
tributaries and usually have a much shorter representative dataset to work with.
4.3 WATER QUALITY CALIBRATION
Water quality calibration is also completed through an iterative process of parameter adjustments and
comparisons of simulated and observed values and is facilitated by using scripted processes in
MATLAB. The model predictions are the integrated result of all of the assumptions used in developing
the model input and representing the modeled sources and processes. Differences in model
predictions and observations require the model user to reevaluate these assumptions for the estimated
model input and parameters and consider the accuracy and uncertainty in the observations.

Water quality monitoring sites will be mapped and assigned to each reach. Sites will be designated as
primary, secondary, and tertiary (i.e., third priority). The most data-intensive, more downstream sites will
be considered primary calibration sites.

A calibration goal will be to keep the parameterization consistent throughout the project area to avoid
curve fitting. Curve fitting is adjusting parameters reach by reach with the intent to force model results
to follow the observed data curve without justification for why two neighboring reaches can exhibit
such different behavior. Calibrating this way often causes many inconsistencies when using the model
to define protection and restoration goals. In addition to completing the input development for the
point-source, atmospheric deposition, and other contributions, the following steps will be performed at
each of the calibration stations after the hydrologic calibration and validation:
1. Estimate all of the model parameters, which include land-use-specific accumulation and
depletion/removal rates, wash-off rates, and subsurface concentrations.
2. Tabulate, analyze, and compare the simulated, annual, nonpoint loading rates with the expected
range of nonpoint loadings from each land use (and each constituent) and adjust the loading
parameters when necessary.
3. Calibrate instream water temperature, sediment, DO, and nutrients to the observed data.
The primary calibration parameters involved in characterizing landscape-erosion processes are the
coefficients and exponents from three equations that represent different soil detachment and removal
processes [EPA, 2006]. Nonpoint sources of total ammonia and nitrate-nitrite will be simulated through
accumulation and depletion/removal and a first-order wash-off rate from overland flow. Because of the
affinity of orthophosphate to bind to sediments, orthophosphate will be simulated using a linear
relationship with sediment washing off of the land. BOD will also be simulated using
sediment-associated wash-off. Subsurface flow concentrations will be estimated on a monthly basis.
Atmospheric depositions of nitrogen and ammonia will be applied to all of the land areas and contribute
to the nonpoint-source load through the buildup/wash-off process.

HSPF nonpoint loading rates (sometimes referred to as export coefficients) are highly variable, with
values occasionally ranging to an order of magnitude depending on local and site conditions of soils,

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slopes, topography, climate, and disturbance. As a quality check, the loadings from the
Chesapeake Bay CAST system from the southernmost Virginia area will be compared to final loadings
in the Neuse River HSPF model.

The model simulates the instream and lake processes that contribute to sediment transport, algal
growth, nutrient consumption, and DO dynamics. The sediment behavior for each size class will be
investigated to ensure that the sediment dynamics reflect field observations. HSPF does not explicitly
simulate streambank contribution dynamics; however, these processes will be implicitly included by
allowing the streambed to contribute those loads. All of the required instream parameters will be
specified for total ammonia, inorganic nitrogen, orthophosphate, and BOD. The processes in the
instream portion of the models include BOD accumulation, storage, decay rates, benthic algal oxygen
demand, settling rates, and reaeration rates. Atmospheric deposition onto water surfaces will be
represented in the models as a direct input to the lakes and river systems. Biochemical reactions that
affect DO will be represented in the model application. The overall sources considered for BOD and DO
include point sources such as wastewater treatment facilities (WWTFs), nonpoint sources from the
watershed, interflow, and active-groundwater flow.

The instream calibration will begin with the temperature and sediment and then dissolved oxygen and
nutrients. The dissolved oxygen and nutrient calibration will be conducted in tandem because these
components depend on one another. The calibration requires developing time-series graphs to
compare the simulated and observed water quality data. Instream water quality calibration will also
include generating monthly boxplots, concentration-duration curves, and scatterplots of
concentrations and corresponding flows. Hourly boxplots will be generated for temperature and
dissolved oxygen to assess the diurnal variability. Sediment scour and deposition in the streambed for
each reach over the period of simulation and the nutrient budget will also be evaluated. USGS
groundwater concentration measurements will be used, when possible, for the water quality calibration.
The parameters calibrated for sediment include buildup, wash-off, and instream scour and deposition.
The parameters calibrated for nutrients include buildup, wash-off, instream cycling, algae growth,
update, death, resuspension, and other parameters.

Lake and impoundment water quality calibrations are often difficult in HSPF because the model
simulates a completely mixed system (homogeneous waterbody with no vertical stratification). Large,
deep headwater lakes with little inflow/outflow can produce an overestimation/accumulation of nitrogen
or phosphorus and low chlorophyll a concentrations. The inherent variability in depth, surface area,
volume, and residence time between lakes in a model application causes each lake to behave
differently, which makes developing a standard approach or solution difficult. To address these issues
and achieve a dynamic, steady-state system, instream parameters for lakes and ponds are generally
very different compared to reaches [AQUA TERRA Consultants, 2015]. Bias as a result of lake
stratification is reduced by calibrating to the observed values taken from the surface to 1 m in depth.

The main goal of watershed water quality calibration is to obtain acceptable agreement of observed and
simulated concentrations (i.e., within defined criteria or targets) while keeping the instream water quality
parameters within physically realistic bounds and the nonpoint loading rates within the expected ranges

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from the literature. The general water quality calibration targets or tolerances for HSPF applications are
shown in Table 4-2. The calibration should be accomplished while maintaining consistent parameters in
each land-use category throughout the watershed, when possible.
Table 4-3. General Calibration Targets or Tolerances for HSPF Applications [Donigian, 2000]
Calibration
Parameter
Difference Between Simulated and Recorded Values
(%)
Very Good Good Fair
Sediment < 20 20–30 30–45
Water Temperature < 7 8–12 13–18
Water Quality/Nutrients < 15 15–25 25–35
Pesticides/Toxics < 20 20–30 30–40
Stipulations:
/ Storm peaks may differ more than monthly and annual values
/ Quality detail of input and calibration data
/ Purpose of model application
/ Availability of alternative assessment procedures
/ Resource availability (i.e., time, money, personnel)
4.4 SENSITIVITY ANALYSIS
Traditional sensitivity analysis involves comparing the relative differences in the results by dividing the
percent change in a variable response by the percent change in a calibration parameter, which provides
an understanding for how parameters impact the physical and biological processes represented in the
HSPF model. Although this analysis is valuable and inherent during the calibration process, several
thousands of parameter-variable options exist for evaluation, and interpreting the results requires a
thorough understanding of HSPF mechanics and terminology. A more useful approach to
understanding model sensitivity involves a detailed evaluation of source contributions using real-world
terminology. By using the HSPF model application results for each day, source, basin, and constituent
of concern, source contributions at all of the locations for selected time periods and/or flow conditions
can be calculated. The source contribution analysis will be included for the outlet of each model
application (i.e., all of the sources contributing to a main-stem river endpoint) as part of the final model
deliverables. A more detailed source analysis can be done with the SAM tool, which will be included as
part of the HSPF and SAM trainings.

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5.0 DATA MANAGEMENT
Two data types will be used to support the Neuse River HSPF modeling project: GIS and time-series
data. The data types must change format as they are integrated into an HSPF model and are thus
subject to possible errors. RESPEC will adhere to electronic data acquisition protocols that were
developed for previous HSPF applications. These protocols ensure that quality assurance
considerations related to preventing, detecting, and correcting electronic data manipulation errors are
properly addressed. RESPEC will also adhere to the protocols for data acceptance criteria described in
the Neuse River Watershed Model Quality Assurance Project Plan (QAPP) [Duda and Kirby, 2023].
RESPEC will maintain a copy of the project files on the network for a minimum of 5 years following the
completion of the project.

Consistent data management procedures will be used during the preprocessing, model calibration, and
postprocessing stages of the project. All of the data and information collected and generated during
this project will be stored in a project folder on RESPEC’s network. Data processing will be completed
using a combination of ArcGIS, MATLAB, Python, and the SARA Time-Series Utility. RESPEC modelers
will be responsible for adhering to and documenting data management practices that ensure the quality
of the data that are downloaded and/or manipulated. Original data sources will be documented to
identify the website or contact person that provided the data, data query parameters, and data request
correspondence. Original, unaltered copies of all data sources used in the project will be retained in the
project folder on RESPEC’s network. Metadata will be included with spatial datasets. The SARA Time-
Series Utility will be used to access the WDM files that will be used to store model-input data such as
meteorological, point-source, atmospheric deposition, and other time-series data.

GIS data will be used in a geodatabase feature-class format. The projection of all of the GIS data will be
consistent. When new GIS data are added to a feature class, ArcPro automatically projects the data to
match the projection of the feature class. Example metadata standards for NC OneMap are available
online (https://www.nconemap.gov/pages/metadata).

Model inputs, including meteorological data, point-source data, and surface-water withdrawals, will be
stored in a WDM file during the calibration process. Model outputs at calibration gages will be stored in
a set of binary (HBN) files. The HBN and WDM files can be accessed using the SARA Time-Series Utility,
which can be downloaded online (https://www.respec.com/product/modeling-optimization/sara-
timeseries-utility). SAM files, which are downloaded to the user’s local drive, will also be used as
storage, and the SAM program will have the capability to allow the user to extract the model data.

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6.0 REFERENCES
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Consultants, Mountain View, CA, for the Minnesota Pollution Control Agency, St. Paul, MN.

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Donigian, Jr., A. S., B. R. Bicknell, J. C. Imhoff, J. L. Kittle, Jr., T. H. Jobes, and P. B. Duda, 2018. HSPF
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Kohler, M. A., T. J. Nordenson, and W. E. Fox, 1955. Evaporation From Pans and Lakes, Research
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Sutherland, R. C., 2000. “Methods for Estimating the Effective Impervious Area of the Urban
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Vaudrey, J. M. P, C. Yarish, J. K. Kim, C. Pickerell, L. Brousseau, J. Eddings, M. Sautkulis, 2016.
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