Appendix K - Surface Water - Water Balance Development Report

Technical Report
2023 Prefeasibility Study
Surface Water: Water Balance Development Report
Kings Mountain Mining Project
Rev 02
Effective Date: April 12, 2024
Report Date: April 12, 2024
Report Prepared for
A ALBEMARLE"
Albemarle Corporation
4250 Congress Street,
Charlotte, NC 28209
Report Prepared by
.•rk consulting
SRK Consulting (U.S.), Inc.
999 17th Street, Suite 400
Denver, CO 80202
SRK Project Number: USPR000576
Albemarle Document Number: KM60-EN-RP-9053
North Carolina Firm License Number: C-5030

SRK Consulting(U.S.), Inc.
2022 KM PFS Report-Surface Water:Water Balance Development Report Page ii
Executive Summary
SRK Consulting (U.S.), Inc. (SRK) was commissioned by Albemarle Corporation (Albemarle) to
support prefeasibility study (PFS)-level water management for the proposed open pit mine and
associated facilities and infrastructure at the Kings Mountain Mine (the Project). Kings Mountain is a
lithium deposit situated primarily within spodumene pegmatites and has been historically mined using
open pit methods. This report presents the development of a water balance model to simulate the
water inflows, outflows, storage, losses, and consumption associated with the Project during
development, operation, closure, and post-closure activities.
SRK developed a site-specific water balance model using the commercial software package GoldSim
(GoldSim, 2021). The water balance model is a simulation of the mine water management system that
incorporates dynamic aspects of the mining activities, specifically:
• Pre-development simulation of the existing streamflows and pit lake filling to establish baseline
conditions
• Simulation of pre-development activities, including dewatering of the existing pit lake and
construction of limited supporting infrastructure such as roads and sediment ponds
• Simulation of the mine development, including development mine activities, facility
construction, and diversion and management of water around the site in anticipation of mine
activities
• Simulation of active mining, including processing rates, facility growth, demand, consumption,
and discharge of treated and untreated water from the mine process, rock storage facilities
(RSF), tailings facilities, and water management ponds and infrastructure
• Simulation of mine closure activities, including remining of potentially acid generating (PAG)
waste rock for pit backfill, removal and decommissioning of ponds and diversions, and filling
of the pit lake leading to eventual pit lake discharge to the adjacent natural channels
The model simulates climate using values based on legacy climate records from the nearby climate
station of Shelby 2 NW(National Oceanic and Atmospheric Administration (NOAA), 2023)that extend
back to 1893, although only the record from 1990 to 2022 was used to avoid temporal bias.The climate
record for this period was infilled with values from the Daymet gridded climate dataset (National
Aeronautics and Space Administration (NASA), 2023) and used to develop deterministic climate
scenarios that include constructed time series for climate forcing. The dataset was also used to
develop a synthetic climate generator based on the WGEN climate model (Richardson, 1984). This
model produces stochastic daily climate time series that allow the model to perform probabilistic
modeling simulations using the Monte Carlo simulation approach.
The model includes climate change predictions, adjusting daily precipitation by a correction factor
based on the AR6 climate change projections (Intergovernmental Panel on Climate Change (IPCC),
2023), and the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset
(National Centers for Environmental Prediction (NCEP), 2023), to provide site-specific climate change
projections. Median climate change predictions were incorporated for both the reasonable expected
Shared Socioeconomic Pathway (SSP) 4.5 as well as the more-conservative SSP8.5. All simulations
were run in this report with the SSP4.5 climate change scenario.
The model was run with multiple deterministic climate forcing scenarios, such as forcing a fifth
percentile very dry year at a specific point late in the mine life to stress the system. Under dry climate
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2022 KM PFS Report-Surface Water:Water Balance Development Report Page iii
forcing, the model predicted the site water balance would remain net positive, meaning that total
inflows to the mine water management system consistently exceed the consumptive use or losses
from the system. Similarly, under wet climate forcing, the model predicted the water management
infrastructure would not be exceeded, and the designed system capacity would be adequate to
address the higher flows.
Probabilistic simulation of the system yielded two key results among the 1,000+ results available from
the model. The model indicated that Water Storage Basin 1 (WSB-1)would always have the capacity
to meet the water demand at the processing plant and that the water level in the pond would be quite
stable, fluctuating less than 6 feet(ft) between wet and dry extremes, as shown on Figure ES.1.
ss0 850
_ 840 840
0
O
7
a
w
ri
'n
830 830
.41410"�Wj
820 820
2023 2025 2027 2029 2031 2033 2035 2037 2039
Time
Statistics for WSB-1 Elevation
5%.,15%/85% 95% is% 25%/75%Z5% 25%„35%/65%,.75%
45%,55% 50%
Source:SRK,2023
Figure ESA: Probabilistic Time Series of WSB-1
The model indicated that the post-closure pit lake would quickly form in the partially backfilled pit and
continue to fill until the discharge point was reached,which is assigned to elevation 850 ft above mean
sea level (amsl). The model predicted the pit lake would not inundate the pit backfill until 12 months
after the backfill was started and reach the discharge elevation sometime around Year 2090, as shown
on Figure ES.2.
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2022 KM PFS Report-Surface Water:Water Balance Development Report Page iv
C: 800 800
O
> MONO
w 700 700
0040
F
Y
000
y 6oa boa
a
a
500 500
400 400
2035 2040 2045 2050 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100
Time
Statistics for Post-Closure Pit Lake Elevation
1%.5%/95%..99% 5%,.15%/85%..95% 15%..25%/75%..85% 25%„35%/65%..75%
45%.,55% 50%
Source:SRK,2023
Figure ES.2: Probabilistic Time Series of the Predicted Pit Lake Water Elevation
Overall, the model indicated the system was consistently net positive, even during the probabilistic
simulations that generated significant dry periods. A sensitivity analysis of the water balance inputs,
including freshwater demand,filtered tailings moisture content, infiltration, percolation rates,and runoff
coefficients, indicated that when varied within reasonably expected ranges, none of these inputs were
able to force the mine water management system to anything other than net positive.
SRK anticipates that this model will be a living tool for the continued development of the Kings
Mountain Lithium Mine. As the Project understanding grows and plans are refined or new
understanding to key inputs is gained, the model is expected to be updated and new simulations
produced so that it can continue to function as a viable tool to aid in the design and evaluation of the
Kings Mountain Lithium Mine.
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Table of Contents
ExecutiveSummary .......................................................................................................... ii
Abbreviations ................................................................................................................... ix
1 Introduction.................................................................................................................. 1
1.1 Property Location................................................................................................................................1
1.2 Property History ..................................................................................................................................3
1.3 Project Overview.................................................................................................................................3
1.4 Project Layout.....................................................................................................................................4
2 Site Environment ......................................................................................................... 6
2.1 General Description ............................................................................................................................6
2.2 Climate................................................................................................................................................6
2.2.1 Temperature............................................................................................................................6
2.2.2 Evaporation .............................................................................................................................6
2.2.3 Precipitation.............................................................................................................................7
2.2.4 Storm Frequency.....................................................................................................................8
2.3 Surface Water.....................................................................................................................................9
2.3.1 Surface Water Streamflow Monitoring ..................................................................................11
2.4 Wind Patterns....................................................................................................................................12
3 Water Balance Development..................................................................................... 13
3.1 General Model Operating Approach .................................................................................................13
3.1.1 Qualitative Assignment of Water Quality...............................................................................13
3.1.2 Project Flowsheet..................................................................................................................14
3.1.3 Water Balance Model Simulation..........................................................................................18
3.1.4 GoldSim Software .................................................................................................................18
3.1.5 General Model Structure.......................................................................................................18
3.1.6 Incorporation of Uncertainty..................................................................................................19
3.2 Climate Simulation ............................................................................................................................21
3.2.1 Climate Change.....................................................................................................................21
3.2.2 Legacy Climate Time Series .................................................................................................24
3.2.3 Synthetic Climate Generator.................................................................................................25
3.3 Environmental Components..............................................................................................................29
3.3.1 Infiltration Simulation.............................................................................................................29
3.3.2 Runoff Simulation..................................................................................................................29
3.3.3 Other Runoff-Infiltration Models............................................................................................31
3.3.4 Percolation and Uptake Simulation.......................................................................................32
3.3.5 Groundwater Inflows .............................................................................................................33
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3.4 Facility Components..........................................................................................................................35
3.4.1 Open Pit.................................................................................................................................36
3.4.2 RoM Pad................................................................................................................................40
3.4.3 Processing Plant ...................................................................................................................40
3.4.4 RSF .......................................................................................................................................43
3.4.5 WS13-1 ...................................................................................................................................44
3.4.6 Other Components................................................................................................................46
4 Water Balance Model Simulations............................................................................ 49
4.1.1 Deterministic Scenarios.........................................................................................................49
4.1.2 Comparison of Pre- and Post-Development Flows in Kings Creek ......................................55
4.1.3 Probabilistic Simulations .......................................................................................................56
4.1.4 Model Sensitivity ...................................................................................................................62
5 Interpretation and Conclusions................................................................................ 65
6 References.................................................................................................................. 66
List of Tables
Table 2.1: Excerpted Tables showing Site-Specific Study and Annual Return Intervals Results......................9
Table 3.1: Precipitation Climate Change Adjustment.......................................................................................23
Table 3.2: Correction from Eto to Lake Evaporation ........................................................................................25
Table 3.3: Surface Infiltration Parameters........................................................................................................29
Table 3.4: GR4J Model Parameters.................................................................................................................31
Table3.5: CN Values by Surface .....................................................................................................................32
Table 3.6: Annual Mine Production Schedule ..................................................................................................37
Table 3.7: Key Pit Input Parameters.................................................................................................................39
Table3.8: Key RoM Pad Parameters...............................................................................................................40
Table 3.9: Key Processing Plant Input Parameters..........................................................................................43
Table3.10: Key RSF Parameters.....................................................................................................................44
Table 3.11: Key WSB-1 Input Parameters .......................................................................................................46
Table 3.12: Dust Control Schedule...................................................................................................................47
Table 3.13: Watershed Input Parameters.........................................................................................................48
List of Figures
Figure ES.1: Probabilistic Time Series of WSB-1 ............................................................................................. iii
Figure ES.2: Probabilistic Time Series of the Predicted Pit Lake Water Elevation...........................................iv
Figure1.1: Location Map....................................................................................................................................2
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Figure 1.2: Preliminary Kings Mountain Mining Project Site Map ......................................................................5
Figure 2.1: Average Monthly Evaporation ..........................................................................................................7
Figure 2.2: Annual Precipitation and Distribution of Monthly Precipitation.........................................................8
Figure 2.3: Existing Streamflow Network and Monitoring Points......................................................................10
Figure 2.4: Wind Rose Data in the Vicinity of Kings Mountain.........................................................................12
Figure 3.1: Kings Mountain Site-Wide Water Balance Pre-Development and Development Flowsheet.........15
Figure 3.2: Kings Mountain Site-Wide Water Balance Operational Conditions Flowsheet..............................16
Figure 3.3: Kings Mountain Site-Wide Water Balance Post-Closure Flowsheet..............................................17
Figure 3.4: Model Screenshot showing the Four Main Level Containers.........................................................19
Figure 3.5: Example of Probabilistic Time Series.............................................................................................20
Figure 3.6: Comparison of Mean Annual Temperature and Mean Annual Precipitation for the Three Climate
Projections...........................................................................................................................................22
Figure 3.7: Change in Daily Maximum Temperatures from Current Climate Conditions.................................22
Figure 3.8: Monthly Temperature Normal Compared to Climate Change Temperature Normal .....................23
Figure 3.9: Comparison of Simulated versus Shelby 2 NW Climates..............................................................27
Figure 3.10: Comparison of Annual Maximum Daily Rainfall from Legacy, Simulated, and NOAA Atlas 14
Values..................................................................................................................................................28
Figure 3.11: GR4J Runoff Model Schematic....................................................................................................30
Figure 3.12: Schematic of Waste Rock Infiltration and Percolation .................................................................33
Figure 3.13: Groundwater Inflows and Outflows from the Pit...........................................................................34
Figure 3.14: Groundwater Inflow Response Curve ..........................................................................................35
Figure 3.15: Screen Capture of Typical Water Balance Module......................................................................36
Figure 3.16: Pit Perimeter Ponds .....................................................................................................................38
Figure 3.17: Process Plant Flow Diagram........................................................................................................41
Figure 3.18: Simplified Process Plant Flow Diagram .......................................................................................42
Figure 4.1: Deterministic Climate Scenarios Annual Precipitation ...................................................................50
Figure 4.2: Outflow from the Contact Water Pond under the Deterministic Climate Scenarios.......................51
Figure 4.3: Volume in WSB-1 under the Deterministic Climate Scenarios ......................................................52
Figure 4.4: Relative Contributions to the Contact Water Pond during the Climate Forcing Period..................53
Figure 4.5: Average Monthly Pumping from the Active Pit Sump under the Deterministic Climate Scenarios54
Figure 4.6: Average Monthly Pumping from the Non-PAG RSF under the Deterministic Climate Scenarios .55
Figure 4.7: Comparison of Flows at Weir#7....................................................................................................56
Figure 4.8: Probabilistic Time Series of the Pre-Development Pit Lake Volume.............................................57
Figure 4.9: Probabilistic Time Series of WSB-1 Volume..................................................................................58
Figure 4.10: Probabilistic Time Series of WSB-1 Elevation .............................................................................59
Figure 4.11: Probabilistic Time Series of the Post-Closure Pit Lake Water Elevation.....................................60
Figure 4.12: Probabilistic Time Series of the Post-Closure Pit Lake Volume ..................................................60
Figure 4.13: Depth of Backfill and Probabilistic Pit Lake Depth during Closure and Post-Closure..................61
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Figure 4.14: Probabilistic Time Series of the Average Monthly Raw Water Supply to the Plant.....................62
Figure 4.15: Sensitivity Study Tornado Chart on Cumulative Discharge from WSB-1 over the Period of Active
Mining ..................................................................................................................................................64
Appendices
Appendix A: Legacy Climate Data
Appendix B: Johnson SB Probability Distribution Fitting to Legacy Precipitation Records
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Abbreviations
Abbreviation Unit or Term
percent
o degree
°F degrees Fahrenheit
3D three-dimensional
ac acre
AEP Annual Exceedance Probability
Albemarle Albemarle Corporation
amsl above mean sea level
AR6 sixth assessment report
ARI annual return interval
AWA Applied Weather Associates
CDF Cumulative Density Function
cm/sec centimeters per second
CMIP6 Coupled Model Intercom arison Project 6
CMP corrugated metal pipe
CN curve number
DEMLR North Carolina Division of Energy, Mineral and Land Resources
DEQ De artment of Environmental Quality
DMS direct media separation
ESRI Environmental Systems Research Institute, Inc.
Eto reference evapotranspiration
FAO Food and Agriculture Organization of the United Nations
FHA Federal Highway Administration
ft foot
ft3 cubic foot
ft3/sec cubic feet per second
GCM eneral circulation model
pm gallons per minute
GR4J Model of Rural Engineering with 4 Parameters Dail
GTG GoldSim Technology Group
HDPE high-density of eth lene
1-85 Interstate 85
OF inflow design flood
IPCC Intergovernmental Panel on Climate Change
km kilometer
kt thousand tons
LoM life-of-mine
M al million gallons
mm millimeter
mm/h millimeters per hour
Model site-wide dynamic water balance computer simulation
Mt million tons
NAG net acid generation
NASA National Aeronautics and Space Administration
NCEP National Centers for Environmental Prediction
NEX-GDDP NASA Earth Exchange Global Daily Downscaled Projections
NOAA National Oceanic and Atmospheric Administration
NPI non-processing infrastructure
NRCS National Resources Conservation Service
PAG potentiallyacid generating
PDF Probability Density Function
PFS refeasibility stud
PMP probable maximum precipitation
Project Kings Mountain Mining Project
Q runoff amount
QQ Quantile-Quantile
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Abbreviation Unit or Term
RoM run-of-mine
RSF rock stora a facility
S mximum retention value
SRK SRK Consulting (U.S.), Inc.
SSP Shared Socioeconomic Pathway
t/d tons per day
t/h tons per hour
TSF tailings storage facility
USDA United States Department of Agriculture
USGS United States Geological Survey
WSB-1 Water Storage Basin 1
WTP water treatment plant
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1 Introduction
The Kings Mountain Mining Project is a legacy open pit lithium mining operation located in the city of
Kings Mountain, North Carolina, in the southeastern United States. The Project is a lithium pegmatite
deposit that is currently being investigated for redevelopment by Albemarle as part of a PFS-level
analysis. Albemarle commissioned SRK to develop PFS-level (evaluate and select phases, per
Albemarle's internal conventions) designs for an expansion of the existing pit, waste rock
management, water management, and ancillary infrastructure to aid Albemarle in making informed
decisions concerning advancement of the Project.
1.1 Property Location
Situated in Cleveland County, the mine is approximately 35 miles west of Charlotte, North Carolina.
Located amidst rolling hills of the Piedmont Plateau, the Project is in a predominantly rural setting
within the city of Kings Mountain. The mine site covers a significant land area, which includes both the
proposed extraction areas and associated processing infrastructure. Figure 1.1 shows the location and
extent of the mine.
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Figure 1.1: Location Map
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1.2 Property History
The following summary highlights the history of the site, compiled from records available to SRK:
• Mining started in 1883 with the discovery of cassiterite, a tin-bearing mineral, within the
outcropping pegmatites.
• Subsequently, open pit mining for tin occurred sporadically between 1903 and 1937 (Horton
and Butler, 1988).
• Between 1943 and 1945, under the sponsorship of the U.S. government, Solvay established
a processing plant and mined for spodumene from the outcropping pegmatites(Garrett,2004).
• In the early 1950s, Foote, a subsidiary of Newmont Mining Corporation, purchased the
property and began open pit mining (assumed at the beginning of 1955)and extracting lithium
from the spodumene.
• In 1993, exploration and mining operations ceased when the open pit bottom reached
approximately 660 ft amsl.
• In early 1994, an open pit lake started to form due to rebounding groundwater, and the pit lake
reached an elevation of 818.67 ft amsl (as of November 2023).
• During the groundwater recovery period (1994 to present), water was sporadically pumped
from Kings Mountain pit lake to a nearby quarry(Martin Marietta)to support quarry operation.
• Albemarle acquired the site in 2015, resuming exploration and mine development activities.
1.3 Project Overview
The Project ore deposit is a lithium-bearing rare-metal pegmatite intrusion that has penetrated along
the Kings Mountain shear zone, a regional structural feature known to host multiple lithium-bearing
pegmatites along its trend. The pegmatite field at Kings Mountain is approximately 1,500 ft wide at its
widest point in the legacy pit area and narrows to approximately 400 to 500 ft in width at its narrowest
point south of the historic pit. The field has a lithium mineralization strike length of approximately
7,500 ft and is predominantly contained in the mineral spodumene. The spodumene pegmatite bodies
exhibit a texture-based variation in lithium grade, spodumene grain size, mineral alteration, and rock
hardness.
After dewatering the legacy pit, the lithium deposit is to be mined using conventional open pit mining
techniques. Blasting will fragment the ore and waste rock where it will be loaded and hauled to either
the processing facilities (ore) or the waste storage facilities (overburden). The current plan includes
mining in the existing pit and expanding the pit to the southwest. Ore would be drilled, blasted, loaded,
and transported by haul truck to a new processing plant at a rate of approximately 2.98 million tons
(Mt) per annum of ore (approximately 8,150 tons per day (t/d)) and processed to produce 380 to
420 thousand tons (kt) per annum of spodumene concentrate. The concentrate will be filtered to
approximately 11 percent (%) moisture by weight and transported off-site for further refinement into
lithium hydroxide monohydrate at a separate facility.
Tailings from the spodumene concentrate process will be filtered to approximately 10% to 15%
moisture content by weight and transported off-site to a nearby facility for disposal. The off-site tailings
storage facility (TSF) is a separate facility and is not addressed in this report.
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1.4 Project Layout
Figure 1.2 presents the Project layout, showing the relative locations of the major components of the
Project.The Project is bisected northeast to southwest by Interstate 85(1-85).The headwaters of Kings
Creek are located immediately northeast of the site, and the creek leaves the Project area at the
southern side of the Project area. The Phase 1 Open Pit outline is shown in the northeast area of the
Project, along with the ultimate (Phase 4) pit extents. Haul roads are shown connecting the pit to the
RSFs: RSF-X, located south-centrally for PAG waste, and RSF-A, located in the southwest for non-
PAG waste. The haul roads will also connect to the non-processing infrastructure (NPI), located in the
northwest portion of the site, and the ore sorting area and the ore stockpiles, located on the east side
of the Project just north of 1-85.A bridge over 1-85 will connect the ore stockpile area to the processing
area, located immediately south of 1-85. South of the processing area, WSB-1 will collect all contact
water produced, including treated PAG effluent water, within the Project area before being discharged
from the site.
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2 Site Environment
2.1 General Description
The Project is located in southwestern North Carolina, USA, adjacent to the city of Kings Mountain on
the 1-85 transit corridor, approximately 33 miles west of the city of Charlotte (Figure 1.1). The property
is located at approximately 35 degrees (o), 13 minutes north latitude and 81°, 21 minutes west
longitude.
The site contains several historic waste rock areas, stream diversions, and tailings facilities; none of
these show any signs of active acid rock drainage or metal leaching except for some iron precipitates,
which are probably related to corroded steal associated with drainage pipes discharging to the
diversion creek around the pit. Most facilities are heavily vegetated, and none of the vegetation shows
signs of metal or acid stress.
2.2 Climate
The Project is situated within the Koppen-Geiger Cfa climate classification (Kottek et al., 2006), which
describes a continental type of climate without a dry season.Temperatures during the warmest months
are above 72 degrees Fahrenheit(°F), and temperatures in the coldest months are between 27°F and
65°F. Average monthly precipitation varies between 3 and 5 inches. Average annual precipitation is
49 inches, with an even distribution of rainfall throughout the year and an average annual snowfall of
4 inches. Southwestern North Carolina is prone to thunderstorms during the summer and ice storms
during the winter.
2.2.1 Temperature
The climate of the Project vicinity is humid subtropical with hot summers and mild winters.The monthly
temperature ranges from a minimum of around YF in January to a maximum of around 104°F in
August,with an average temperature of around 60°F. Legacy data show that temperatures in the area
have been increasing, with an average rise of 0.3°F per decade since 1970, or roughly 1.7°F from
1895 to 2020. Climate change is expected to further contribute to this warming trend, potentially
impacting surface water conditions such as increased evaporation rates and altered streamflow
patterns. Predictive climate models suggest further warming in the future, potentially resulting in more
frequent and severe heatwaves and droughts.
2.2.2 Evaporation
Evaporation rates at the Project vary based on temperature, humidity levels, wind speed, and solar
radiation. Legacy data from regional climate stations provided by NOAA (NOAA, 2023) (Clemson
University, SC GHCND:USC00381770, Chesnee 7 WSW, SC GHCND:USC00381625, and Chapel
Hill 2 W, NC GHCND:USC00311677) show that evaporation rates are highest in summer, averaging
around 6 to 7 inches per month, and lowest in winter,with around 2 to 3 inches per month (Figure 2.1).
Overall,typical annual potential evaporation ranges from 54 to 58 inches. Evaporation impacts surface
water availability by contributing to water loss from lakes, rivers, and streams. Factors such as
vegetation cover, land use practices, and soil moisture levels influence evaporation variability. Climate
models predict that evaporation rates will continue to increase in the future due to warming
temperatures and changes in precipitation patterns.
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S
7
6
0 5
Y
o
a 4
7
W
s 3
r
o
1
0
January February March April May June July August September October November December
Clemson Univ Chesnee 7 WSW Chapel Hill 2 W — — —Average GOIdSim
Source:SRK
Figure 2.1: Average Monthly Evaporation
2.2.3 Precipitation
Precipitation totals at the Project vary throughout the year. Based on the last 30 years of records from
the nearby NOAA station (NOAA, 2023)(Shelby 2 NW, NC GHCND:USC00317845),the area typically
receives between 40 to 60 inches of rainfall annually (Figure 2.2), with precipitation being distributed
relatively evenly throughout the year without a clear wet or dry season. The region is susceptible to
extreme precipitation events, such as tropical storms and hurricanes, which can bring heavy rainfall
and cause flooding.
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Distribution of Precipitation
12 80
70
v to —
L y
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0 0 50
a 5 e 40
y 30
T 4
CCC 20
a z C
10
a p
January February M—h April May June July August September October November December Annual Total
2.59G5% 5%-10% ■10%-25% ■25%-50% ■Median ■50%-75% ■75%-90% ■90%-95% 95%-97.5%
Annual Precipitation
ao
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20
0
1925 1930 1931 1940 1945 1950 1955 1950 1%5 1970 1925 19 0 199i 1990 1995 2000 2005 2010 2015 2020
Source:SRK
Figure 2.2: Annual Precipitation and Distribution of Monthly Precipitation
2.2.4 Storm Frequency
Storms encountered at the Project vary in frequency and intensity throughout the year. For example,
a 100-year, 24-hour storm event produces, on average, 7.96 inches of precipitation (NOAA, 2023).
The region experiences thunderstorms, tropical storms, and hurricanes, which can bring heavy rainfall
and high winds. Thunderstorms, which are the most common type of storm in the area, see peak
activity in July and August. Factors such as temperature, humidity, wind patterns, and topography
influence storm occurrence. These storms can impact surface water availability and quality by causing
flooding, erosion, and sedimentation. Climate change could increase the frequency and intensity of
storms in the future, posing higher risks of flooding and erosion.
Applied Weather Associates (AWA) completed the Site-Specific Probable Maximum Precipitation
Study for Kings Mountain Mining Operations, North Carolina(AWA,2022)for the Kings Mountain basin
in North Carolina. AWA utilized a storm-based approach to derive the site-specific probable maximum
precipitation (PMP) depths to update the PMP depths originally developed in hydrometeorological
reports developed by the National Weather Service. Table 2.1 shows Tables 10.4 and 10.5 excerpted
from AWA (2022), which show the results of the site-specific study with annual return intervals (ARI)
to 1:10,000 years and beyond.
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Table 2.1: Excerpted Tables showing Site-Specific Study and Annual Return Intervals Results
Table 10.4: King% Mountain hasin AEP for 6-,24-,And 72-hour PMP
Kings Mounuin AEP
Estimate PMP(in) AEP ARI
6hr 28.5 2.5 F9 39,829,769
24hr 32.4 1.09'7 9,144,104
72hr 32.4 3.93'7 2,540,551
Table 10.5: Kings Mauntain hstsin a.erall frequent~• analysis for 6-,24-,and 72-hour
pr Kings Mnt Frequency Analysis 6-hour 24-hour 72-hour
ARI AEP AEP 50% 5% 95% 50% 5% 95% 50% 5% 95%
1.01 0.99010 9.91 1.0 0.9 1.1 2.0 1.8 2.2 2.4 2.2 2.6
2 0.5000D 5.01 2.3 2.1 2.5 3.6 3.4 3.9 43 4.0 4.6
5 0.2000D 2.01 3,2 2.9 3.4 4.8 4.4 5.1 5.7 5.2 6.1
10 0.10000 1.01 3.8 3.5 4.1 5.5 5.1 6.0 6.6 6.1 7.1
25 0.04000 4.0 4.7 4.3 5.0 6.6 6.1 7.2 7.9 7.3 8_5
50 0.02000 2.0'1 53 4.9 5.8 7.5 6.9 8.2 8.9 8.2 9.7
100 0.01000 1.01 6.0 5.5 6.7 8.4 7.7 9.2 9.9 9.1 10.9
200 0.00500 5.01 6.3 6.2 7.6 93 8.3 10.4 11.0 10.1 123
500 0.00200 2.01 7.9 7.1 8.9 10.6 9.6 12.1 12.6 11.4 143
1,000 0.00100 1.0' 8,7 7.$ 10.1 11.7 10.4 13,5 13.9 12.4 16,0
5,000 0.0002D 2.0° 10.9 9.5 13.1 14.4 12.5 17.2 17.1 14.9 20.4
10,000 0.00010 1.0° 11.9 10.3 14.5 15.7 13.5 19.1 18.6 16.0 22.6
100,000 0.00001 1.c) 15.9 13.1 20.5 20.4 16.9 26.5 24.2 20.0 313
1,000,000 0.000401 1.c) 20.5 16,3 28.3 26.2 20.7 36.1 31.0 24.5 42.7
10,000,000 0.0000401 1.0' 26.2 19.9 38,7 33.1 25.0 48.6 39.1 29.7 57.8
100r000,000 0.00000001 1.01 33.1 24.0 52.5 41.3 29.9 65.5 48.9 35.4 77.6
1,000,000,000 0.000000001 1.01 41.4 28.6 70.7 51.2 35,3 97.4 60.6 41.9 103.5
10,000,000,000 0.0000000001 1.010 51.4 33.9 94.7 63.1 41.5 1161 74.7 49.2 137,5
Source:AWA,2022
AWA (2022) also included a site-specific climate change assessment to "understand and quantify
whether the climate change projections related to temperature, moisture, and precipitation are
expected to change significantly in the future (through 2100), and whether those changes would
warrant a change to the currently derived PMP depths." The results of the study indicate "an increase
in precipitation and temperature in the future" with "the most likely outcome regarding precipitation
over the basin going forward is that the mean annual and seasonal amount will increase, but the
individual extreme events will stay within the range of uncertainty currently calculated" (AWA, 2022).
2.3 Surface Water
Surface water hydrology and stormwater control system design at the site are described in detail in
SRK's Surface Water Management Report, Kings Mountain Mining Project, North Carolina (SRK,
2023d), and the following sections are incorporated from that report.
The natural drainage network in the vicinity of the Project is heavily influenced by legacy and active
mining activities. The contributing watersheds to the Project area are roughly defined by Battleground
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Avenue to the north, Tin Mine Road to the west, Church Road to the south and east, and Cardio Hill,
a legacy RSF, to the northeast. The drainage network consists of two main drainages and several
constructed water bodies, as shown on Figure 2.3.
WV
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-.. 1.F.!y^c.'.:'U.!r•, ..
A"
` eir
t w
pa
Source:SRK,2O23d
Figure 2.3: Existing Streamflow Network and Monitoring Points
Kings Creek passes through the Project area from northeast to southwest. Upgradient of the Project,
Kings Creek is intercepted by the Martin Marietta Quarry Pit. Water intercepted by the Martin Marietta
Quarry Pit is pumped out on a regular basis and discharged into Kings Creek. The pumping system
was recently upgraded to a capacity of 2,500 gallons per minute (gpm).
As Kings Creek enters the Project area, it is routed under the current Albemarle research building in a
620-ft long, 4-ft-diameter corrugated metal pipe (CMP) culvert. After exiting the culvert, Kings Creek
flows to the southwest and joins with flows from the South Creek Reservoir before crossing under 1-85
in three 7-ft-wide by 10-ft-high concrete box culverts. Flowing south, Kings Creek joins with flows from
WSB-1 before flowing off the Project area to the southwest.
South Creek begins northwest of the Project area in an area of residential neighborhoods. The creek
flows generally southwards as it passes the existing Foote Mineral Tailings Impoundment before
entering the South Creek Reservoir, formed as part of the legacy mining activities in the area. Runoff
from the legacy tailings impoundment does not discharge directly to South Creek, but meteoric water
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infiltrating into and through the tailings likely reports to South Creek and South Creek Reservoir. The
recently upgraded spillway from the South Creek Reservoir consists of two 32-inch-diameter high-
density polyethylene (HDPE) culverts through the embankment. The culverts flow into a rock down-
chute that joins Kings Creek.
Kings Mountain Pit Lake is formed in the legacy Kings Mountain Pit and does not currently contribute
surface water flows to the stream network. The current pit lake elevation is approximately 800 ft amsl
and would need to rise at least 50 ft before overflowing from the pit into Kings Creek.
There are several small, manmade ponds in the Project area which generally contribute to the Kings
Creek drainage system. The most notable of these ponds is Pond #1, a legacy water management
structure used by the existing industrial activities to manage stormwater runoff from the Project area.
Pond #1 collects water from the site and infrequently discharges through a culvert under the railroad
spur into Kings Creek.
WSB-1 (also referred to as Executive Club Lake) is formed by the legacy Foote Minerals Chem tailings
impoundment south of 1-85. Post-mining, the embankment was breached down to an elevation of
approximately 820 ft amsl, and flows exiting WSB-1 flow freely over a rock spillway,joining with Kings
Creek approximately 1,500 ft downstream of the lake. The area below the confluence of Kings Creek
and WSB-1 outflow is currently blocked by a beaver dam,forming a large marshy area in the drainage
and resulting in localized flooding.
A conceptual final discharge point from the Kings Mountain Project area was developed at the legacy
Weir#7 structure located in the vicinity of the beaver dam, although this site is not currently included
in the surface water management plant or monitored for discharge.
2.3.1 Surface Water Streamflow Monitoring
Streamflow at the Kings Mountain site is continuously monitored at two locations.The first site, located
at Monitoring Point KMSW-3, a legacy concrete and steel plate weir designated as Weir#3, is located
on Kings Creek below the confluence with South Creek and upstream of the culvert crossing under
1-85. The second site is located at the outlet of South Creek Reservoir,just upstream of the confluence
with Kings Creek, at Monitoring Point KMSW-8.
Weir#3 is a multi-stage weir, consisting of a 1.4-ft-deep V-notch below a 26.5-ft-wide rectangular weir.
Flow depth over the weir is continuously monitored at 1-minute intervals by a Vega 11 lookdown radar
sensor and Campbell Scientific data logger with solar power supply. Data from the logger are
transmitted via cellular network and are available through a web interface. A stage-discharge
relationship for the weir was developed using theoretical weir equations and validated with manual
streamflow measurements as described in a technical memorandum (SRK, 2022). The stage-
discharge relationship for high flows above the weir were established using hydraulic simulation of
Kings Creek adjusted to align with the theoretical weir discharge when flow depths are at the top of
the weir. The stage discharge relationship is incorporated into the web interface so that the monitoring
data can be presented as water levels, water elevations,flow in cubic feet per second (ft3/sec), or flow
in gpm.
The outlet from South Creek Reservoir consists of two, 32-inch-diameter HDPE culverts installed
during the most-recent dam improvements in July 2019. Water levels in the reservoir are monitored
along with the groundwater monitoring system using a Rugged Troll pressure transducer suspended
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in the reservoir from an existing platform and associated data logger. A stage-discharge relationship
for the culvert outlets was developed using the software package HY-8 (Federal Highway
Administration (FHA), 2021) for heads on the culvert from 0 to 18.63 ft (depth to dam overtopping,
corresponding to flows from 0 to 152 ft3/sec). A hydraulic analysis of the concrete inlet structure
confirmed that the culverts are the hydraulically limiting structure. Measurements from the data logger
are manually downloaded, along with the groundwater monitoring measurements, and converted to
flow rates using the stage discharge relationship for the culverts.
On a regular basis, water level and flow data for the two monitoring stations are downloaded and
streamflows are calculated for reporting. In addition to the continuous monitoring, Albemarle regularly
performs manual streamflow measurements using a handheld acoustic instrument following the United
States Geological Survey (USGS) discharge measurement method (ISO 748:2021-Hydrometry). The
first sampling event was performed in May 2022.
Monitored streamflows and observed precipitation data at the site were used to produce a rainfall-
runoff model, which is used in surface water design calculations and water balance modeling to
estimate the amount of runoff that will report to the various hydrologic structures in response to
hypothetical rainfall events, both normal rainfall and extreme events. The runoff values were also used
as a basis for estimating the rainfall-runoff behavior from other surfaces that will be developed as the
mine progresses, such as reclaimed RSFs.
2.4 Wind Patterns
Wind rose data were obtained from the Department of Environmental Quality(DEQ)Air Quality Portal
available online at https://airguality.climate.ncsu.edu/wind/. Figure 2.4 presents wind rose data for the
Shelby Municipal Airport, located about 13 miles west of Kings Mountain, and for the Gastonia
Municipal Airport, located about 13 miles east of Kings Mountain. The data for both sites indicate the
predominant wind direction is out of the north and northeast, with a good portion of the winds out of
the south and southwest.
Wind rose for KAKH in Gastonia,NC Wind rose for KEHO in Shelby,NC
For Feb 1,14A9 m Mey 24,2023 191%01 deu eveileble7 --
en 3,2454 m Mey 24,2@3193%of date eveileble7
N N
Nrm ---- Nrae HNvv ---.!Nr1e
NE NE
ENE
i
W all r W E
Wsvv E=E �
19 z I Wipdsf¢mphi.. , �E
w, nbeenedene s Ce1m W nd. -hi: s
311 d observations
Wind Speed:
i mph mp pM1 15 mph ,u Wind Speed_
�6tn]mph -fa ro l5m h �?2a moh _'a ' A__ 0 2m5mph �]mta mph �15 mph ,uL)
0-7,ph •10 to 15mph >_20 mph
Source: DEQ Air Quality Portal, https://airquality.climate.ncsu.edu/wind/
Figure 2.4: Wind Rose Data in the Vicinity of Kings Mountain
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3 Water Balance Development
3.1 General Model Operating Approach
SRK developed a conceptual model of the Project, representing each of the major facilities that impact
the mine water management system, described in Section 1.4. The conceptual model addresses how
each of these facilities generate, consume, store, and/or convey water(and solids if necessary)within
their defined limits. The conceptual model also describes how these facilities are interconnected and
under what conditions water and/or solids will pass from one facility to another and how the quality of
the water will be impacted as it moves through the Project.
The physical processes and logical structures by which water is generated, consumed, and conveyed
were developed specifically for each component of the conceptual model and are described in detail
in the facility descriptions in Section 3.4.
Within each component of the water balance, the continuity equation shown in Equation 1 is applied
to determine the amount of water that must be stored, released or provided by that component, based
on the understanding of how each component functions.
Continuity equation: (Y_Inflow - Z outflows)At= A Storage Equation 1
3.1.1 Qualitative Assignment of Water Quality
As an overarching goal of the mine water management system, the site-wide water balance attempts
to provide sufficient water for the processing plant to operate at design levels while controlling the
undesirable release of impacted water from the site. To achieve this end, the water balance attempts
to consume the most-impacted water at the site first before attempting to consume water from less-
impacted sources. Similarly, if water needs to be released from the site, the model attempts to release
(or not capture)the least-impacted water first before releasing other, more-impacted water.
While the model does not explicitly calculate or assign water quality to the water flows within the model,
flows are generally considered to conform to four different water categories and are managed
accordingly. The following subsections describe the water quality categories in order of least impacted
to most impacted.
Non-Contact Water
Water that has not come into contact with mining activities is assigned the least-impacted water quality.
This water has only contacted vegetated or newly constructed native soil surface and can be released
to the environment with only appropriate sediment controls. Generally, all surrounding undisturbed
watersheds and any reclaimed surface are defined as generating non-contact water.
Non-PAG Contact Water
Water that has come into contact with mining activities but is expected to meet discharge quality is
assigned the second least-impacted water quality. Generally, water that has come into contact with
non-PAG waste rock, pit walls, and haul roads are assigned as generating non-PAG contact water.
Based on the Geochemistry: Water Quality Predictions Report and the Baseline Geochemical Report
conducted for the project (SRK, 2023b, and SRK, 2023c), this contact water is expected to meet
discharge water quality but will undergo de-sedimentation and monitoring prior to release to the
receiving environment.
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PAG Contact Water
Water that has come into contact with PAG waste is assigned to a more-impacted water quality. PAG
contact water will be treated in a water treatment plant (WTP) prior to transferring into WS13-1.
Process Water
Water sourced from the flotation process system is assigned to a more-impacted water quality, which
will also be treated in a WTP prior to re-use in the process or, if excess treated process water is
produced, transferred to WSB-1.
3.1.2 Project Flowsheet
The interconnections between the facilities are displayed across three project flowsheets for different
phases of the Project. Figure 3.1 shows pre-development and development facilities and flow
connections, Figure 3.2 displays operational facilities and flow connections, and Figure 3.3 depicts
closure and post-closure facilities and flow connections. Flow arrows are color coded to correspond to
the different water categories described above, as well as different color codes for the flow of solids
tracked by the model, such as waste rock, ore, tailings, and product.
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Figure 3.1: Kings Mountain Site-Wide Water Balance Pre-Development and Development Flowsheet
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000u KIVI PFS Surface Water:Water Balance
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SRK Consulting(U.S.), Inc.
2022 KM PFS Report-Surface Water:Water Balance Development Report Page 17
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Figure 3.3: Kings Mountain Site-Wide Water Balance Post-Closure Flowsheet
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3.1.3 Water Balance Model Simulation
The conceptual model of the mine water balance was incorporated into a site-wide dynamic water
balance computer simulation (the Model) designed to simulate the physical processes identified for
each component, and transfer water between the facilities according to the flowsheet.The Model forms
an electronic analog of SRK's understanding of how the mine water management system at the Project
is expected to behave and allows for simulations of the system in an effort to explore the behavior of
the system under typical, legacy, projected, and extreme conditions.
The Model incorporates the water-related aspects of the identified mine components as well as
environmental factors, such as groundwater and precipitation, and the interaction with the physical
and mine processes as they relate to water. Each Model component is specifically constructed to
represent the particular function, geometry, and schedule of the facility components.
Outflows from each component simulated in the Model are carefully linked to the inflow of the
downstream component, and no component is allowed to pass the water to multiple destinations.
Inflows and outflows internal to each component, such as precipitation captured or water stored, are
included in the summation and the change in storage computed.
3.1.4 GoldSim Software
The Model was developed using the GoldSim Version 14.0 Simulation software (GoldSim, 2021).
GoldSim is a highly graphical simulation modeling platform that supports dynamic and probabilistic
modeling. GoldSim also strongly supports top-down modeling and embedded documentation graphics,
both of which have been widely incorporated into the Model.
A GoldSim model is built by describing functional relationships mathematically using elements that
show the linkages graphically.The model can create a dynamic system that evolves through time and
incorporates uncertainties to simulate the system under future conditions and run multiple scenarios
to compare how they will affect future performance.
The availability of several custom components and reporting tools facilitates the development of water
balance models using GoldSim. Although not specifically designed for mine water balance modeling,
GoldSim is very well suited to simulate complex dynamic systems that follow a distinct logical structure
and can be described using mathematical and logical structures. GoldSim is considered the industry
standard software platform for the development of dynamic and probabilistic mine water balances.
3.1.5 General Model Structure
GoldSim models are typically organized by multiple levels of containment (containers or modules)for
clarity. The Model has been organized into four main high-level containers, which contain additional
levels of sub-containment, as needed. Figure 3.4 shows a screenshot of the four main high-level
containers and reporting container, which are described as follows:
• Climate: The climate container develops the daily rainfall and evaporation used by all facilities
in the model. The climate is developed from legacy precipitation records that are reused in
chronological order or reshuffled to form a wide range of climate conditions, from extreme wet
cycles to average and extreme dry cycles. The Model can also simulate the climate using a
synthetic climate generator that produces a climate similar to the legacy conditions but is not
constrained to only values observed in the legacy record.
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• Runoff: Runoff from the various surfaces is calculated using different methods, as described
in Section 3.3.2. The unit runoff rates from the different surfaces are used throughout the
Model to produce rates of runoff from the facilities in different proportions and at different
times, depending on the extent and state of each facility.
• Common: The common container develops parameters that represent Project-wide
parameters or behavior where they are available for use by the individual facility components
located in the water balance container. Section 3.4.6 presents the common container.
• Water balance: The majority of the water balance calculations take place within the water
balance container. The water balance container is described here in general terms.
Section 3.4 describes individual components of the water balance model in more detail,
including input parameters, methodology, and operating logic for each facility.
• Reporting: The reporting container is used for organizing reporting elements and is not part
of the water balance calculations.
G_-1r7 r7
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water Balance Common
O
Climate
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Runoff
Figure 3A Model Screenshot showing the Four Main Level Containers
3.1.6 Incorporation of Uncertainty
Simulation models developed using the GoldSim software can easily be switched between
deterministic simulations and probabilistic simulations. A deterministic simulation is one where all
inputs are known at the beginning of the simulation and are the same for all subsequent simulations,
while a probabilistic simulation allows the inputs to be stochastically determined using probabilistic
distributions during the simulation and will vary according to defined limits.
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While deterministic simulations are useful to understand a system's response to possible changes in
configurations and require less computational time to execute, they fall short in predicting future
outcomes. The Model was built as a simulation of the real-world environment and, as such, it must
contain uncertainty; the Model addresses this through the use of Monte Carlo simulation capacity
within GoldSim that helps propagate uncertainty through the Model.
In a Monte Carlo simulation, each uncertain value is varied using probability distributions. The
distributions, which are typically developed from statistically analyzing legacy data, define and bound
the uncertain inputs within the Model. This means that while the Model is designed to run as a single
deterministic simulation, the Monte Carlo portion of GoldSim runs through the entire simulation period
multiple times while continuously re-sampling the distributions to simulate the variability of the
uncertain inputs each time. One single run with a single set of inputs sampled from the probabilistic
distributions can be referred to as a single realization of the Model. A Monte Carlo model will typically
run through hundreds of realizations, each with a different sample of uncertain inputs.
Running multiple realizations consecutively produces a large number of independent results, each of
which represent possible future outcomes. These independent results are then statistically assembled
by the modeling software into probability distributions for each output. Probabilistic model results are
usually presented as a probabilistic time-series (e.g., annual precipitation totals), which represents a
range of values through time (as shown on Figure 3.5), with the most likely result at any time along
the x-axis presented as the median, or 50% value in the darkest color. At that point in time of the
simulation, 50%of all simulations produced a value less than that value. Less-likely results are typically
plotted as increasingly lighter shades of color, indicating that fewer realizations (i.e., less likelihood)
produced values greater or less than that value. A band of color at the upper edge of the graph might
represent the 95%to 99% probability, indicating that the bottom of the color represents a value where
95% of the all the realizations were less than that, and the top of the color band represents a value
where 99% of all the realizations were less than that. The bands are presented in matching pairs,
below the median (e.g., 1% to 5%), and above the median (e.g., 95% to 99%).
2200 2200
2000 A 2000
i AA
r���
1800 1800
1400
a120a ' 1200
1000 1000
2020 2030 2040 2050 2060 2070 2080 2090
Time
Statistics for Annual Precipitation
25%,.35%/65%,.75% 35%„45%/55%.,65% 45%.35%
50
Source:SRK,2023
Figure 3.5: Example of Probabilistic Time Series
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All of the uncertainty within the Project water balance is addressed through stochastically generated
daily climate using one of two methods: probability distributions developed from legacy data or
randomly re-sampling the legacy precipitation and evaporation data. Section 3.2 presents an in-depth
description of the development of the climatic parameters for the Project water balance.
3.2 Climate Simulation
The impact of climate on the water balance simulation represents the most uncertain of the inputs and
the one that the operator has the least control over. Incorporating a thorough understanding of the
climate at the site and addressing the uncertainty that it brings to the Model is a critical component of
the Model.
3.2.1 Climate Change
Climate change effects in the Project area will develop over the long timescales anticipated for the
post-closure period. The climate change modeling, described in more detail in the PMP and Climate
Change study (AWA, 2022), considers potential effects up to Year 2100 based on a legacy period of
record from 1975 to 2005. This legacy period represents current climate conditions and is called the
baseline, while future differences with respect to this period are called anomalies. To simulate climate
change, the study produced a projected climate out to Year 2100 using the information available from
the IPCC from the sixth assessment report (AR6) (IPCC, 2021). The climate change evaluation was
based on downscaled climate general circulation models (GCM) provided in the NASA NEX-GDDP
dataset (NCEP, 2023).
The climate change predictions are based on 35 GCMs, of which 26 had parameters that were useful
to this study, and across five SSPs. The SSPs are based on five narratives describing broad
socioeconomic trends that could shape future society. Although the IPCC AR6 does not assign
likelihoods to the different SSPs, these are intended to span the range of plausible futures and range
from pessimistic and highly unlikely (SSP8.5), to optimistic and highly unlikely (SSP1.9), and most
likely (SSP4.5).
The results of modeled trends and estimated precipitation frequencies have a large variability that can
be attributed to the uncertainty inherent with GCM projections. The different climate models used for
the Kings Mountain basin represent a significant component of future climate uncertainty in climate
models.This uncertainty is represented by the range of climate futures indicated by the Coupled Model
Intercomparison Project 6 (CMIP6)ensemble of projections.
The median of the 26 models shows an increase in mean annual temperature and mean annual
precipitation (Figure 3.6). Temperature, in regard to daily maximum (frequency based) and monthly
averages, show an increase by 2100 for both the SSP4.5 and SSP8.5 projections (Figure 3.7 and
Figure 3.8).
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Historical SP45 SP85
Model
G A Modell A Model26
A Model10 A Model27
U 22.5 22.5 215 Model 11 Model28
m
QQ Model12 Model29
I Model13 Model30
E 20.0 20.0 20.0 �p Model 14 Model 33
a) ,A ---�- A---- Q Model15 A Model34
~ D- -e�S� ---- A Model 16 A Model4
A Model17 A Model
Q17.5 17.5 17.5 Modell A Model
A Model21 A Modell
Model22 A Model
1150 1200 1250 1300 1350 1150 1200 1250 1300 1350 1150 1200 1250 1300 1MO A Model23 A Model
Annual Precipitation(mmtyr)
Source:AWA,2022
Figure 3.6: Comparison of Mean Annual Temperature and Mean Annual Precipitation for the
Three Climate Projections
0
..r
09
y Projection
E 6- SP45
o E
� SP85
�3
U
All Summer Winter
Source:AWA,2022
Note: Results are based on annual maximum frequency analysis.
Figure 3.7: Change in Daily Maximum Temperatures from Current Climate Conditions
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30
U • Projection
20 . • F� Hist
y • F� SP45
CL
� F� SP85
H
10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Source:AWA,2022
Note: Results are based on daily normal calculations.
Figure 3.8: Monthly Temperature Normal Compared to Climate Change Temperature Normal
A correction for climate change based on the simulation year is incorporated into the Model for both
the SSP4.5 and SSP8.5 climate scenarios, using the mean result of the climate change predictions.
The Model adjusts precipitation by linearly interpolating data from Table 3.1, with the simulation year
between 2005 (no correction)and 2100 for the selected climate scenario.
Table 3.1: Precipitation Climate Change Adjustment
Month Correction Factor for Current Conditions to Projected Conditions
Current, 2005 SSP4.5, 2100 SSP8.5,2100
January 1.00 1.08 1.10
February 1.00 1.08 1.09
March 1.00 1.06 1.05
April 1.00 1.10 1.11
May 1.00 1.05 1.05
June 1.00 1.10 1.09
July 1.00 1.09 1.06
August 1.00 1.12 1.12
September 1.00 1.11 1.09
October 1.00 1.00 1.00
November 1 1.00 1 1.05 1.08
December 1 1.00 1.07 1.04
Source:AWA,2023
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3.2.2 Legacy Climate Time Series
To present model results that are both easily understood and meaningful, SRK performed many of the
screening and summary simulations with deterministic climate time series. These series were
developed using recycled or shuffled legacy climate records from the nearby climate station, after
being compared and corrected for distance from site, length of record, completeness, and matched to
the limited legacy records of both closer climate stations (with limited record lengths)and legacy data
from the site. The Shelby 2 NW Climate Station (USC0317845), located 12.6 miles northwest of the
Kings Mountain pit, was identified as the station with a suitably long record and similarity to the site
climate.
Precipitation
Daily precipitation records were obtained from NOAA's Climate Data Online website(NOAA, 2023)for
the Shelby 2 NW station for the period of 1893 to 2022. The precipitation analysis only used the data
from 1990 to 2022 to avoid temporal bias in the data. The first decade (1990s) of records has some
missing data points (up to 19%) but is nearly 100% complete after Year 2000. To develop a complete
record, SRK used the Daymet gridded dataset (NASA, 2023) to infill the missing data points in the
Shelby 2 NW dataset. Daymet is an interpolated and extrapolated dataset of daily records at
1-kilometer(km)grid spacing available for North America and Hawaii provided by NASA.A comparison
of the Shelby 2 NW and Daymet datasets indicated a difference of less than 1% in accumulated
precipitation over the 30-year period. Appendix A presents monthly values of the 32-year precipitation
time series.
The water balance model is configured so that the legacy time series may be used in sequence,
repeating the legacy climate as it was experienced and merely shifted in time. Alternatively, the years
and months of the time series can be reshuffled intentionally, as well as stochastically, to stress the
system with new climate scenarios (climate forcing). SRK also developed five synthetic climate years:
one that combined the most representative of the monthly records of daily precipitation to produce an
average year of precipitation and four others that combine select monthly records to produce extreme
dry (fifth percentile) to extreme wet (95th percentile) conditions. The following climate forcing years
were produced:
• Fifth percentile year: total annual precipitation = 33.71 inches
• 25th percentile year: total annual precipitation =41.10 inches
• 50th percentile year: total annual precipitation =49.59 inches
• 75th percentile year: total annual precipitation = 54.65 inches
• 95th percentile year: total annual precipitation = 67.17 inches
The synthetic years can be mingled with the legacy years to provide additional climate forcing.
This method of synthetic climate series provides an easy-to-understand way to force the system to
respond to stressors on the system, such as forcing back-to-back wet years or to produce results in
response to an average or extreme climate.
Evaporation
Reference evapotranspiration (Eto) is an evaporation term specifically developed for soils and crop
surfaces. The water balance model uses Eto to calculate losses from land surfaces as part of the
runoff calculations discussed in Section 3.3.2. The water balance model also calculates the losses
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from open water bodies, such as ponds and reservoirs, using a lake evaporation term, which is
evaporation from a surface with unlimited moisture but less energy than Eto.
The Daymet climate datasets for the site included evaporation calculated using the Food and
Agriculture Organization of the United Nations (FAO) Eto method, based on the modified Penman-
Monteith method (Allen et al., 1998). SRK also calculated lake evaporation using the Hamon method
(Hamon, 1961), which requires daily temperatures. Appendix A presents the calculated monthly Eto
for the legacy period.
Lake evaporation and reference evaporation were compared (also shown in Appendix A), and a
correction factor from Eto to lake evaporation was determined on a monthly basis, as shown in
Table 3.2. These correction factors are similar to those recommended by the FAO when correcting
Eto to lake evaporation. The evaporation records are incorporated into the water balance model in a
structure matching the legacy precipitation records, so scenarios run with sequenced or reshuffled
precipitation uses the matching Eto values. Lake evaporation is calculated by correcting Eto.
Table 3.2: Correction from Eto to Lake Evaporation
Month Eto to Lake Evaporation Factor
January 0.44
February 0.41
March 0.43
April 0.48
May 0.60
June 0.71
July 0.75
August 0.71
September 0.63
October 0.53
November 0.47
December 0.48
Annual 0.60
Source:SRK,2023
3.2.3 Synthetic Climate Generator
To provide a more-thorough means of stressing the system by producing climatic inputs that may be
more extreme than those observed in the legacy record, SRK incorporated a stochastic synthetic
precipitation generator based on the WGEN climate generator model developed by the United States
Department of Agriculture (USDA) (Richardson and Wright, 1984) into the water balance model and a
separate model to produce daily Eto values using fitted probability distributions.
The following sections describe the structure of the stochastic models and the methodology used to
develop and validate the model parameters. When stochastic simulations are configured in the model,
both the stochastic precipitation model and the stochastic evaporation model are used instead of the
legacy records.
WGEN Stochastic Precipitation Model
The WGEN model is a second-order Markov Chain model where the presence or lack of rain is
determined from daily probabilities of rain, and a subsequent depth of rain is stochastically generated
only if rain is indicated. Parameters for probability of rain and depth of rain vary monthly. The model is
further refined by developing two separate probabilities of rain: one for days following rainy days and
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a second probability for days following dry days; this allows the WGEN model to simulate many
different climates, from intense but infrequent thunderstorms to multi-day monsoonal rainfall patterns.
The original WGEN model uses the probability of occurrence for presence or absence of rain and a
two-parameter gamma distribution to determine the daily depth of rain. SRK has found that other
probability distributions may be more representative of daily depth of rain at the site and evaluated
multiple distributions as alternates to the gamma distribution to find the best fit to the available data.
Development of Probabilistic Precipitation Parameters
SRK used the daily precipitation from the infilled Shelby 2 NW time series to develop a set of
parameters that would allow the modified WGEN climate model to produce synthetic daily precipitation
values that are representative of the observed site precipitation and can also extend the legacy record
beyond observed values.
SRK analyzed the daily precipitation records to determine the number of days of rain, rain following
rainy days, and rain following dry days on a monthly basis. These counts were used to develop the
probability of rain/no rain for each month given the presence or absence of rain during the preceding
day of the simulation.
Using the rainfall depths from the Shelby 2 NW infilled time series, a probability distribution to simulate
the depth of rain was developed. Non-zero daily precipitation records were examined for each month,
and several continuous probability distributions were fitted to the available data. Nearly 60 different
distributions were examined using a commercial statistical analysis package (EasyFit, 2015), and the
top four or five distributions were examined manually. The final selection of the probability distribution
was based on overall fit, similarity to other distributions fitted to different months, and visual
determination of outliers influencing the fit. Based on this manual selection process, SRK selected the
four-parameter Johnson SB distribution (Johnson, 1949)to represent the daily rainfall depth probability
distribution. The fitted distribution for each month is shown as Cumulative Density Function (CDF),
Probability Density Function (PDF) and Quantile-Quantile (QQ) graphs included as Appendix B.
Appendix B also includes a summary of the probability of rain and Johnson SB distribution parameters.
Synthetic Precipitation Generator Validation
This synthetic precipitation generator allowed SRK to produce both the means and extremes
representative of the precipitation time-series for the site. Statistics from the legacy period presented
in Appendix A are compared to the statistics from 10,000 realizations of a single year (shown on
Figure 3.9).The statistics for monthly precipitation, annual precipitation, and heavy rainfall (a measure
of rain above the 95th percentile daily amount) are compared and indicate that the model is a good
representation of the precipitation experienced at the site.
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Comparison of Simulated Climate vs Daily Precipitation for 1990-2022 Shelby 2 NW Record(Infil led
}
Nine kWT Series
P--k J-. FeEro Mardi Alwl My June J rt sm..ber Odher N-b- 0emnher Annual Prec r- Sm Ile Rain
5% Hi..m ll5.0"%y 2.11 LW 153 IM 1.32 1.26 L56 0.92 032 OA9 0.75 2.21 34.17 34.17 14.65 14.65
25% Hismriol 25.0% 2,84 2.43 37d 266 2.46 212 252 294 2.64 SA9 1.i4 2.78 43.15 43.15 208E 20.8E
50% Hirterical E. 3.69 320 ZN 3.56 3.9T 3.45 3T7 Z. 3.66 3.fd 3.46 4.1a 50.35 50.36 26.71 26.71
75% Hirterical 75.O% &bJ 4A5 622 5.70 5.53 5.6& SS+S SA6 SJIfi 5.1E 5.15 4.90 5i65 5i8 3L50 31.60
95% Hi..i= .O% 4.10 &-% 9.18 7.98 7.30 039 &72 &M 6.73 10A3 8.111 &19 67-95 670 dlf 48 49AS
Sinuletion Oi�_MOOD 1 redh toti
Per-ile Jawa Feb-y Mardi APF1 hb June July Aug- her O.W- Nmanher Oecanher Annual Annual Sum He Rain
5% 3inulomd 134 LM 1.17 1.10 10& L16 147 114 1111 011 0.>q 1.10 37.93 37.93 L5-73 15.73
25% Simulatrd 25.0°6 L94 2.20 3.10 25f 2.57 2.57 2.90 La 19d Im Z17 2.71 44.99 44.99 2176 22.75
50% Sim ulahad SO.OX 4.37 1.29 4AS 326 3.94 111111. 4.23 4.A5 3.7 3.42 3.54 4.09 -90.26 50.26 26.46 26.48
75% Simulxed 75.0% aw &CA 611 5.45 5.57 147 SA4 576 5.16 5.37 5.30 5.66 55.65 55.65 3fA0 31,40
95% Simu lxed 95A% &W 7-17 920 1.13 8.35 &11 &77 975 &55 &ST 8.47 &35 64.23 64.23 39Ad 39A1
Comparison of Simulated Climate vs Daily Precipitation for 1990-2022 Shelby 2 NW Record (Infilled)
u.0 - as 30
c]
9
>0.4 I
........... ._._._._.._�
/•.� sa
- v 1 wwwww
r -----------
xo
�xo
0.0
10
x.o -
0
Jews' FeE-y MerVi Apil Mel line All A%6 SPtreher O[mns Noeemoer Pc2mW Sum HeevY Rein
-----Hlaoriul b.MO ,_Hlna%.:IFS Oii ��N� -•Is4A%I +-H�erlul nSAlif -----HI¢arlul[95.4ly -----9i•Wretl{..� ,_SImWSN II.A3LI ��Slmuhud150.0%I + V,m ,r LMMJ -----Smulnr415fi.113L)
Source:SRK,2023
Figure 3.9: Comparison of Simulated versus Shelby 2 NW Climates
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Figure 3.10 presents a comparison of the annual maximum precipitation of the legacy record and
simulated climate against AWA's Annual Exceedance Probability(AEP) precipitation depths(provided
in Table 2.1) indicate that while the synthetic climate generator produces values very similar to the
legacy record, both values are 10%to 15% below AWA's AEP values for the same annual exceedance
probability (AWA, 2022). This is expected as the methodology used to determine AEP values
increases the midnight-to-midnight precipitation totals reported by the climate stations by a factor of
1.13 to account for 24-hour storm events which may span the midnight divider. This adjustment is not
appropriate in a water balance where total volume of rainfall is more relevant than rainfall intensity.
t_'
12
10
= o
a �
E.Q •
E a 6 _
•
4 ♦ - —
•
•
♦ I I
0
0.000 0.200 0.400 0.600 0.800 L" 1.200
Annual Exceedance Probability
• AWAAnnual Exeedance Probability —Simulated Precipitation ♦ Historical Precipitation
Source:SRK,2023,and NOAA,2023
Figure 3.10: Comparison of Annual Maximum Daily Rainfall from Legacy, Simulated, and
NOAA Atlas 14 Values
Evaporation
SRK examined the daily precipitation and evaporation time series and found essentially no correlation
on a daily basis (r2= -0.16). Based on this, SRK fitted a two-parameter gamma distribution to the daily
Eto, on a monthly basis, to produce stochastic Eto. Appendix B includes the fitted gamma distribution
parameters. The model uses the same correction factor for Eto to lake evaporation that was
determined for the legacy record (Table 3.2).
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3.3 Environmental Components
The water balance model simulates the influence of the environment on the water management
system. Environmental components, such as runoff, infiltration, and percolation, are simulated using
mathematical models that approximate the behavior observed or inferred at the site.
3.3.1 Infiltration Simulation
Infiltration into the various facilities was estimated using a percent of rainfall equation, where a
percentage of rainfall falling on the surface is modeled to infiltrate into the material below based on
soil characteristics and observed behavior for similar materials. A Green-Ampt (Green and Ampt,
1911) simulation of the uncovered tailings materials using a sandy clay loam soil texture and a
hydraulic permeability of 2.52 millimeters per hour(mm/h) (7 x 10-5 centimeters per second (cm/sec))
was performed to benchmark the infiltration parameter used for the tailings surface. Table 3.3 presents
the infiltration percentages were assigned to surfaces in the model. To assess the impact of these
assumptions, a sensitivity study, described in Section 4.1.4, was performed to quantify the impact of
these values have on the model results.
Table 3.3: Surface Infiltration Parameters
Modeled Surface Soil Texture Infiltration as Percentage of Rainfall (%)
Pit wall Hard rock 0
Active waste rock Cobbles and boulders 65
Reclaimed waste rock cover Silty clay 5
Source:SRK,2023
3.3.2 Runoff Simulation
Overland flow contributions to the water balance model will be produced by undisturbed ground,
disturbed ground, active mining surfaces, and reclaimed surfaces. A different rainfall-runoff
relationship has been developed for each of these representative surfaces.
GR4J Runoff-Infiltration Lumped Parameter Model
To represent runoff from large, natural watersheds, SRK selected the Model of Rural Engineering with
4 Parameters Daily (GR4J) lumped parameter model (Perrin, 2002). As shown on Figure 3.11, the
GR4J model uses a production store, a routing store, and two routing hydrographs (UH1 and UH2)to
collect, store, release, and attenuate runoff and infiltration flows. Losses or gains from groundwater,
labeled F(X2), are added or removed from both the slow-release hydrograph (UH1) and the rapid
response hydrograph (UH2).
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T
p
interception
En Pn
Es Ps Pn-Ps
Production
store Xl S
Perc Pr
0.9 0.1
UH1 UH2
H
X4 2.X4
Q9 Q1
Routing
store X3 R F(X2) F(X2)
Qr Qd
Q
Source: Perrin,2002
Figure 3.11: GR4J Runoff Model Schematic
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SRK used the GR4J model converted to the GoldSim platform by GoldSim Technology Group (GTG)
(2021)for surfaces that behaved like rural surfaces, meaning natural soils with sufficient thickness to
store and release water, to simulate infiltration and runoff resulting from rainfall in the water balance
model.
The GR4J model is very good in replicating the behavior of surfaces where there are observed flows,
which applies to the natural ground surfaces of the Project site for which stream gauging was
performed. The GR4J model does not use the physical soil properties as part of the inputs when
developing the parameters, so without observed flows, the GR4J model parameters are challenging
to develop.
Using observed flows in Kings Creek and South Creek from the 2022 and 2023 streamflow monitoring
program, SRK iteratively developed the parameters for the GR4J model to simulate natural flows at
the Project site, as shown in Table 3.4.
Table 3.4: GR4J Model Parameters
Parameter Value
X1 Production store capacity 1,045.5 millimeters mm
X2 Catchment exchange coefficient 0.00102 mm
X3 Routing store capacity 0.1028 mm
X4 Unit h dro raph time base 0.999 day
Source:SRK,2023
The GR4J model with these parameters produces daily overland flow and interflow in response to
rainfall events. The GR4J model was simulated using legacy rainfall patterns and produced with an
average annual basin yield (total volume runoff/total volume precipitation) of 13.3%.
3.3.3 Other Runoff-Infiltration Models
For surface configurations used in the water balance modeling that did not behave like typical soil
surfaces or where there are no observed flows, SRK used the curve number (CN) method (National
Resources Conservation Service (NRCS), 2004) to produce runoff from the surface in response to
daily rainfall. The CN method uses a single indicative value between 1 and 100 to determine the non-
linear rainfall-runoff behavior. The CN value is used to determine the maximum retention value (S)
using S = 25.4(1 CN — 10) Equation 2:
S = 25.4('00— 10) Equation 2
CN
Where: S = maximum retention (mm)
CN = curve number(1 to 100)
The runoff amount(Q) is then related to the rainfall with Q = (P-0.2S)2 Equation 3:
P+0.8S
Q = (P-0.2S)2 Equation 3
P+0.8S
Where: Q = runoff(mm)
P = precipitation (mm)
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SRK assigned CN values to surfaces within the water balance model, as shown in Table 3.5. To
assess the impact of these assumptions,a sensitivity study(described in Section 4.1.4)was performed
to quantify the impact of these values have on the model results.
Table 3.5: CN Values by Surface
Surface CN
Pit wall 85
Active waste rock 40
Reclamation cover 76
Haul road 82
3.3.4 Percolation and Uptake Simulation
Percolation of infiltration into the coarse waste rock surfaces is expected to experience both lag and
attenuation, as well as moisture uptake into the dry waste rock placed in the RSFs. SRK developed
an empirical model that simulates the behavior of the RSF based on observations of similar facilities,
assuming three possible pathways for moisture to percolate through the RSF.
Active Waste Placement Zone
Once moisture infiltrates beyond the upper surface of the waste rock,the model simulates this moisture
percolating downwards and could potentially encounter dry waste rock. If the waste rock is below the
breakthrough moisture content, the percolating moisture will be permanently absorbed into the waste
rock, raising the moisture content of the waste rock. Once the waste rock has achieved breakthrough
moisture content, moisture will begin to percolate through the waste rock at some downwards velocity
dependent on the moisture content of the rock. Although this behavior is non-linear, due to the coarse
nature of the waste rock, the range of moisture content for this behavior is fairly limited. For simplicity,
SRK has assigned a single breakthrough moisture content with a corresponding percolation velocity.
The percent of infiltration assigned to the active zone encountering waste rock below the breakthrough
moisture content will produce no seepage, while moisture encountering waste rock above the
breakthrough moisture will percolate at a single velocity. The lag time for moisture moving through the
system is based on the height of RSF (travel time = height/velocity), while the attenuation of the flow
is simulated using a GoldSim delay element, which allows for the outflows to be distributed temporally
in a bell shape. The outflow of the delay element is modeled to report as seepage to the bottom of the
RSF.
Short Circuit Zone
Not all moisture infiltrating into the waste rock will encounter waste rock below the breakthrough
moisture content. The model assigns a small percentage of the infiltration to short-circuiting the
majority of the waste rock, either through preferential pathways through the RSF or by being located
near the periphery of the RSF such that it is a very short pathway to the base of the RSF. This waste
rock is modeled as having achieved breakthrough moisture content shortly after being placed.
The percent of infiltration assigned to the short-circuit zone is modeled as reporting immediately as
toe seepage from the RSF.
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Inactive Waste Placement Zone
Some infiltration will also encounter waste rock already at the breakthrough moisture content. This
infiltration will not experience the moisture uptake mechanism described above but will be subjected
to the lag and attenuation of moisture moving through the waste rock.
The percent of infiltration assigned to the inactive zone will be added to the GoldSim delay element
and be subjected to the lag and attenuation calculation before reporting as seepage to the bottom of
the RSF.
Infiltration to the Ground
Any moisture percolating through the waste rock that reaches the base of the RSF will potentially seep
into the ground and be lost. If the RSF includes a low-permeability liner, such as that proposed for
RSF-X(SRK, 2023d),the potential seepage to the ground is defined as zero.Any seepage to the base
of the RSF (above the potential infiltration rate)will report as toe seepage from the RSF. Toe seepage
is modeled to be collected by the RSF under drains and report to the seepage collection sumps at the
RSF perimeters.
Figure 3.12 shows a schematic of these pathways.
1111 irep�iti°i 11111
Closed Operational0
�,a ■I1 Infiltration■ Infiltrationion �G cos
No uptake Uptake in
Non-contact
in Inactive Active Area Short
Flow Area Delay Circuit
Toe Contact
Seepage to I01 Seepage Flow
Groundwater I
■ GKound�atQY tnf�o.N
Source:SRK,2023
Figure 3.12: Schematic of Waste Rock Infiltration and Percolation
The model distributes the total infiltration into the waste rock amongst these three mechanisms, based
on the RSF geometry and relative surface areas of the RSF periphery, active waste placement area,
and area of inactive waste rock surface (defined as at breakthrough moisture content).
3.3.5 Groundwater Inflows
Groundwater inflows to the model are used to define the seepage from the pit walls to the pit sump as
the mine extends below the groundwater table. SRK performed detailed studies as part of the
hydrogeologic evaluation of the Project site (SRK, 2023a) that were incorporated into the water
balance model.
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The hydrogeological three-dimensional (3D) finite element modeling predicted groundwater inflows
into the open pit based on the period of mining, as well as the water levels (or pit bottom elevation if
no pit lake is present). Groundwater inflows for different stages of pit activities were represented in the
water balance model, as follows:
• Groundwater inflows during the existing pit lake (pre-development)
• Groundwater inflows during the pit lake dewatering (from current to existing pit bottom)
• Groundwater inflows during active mining (based on pit bottom and simulation year)
• Groundwater inflows during the closure/post-closure pit lake recover period (based on pit lake
water level)
Groundwater inflows were provided on an average annual basis from the groundwater model, as
shown on Figure 3.13. Later in the post-closure period, the Kings Mountain pit lake is expected to act
as a flow through system, so there will be both a groundwater inflow to the pit and an outflow to
groundwater from the pit.These values were interpolated into monthly values for the operational period
to smooth the inflow values. During the post-closure period,the stochastic nature of the water balance
model will impact the water level in the post-closure pit,which will impact the groundwater inflows.The
groundwater inflows were correlated to pit lake level, and a groundwater response curve was
developed that simulated the groundwater inflows and outflows as a function of pit lake level, as
opposed to time. Figure 3.14 shows this responsive curve.
300 900
800
a 700
tip o
L (O
200 600
3 "'
500 m
V)
E400
o +
c�
00 100 300
a)
3 Y
LL 200
a
100
0 0
2030 2040 2050 2060 2070 2080 2090 2100
Time
Groundwater Inflows — Groundwater Outflows Pit Lake Water Surface Elevation
Source:SRK,2023
Figure 3.13: Groundwater Inflows and Outflows from the Pit
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350
300
s=
250
3
0
200
0
au 150
3
� 100
0
C7
50
0
550 600 650 700 750 800 850 900
Pit Lake Water Surface Elevation(ft)
Source:SRK,2023
Figure 3.14: Groundwater Inflow Response Curve
An interesting effect was noted in the groundwater model after the pit lake had achieved the discharge
elevation, where the outflow to the groundwater decreased by about 7 gpm over 6 years (as can be
seen on Figure 3.13 around Mine Year 87), likely due to stabilization of the shallow groundwater
system adjacent to the pit. This effect was included in the model based on the actual date the
simulation achieved the discharge elevation.
3.4 Facility Components
Each of the major facilities that impact the mine water management system are incorporated into the
model with GoldSim containers to form a facility module. Each module is specifically constructed to
represent the particular function, geometry, and schedule of the facility. Individual modules within the
water balance container have typically been developed using a common template for clarity and
organization, as shown in a screen capture of the GoldSim model on Figure 3.15. Key points of the
typical layout shown on Figure 3.15 are as follows:
• All inflows from outside the module are linked in the green box on the left side of the screen.
• Outflows from this module to another module are located in the red box on the right side of
the screen and will typically be the only link to other modules in the model (other linkages may
be utilized for reporting or verification).
• Demands on this module from other modules or other information provided by other modules
are located in the blue or magenta boxes on the top of the screen. Demands (e.g., water
needed to meet process makeup requirements) are passed through this element, and the
module will attempt to satisfy them. For every demand passed to this module, there will be a
corresponding outflow sending available water to the module making the request.
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• Demands on other modules are located in a yellow box at the bottom of the screen and
represent water needed by this module (e.g., water from a feed pond to treatment). Other
facilities will receive this demand and attempt to send water back to meet it. For each demand
this facility defines, there will be at least one corresponding inflow to the facility.
Links ® Water Storage Basin 1 (Formerly Executive Club Lake)
Returr Da sncrara
Demands on This Module Module Switches and Info exchanged
fx
Dema nd_Ha ulAd_Dust_f.tn _Makeup
Impacted Area
Module Inflows / Module Outflows
k �(
fV N Jx .
Pit_Dewatering WSBS_Inputs Ma keupto_Process W!P
fx f1
Haul_Roatl_CorKaR_R.n F� Haul_Rtl_Dust_Con I
� �
,r o
o 1.19 _in_fnntac[_Piii W 131 Cal— ,tl
NPI fn—a Ri, H
L
f bvercop to_Env
NA_R F_A Pumi 1`
fx a W56_QuHlow\\\
PAG_RSF_%_Pu mpo
fWSRSummary
x
ROM_Water Pum t Ffx
Runufl_Below_Embankmert Flow to_Kmn Creek
Tre ate tl_PAG_LVa[er
Module exchanged
Demands on other Modules
Source:SRK,2023
Figure 3.15: Screen Capture of Typical Water Balance Module
Within the module, there will be additional inputs and calculations in the area between the boxes,
organized by additional levels of containment, if needed. Water flows, water demands, ore and waste
tonnages, and other information are passed between the modules to determine the flows and
accumulation of water in the system according to the conceptual model.The following section presents
a detailed description of the facilities and key inputs used by the model.
3.4.1 Open Pit
The Kings Mountain open pit will produce ore and waste rock during the active mining period and
accept, discharge, and accumulate water over the entire simulation period.
Existinq Conditions and Pre-Development Stages
The open pit is an existing excavation with a bottom elevation of approximately 655 ft filled with a pit
lake at an approximate elevation of 800 ft that is steadily rising a few feet each year as a result of
natural recharge and runoff. As part of the pre-development mining activities, the model simulates the
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dewatering of the existing pit lake to the existing South Creek Reservoir in preparation for mining
activities. South Creek Reservoir discharges to Kings Creek near the southern edge of the site. Once
the pit lake has been drained, pumping will continue to maintain a dry pit, discharging to WSB-1 once
it has been constructed.
Development and Mining Stages
Once the development stage of the mine begins (Mine Year-1), waste rock will be mined from the pit,
and non-PAG waste rock and overburden will be disposed of in RSF-A. Any PAG waste rock will be
placed in RSF-X.
During operations (Mine Years 1 through 8), the pit will send ore to the run-of-mine (RoM) pad, non-
PAG waste rock and overburden to RSF-A, PAG waste rock to RSF-X, and aggregate rock off-site for
commercial use according to the schedule presented in Table 3.6.The ore and waste production were
converted to tons per day for input the model. Additionally, the last year of production was distributed
quarterly to produce the nominal production in Q1 of Mine Year 9 and a reduced production in Q2 of
Mine Year 9 only, rather than a lower production rate for the whole year.
Table 3.6: Annual Mine Production Schedule
Mine Annual Production (kt)
Year Total Ore Feed Non-PAG PAG Overburden Aggregate
(includingStockpiles Waste Waste Waste
-1 187 1,464 349 263 97
1 2,281 2,075 1,004 0 1,295
2 2,902 869 647 0 1,744
3 3,081 1,345 674 580 2,927
4 2,922 2,505 1,623 545 5,174
5 2,732 2,186 2,300 350 5,432
6 2,916 2,017 2,157 482 7,429
7 3,110 1,583 1,094 182 5,758
8 3,064 619 617 1 7,711
9 1,309 118 100 0 3,992
Total life-of-mine(LoM) 24,505 14,780 10,563 2,403 41,560
Source:Albemarle,2023
During the development and mining stages, run-on to the pit, runoff from the pit walls, and groundwater
inflows reporting to the pit walls will be managed by collecting water in a transient sump in the pit
bottom and pumping to WS13-1. Run-on to the pit from external watersheds will be minimized through
a series of diversion ditches and collection ponds in low points along the perimeter, routing non-contact
run-on around the pit to discharge into Kings Creek, as shown on Figure 3.16.
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PERIMETER PIT
000*0 POND 71
�►
<v� o m
f -
PIT PHASE 4
t ERIM PONDER TP73 COUNTOURS
f
TFMPDRARY
PERIMETER PIT
POND 72
\ %
Figure 3.16: Pit Perimeter Ponds
The topography of a low area on the north perimeter of the pit is not suitable for diversion, and three
ponds will be constructed to intercept run-on before it enters the pit and pump it to the nearby diversion
channel. The pit perimeter ponds have been sized to intercept the runoff from a 25-year storm and
pump out the resulting water volume within 7 days. Ponds 71 and 73 will be active during the entire
LoM, while Pond 72 will be removed during the Phase 4 pit expansion.
Groundwater inflows to the open pit have been predicted during the development and mining stages
as part of a numerical modeling effort for the Project, as described in Section 3.3.5.
The model allows the pipeline conveying pit sump water to WSB-1 to be used to provide haul road
dust control water during operations along various points of the pipeline. Section 3.4.6 discusses the
demand for dust control.
Closure and Post-Closure Stages
At the end of active mining, the pit will stop producing ore and waste rock, and the pit pumping will
cease, allowing the post-closure pit lake to begin forming. At the same time, PAG waste rock will be
mined from RSF-X and used to backfill the lower portions of the pit. Based on the end of mining pit
shells developed for the mine, the backfill will occupy the lower 280 ft of the pit once the backfill
operations are completed.
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The waste is simulated as being placed in level lifts in the bottom of the pit, speeding the time needed
to inundate the PAG waste. The water balance model accounts for the displaced water in the lower
portion of the pit lake by the solids portion of the waste rock backfill using the densities provided by
the mine planning team to determine effective porosities in the backfill voids.
During the closure period, the diversion channels and collection ponds will be removed, allowing the
diverted watersheds to flow directly into the pit, increasing the rate of filling.
The rate of pit lake level rise will initially be quite rapid due to the constrained nature of the pit bottom.
To maintain the level of the pit backfill above the pit lake level, SRK iteratively adjusted the rate at
which waste rock is remined from RSF-X to keep the top of the waste rock above the pit lake level,
arriving at a value of 50,000 t/d.
During the post-closure period, after several decades of run-on, runoff, and groundwater contributions,
the pit lake is projected to reach the discharge elevation of 850 ft and begin discharging into Kings
Creek.
Table 3.7 shows key inputs used by the model to simulate the open pit.
Table 3.7: Key Pit Input Parameters
Parameter Open Pit Value
Phase 1: 0 Mt
Pit phase by ore tonnage produced Phase 2:4.10 Mt
Phase 3: 7.70 Mt
Phase 4: 14.11 Mt
Pre-development: 655 ft amsl
Phase 1: 655 ft amsl
Pit bottom by pit phase Phase 2:465 ft amsl
Phase 3: 375 ft amsl
Phase 4: 286 ft amsl
Pre-development: 99.59 acres(ac)
Phase 1: 105.2 ac
Pit footprint by pit phase Phase 2: 110.4 ac
Phase 3: 124.7 ac
Phase 4: 124.7 ac
Pre-development: 61.07 ac
Phase 1:49.0 ac
Run-on contributing area by pit phase Phase 2:49.2 ac
Phase 3: 5.0 ac
Phase 4: 5.0 ac
Run-on area diverted around pit operational 27.24 ac
Run-on area contributing to Pond 72 14.64 ac
Top area Pond 72 0.48 ac
Pumping capacity Pond 72 100 gpm
Pit rim discharge elevation 850 ft amsl
Backfill waste rock density 0.0707 tons/cubic feet ft3
Backfill waste rock porosity 0.15
Pre-development pit clewatering rate 2,500 gpm at 95%availability
Operational pit clewatering rate 5,000 gpm
Initial pit lake level January 1,2023 800 ft amsl
Initial moisture of rock from pit 0.5%
Source:SRK,2023
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3.4.2 RoM Pad
Ore from the pit will report to the RoM pad for stockpiling and sorting. The stockpiles are simulated
dynamically using the target production rate to deplete the stockpile as the ore from the pit is added.
Precipitation falling on the RoM pad is allowed to produce runoff and infiltration into the stockpiles, but
as the stockpiles are being constantly depleted and replenished, the RoM pad is simulated as
producing no seepage. The water balance model routes all non-PAG contact water from the RoM pad
to WSB-1.
Ore sorter rejects account for a loss of 1.75 Mt of ore over the LoM from the RoM pad. These rejects
are sent to RSF-X and combined with the PAG waste. The remainder of the ore is sent to the
processing plant.
Table 3.8 shows key input parameters for the RSFs.
Table 3.8: Key RoM Pad Parameters
Parameter RoM Pad Value
Ultimate footprint 17 ac
Ore sorter rejects final moisture content 5.26%
Source:SRK,2023
3.4.3 Processing Plant
The processing plant accepts ore from the RoM pad and produces direct media separation (DMS)
product, concentrator product, DMS reject, DMS concentrator waste, and concentrate tailings.
Figure 3.17 shows the full flowsheet of the process provided by the process engineering team, which
was simplified for the site-wide water balance to that shown on Figure 3.18.
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1 1 2 1 3 1 4 5 6 7 1 8 1 9 1 10 11 12 13 14 15 16 1T 18 19 20 21 22 1 23 24
A rFRrIAaT causHwr.
5EcoNOAMr cRusnINc�
I I
o
,
1 � �sw, 111111 f
_ Izrsen®ac_omne
ROUGHER OMS D78J.tlG MAGNETIC CFPARATIf]N r INIfA Fl(IT0.TION��� 0, p
F
-41
---
I�P�,�/ __—__—_ I I I I - -• H
�6SPODUMENE(Li
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II I I
I -
, I
(27 CI FANFR OMS
�I I� I K
II f.ONC.FNTRATF DFWAIFRING � I -- I
F-1 FIL
:j L
i L
761.--7�
SEPARATION i L-- i L-- M
MCd CONCENTRATE I SPODUMENE(Li)TAILS
DEWA7ERING DFWATFRINf
`°` L - ----- -- Z- - ------------
� � N�_aN II IIOL�'E U O O coucENTRAr Loa N
PRELIMINARY 'I` II
0 0 OT FOR CDNSTRUCTIO o a p
vans.a,o oin H LET[H NINGS MOUNTAIN
�Ap 9EMARLS
_ _ Q
uNlxGwxo eo„ceNrrsnra,�EACI r�
e EWIOE
MINE SEIELTPWSE
R
�KM60-PR-BF_0100
N NTE o -EOiNL]]I
Source: Hatch, 2023
Figure 3.17: Process Plant Flow Diagram
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RSF-A RSF-X Off-Site Off-Site
it .- I L
Direct Magnetic Separation and Mica Flotation new Off-Site
Raw Water Process Water
.. Storage
Figure 3.18: Simplified Process Plant Flow Diagram
During operations(Mine Years 1 through 8), the pit will send ore to the RoM pad, non-PAG waste rock
to RSF-A and off-site for commercial sale, and PAG waste rock to RSF-X according to the schedule
presented in Table 3.6. Based on the proportions of nominal tons defined in this flowsheet,the percent
of incoming ore from the RoM pad is split into the product streams for shipment offsite, DMS rejects
added to the net acid generation (NAG) waste stream, and tailings sent to the off-site TSF. Each
stream is assigned a water content after dewatering at the filter plants, which determines the amount
of water leaving with each stream.
As part of the process flowsheet, the processing plant requires a minimum stream of relatively clean
water for gland seal water and reagent use. The minimum stream results in a net positive water
balance at the processing plant. The model routes this excess process water through a WTP at the
processing plant area, and all effluent from the WTP is reused in the process as clean makeup water.
Table 3.9 shows key input parameters for the processing plant.
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Table 3.9: Key Processing Plant Input Parameters
Parameter Processing Plant Value
Percent of RoM to DMS rejects(non-PAG waste) 33.85%
Percent of RoM to DMS concentrate (product) 13.30%
Percent of RoM to flotation concentrate (product) 7.88%
Percent of RoM to DMS concentrate rejects(PAG waste) 1.1%
DMS rejects moisture content(gravimetric) 10%
DMS concentrate moisture content(gravimetric) 1%
Flotation concentrate filtered percent solids 96.1%
Flotation tailings filtered percent solids 85%
Plant clean water demand 35.45 gallons/ton
Dust control makeup demand 13.8 gpm
Source:SRK,2023
As described in Section 3.4.4, PAG contact water seepage and runoff from RSF-X are also routed
through a WTP in the vicinity of the processing plant before they can be discharged. The model uses
the effluent from the WTP as clean makeup water in the process. If there is still a demand for clean
water makeup in the process after all the treated process and PAG water are used, additional clean
makeup water is drawn from WSB-1 to meet the target demand of clean water at the processing plant.
3.4.4 RSF
The water balance model includes two separate RSFs: RSF-A for the storage of non-PAG waste and
RSF-X for the storage of PAG waste. Additionally, rejects from the DMS process and a small stream
of aggregate tailings from off-site processing activities will be mixed with the non-PAG waste, while
ore sorter rejects from the RoM pad and a small stream of DMS concentrate waste will be mixed with
the PAG waste. Clean waste rock may also be used as a construction material during development of
the mine. Select aggregate waste will be shipped directly off-site for commercial sale and is not
considered further in the water balance.
Conceptually, the RSFs are all modeled according to the same model, described below:
• The footprint of the RSF is determined from the current tonnage placed on the RSF. In general,
the model simulates the RSF footprint as expanding rapidly until the full extent is achieved,
followed by the placement of subsequent lifts, increasing the height of the RSF without further
increasing the footprint.
• The footprint is separated into reclaimed and active waste rock. While waste is being placed
on the RSF, the surface is as active, consisting of uncovered waste rock. When the waste
rock slope has achieved ultimate grades and if the RSF is a permanent structure, the model
simulates the active waste rock as being regraded, covered with growth media, and
revegetated.
• For each of the active and reclaimed areas of waste rock, the model calculates infiltration and
runoff from the surface, as described in Sections 3.3.1 and 3.3.3. Runoff from the active waste
surface is assigned to non-PAG contact water in the case of RSF-A and PAG contact water
in the case of RSF-X, while runoff from the reclaimed surfaces is considered non-contact
water.
• Infiltration enters the waste surface and is percolated through the RSF, as described in
Section 3.3.4, with the percentages of short-circuit, active, and inactive proportions assigned
based on the general geometry of the RSF and anticipated extent of active placement. The
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travel time used to estimate lag and attenuation is derived from the average height of the RSF
through time.
• Toe seepage and active runoff are combined in a collection pond at the perimeter of each
RSF. A pump and force main system transfers the non-PAG contact water from RSF-A to
WSB-1 and PAG contact water from RSF-X to the WTP. The size of the pond(s) and nominal
pumping capacity were iteratively selected to minimize the likelihood of the pond overtopping
into the non-contact water management system.
RSF-A
RSF-A is located in the southwest corner of site, as shown on Figure 1.2. Non-PAG contact water from
RSF-A is collected in a single collection sump (Pond 61), located at the low point on the southern
perimeter and transferred by pump and force main to WSB-1. RSF-A will be closed and reclaimed at
the end of mining.All runoff from the closed cover will be routed to the non-contact perimeter channels,
and the collection sump will be breached to allow seepage flows to passively discharge to South Creek.
RSF-X
RSF-X is located south of the pit (as shown on Figure 1.2) and will be the only RSF containing PAG
waste. To minimize the potential release of PAG contact water, the PAG waste will be underlain with
a synthetic liner. Contact water and seepage from RSF-X will be collected in a single collection sump
(Pond 52) located at the low point along the southern perimeter before being transferred to a WTP
located near the process plant infrastructure. During closure activities, all waste materials in RSF-X
will be re-mined and relocated to partially backfill the pit, as described in Section 3.4.1. At the end of
closure activities, there will not be any PAG waste remaining in RSF-X to require reclamation, and the
synthetic liner will be removed and disposed of.
Table 3.10 shows key input parameters for the RSFs.
Table 3.10: Key RSF Parameters
Parameter RSF-A Value RSF-X Value
Waste rock capacity 41.80 Mt Phase 1: 13.60 Mt
Phase 2: 34.00 Mt
Aggregate tailings 410.7 t/d Not applicable
Ultimate footprint 87.88 ac Startup:29.50 ac
Expansion: 51.00 ac
Ultimate height 230 ft 100 ft
Short-circuit 5% 5%
Active area 75% 75%
Inactive area 20% 20%
Low-permeability liner No Yes
Collection pond capacity Pond 61: 0.66 million Pond 51: 2.40 Mgal
gallons M al Pond 52: 2.93 M al
Transfer pump capacity Pond 61: 200 gpm Pond 51: 250 gpm
Pond 52: 250 qpm
Source:SRK,2023
3.4.5 WSB-1
WSB-1 is located south of 1-85. A significant portion of the watershed between the lake and 1-85 is
diverted around the lake. During the development period, the diversions will be removed or directed
into WSB-1, and the former TSF embankment will be restored and stabilized. A low-level outlet,
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suitable to maintain a minimum pond volume and retention time, will be constructed for sediment
control. Although not simulated in the model, WSB-1 can be used to limit discharge of contact waters
that are expected to meet discharge quality during upset conditions. WSB-1 serves as a collection
node for all contact waters produced by the site that are assigned water qualities generally suitable for
discharge to Kings Creek. WSB-1 accepts contact water flows from the following sources:
• Pit dewatering flows from the open pit sump
• Seepage and runoff from RSF-A collection sump (Pond 61, Figure 1.2)
• Runoff from portions of the process and non-process infrastructure areas that are expected to
produce non-PAG contact water and RoM pad collection sump.Water management and pond
sizing for these components is described in the process area stormwater management plan
(Hatch, 2024).
• Unused treated PAG contact water from the WTP
The model was constructed to allow additional sources to be added to WSB-1 if needed, including
runoff from the haul roads and runoff from the NPI that does not directly flow into WSB-1. For the
purposes of this report, both of these streams are modeled to discharge directly to Kings Creek or
South Creek instead of being routed to WSB-1, as they are expected to meet the discharge
requirements of North Carolina Division of Energy, Mineral and Land Resources(DEMLR). In addition,
WSB-1 will receive non-contact inflows from runoff from the surrounding watersheds and direct
precipitation on the pond open water.
WSB-1 will supply makeup water to the processing plant, if necessary, as described in Section 3.4.3,
and serve as a backup source for haul road dust control water, as described in Section 3.4.6. WSB-1
will discharge directly to the drainage below the pond, which joins Kings Creek south of 1-85,
approximately 1,750 ft downstream of the toe of the WSB-1 embankment.
Under normal operating conditions, discharge from WSB-1 will be passively controlled through a low-
level outlet works consisting of an 18-inch-diameter vertical riser pipe set at an elevation of 830 ft amsl,
10 ft above the lowest point in WSB-1 and 20 ft below the crest of the WSB-1 embankment at an
elevation of 850 ft amsl.Additional riser pipes will be installed at elevations of 825, 835,and 840 ft amsl
as part of the design of WSB-1, although these are not used in the model simulations.
The WSB-1 emergency spillway is incorporated in the model at an elevation of 843 ft amsl, designed
to convey the inflow design flood (IDF) with less than 2 feet of flow depth, resulting in a maximum
water level in WSB-1 of 845 ft amsl. The water balance model does not simulate the use of the
emergency spillway, as it is only utilized during upset conditions when the pond is prevented from
discharging water for an extended period of time prior to generating the IDF.
Prior to operations and during the post-closure period, the model simulates the existing outfall of
WSB-1 as a spillway, with an invert at an elevation of 823 ft amsl. During operations, the spillway and
outlet elevation are set as described above. The model incorporates an elevation-dependent outflow
function for flow through all outlet structures, which simulates the rise and fall of the WSB-1 water
levels in response to an change in flows into the lake.
The model simulates WSB-1 being breached at the end of mining, returning the outlet invert to an
elevation of 823 ft amsl. Table 3.11 presents a summary of key parameters used to simulate WSB-1.
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Table 3.11: Key WS13-1 Input Parameters
Parameter Value
Pond base elevation 820 ft amsl
Pond crest elevation 850 ft amsl
Direct contributing watershed 115.92 ac
Diverted watershed 151.81 ac
Spillway invert elevation 843 ft amsl
Low-level outlet elevation 830 ft amsl
Contributing watershed 281 ac
Spillway bottom width 16 ft
Riser pipe diameter 18 inches
Source:SRK,2023
3.4.6 Other Components
Additional components are simulated in the water balance model to represent the complete structure
of the proposed mine facilities and mine water management infrastructure; these include the following
model components.
South Creek Reservoir
South Creek Reservoir is an existing retention pond located on the south side of the mine site. The
pond is used as a sediment control structure for South Creek collecting flows from the residential area
upstream of the mine site, the NPI area, haul roads, and other non-mining activities in the western
portion of the Project area. The South Creek Reservoir is represented as a node for flows passing
through the southern portion of the mine site.
Non-Contact Collection Node
A collection node for all non-contact water collected by the various diversion structures, runoff from
reclaimed surfaces, and post-closure discharges from the RSF collection sumps is included for clarity.
All flows from the non-contact collection nodes are routed to Kings Creek.
Dust Control Demand Module
Haul road dust control is calculated in the dust control module using recommended application rate
from National Institute for Occupational Safety and Health (NIOSH) (Cecala et al., 2012), estimating
the number of 3,000-gallon dust control tankers needed per day on a seasonal basis, as shown in
Table 3.12. Seasonally varying flows are applied during the times the mine is operational and during
the closure period. No dust control demand is calculated during the pre-development or post-closure
periods.
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Table 3.12: Dust Control Schedule
Month Active Mining Closure Activities
(Trucks per Day) (Trucks per Day)
January 1 1
February 1 1
March 1 1
April 2 1
May 2 1
June 3 2
July 3 2
August 4 2
September 3 2
October 2 1
November 2 1
December 1 1
Source:SRK,2023
Dust control demand is first withdrawn from the pit sump dewatering flows at various points along the
pit ramp and haul road leading from the pit. If there are insufficient pit dewatering flows to meet the
dust control demand, additional dust control water will be withdrawn from WS13-1.
Pre-Development Pit Discharge Treatment
As described in Section 3.4.1, the pre-development dewatering of the existing pit lake will discharge
to the South Creek Reservoir. A minimal level of treatment is required prior to discharge, so the flows
are passed through a tracking node to simplify reporting and volumes requiring treatment.
Waste Rock Distribution Node
Due to the complexity of routing non-PAG, PAG, ore sorter rejects, and DMS concentrate waste and
rejects to the various RSFs and off-site destinations, a distribution node for all waste rock and rejects
was incorporated into the model to simplify the distribution of the waste materials to the various
facilities.
South Creek and Kings Creek Watersheds
Runoff from the undisturbed watersheds upstream or within the mine site are calculated using the
watershed models described in Section 3.3.2 and produce streamflow that is routed through the model
facilities, as described in the sections above.
Pre-development watersheds contributing to Kings Creek within the Project area are incorporated into
the model, and the contributing areas are adjusted during the LoM as the facility footprints within the
watersheds expand during development or contract during closure of the mine. Generally, the flow in
Kings Creek and South Creek do not impact any facilities in the mine water balance and are passed
through the site in the creeks and non-contact diversions; they are included for reporting purposes
only. The Kings Creek watershed upstream of the Project areas includes a large aggregate quarry
which intercepts the watershed. Excess water from the aggregate quarry is routinely discharged into
to Kings Creek upstream of the Project. An evaluation of the flows in Kings Creek allowed a
representative pumping frequency to be estimated that simulates the pumping flows contributing to
Kings Creek.
A parallel watershed component was incorporated in the water balance model that only incorporated
the pre-development watersheds and aggregate quarry pumping system. This model was subjected
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to the same climate as the rest of the model, allowing for a direct comparison of the flows in South
Creek and Kings Creek as if the Kings Mountain Project had not been developed, versus the predicted
flows in South Creek and Kings Creek during the development, active mining, closure, and post-
closure activities of the Kings Mountain Project.
Table 3.13 shows key contributing watershed inputs for these pre-development catchments.
Table 3.13: Watershed Input Parameters
Watershed Area(ac)
South Creek watershed above haul road 292.39
South Creek watershed below haul road to Kings Creek 218.77
Closed basins north of railroad 81.60
Closed basins south of railroad 51.78
Watershed of Pond#1 23.24
Kings Creek watershed above research center 81.70
Kings Creek watershed from research center to Pond 1 discharge 53.88
Kings Creek Watershed from Pond 1 discharge to Weir 3 27.84
Closed basins east of Kings Creek 3.79
Kings Creek watershed from Weir 3 to 1-85 17.98
Kings Creek watershed from 1-85 to Weir 7 157.56
Watershed diverted around WSB-1 151.81
Watershed of WBS-1 115.92
Kings Creek watershed below WS13-1 16.83
Upstream Pit Pumping Parameter Value
Upstream pit pumping rate 2,500 gpm
Mean: 3 days
Upstream pit pump on duration gamma distribution parameters Standard deviation: 1.5 days
Minimum: 1 day
Maximum: 10 days
Mean: 3 days
Upstream pit pump off duration gamma distribution parameters Standard deviation: 1.0 day
Minimum: 1 day
Maximum: 10 days
Source:SRK,2023
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4 Water Balance Model Simulations
The model performs this balance every timestep using discrete, constant timesteps which, for this
model, have been configured to 1 day. Additionally, the GoldSim simulation software may insert
additional timesteps during the simulation process in order to capture rapidly changing conditions.The
model is simulated from pre-mining conditions in 2024 through operations and closure and extends
into post-closure to capture the pit lake filling by 2100.
The water balance model was developed to provide information on the water management flows and
storage components to aid in the design and sizing of the water management infrastructure and to
provide flow contributions to other studies, such as the geochemical modeling and groundwater
modeling studies. The number of results available within the water balance is quite large, with over
1,000 time history elements available for reporting in any of the simulations. This section provides
some key results to summarize the results of the modeling efforts.
4.1.1 Deterministic Scenarios
As part of the overall Project study, SRK produced results from the water balance model using
deterministic, forced scenarios. These scenarios provide a valuable understanding of the model
behavior under easily understood parameters, where all the values reported can be averaged and
summed to produce consistent values. Additionally, deterministic scenarios can be run quickly, and a
large number of results were reported for use with the groundwater and geochemical modeling efforts.
For these scenarios, five different climate scenarios were constructed using the climate forcing
datasets described in Section 3.2.2. Note that the climate scenarios were developed using daily data
from select months in the legacy record to produce the average, wet, or dry scenarios. Therefore,
these scenarios represent actual precipitation patterns and depths for the climate record. These
scenarios were developed to simulate the system under generally average conditions from 2023 to
2120 with Climate Change Scenario SSP4.5 but with a single year stressed with a specific climate
forcing.
The scenarios evaluated were:
• Scenario 1: Average with one extreme dry year. All years are simulated at 50th percentile
precipitation except 2034, which is simulated using the fifth percentile climate year.
• Scenario 2: Average with one dry year. All years are simulated at 50th percentile precipitation
except 2034, which is simulated using the 25th percentile climate year.
• Scenario 3: Average every year. All years are simulated at 50th percentile precipitation.
• Scenario 4: Average with one wet year. All years are simulated at 50th percentile precipitation
except 2034, which is simulated using the 75th percentile climate year.
• Scenario 5: Average with one extreme wet year. All years are simulated at 50th percentile
precipitation except 2034, which is simulated using the 95th percentile climate year.
Figure 4.1 shows the annual total precipitation values.
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70
60
0
a
u 50
w
d
c
Q
40
30
2030 2040 2050 2060 2070 2080 2090 2100
Time
Annual Precipitation
5th in 2034 25th in 2034 - 50th every year - 75th in 2034 - 95th in 2034
Source:SRK,2023
Figure 4.1: Deterministic Climate Scenarios Annual Precipitation
The data provided to the other modeling efforts included 125 different results from the model, including
flows, volumes, elevations, areas, and tonnages for each month of the simulation out to the pit lake
overtopping for each of the five scenarios in tabular format. The following subsections present key
results of the model.
Discharge from WS13-1
The water balance model simulated discharge through the low-level outlet in response to the sum of
net inflows and minor losses (seepage and evaporation). Figure 4.2 shows the outflow for the five
scenarios for the operational period of the mine.
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1400
1200
0_
.29
-0 1000
0
a
800
0 O
CJ
E 600
0
w
N
30 400
Y
O 200
0
2023 2025 2027 2029 2031 2033 2035 2037 2039
Water to Discharge
5th in 2034 25th in 2034 50th every year 75th i n 2034 95th in 2034
Source:SRK,2023
Figure 4.2: Outflow from the Contact Water Pond under the Deterministic Climate Scenarios
Of particular relevance is the discharge during the extreme dry year, which drops to zero during this
scenario; this indicates that even though the site-wide water balance is generally net positive, during
extreme droughts the WSB-1 water balance is slightly negative,consuming stored inventory. However,
as shown on Figure 4.3, even during the fifth percentile dry year, the pond volume is not significantly
depleted, indicating that there is sufficient water to meet Project demands.
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60
50
40
4-1
>_
30
O
ti
m
N 20
10
0
2023 2025 2027 2029 2031 2033 2035 2037 2039
Time
WSB-1 Volume
5th in 2034 25th in 2034 50th every year 75th in 2034 95th in 2034
Source:SRK,2023
Figure 4.3: Volume in WS13-1 under the Deterministic Climate Scenarios
Contributions to the Contact Water Pond During 2038
The model was configured to track all water contributions to WS13-1, and Figure 4.4 presents a series
of pie charts showing the relative contribution from each source. Although there are some changes to
the contribution as a result of the climate forcing, the relative contribution from each source is fairly
consistent, with 45% to 47% of the flows coming from pit dewatering and 47% to 49% of the treated
PAG contact water flows remaining after meeting the process makeup demand. The remaining inflows
to WSB-1 are provided by the seepage collection from RSF-A and RoM pad. The pit dewatering is fed
by a relatively constant supply of groundwater, while the PAG contact water is higher than the other
non-PAG contact water flows as RSF-X is underlain by a synthetic liner, collecting 100% of the
seepage flows from the waste rock.
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Deterministic
Deterministic
5th in 2034 25th in 2034
Pit Dewatering NAGContact Water
ROM Pad Contact Water Treated PAG Water Pit Dewatering NAG Contact Water
ROM Pad Contact Water Treated PAG Water
Deterministic
SOth every year
Pit Dewatering NAGContact Water
ROM Pad Contact Water Treated PAG Water
Deterministic Deterministic
75th in 2034 95th in 2034
Pit Dewatering NAGContact Water Pit Dewatering NAGContact Water
ROM Pad Contact Water Treated PAG Water ROM Pad Contact Water Treated PAG Water
Source:SRK,2023
Figure 4A Relative Contributions to the Contact Water Pond during the Climate Forcing
Period
Pumping from the Pit Dewatering
The pit dewatering system has been designed to collect pit inflows in a sump in the active pit bottom.
Inflows from groundwater, runoff from the pit walls, run-on from undiverted watersheds, and direct
precipitation on open water would accumulate in the active pit bottom unless they were pumped out
quickly enough to minimize impacts to mining activities. The model simulates the maximum pumping
rate at 5,000 gpm from the pit during and immediately after storm events, but typically is just removing
groundwater inflows and runoff and run-on from day to day rainfall at a much lower rate. The outflow
structure of WSB-1 allows these infrequent high pumping discharges to be significantly attenuated
before release. Figure 4.5 shows a graph of monthly average pumping from pit dewatering to WS13-1.
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400
300
n
ubu
on
c
Q
E 200
a
Y
d
1
U
Q 100
0
2023 2025 2027 2029 2031 2033 2035 2037 2039
Time
Active Pit Pumping
5th in 2034 25th in 2034 50th every year 75th in 2034 95th in 2034
Figure 4.5: Average Monthly Pumping from the Active Pit Sump under the Deterministic
Climate Scenarios
Pumping from the Rock Storage Facilities
Contact water from the RSFs is largely the result of infiltration into the waste rock greater than the
percolation potential of the underlying saprolitic soils. The coarse nature of the rock precludes runoff
from the waste rock surface except during significant rainfall events. Once the RSF is closed and
covered with a reclamation cover, the collected flows drop to near zero and are no longer transferred
to WSB-1. Figure 4.6 presents the monthly flows from the non-PAG RSF collection sump under the
50th percentile climate scenario through the LoM.
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za
22
20
18
E 16
uao
m 14
.a
E
a
12
c
i 10
Q
Z
8
6
4 Istr
2
0
2023 2025 2027 2029 2031 2033 2035 2037 2039
Time
NAG Pond 61 Pumping
- 5th in 2034 25th in 2034 50th every year - 75th in 2034 95th i n 2034
Source:SRK,2023
Figure 4.6: Average Monthly Pumping from the Non-PAG RSF under the Deterministic Climate
Scenarios
4.1.2 Comparison of Pre- and Post-Development Flows in Kings Creek
The site-wide water balance includes a parallel component that produced streamflows in South Creek
and Kings Creek in response to the same climatic conditions that the site-wide water balance is
subjected to, allowing a direct comparison of the flows in South Creek and Kings Creek as if the Kings
Mountain Project had not been developed (no-development flows),versus the predicted flows in South
Creek and Kings Creek during the development, active mining, closure, and post-closure activities of
the Kings Mountain Project (predicted flows).
Flow contribution from the reclaimed OSFs is not significantly different on an annual/monthly basis
from natural ground currently in existence. Thus, this data has not been revised.
The model was simulated from pre-development into closure using the recycled legacy climate from
1990 to 2022 described in Section 3.2.2, mapping the 1990 record to Year 2023 and the 2006 record
to Year 2039. This climate records were adjusted for the SSP4.5 climate change described in
Section 3.2.1.
The model indicated that the flows at Weir#7, located at the downstream discharge point on the Kings
Mountain property, would typically experience moderate to slightly higher flows during the LoM, as
shown on Figure 4.7. The graph of the flows in response to daily rainfall events shows the variability
in flows is comparable to the increase in flows resulting from the initial dewatering of the Kings
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Mountain pit lake during the pre-development period. During the LoM (2026 to 2032), the predicted
flows in Kings Creek are slightly higher than the no-development flows, largely as a result of the
addition of pit dewatering flows and the capture of flows previously contributing to closed basin (e.g.,
Kings Mountain pit lake). The closure/post-closure period still shows a minor increase in flows at
Weir#7, as significant closed basins upstream of the weir (e.g., Kings Mountain Mine tailings basin)
have been graded to drain into the stream network.
6000
Monthly Average Flows
's
5000
a
m
v
3: 4000
v
L
U
0 3000
Y
U!
O1
V
to
C
Y
2000
1000
2023 2025 2027 2029 2031 2033 2035 2037 2039
Ti me
Predicted Flows at Weir#7 No-Devel pment Fl ows at Wei r#7
Source:SRK,2023
Figure 4.7: Comparison of Flows at Weir#7
4.1.3 Probabilistic Simulations
To fully examine the impact of a wide range of climatic inputs to the model, SRK produced results from
the water balance model using a probabilistic Monte Carlo simulation. The model was run from pre-
development(January 1,2023)through development,operations,closure, and approximately 60 years
into post-closure (2100). The model was simulated with the WGEN synthetic climate generator and
probabilistic evaporation model for 250 realizations, with the results compiled and statistical time
histories produced to indicate the range of results predicted by the model. As with the deterministic
simulations, the number of results available from the model is very large. The following subsections
present a select number of key results.
Time to Dewater the Existing Pit Lake
The existing lake in the Kings Mountain pit will need to be removed prior to any mining activities. The
current pit level (as of January 2023) is 800 ft amsl, which corresponds to approximately 1,300 Mgal
of water. This volume is steadily increasing as a result of natural groundwater inflow and surface water
contributions to the pit and is expected to reach 1,450 Mgal by the end of 2023. A pump, treat, and
discharge program is planned for 2024/2025,and the model simulates the maximum pumping capacity
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as 2,500 gpm, with a 95% availability, for a nominal 2,375 gpm removal rate. At this rate, the water
balance model predicted that the pit would be dewatered by mid-2025, depending on climatic inputs,
as shown on Figure 4.8.
1600 1600
1400 0011111010` 1400
m 1200 ` 1200
M
1600 �` 1000
E
� 800 ` 800
0
� 606 ` 600
v
a 400 ` 400
200 ` 200
0 bib 10
Jan 2023 Jul 2023 Jan 2024 Jul 2024 Jan 2025 Jul 2025
Time
Statistics for Pit Lake Volume
1%.,5%/95% 99% 5% 15%/85% 95% 15% 25%/75%..85% 25%.35%/65%.75%
35%.45%/55%.,65% 45%,.55% 50%
Figure 4.8: Probabilistic Time Series of the Pre-Development Pit Lake Volume
WSBA Volume
WSBA was implemented to collect contact water from sources around the site and maintain a
consistent volume in the pond to provide a backup source of water and provide mixing,
de-sedimentation, and detention of contact water flows prior to discharging to Kings Creek. Figure 4.9
presents a probabilistic time series of the volume in the WSBA. The median (50%) line indicates that
the pond is able to maintain a consistent volume through the operational period, increasing during
extreme events and occasionally falling below the operating volume but recovering quickly. Figure 4.10
shows a corresponding graph of the water level elevation in WSBA, indicating the water level in the
pond fluctuates less than 4 ft below and 2 ft above the nominal operating level, even under extreme
storm events. During the pre-development and post-closure periods, the model was constructed with
a spillway elevation of 823 ft amsl, resulting in a nominal 3 ft of water retained in WSBA during those
periods.
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180 180
160 _,,,�yaWa,.w��l..a�LL„���°,I�Ih _ �lu��,. � 1160
w 140 �r MW � 1M 11 'T T 140
t'C 120 120
2
a, 100 100
M
> 80 � �80
m
6D 60
4D 40
2D r 20
D ► 0
2023 2025 2027 2029 2031 2033 2035 2037 2039
Time
Stati sti cs for W513-1 Vol ume
1%..5%/95%..99% 5%..15%/85%..95% 15%..25%/75%..85% 25%..35%/65%..75%
35%.A5%/55%..65% 45%..55% 50%
Source:SRK,2023
Figure 4.9: Probabilistic Time Series of WS13-1 Volume
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850 850
9
x 840 840
c
0
m
w
ti
m
Ln
830 830
820 820
2023 2025 2D27 2029 2031 2D33 2035 2037 2039
Time
Statistics for WSB-1 Elevation
1%.5%/95% 9991 5%.15%/85% 95% 15%..25%/75%.8591 25%..35%/65%.7591
35% 4591/5591,.65% 45%..55% 50%
Source:SRK,2023
Figure 4.10: Probabilistic Time Series of WS13-1 Elevation
A relatively stable water elevation is conducive to the establishment of wetlands in the margins of the
WSB-1.
Post-Closure Pit Elevation and Volume
Due to the consistent inflow of groundwater, direct precipitation on the pit lake, runoff from the pit walls,
and run-on contributions to the pit lake, the pit lake water elevation is expected to increase throughout
the post-closure period until reaching the low point on the pit rim that allows discharge to Kings Creek,
which is at an elevation of 850 ft amsl. As shown on Figure 4.11, the pit lake is expected to achieve
this elevation sometime around Year 2090, with the earliest predicted discharge to Kings Creek in
2087 (95th percentile) and the latest predicted discharge (5th percentile) in 2096. Figure 4.12 shows
the corresponding volume of water in the pit lake.
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1300 soD
0900 mom
700 P 700
aj
w _ F
ru
v 600 600
Y
ru
d 500 500
400 � �400
2035 2040 2045 2050 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100
Time
StatiAcs for Pit Lake Elevation
1%..5%/95%..99% 5%..15%/85%..95% 15%..25%/75%..85% 25%..35%/65%..75%
35%.A5%/55%..65% 45%..55% 50%
Source:SRK,2023
Figure 4.11: Probabilistic Time Series of the Post-Closure Pit Lake Water Elevation
soon �sooD
7000 7000
bn
5000 � �5000
0 4000 Jda� 40DO
� 3000 �. 30DD
Y
N
d 2000 "000"­
�20DO
1000 �1DDD
0 0
2035 2040 2045 2050 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100
Time
Statistics for Pit LakeVolume
1%..5%/95%..99% 5%..15%/85%..95% 15%..25%/75%..85% 25%..35%/65%..75%
35%.A5%/55%..65% 45%..55% 50%
Source:SRK,2023
Figure 4.12: Probabilistic Time Series of the Post-Closure Pit Lake Volume
Time to Inundate the Pit Backfill
During closure, the entire volume of PAG waste from RSF-X will be remined and placed as backfill in
the exhausted pit. The waste rock is simulated as being placed level to decrease the time necessary
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to completely inundate the waste backfill. Figure 4.13 shows the elevation of the pit backfill and water
level in the pit at the fifth, 5Oth, and 95th percentiles; this shows the range of times that the pit lake
takes to inundate the waste rock. As no uncertain inputs are associated with the schedule of waste
rock or the remining of the PAG waste, there is no uncertainty in the model associated with the timing
or level of pit backfill. The model indicates that even at the 95th percentile the waste rock will be
inundated in around 12 months after pumping ceases from the pit bottom. During the early stages of
pit filling, the two curves are very close, suggesting the timing of when the backfilling begins and the
pit lake being allowed to form may be critical.
600
500
400
a
3nn --------
W _ �
200
100
0
lan 2035 Jul 2035 Jan 2036 Jul 2036 An 2037 Jul 2037
Time
Height of Backfill(50%) — — — Pit Sump Depth(Sth percentile)(5%)
Pit Sump Depth(50th percprMl )(50%) ------ Pit Sump Depth(9Sth percentile)(95%)
Source:SRK,2023
Figure 4.13: Depth of Backfill and Probabilistic Pit Lake Depth during Closure and Post-
Closure
Raw Water Supply to the Processing Plant
The makeup water demand at the processing plant is designed to be met from a number of different
sources in a hierarchical manner. The model logic is constructed so that the first source of water is the
treated excess process water from the plant, the second source of water is the treated PAG contact
water, and the third and final source of water is from WSB-1. As indicated by the probabilistic time
series of the WS13-1 water surface elevation, WSB-1 is always able to supply water to the plant.
Figure 4.14 shows a probabilistic time series of the average monthly flow of water to the processing
plant from WS13-1. This flow is very erratic as a result of it being strongly dependent on precipitation-
driven excess water.
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220 220
CL
180 - ' 180
10 �_--1 �10
10 �_--� 140
0 1240 �___■ 120
0 10 0
__--■ 0
8 8
C 50 I����■ -40
2 --- �00
0
2023 2025 2027 2029 2031 2033 2035 2037 2039
Time
Statistics for Makeup W ater to Process Demand
5%..15%/85%..95% 15%..25%/75%..85% 25%.35%/65%.,75%
457..55% 50%
Source:SRK,2023
Figure 4.14: Probabilistic Time Series of the Average Monthly Raw Water Supply to the Plant
4.1.4 Model Sensitivity
The sensitivity of the water balance model of key inputs is important at this stage of the Project. Field
measurements of many of the inputs to the model are not available, and the inputs are necessarily
based on analog materials or conditions. Determining which of these inputs the model is sensitive will
allow further studies to better define these parameters prior to final design efforts. The sensitivity
analysis requires a single model output to represent the model response to the sensitivity analysis.
For this study, the total volume of water discharged from WS13-1 during the period of active mining
was used as the representative model output; this provides a comparative measure of the amount of
excess water available in the Project during the active mining period.
The following inputs to the model were included in the sensitivity analysis.
Runoff Curve Number of Active Waste Rock Surfaces
The model simulations were made using a runoff CN of 40 for active waste rock surfaces, which is
representative of coarse waste rock with very high infiltration behavior; the sensitivity analysis allows
the CN value to vary from 30 to 60.
Infiltration Percentage of Active Waste Rock Surfaces
The model simulations were made assuming 65%of precipitation falling on the active waste rock would
infiltrate into the waste rock, creating seepage flows that report to the base of the RSF. The sensitivity
analysis allowed the infiltration rate for all waste rock surfaces to vary from 50% to 80%.
Percolation Rate of the Native Soils
The material underlying RSF-A was simulated with a vertical percolation rate of 5 x 10-5 cm/sec in the
simulations, based on permeability testing of the saprolitic soils at the site. Any seepage from the rock
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2022 KM PFS Report-Surface Water:Water Balance Development Report Page 63
storage or tailings facilities that reaches the base of the facility will first satisfy the percolation capacity
of the underlying soils (essentially lost to regional groundwater) before producing seepage that can be
collected in the various collection sumps. The sensitivity analysis allowed the percolation rate into the
underlying soils to vary from 1 X 10-5 to 1 X 10-4 cm/sec.
Dust Control Use
Dust control use required at the mine site is highly variable depending on haul road construction, types
and use of vehicles, and rainfall patterns. The model simulations simulated with between one to three
water tankers per day would be used for dust suppression depending on the season and mine activities
(see Table 3.12). The sensitivity analysis allowed the calculated water demand to vary by 75% to
125% of that number.
Final Moisture Content of the Filtered Tailings
Tailings will leave the process plant after filtering to approximately percent 85% solids at typical rate
of 635 dry tons per day. The moisture content can be expected to vary in response to material
properties of the tailings solids as well as the efficient of the filtration plant. The sensitivity analysis
allowed the moisture content of the filtered tailings to vary from 75%to 95%.
Final Moisture Content of DMS Rejects
Coarse DMS rejects comingled with the waste rock for disposal are simulated as being placed at 10%
moisture content (by weight) as determined in the process flowsheet (Hatch, 2023). This number is
dependent on the physical properties of the DMS rejects and moisture content of the DMS process.
Water in the DMS rejects is lost to the plant and is permanently entrained in the RSF. The sensitivity
analysis allowed the moisture content of the DMS rejects to vary from 7%to 13%.
Process Clean Water Demand
The plant will require a relatively clean source of water for gland seal and reagent use, in addition to
raw water for dust suppression with the plant. The simulations assigns a demand of 30.45 tons per
hour (t/h) (Hatch, 2023) of clean water at the process as determined in the process flowsheet. This
water can change as different processes and efficiencies change the amount of fresh water demanded
by the process. The sensitivity analysis allowed the clean water demand to vary from 20 to 50 t/h.
Sensitivity Analysis Results
Figure 4.15 provides a tornado chart presenting the results of independently varying the inputs
described above using a 5Oth percentile climate scenario during the pre-development through closure
periods (2023 through 2040). The horizontal bars indicate the range of total water discharged from
WSB-1 during the active mining period resulting from varying that parameter. The greater the length
of the bar, the more impact varying that input has on the model. The results have been sorted from
highest to lowest impact, creating the characteristic tornado shape.
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Tornado Sensitivity Chart-Analyzed Result:Cumulative Outflow from WSB-1 (Mgal)
58C 59C 600 610 620 630 W 650 660 670 680 69C 70C 710 720 730
Filtered Tailings Percent Solids
Clean Water Demand
H Active Infiltration Percent
v
7
v ❑MS Reject Moisture Content
C
Q
C
Dust Control Adjustment
Curve Number CN Active Waste
Native Ground Percolation
0 Low M High
Source:SRK,2023
Figure 4.15: Sensitivity Study Tornado Chart on Cumulative Discharge from WSB-1 over the
Period of Active Mining
The sensitivity analysis indicates that the moisture content of the filtered tailings has the strongest
impact on the amount of excess water in the Project. However, this impact still results in a net positive
water balance in the process, only varying the cumulative discharged amount by -4% to +17%. The
next most impactful variable, clean water demand at the process, only varied the total amount of
excess water produced by the site by -4%to 12%.
Overall, the model predicts that the site will produce excess water from WSB-1 under the most
reasonable ranges of inputs.
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5 Interpretation and Conclusions
The water balance model for the Kings Mountain Lithium Mine PFS was developed to provide a tool
to interpret, understand, and plan for the mine water management aspects of the Project. Within the
understanding of the Project at this stage in the design process, the model is a dynamic simulation of
the development of the mine, mining activities, closure, and post-closure activities.The model includes
the time-dependent nature of the pit, including mine tonnage schedules, facility growth, and sequence
of facilities coming online or being removed by mining activities. The model incorporates a synthetic
climate generator that can produce probabilistic climate on a daily basis as well as several pre-
configured climate scenarios to provide deterministic climate forcing. Both kinds of climate series are
corrected for site-specific climate change projects on a yearly basis.
The model indicates that the Project will be consistently net positive with regards to water. Pit
dewatering flows, runoff, run-on, and seepage flows are greater than the water demands calculated
for the process, and the makeup supply that is provided by WSB-1 during periods of low to little rain
(180 to 200 gpm) is easily met without depleting the pond.
The model was also used to evaluate the pit lake filling timeline. The model indicates that the pit lake
will fill to the discharge elevation of 850 ft amsl around after Year 2090. Additionally, the model
indicates that the pit backfill will be inundated 12 months after the pit backfilling begins and that it will
be critical to implement the backfill a quickly as possible to prevent the pit backfilling from being
inundated during placement.
A sensitivity analysis of the model indicates the model is relatively sensitive to the amount of moisture
leaving the site with the tailings, as well as the freshwater demand at the mill. However, none of the
model inputs resulted in a net deficit to the water balance, indicating that the overall water balance
remains positive under the range of parameters explored. Other uncertain inputs to the model, such
as runoff coefficients, percolation parameters, or dust control use, had even less impact on the mine
water balance.
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6 References
Albemarle Corporation (Albemarle), 2023. Final Select Mine Plan 07/31/2023 EXTERNAL Minemax
Dashboard - Ph1 Base Case v01.xlsx.
Allen, R., Pereira, L. S., Raes, D., and Smith, M., 1998. Crop evapotranspiration — Guidelines for
computing crop water requirements. FAO Irrigation and Drainage Paper N° 56. Rome, Italy.
Cecala, A. B. et al., 2012. Dust Control Handbook for Industrial Minerals Mining and Processing,
NIOSH RI 9689, January 2012.
Applied Weather Associates (AWA), 2022. Site-Specific Probable Maximum Precipitation Study for
Kings Mountain Mining Operations, North Carolina, Applied Weather Associates, September 2021,
KM60-EN-RP-9431.
EasyFit, 2015. EasyFit Professional, Version 5.6. MathWave Technologies, January 13, 2015.
Environmental Systems Research Institute, Inc. (ESRI), 2023. Please provide this reference.
Federal Highway Administration (FHA), 2021. HY-8 culvert analysis software, Federal Highway
Administration. Version 7.70.10.0, June 2, 2021.
Garrett, D. E., 2004. Handbook of Lithium and Natural Calcium Chloride: Their Deposits, Processing,
Uses and Properties, Kidlington, Oxford, Elsevier Ltd., First Edition, p. 99, 157-160.
GoldSim, 2021. GoldSim, Version 14.0. GoldSim Technology Group, November 2021.
GoldSim Technology Group (GTG), 2021. Model of Rural Engineering with 4 parameters Daily
(GR4J) in GoldSim, (https:Hsupport.goldsim.com/hc/en-us/articles/360056312214-Model-of-Rural-
Engineering-with-4-parameters-Daily-GR4J-in-GoldSim), GoldSim Technology Group, downloaded
November 2021.
Green, W. H., and Ampt, G. A., 1911. Studies on soil physics, Journal of Agricultural Science, 4(1),
1-24.
Hamon,W. R., 1961. Estimating potential evapotranspiration J. Hydraul. Div. Proc. Am. Soc. Civ. Eng.
Hatch, 2023. Kings Mountain Mine Process Flowsheet, 2023.
Hatch, 2024. Development Erosion and Control Plan {not provided yet}.
Horton, J. W., Butler, J. R, and Milton, D. M., 1981. Geological investigations of the Kings Mountain
belt and adjacent areas in the Carolinas, Carolina Geological Society: Field Trip Guidebook,
October 24-25, 247 pages.
Intergovernmental Panel on Climate Change (IPCC), 2021. Climate Change 2022: Impacts,
Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change [H.-O. Portner, D.C. Roberts, M. Tignor, E.S.
Poloczanska, K. Mintenbeck, A. Alegria, M. Craig, S. Langsdorf, S. Loschke, V. Moller, A. Okem, B.
Rama (eds.)]. Cambridge University Press. Cambridge University Press, Cambridge, UK and New
York, NY, USA, 3056 pp., doi:10.1017/9781009325844.
IPCC, 2023, Assessment Report 6 Synthesis Report, International Panel on Climate Change
March 19, 2023.
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SRK Consulting(U.S.), Inc.
2022 KM PFS Report-Surface Water:Water Balance Development Report Page 67
Johnson, N. L., 1949. Systems of Frequency Curves Generated by Methods of Translation. Biometrika.
36 (1/2).
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F., 2006. World Map of the Koppen-Geiger
Climate Classification Updated. Meteorol. Z., 15, 259-263. DOI: 10.1127/0941-2948/2006/0130.
National Aeronautics and Space Administration (NASA), 2023. Daymet: Daily Surface Weather Data
on a 1-km Grid for North America, Version 4 R1 from their website at https://doi.org/10.3334/
ORNLDAAC/2129.
National Centers for Environmental Prediction (NCEP), 2023. Reanalysis data provided by the
NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at http://www.esrl.noaa.gov/psd/
data/reanalysis/reanalysis.shtml.
National Oceanic and Atmospheric Administration (NOAA), 2023. National Center for Environmental
Information, Station ID: GHCND:USC00317846 Shelby 2NW, from their website at www.ncei.noaa.
gov.
National Resources Conservation Service (NRCS), 2004. Part 630 Hydrology National Engineering
Handbook, Chapter 10 Estimation of Direct Runoff from Storm Rainfall. United States Department of
Agriculture Natural Resources Conservation Service, 210-V1-NEW, July 2004.
Perrin, C., 2002. Vers une amelioration d'un modele global pluie-debit au travers d'une approche
comparative. La Houille Blanche, n°6/7, 84-91.
Richardson, C. W., 1984. WGEN: a model for generating daily weather variables. Washington, D.C.:
U.S. Dept. of Agriculture, Agricultural Research Service.
Richardson and Wright, 1984. WGEN: A Model for Generating Daily Weather Variables. U. S.
Department of Agriculture, Agricultural Research Service, ARS-8, 83 p.
SRK Consulting (U.S.), Inc. (SRK), 2022. Development of Stage Discharge Relationship for South
Creek Outlet Culverts and No. 3 Weir, September 27, 2022.
SRK, 2023a. Numerical Report to support EA, Hydrogeological Study and Groundwater Modeling,
KM60-EN-RP-9044.
SRK, 2023b. Baseline Geochemistry Characterization Kings Mountain Mining Project, KM60-EN-RP-
9055.
SRK, 2023c. Geochemistry:Water Quality Predictions, Kings Mountain Mining Project, KM60-EN-RP-
9151.
SRK,2023d. RSF-X Design Technical Memorandum, KM60-EN-RP-9480. SRK Consulting(U.S.), Inc.
September 15, 2023.
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Appendices
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2022 KM PFS Report-Surface Water:Water Balance Development Report Appendices
Appendix A: Legacy Climate Data
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Table A-1: Shelby 2 NW(Infilled) Precipitation Monthly Time Series
Mm Preri itzd nsmn r«
1kmN PmuPft&Dn for ifflQ-2W25hdbT2 HN
.:r�_ Xtru3r- FAkrcn i lure 1 Omu6er N-r b: Dem�Yc annum7aad
Irl 19 IIR IIr1.l n I IH n IH IH
7A1 2RD 6.65 CL25 4.99 Z94 0.66 1LL73 LS4 418 6512
6.36 ISO 44C 733 5.14 430 9.73 7.95 0.42 Dim L29 UK 52A6
389 SM 9.32 .1.16 7.29 5.71 3.29 6.27 321 1RGn 821 fa4 7037
76R 4.7D 9.6E 4.0 O.DO Z22 4AO 195 L23 2A9 3A5 Iku nA3
_=i4 E32 229 7.32 3M 1.H3 LLL3 3.90 C34 3.13 Sal 3O9 L54 5723
1?93 2,33 6a3 2.78 0-% A.6A 3.8E 9.03 972 3a6 916 68E 2111 70%
19% E.33 232 727 337 AAi 436 3.91 124 6.36 0.71a 3.15 43O X.37
1997 490 3..6E 4U4 3O3 3.20 463 3.19 416 AM 5.12 326 49O 91.1S
19M 7a7 3-3D 3.% 504 7.21 3E3 521 4E4 3.35 LE9 2.72 4O3 Si 03
1935 6.17 3A3 4,39 a 17 Lin 3.98 L" Z43 3.66 4E8 2.L9 L56 41.M
20M 363 Z15 4A6 330 2.1E 339 3.99 179 1.37 D.DD 3i62 236 35.73
200L LSD 2!6 6.22 0M 3A1 7.LD 5.5- 0.95 6.99 1D6 La7 3.9E 4L.11
2002 419 IAZ 3.36 LO 2.30 1D7 327 aD3 3.31 5.12 4791 1. 4731
200a L76 4AZ 5.33 10.01 9.32 3.97 9.6i a7D 3.15 L67 LM L80 0-0
2004 LM 494 209 3S6 3A7 9.60 1.36 395 ii-M Me 876 3.D9 310
2003 331 330 436 3.39 9.97 7.33 6.77 101. OAO 1.94 399 419 50.74
2AC6 337 am III 2A3 L73 3.33 3.26 39D 3113 4,22 3M 434 3915
K07 2.78 L97 39D 2.77 L64 218 0.93 (122 O.M L3D L17 3.11 24%
200& 2.47 LS4 403 2.66 3.19 LED 2AO 10D9 3.33 1-29 i64 3,914 %.1D
Korn 2.24 Sao 132 3-w 5.39 473 1.97 1D2 4.16 4,02 Sal 7.90 48M
20i0 rMo 42D 470 2A4 5.27 3S3 1.76 C39 3.95 3.63 LW L® 46_R
2011 330 23D 633 367 317 M34 3.55 4,43 5.26 2-U 617 33a AEM
2012 2.61 L22 L71 2.74 7.53 L89 7.04 3S3 2.E4 L33 0.43 482 39,64
2013 611E mm 334 3-% 6.91 46L 1251 426 4.95 362 459 657 6633
2X4 317 290 4-33 497 2A0 7.39 2.32 439 SAC L33 437 L7E 4722
2013 284 2a5 2A0 6.92 0.83 Ln 1.90 249 2.74 6.33 925 0119 9336
2016 3,24 3.M 12E 1O6 E.O4 L644 3.3E 4ES 1.H1 09D 063 L29 3034
2017 483 am 412 Cu E.90 ]'25 3.77 146 4.17 442 am L30 4524
20is 233 4A6 4,30 6.74 E.H3 729 3.13 46D 1.70 663 HAD 2.E3 63A7
2019 431 61R 364 4.73 AD2 7.21 3.32 416 OAS 3.43 LE7 61A 3264
2020 r w 9a9 3.12 7OL 713. 331 L40 142 3a5 5.8d GM 4,34 6618
2021 3,38 51B 9.08 2A6 246 2.1E 41.6E Z66 LEI 2-36 am 193 1U13
2022 473 287 301 3ffi 387 L34 2.70 4EL 163 4,00 627 40 44A9
Aye 4EH 3m "1 4.13 A.L9 41s 4.30 4,36 3.73 391 393 1131 50AD
3w 2.33 187 226 226 L17 Z60 LE7 23L 2.34 LM26fi 20d i1.Li
Mn LM I'M iii 0.68 Oa0 NS O.S Qi6 O.iO 0.O0 OA3 I 24%
Mm 1419 %AD 9.6E SQOL 9.32 3113 1231 1009 SLAT IA73 923 1019 70%
ss a
34 70
33 w
S
I`1 lIl j
� M
0
�NP..k�W -Mrrdh.*kr
Source: NOAA,2023
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2022 KM PFS Report-Surface Water:Water Balance Development Report Appendices
Table A-2: Penman-Monteith Reference Evapotranspiration Monthly Time Series
Wrghly Ewaparation Sim nry for
Purrwh wmem xerce B m a7s'ratoniQ 799PMu
1r- Fehnn M-11 Jurc 1 Omoh[r IY�n�e Demmer Am 170.
Year Aln i n i n p n ,m,
19x. 2.0 L 5E 3.FA 331E 629 7.54 7.59 7.02 5A5 366 Z80 LEM 55 M
139G =.E1 Li� 3F.11 50E 6.56 7.D7 7.59 SL37 5A0 4CE 723 '_.36 5±0E
1392 =.S3 L�s 371 5 iC 5.00 fi32 7.84 SJ 5.83 3E_ LOB =.43 51 x
1993 =.72 Los 3,23 5Ca 6A3 7.58 9.7E SSE 3A6 323 L;± Lei 550E
1994 LGO 2s2 399 3-0 EAO fiS5 7A6 SAC. 4.94 3,47 257 L71 5294
1393 L73 2.02 "3 3.44 5.32 fiE9 TO fi67 AM 369 2AL LE4 33.10
L9% ifi1 2A3 3,23 432 6.73 7.15 7A9 E39 5.99 3.E4 L% LO E]2
LA9F 179 233 4,20 4.7 6.L3 C15 7.86 7.14 3.24 372 2A7 156 52.64
is% Lai 226 359 421 6.80 7.46 7.74 E91 SA9 3,96 239 184 550E
199E 2D8 2A6 359 330 E.54 E4S 7.59 7.24 5.77 3,45 755 L97 x�O
2x0 L68 179 41S 422 7.03 7A4 7.18 fi79 5.69 4,14 7 w =.23 5394
230L iS7 L-S 3,41 537 6.37 7.D4 6.82 7.09 5.92 3.ES 7a7 =.8-0 S-i'
Im 2.07 10 390 5A4 GAS 7.2s 9.29 E22 4.94 3.22 ili =.59 55 cc
2003 L64 207 3,M 410 1.60 EM 6.93 617 5A6 3.12 299 76 5075
2004 L33 2 L' "I 336 7101 6.39 7A2 6.29 A.SE 3,43 239 1-74 SL-7
L001 193 2 N 3fi2 3AD 6.20 M 4 726 E54 5.'5 36L 2.53 168 53 36
bx6 211 2 i= 3,87 5M 6.79 -._S 7.52 S25 4.93 356 2.5L MR 5.53
bh7F 139 L 24 4,35 331 6.72 7.33 7.69 d41 6.09 419 253 196 MAL
20M L78 2.37 3,83 4M E.59 3IE 7.77 E73 4.95 3,65 LU 1-64 3423
200E 171E 236 3.43 312 3.71 7.23 7.23 5.79 4.91 337 233 1-45 51.75
201D 174 18L 373 3.98 6A1 7.67 7.91 fi91 3.67 4D4 L41 L39 3516
2011 LfiE 2-57 3,56 5 R 6.39 7.43 7.74 7.D4 419E 3,63 ZAS L96 54.9E
2012 194 2S3 4,50 5_D 6.62 7.14 7.57 fi5D JAR 3.5E 239 1-72 54.64
2013 L39 ZJ37 339 456 3.93 fi59 6Ai 6.3D 3.19 37L 219 1-30 50A6
2014 L70 235 354 am 6.77 fi84 S.93 fi37 AM 396 22fi 1_69 5225
2OU L.77 S,aa 3,91 4390 7.03 7.46 OA4 7.05 5.9E 3,46 220 1-90 54M
Mir, L.70 114 419 5Z7 6.14 7.49 9.01 &23 3.71 406 L79 Lfia 361M
2017 L98 197 3,90 3.15 6.23 E69 7.63 fi55 1.29 387 7A0 167 54A3
2015 L23 2 53 3A4 49S 6.12 7.57 7A2 fi33 3.27 322 2J03 L62 33-0
2019 L73 L 0 361 3..15 6.92 fi63 7.71 fiS7 S.L3 4D2 218 LSS 5524
202E 181 2 it 3,92 531 3.66 C29 7.91 fi5L 4,97 373 L53 L74 53.7E
h72j- 172 205 3,91 532 6A2 &% 7.35 fi91 N.
3S1 L6 215 SLib
2M 1A 2� 4A5 53a 6A3 7.44 7.36 .71 N. 364 zM 147 Mm
181 L 0 3,79 3.16 6A3 7.DS 7.35 fi12 1.13 373 237 12G I1`O
sw a14 0 24 MR 03G 0.39 646 OA4 '. 0.37 IX24 0 24 IX2D i�
Mir LID 1�-_ 3,23 4A5 5.60 C15 SM CO 4.SS 3,22 i36 129 544E
Mm 211 197 1 4,50 323 7A5 2AR a.78 SAL 6.13 4,19 2S7 213 MM
]6O0 p
lily 7ff
3
s
6 cc
�m
x
am ao-
im io
am o
�ue.x#en �.ru.i ns
Source:SRK,2023
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2022 KM PFS Report-Surface Water:Water Balance Development Report Appendices
Table B-3: Hamon Method Lake Evaporation versus Reference Evapotranspiration Monthly
Time Series
Del ken FG119942022 2HW
Feline Mm� lunr 1 her ilmobl Wmrwb r Ormwc A-17o4l
yp n m n I n n I n m
L99D D.97 1.38 LEIS 2.47 339 S.D3 3.73 4S7 3.13 2L1 12E a9Z 33A7
3%L 022 1O6 1.73 2.7L 4.31 4,91. 171 439 3.17 L99 LSO 0.93 MD7
L%z D.SA 1OE i60 23Z IN 472 3.75 4,41. 3.is L73 US 0.77 30.93
L63 D.S7 0336 iAE 219 3.9E 3.21 SAS 4,63 3.37 LS3 iC6 D.72 3211
L94 D.66 0.9L L71 2Fi 3A2 S.D4 3.15 4,33 2.93 LTT 1.3E D.20 3O 53
i91A 0.76 O33i i77 137 3.79 4,33 3A1 4,53 3.D1 208 0.95 0.73 3123
19% 0.72 10E i38 1 4.01 4,24 3.39 4,40 2.97 LS7 Loi 0.28 30?_
L997 0.80 102 L83 1L2 3.31 4,33 3A3 445 3.1E 1-77 0% a73 Z'96
19% 0.37 102 i47 13L 3.9E Ali. 3.39 433 3A4 2D3 5.5a 0.73 311E
19% 0.22 094 i39 1.57 3.35 4,63 3.6i SOD 2.98 LSL LZA 0.80 3136
20w D.72 104 i7A 2AS 3.9E S.18 9.25 4,63 1.96 LSS LOO 0.39 31 iG
20M D.73 1.00 LAi 2,90 3.77 4S7 4.96 4,73 b.86 1-73 Lr D.92 3)aE
2CU0 0.5E o3a6 L60 2M 3.37 3.L3 IV 4,67 3.w L93 0.93 O.EB 31.94
2CU0 O.ES ORL i61 216 3A3 439 lii 450 3AO L55 L39 0.71 30.17
2604 0.76 024 i76 2.33 4A0 499 SAY 43S 3.97 2D7 L22 0.7E 322Z
won 0.52 093 L33 ILL 3.61 4,90 3.82 SLE 3.54 2C6 LYO D.70 32.77
Lo:6 0.99 011E L6A 13a7 3.7E AD3 172 SDa 3.03 1-77 L15 D.93 32E8
LC•77 0.56 oX i99 2.47 3.02 326 9A3 163 3.80 2.30 Lio 0.96 3455
bxa 0.77 10_ LSE 2AS 3.60 511. 5.65 4,72 3.20 L92 1jy- Dig 3228
bX& 0.76 0.93 L61 2.47 3.91 312 3.73 49S 3.13 1-77 L21 D.68 31.92
201D 0.EB 0.71. iA8 2h7 A.ZE Ise SA9 123 3118 1-96 i11 0.38 34.M
2011 DIM L@ i60 2.71 3.99 S.32 3.66 4.52 3.Z8 L34 i r, i.D1 31.13
26i7 DLM 1M 2Z4 210 4.Z9 478 1.06 463 3.23 L39 i n, 0.98 33A5
2013 D.91 0&4 LZ5 2A9 3.3E 3.0Z 3A1 433 3.ZE LOS lA2 0.87 3124
2014 D.® C96 L33 2.46 4.1E 3.27 3.23 446 3.3E 1-96 097 0.87 32-W
200 0.73 0.71. i69 2M 4.23 3LE9 3.915 4,96 3AO L90 L27 i13 3412
2016 D.71 U-94 L96 2-1a 3.78 536 6.12 S22 3.72 2L3 Li9 0.83 3454
2017 0.95 L37 L52 2.SZ 3.87 S.DD 3.92 4,72 3.1-9 2L6 L12 0.1m 333Z
2013 0.6' L27 L44 233 AAE A34 3.56 493 3.98 225 0.99 0.ffi 3430
2015 0.E2 LM L47 22& 4.36 49S 3.89 4,93 3.E2 230 0.92 0.88 3408
2026 0.52 LM 191 2_8 339 496 6.03 497 3.Z2 210 L30 D.76 329L
2021 D.78 0m 173 233 3.63 S.DZ 3.64 497 3.1E LET LOD LD3 KA3
2O2Z D.73 C98 168 234 4AM 1ZD 6.10 424 3.1E 171 L25 0T7 3L82
�.e 0.79 096 1.6A 2A9 In SD4 9.0 LSD 3.23 1-96 iio O.Sd 323f
Sbtl O.LD OS3 RZi aM U.33 D.33 0.32 C131 D.26 D.L7 0.13 O.23 L31
Mil 0.63 471 128 2]2 3.31 433 A.9fi 433 28fi 1-71 I'M 0a �98
M17ml 0.97 177 Z.N 2.57 4AE S.68 6.i9 105 3.98 2.30 L39 113 3456
16m p
JIL
.i n co
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Source:SRK,2023
D H/J O KingsMountain_WaterBalanceModeling_Report_USPR000576_Rev02.docx April 2024

SRK Consulting(U.S.), Inc.
2022 KM PFS Report-Surface Water:Water Balance Development Report Appendices
Appendix B: Johnson SB Probability Distribution
Fitting to Legacy Precipitation Records
D H/JO KingsMountain_W aterBalanceModel ing_Report_USPR000576_Rev02.docx April 2024

SRK Consulting(U.S.), Inc.
2022 KM PFS Report-Surface Water:Water Balance Development Report Appendices
Table B-1: Probability of Rain and Johnson SB Distribution Parameters for the WGEN Synthetic Climate Generator
Da iIV Precipitation Probabi litV of Rain
Number of Days Probability of Day
Number of Days with Rain with Rain Fallowing PrcbaNlity of Day with with Rain Follow Probal3ility of Day with
Month Number of Days Number Df Rain Days Number Df Dry Days FDIIDwng Rain Days Dry D�ars Rain Rain Day Rain Fo§Dw
January 1024 338 686 166 171 D.3301 D.4926 0.2493
Febmary 932 295 637 134 161 D.3165 0.4558 0.2527
March 1023 339 684 16D 179 D.3314 0.4734 0.2617
April 990 287 703 132 155 0.2899 0.4615 0.2205
May 1023 328 695 172 156 0.3206 0.5260 0.2245
June 990 347 643 182 165 0.352 D.5260 0.2566
d uty 1023 381 642 184 197 0.3724 D.4642 0.3069
August 1023 360 663 181 179 D.3519 0.5042 0.27D0
ember 990 253 737 125 128 D.2556 D.4960 0.1737
October 1023 256 767 125 131 0.2502 0.4902 0.1TDB
November 990 264 726 124 140 D.2667 0.4715 0.1928
December 1023 332 691 17D 162 0.3245 D.5136 0.2344
Annual 12054 3780 0274 1655 1924 D.3136 D.4909 0.2325
Daily Precipitation Johnson SB Probability Distribution Parameters for Precipitation Depth
Morph ❑istribudon Fitted Parameters for Selected Distributions
January Johnson SB Scale Gamma 2.0242 Scale Delta 0.8182 Scale Lambda 4.4092 Location A -0.07673
February Johnson SR Scale Gamma 3.1908 Scale Delta 0.9449 Scale Lambda 8.4554 Location -0.03685
Mardi Johnson SB Scale Gamma 3.1636 Stale Delta 0.9594 Scale Lambda 9.7596 Location A -0.06923
April Johnson SB Scale Gamma 1.6954 Scale Delta 0.7513 Scale Lambda 3.4790 Location A -0.04545
May Johnson SB Scale Gamma 2.0010 Scale Delta 0.7180 Scale Lambda 4.2443 Location A -0.02775
June Johnson SB Scale Gamma 1.7186 Scale Delta 0.5995 Scale Lambda 3.3226 Location A 0.00655
July Johnson SB Scale Gamma 3.0942 Scale Delta 0.8813 Scale Lambda 9.2951 Location A -0.06948
August Johnson SB Scale Gamma 2.9964 Scale Delta 0.8595 Scale Lambda 9.3422 Location]0 -0.07835
September Johnson SB Scale Gamma 3.2339 Scale Delta 0.9187 Scale Lambda 12.6210 Location 70 -0.10147
October Johnson SB Scale Gamma 1.8%9 Scale Delta 0.6108 Scale Lambda 4.9435 Location A -0.03236
November Johnson SB Scale Gamma 2.9333 Scale Delta 0.9405 Scale Lambda 9.0758 Location A -0.09873
December Johnson SB Scale Gamma 2.7451 Scale Delta 0.9901 Scale Lambda 6.2177 Location Al -0.09479
Source:SRK,2023
DH/JO KingsMountain_WaterBalanceModeling_Report_USPR000576_Rev02.docx April 2024

SRK Consulting(U.S.), Inc.
2022 KM PFS Report-Surface Water:Water Balance Development Report Appendices
Table B-2: Gamma Distribution Parameters for Daily Reference Evapotranspiration in
inches/day
Month Alpha Beta
January 10.7993 0.0054
February 11.4785 0.0072
March 13.9176 0.0088
April 24.8706 0.0069
May 28.3117 0.0073
June 37.5546 0.0063
July 48.8532 0.0050
August 40.3850 0.0054
September 25.0807 0.0069
October 19.0411 0.0063
November 14.3417 0.0055
December 11.2369 0.0050
Source:SRK,2023
D H/JO KingsMountain_W aterBalanceModel ing_Report_USPR000576_Rev02.docx April 2024