Appendix H - TSF Water Balance Model_Rev04_20240605

Technical Report
2023 Prefeasibility Study
Surface Water: Water Balance Development Report
Archdale TSF Kings Mountain Mining Project
Rev02
Effective Date: April 2, 2024
Report Date: April 2, 2024
Report Prepared for
ALBEMARLE`
Albemarle Corporation
4250 Congress Street
Charlotte, NC 28209
Report Prepared by
mirk consulting
SRK Consulting (U.S.), Inc.
999 17th Street, Suite 400
Denver, CO 80202
SRK Project Number: USPR000576
Albemarle Document Number: KM60-EN-RP-9499
North Carolina Firm License Number: C-5030
Signed by Qualified Persons:
David Hoekstra, BS, PE, NCEES, SME-RM
Reviewed by:
Mark A. Willow, M.Sc., NV-CEM, SME-RM

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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 Mining Project(KMMP or Project). KMMP
is a lithium deposit situated primarily within spodumene pegmatites and has been historically mined
using open pit methods. The Project will generate tailings material that will be disposed of at a remote
site approximately 3 miles south of the Project at the Archdale Tailings Storage Facility (TSF). This
report presents the development of a water balance model to simulate the water inflows, outflows,
storage, losses, and consumption associated with the Archdale TSF used by 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:
• Groundwater inflows to the TSF during and after operations.
• Surface run-off from the active TSF during operations.
• Infiltration into the TSF during and after operations.
• Storage of stormwater and seepage water volumes within the Contact Water Management
System.
• Simulation of mine closure activities, including placing cover on the tailings and the reduced
seepage produced by the tailings.
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.The time dependent
contribution of seepage from the filtered tailings, seepage from the exposed waste rock berm,
groundwater inflows, and other components of the model were provided to the groundwater modeling
study team as well as the geochemical water quality modeling team to provide base water volumes
for water quality predictions.
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Additionally, the water balance model was developed iteratively during the tailings development
process to provide feedback to infiltration, surface ponding, and water management strategies for the
TSF.
The water balance incorporates the overall mine water management strategy of the Kings Mountain
Project, which is to divert as much of the non-contact water(water that has not come into contact with
mine activities) around the site to avoid contacting the TSF Placement activities
Estimates of the tailings water quality for both seepage and runoff streams indicate that contact water
will be suitable for discharge, so the mine water management strategy for the TSF involves collecting
and monitoring contact water from the TSF perimeter corridor before releasing to Archdale Creek with
appropriate sediment controls and collecting and monitoring contact water from the TSF interior for
sediment and water quality.
Stormwater runoff from the interior of the TSF is removed from the TSF surface as quickly as possible
(approximately 2 days for the 100-yr storm) to avoid excessive infiltration into the previously placed
filtered tailings, while seepage from the TSF will be collected at the base of the TSF, along with
intercepted groundwater flows to minimize the zone of saturated tailings at the base of the TSF. Both
sources of contact water are pumped to the Contact Water Pond located in the adjacent Non Process
Infrastructure Pad where it can be monitored and released at a sustainable rate to the downstream
drainage. The pond is designed to release water from the 10-yr storm event through a skimmer device,
while the spillway is designed to convey the flows from the PMP on the TSF Interior.
Overall, the model indicated the system was able to manage stormwater and seepage flows in a
controlled manner, even during the probabilistic simulations that generated significant wet periods and
storm events. A sensitivity analysis of the water balance inputs, including groundwater inflow,
infiltration, and percolation rates, indicated that when varied within reasonably expected ranges for
annual precipitation scenarios that included the 5th percentile climate year (1 in 20 drought) and 95th
percentile climate year (1 in 20 wet). Additional scenarios were included that addressed single, one
day events up to the Probable Maximum Precipitation. For all these cases, the system was able to
manage the TSF contact water within the design constraints.
SRK anticipates that this model will be a living tool for the continued development of the Archdale TSF
supporting 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 Archdale TSF supporting the Kings Mountain Lithium Mine.
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Table of Contents
ExecutiveSummary .......................................................................................................... ii
Abbreviations .................................................................................................................. vii
1 Introduction .................................................................................................................. 1
1.1 Property Location................................................................................................................................1
1.2 Property History..................................................................................................................................2
1.3 Project Overview.................................................................................................................................3
1.4 Project Layout.....................................................................................................................................4
2 Site Environment ......................................................................................................... 5
2.1 General Description ............................................................................................................................5
2.2 Climate................................................................................................................................................5
2.2.1 Temperature............................................................................................................................5
2.2.2 Evaporation .............................................................................................................................5
2.2.3 Precipitation.............................................................................................................................6
2.2.4 Storm Frequency.....................................................................................................................7
2.3 Surface Water.....................................................................................................................................8
2.4 Wind Patterns....................................................................................................................................11
3 Water Balance Development..................................................................................... 12
3.1 General Model Operating Approach.................................................................................................12
3.1.1 Qualitative Assignment of Water Quality...............................................................................12
3.1.2 Project Flowsheet..................................................................................................................13
3.1.3 Water Balance Model Simulation..........................................................................................13
3.1.4 GoldSim Software .................................................................................................................13
3.1.5 General Model Structure.......................................................................................................16
3.1.6 Incorporation of Uncertainty..................................................................................................17
3.2 Climate Simulation............................................................................................................................18
3.2.1 Climate Change.....................................................................................................................19
3.2.2 Legacy Climate Time Series .................................................................................................21
3.2.3 Synthetic Climate Generator.................................................................................................23
3.3 Environmental Components..............................................................................................................26
3.3.1 Infiltration Simulation.............................................................................................................26
3.3.2 Runoff Simulation..................................................................................................................27
3.3.3 Other Runoff-Infiltration Models ............................................................................................29
3.3.4 Groundwater Inflows .............................................................................................................30
3.4 Facility Components..........................................................................................................................31
3.4.1 Processing Plant Tailings Delivery and Filtered Tailings Stockpile ......................................33
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3.4.2 Filtered TSF...........................................................................................................................34
3.4.3 Contact Water Management Pond........................................................................................36
4 Water Balance Model Simulations............................................................................ 37
4.1.1 Deterministic Scenarios.........................................................................................................37
4.1.2 Probabilistic Simulations .......................................................................................................40
4.1.3 Model Sensitivity ...................................................................................................................42
5 Interpretation and Conclusions................................................................................ 44
6 References.................................................................................................................. 45
List of Tables
Table 2-1: Excerpted Tables showing Site-Specific Study and Annual Return Intervals Results......................8
Table 3-1: Precipitation Climate Change Adjustment.......................................................................................21
Table 3-2: Correction from Reference Evapotranspiration to Lake Evaporation..............................................23
Table 3-3: Surface Infiltration Parameters........................................................................................................27
Table 3-4: GR4J Model Parameters.................................................................................................................29
Table3-5: CN Values by Surface.....................................................................................................................29
Table 3-6: Scheduled Rate of Tailings Delivery to the Archdale TSF..............................................................33
Table 3-7: Key TSF Input Parameters..............................................................................................................35
Table 3-8: Key Water Storage Basin 3 Input Parameters................................................................................36
List of Figures
Figure1-1: Location Map....................................................................................................................................2
Figure 1-2: Preliminary Kings Mountain Mining Project Site Map......................................................................4
Figure 2-1: Average Monthly Evaporation..........................................................................................................6
Figure 2-2: Annual Precipitation and Distribution of Monthly Precipitation ........................................................7
Figure 2-3: Existing Streamflow Network.........................................................................................................10
Figure 2-4: Wind Rose Data in the Vicinity of Kings Mountain.........................................................................11
Figure 3-1: Archdale Site-Wide Water Balance Operational Conditions Flowsheet........................................15
Figure 3-2: Model Screenshot Showing the Four Main Level Containers........................................................17
Figure 3-3: Example of Probabilistic Time Series ............................................................................................18
Figure 3-4: Comparison of Mean Annual Temperature and Mean Annual Precipitation for The Three Climate
Projections...........................................................................................................................................19
Figure 3-5: Change in Daily Maximum Temperatures from Current Climate Conditions.................................20
Figure 3-6: Monthly Temperature Normal Compared to Climate Change Temperature Normal.....................20
Figure 3-7: Comparison of Simulated vs. Shelby 2 NW Climates....................................................................25
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Figure 3-8: Comparison of Annual Maximum Daily Rainfall from Legacy, Simulated and NOAA Atlas 14
Values..................................................................................................................................................26
Figure 3-9: GR4J Runoff Model Schematic......................................................................................................28
Figure 3-10: Groundwater Inflows and Outflows to the TSF............................................................................31
Figure 3-11: Top Level Screen Capture of the GoldSim Water Balance .........................................................32
Figure 3-12: Screen Capture of Typical Water Balance Module......................................................................33
Figure 3-13: Tailings Infiltration and Runoff Flow Schematic...........................................................................34
Figure 4-1: Deterministic Climate Scenarios Annual Precipitation...................................................................38
Figure 4-2: Outflow from WSB-3 Through the Skimmer under the Deterministic Climate Scenarios..............39
Figure 4-3: Volume in the CWMP under the Deterministic Climate Scenarios................................................40
Figure 4-4: Probabilistic Time Series of CWMP Volume..................................................................................41
Figure 4-5: Probabilistic Time Series of CWMP Elevation...............................................................................41
Figure 4-6: Sensitivity Study Tornado Chart on Total Water Released from the Tailings over the Period of
Deposition............................................................................................................................................43
Appendices
Appendix A: Historical Climate Data
Appendix B: Johnson SB Probability Distribution Fitting to Legacy Precipitation Records
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Abbreviations
Abbreviation Unit or Term
percent
° de ree(degrees)
AR6 sixth assessment report
amsl above mean sea level
AEP annual exceedance probability
AWA Applied Weather Associates
CDF cumulative density function
cfs cubic feet per second
CMP corrugated metal pipe
CN curve number
DEMLR North Carolina Division of Energy, Mineral and Land Resources
dia. diameter
DMS direct media separation
DEQ Department of Environmental Quality
ETo reference evapotranspiration
FAO Food and Agriculture Organization of the United Nations
ft foot feet
ft2 square foot feet
ft3 cubic foot feet
al gallon
GCMs General circulation models
m gallons per minute
HDPE High Density Polyethylene
OF Inflow design flood
i85 U.S.Interstate Highway 85
in inch
IPCC Intergovernmental Panel on Climate Change
Ib pound
LoM Life-of-Mine
mm/h millimeter per hour
Mt million tons
NEX-GDDP NASA Earth Exchange Global Daily Downscaled Projections
NPI Non-processing infrastructure
PAG Potentially acid generating
PDF probability density function
PFS Prefeasibility Stud
PIMP probably maximum participation
QQ Quantile-Quantile
RoM Run-of-Mine
sec second
SSP shared socioeconomic pathway
t ton (imperial ton 2,000pounds)
t/d ton per day
t/h ton per hour
Vy ton per year
TSF tailings storage facility
USDA United States Department of Agriculture
USGS United States Geological Survey
WTP water treatment plant
y year
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1 Introduction
Kings Mountain Mining Project(KMMP or 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 Corporation
(Albemarle) as part of a prefeasibility (PFS)-level analysis. Albemarle commissioned SRK Consulting
(U.S.), Inc. (SRK) to develop prefeasibility-level 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. The Project will generate tailings material
that will be disposed of at a remote site approximately 3 miles south of the Kings Mountain Project at
the Archdale Tailings Storage Facility (TSF).
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 of
the mine and the location of the Archdale TSF.
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^�� '1' 91ue Ridge
r Perkxey
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i
Statesville
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A10l1nShc n5
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/7
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Green River -` Shelby
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Wade Hampron 7R60 pQa
Greenville R;e
- South Carolina
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Source: ESRI,2023(modified by SRK)
Figure 1-1: Location Map
1.2 Property History
The following summary highlights the history of the Kings Mountain site, compiled from records
available to SRK:
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• 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 feet(ft) above mean sea level (amsl).
• In early 1994,an open pit lake started to form due to rebounding groundwater, and the pit lake
reached an elevation of 817 ft amsl (as of January 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.
The proposed Archdale TSF will be located at the site of a former mica mine. The following summary
of the site is compiled from records available to SRK:
• The site was formerly owned by the Kings Mountain Mica Company, which began operation
in 1949.
• The site was owned by several different companies between 1994 and 2021, including
Franklin Minerals, Oglebay Norton, Zemex, General Chemical and Imerys.
• Imerys expanded mining to the property north of Archdale across Highway 29 in 2011
• Albemarle acquired the site in 2023 for use as a permanent storage of filtered tailings from the
KMMP.
Available aerial photography of the site suggests that mining activities continued through about 2013.
The current site layout encompasses several shallow in-pit ponds formed during the previous mica
mining operations. Figure 1-2 shows a detailed map of the current site layout.
1.3 Project Overview
Tailings from the spodumene concentrate process at Kings Mountain will be filtered to approximately
10 to 15% moisture content by weight and transported off-site to the proposed Archdale TSF for
disposal. A portion of the waste rock mined at the Kings Mountain site will be transported to Archdale
for construction of the TSF embankment.
An initial TSF embankment will be constructed on site to hold approximately one to two years of filtered
tailings. Thereafter, filtered tailings material will be placed and compacted with mobile equipment at
the same time the TSF perimeter embankment is raised with compacted waste rock and/or fill. The
TSF will be constructed in this manner until the facility reaches its capacity of approximately 7.14
million CY, at which time the facility will be closed and reclaimed.
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1.4 Project Layout
The Project layout and the relative locations of the major components of the Project are shown in
Figure 1-2.The Project is bounded by Interstate 85 on the south and Highway 29 on the north.Access
to the TSF will be from Highway 29 with separate truck and light vehicle entrances.The site will include
a small office and maintenance facilities, parking, water storage and sediment control facilities, and a
TSF perimeter access road. Spaces for a small road base stockpile and a growth media storage area
are included in the site plan.
SITE ENTRANCE - o xa eoo
a�
EEC
IWFT.PERIMETER HIGH POINT
HAUUACCESSROAD
FILTEREDTAILIN-
STORAGE
LIGHT VEHICLE ENTRANCE �
ROCK FILL EMBANKMENTS
CREST WIELEV 1
SEEPAGE INTERCEPTION DRAIN
PERIMETER ACCESS ROAD
WATER AND SEWER MAIN \.
LIGHT VEHICLE ACCESS ROAD /
CULVERTS(TVP.)
PROPOSED CULVERT( EXISTING TYP.I �
/\ SEDIMENT BASINS
FUEL PAD
MAINTENANCE SHOP SEEPAGE COLLECTION TANK
/ � �/
CULVERT ABLE TO PASS PIMP
PROPERTY BOUNDARY I OVERHEAD POWER
CONTACT WATER POND
STOCKPILE GROWTH k CV'9)MEDIA �_// LAY DOWN AREA
( /
TRUCK PARKING
PARKING
V
Figure 1-2: Preliminary Kings Mountain Mining Project Site Map
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2 Site Environment
2.1 General Description
The Project is located in southwestern North Carolina, USA, approximately 3 miles southwest of the
city of Kings Mountain on the Interstate 85 (1-85) transit corridor, approximately 33 miles west of the
city of Charlotte(Figure 1-1).The property is located at approximately 35 degrees(°), 11 minutes north
latitude and 81°, 24 minutes west longitude.
The Project property boundary encompasses approximately 100 acres and includes several legacy
waste rock areas and an existing mine pit with several small ponds. None of the ponds show signs of
active acid rock drainage or metal leaching. Much of the site is heavily vegetated, and the vegetation
does not show signs of metal or acid stress.
2.2 Climate
The Project is situated within the K6ppen-Geiger Cfa climate classification (Kottek, 2006), which
describes a continental type of climate without a dry season.Temperatures during the warmest months
are above 72 degrees Fahrenheit ff), 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
42 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 1040F 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 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,
average annual 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
c �
o S
`o
F 4
w
T �
r 3
c
o i
2
1
0
January February March April May June July August September October November December
Clemson Univ Chesnee 7 WSW Chapel Hill 2 W —— —Average GoldSim
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, 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
N 10
L
60
0 9 c ■
O c 50
•0 6 a40
d` 4 30
_T —
L c 20
C
O 2 = 6
10
0
January February March April May June July August September October November December Annual Total
2.596-5% 5%-10% ■10%-25% ■25%-50% ■Median ■50%-75% ■75%-90% 9D%-95% 95%-97.5%
Annual Precipitation
80
J0
t 60
50
r 40
i 30
20
a 10 I I
0
1 19U 1135 .1 _ ��- 1970 19]S 1980 _ 2C"' ��_. _J10 2�l_,
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 (National Oceanic
and Atmospheric Administration (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
StudyforKings 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 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 Mountain AEP
Estimate PMP(in) AEP ARI
6hr 28.5 2.5 F8 39,929,769
24hr 32.4 1.09'7 9,144,104
72hr 32.4 3.93'7 2,540,551
TAhle 10.5: Kink Mountain hasin overall frequency analysis for 6-,24-,and 72-hour
Kings Mnt Frequency Analysis 6-hour 24-hour 72-hour
ARI AEP AEP 50% 5% 95% 50% 5% 95% 50% 5% 95%
1.01 0.99016 9.9' 1.0 0.9 1.1 2.0 1.8 2.2 2.4 2.2 2.6
2 0.5000D 5.0' 2.3 2.1 2.5 3.6 3.4 3.9 4.3 4.0 4.6
5 0.200o0 2.0'' 1.2 2.9 3.4 4.2 4.4 5.1 5.7 5.2 6.1
10 0.10000 1.0' 5.3 3.5 4.1 5.5 5.1 6.0 6.5 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 0.0200D 2.0' 5.3 4.9 5.8 7.5 6.9 8_2 8.9 8.2 9.7
100 0.01000 1.0' 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 8.5 10.4 11.0 10.1 123
500 0.00200 2.0 3 7.9 7.1 8.9 10.6 9.6 12.1 12.6 11.4 14.3
1,000 0.00100 1.c) 8,7 7.8 10.1 11.7 10.4 13,5 13,9 12.4 16,0
5,000 tl_00020 2.0-0 10.9 9.5 13.1 14.4 12.5 17.2 17.1 14.9 20.4
10,000 0.00010 1.c) 11.9 10,3 14.5 15.7 13.5 19.1 18.6 16.0 22.6
100,000 0.00001 1.c) 15.8 13.1 20.5 20.4 16.9 26,5 24.2 20.0 31.1
1,000,000 0.000001 1.c) 20.5 16,3 28,3 26.2 20.7 36.1 31.0 24.5 42.7
10,000,000 0.0000001 1.c) 26.2 19.9 38,7 33.1 25.0 48,8 39.1 29.7 57.8
100rO001000 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 1.0'9 41.4 29.6 70.7 51.2 35.3 97.4 60.6 41.9 103.5
10,000,000,000 0.0000000001 1 O" 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, Archdale Site, Kings Mountain Mining Project, North
Carolina (SRK, 2024), and the following sections are incorporated from that report.
The project is located within the Dixon Branch watershed, a tributary to Kings Creek which is part of
the Broad River basin which stretches from western North Carolina into northwestern South Carolina
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(Figure 2-3)and encompasses a total 1,513 square miles.The headwaters originate in the Blue Ridge
Mountains and generally flow southeastward ly. The basin has a varied landscape of forested land,
asture and row crop agricultural land, and urban land and is made up of 17 sub-watersheds, including
Kings Creek.
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122— 1240000 1260000 1260000 130 moo
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SOURCE:SRK 2023 1—E 03.05.2024 KINGS MOUNTAIN MINING PROJECT nGuaE NO 2.1
raE N-E:Fig 2-1.1.Creek Watershed SRKPROJECTND._USPR#0000576
r:enw�nnE�eo�me sau coy:wa�g��iswE nsaw
Figure 2-3: Existing Streamflow Network
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2.4 Wind Patterns
Wind rose data were obtained from the Department of Environmental Quality(DEQ)Air Quality Portal
available online at https:Hairguality.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.
Em Wind rose for KAKH in Gastonia,NC Wind rose for KEHO in Shelby,NC
For Feb 1,1999 m Msy24,2023191%of dale evsilane) For Jan 3,—m May 24,2@3 93%of date available)
N N
be aE
., .. EaE is
i
1
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sw se sw sE
Calm Wintls l<2 mph'. SSVJ S Celm Winds[<2 mphl: S`: SSE
39.2v,pf observalions S
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Wind Speed:
11
mph mpo 15 mph ,u ) Wind Speed_
�6[O 11Z'1 10-5,h >_20h,ph =ime ants_- �21p6 mph 7--ph 161.20 mph
♦5tp7mh 10to15 mph -20 nph time ncsu_
Source: DEQ Air Quality Portal, https://airguality.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 address 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.
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: (Z Inflow - Y_outflows)At= A Storage Equation 1
3.1.1 Qualitative Assignment of Water Quality
The mine water management system of the Archdale site is focused on separating water that has
come into contact with mining waste materials from the KMMP, notably filtered tailings stored at the
site and waste rock used to construct stable perimeter berms. Waste used for structural purposes at
Archdale will be selected from materials classified as non-potentially acid generating (Non-PAG).
Based on geochemical analysis(SRK,2023b and SRK 2023c)of the tailings and waste rock materials,
SRK believes the impacted water produced by the TSF stormwater and seepage collection systems
will be suitable for discharge off-site.
As an overarching goal of the Archdale TSF water management system, the Site-Wide Water Balance
focuses on providing sufficient storage to prevent the undesirable release of impacted water from the
site. To achieve this end, the water balance provides storage for stormwater from the TSF with
sufficient detention time to allow the discharges to be monitored before release off-site.
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 two different water categories and are managed
accordingly. The water quality categories are, in order of least impacted to most impacted, described
below:
Non-Contact Water
Water that has not come into contact with mining activities is assigned the least impacted water quality.
Non-contact water that has only contacted revegetated, paved, or undisturbed native soil surface can
be released to the environment with only temporary sediment controls during initial construction.
Generally, all surrounding undisturbed watersheds, non-process infrastructure areas, and any
reclaimed surface are defined as generating non-contact water.
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New Disturbance Non-Contact Water
Water that has not come into contact with mining activities is assigned the least impacted water quality
but runoff from newly disturbed areas or areas that will not be immediately revegetation is managed
differently that other non-contact water. New Disturbance Non-Contact Water can be released to the
environment with appropriate sediment controls during the life of the facility. Generally, all runoff from
the reclaimed TSF perimeter berms, the active TSF perimeter and haul roads, and surrounding areas
that cannot be diverted are defined as generating new disturbance non-contact water.
Contact Water
Water that has come into contact with mining activities, but is expected to meet discharge quality, is
assigned a more impacted water quality. Generally, water that has come into contact with the filtered
TSF or the exposed waste rock perimeter berms is assumed to be contact water. Contact water is
defined as being able to meet discharge water quality, but may require de-sedimentation and
monitoring prior to release.
3.1.2 Project Flowsheet
The interconnections between the facilities are shown for operational facilities and flow connections in
Figure 3-1. 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.
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 SRKs understanding of how the mine water management system at the
Archdale TSF site is expected to behave, and allows for simulations of the system, in an effort to
explore the behavior of the system under typical, historical, 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, is
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.
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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.
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c'
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Figure 3-1: Archdale Site-Wide Water Balance Operational Conditions Flowsheet
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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. A screenshot of the four main high-level containers and
reporting container is shown as Figure 3-2 and 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 historical conditions but is
not constrained to only values observed in the legacy record.
• Runoff: Runoff from the various surfaces is calculated using different methods, as described
in Section 3.3.2 below. 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. The common container is presented in Section 3.4.
• Water Balance: Within the Water Balance container, the majority of the water balance
calculations take place. The Water Balance container is described here in general terms.
Individual components of the water balance model are described in more detail in Section 3.4,
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.
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L
Water Balance
_ Common
r7
O
Climate r7
Results
Runoff
Figure 3-2: 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.
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.
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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 in Figure 3-3, 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
E 2000 ' 2000
0 laoo ' '' 1800
1600 �� 1600
V
N
a- 1400
—� 1400
c
Q 1200 —� 1200
1000 1000
2020 2030 2040 2050 2060 2070 2080 2090
Time
Statistics for Annual Precipitation
1%..5%/95%..99% 5%..15%/85%..95% 15%..25%/75%..85%
25%..35%/65%..75% 35%..45%/55%..65% 45%..55%
50%
Source:SRK,2023
Figure 3-3: Example of Probabilistic Time Series
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. An in-depth description of the
development of the climatic parameters for the Project water balance is presented in Section 3.2.
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.
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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 an historical period
of record from 1975 to 2005.This historical period represents current climate conditions,and it 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 the year 2100 using the information
available from the Intergovernmental Panel on Climate Change (IPCC) from the sixth assessment
report(AR6) (IPCC, 2021). The climate change evaluation was based on downscaled climate General
Circulation Models (GCMs) provided in the NASA Earth Exchange Global Daily Downscaled
Projections (NEX-GDDP) data set (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 Shared Socioeconomic Pathways (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 GCMs 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 twenty-six models shows an increase in mean annual temperature and mean annual
precipitation (Figure 3-4). 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 3-5 and
Figure 3-6).
Historical SP45 SP85
Model
Modell A Model26
Model10 A Model27
NU 22.5 22.5 22.5 A Model 11 A Model28
�5 Model12 Model29
m 0 Model 13 Model 30
c 20.0 20.0 20.0 ® Model14 Model33
F p A Model15 A Model34
Model 16 Model 4
Model17 Model
c 17.5 175 � 175 Modell Model
Q
Model21 Model
Model22 Model
1150 1200 1250 1300 1350 1150 1200 1250 1300 1350 1150 1200 1250 1300 1350 Model23 A Model
Annual Precipitation(mm/yr)
Source:AWA,2022
Figure 3-4: Comparison of Mean Annual Temperature and Mean Annual Precipitation for The
Three Climate Projections
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V
Y
`0
N Projection
E 5 SP45
O E
� SP85
03
ca
All Summer Winter
Source:AWA,2022
Note: Results are based on annual maximum frequency analysis.
Figure 3-5: Change in Daily Maximum Temperatures from Current Climate Conditions
30
E� • ` ¢•
. _
Projection
20 . ' Hist
a • SP45
SP85
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 3-6: 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 from Table 3-1 with the simulation year
between 2005 (no correction) and 2100 for the selected climate scenario.
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Table 3-1: Precipitation Climate Change Adjustment
Correction Factor for Current
Conditions to Projected Conditions
Month Current SSP4.5 SSP8.5
2005 2100 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 1.08
December 1 1.00 1.07 1.04
Source:AWA,2023
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 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 match 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 (1990's) of records has some
missing data points (up to 19%) but is nearly 100% complete after the year 2000. To develop a
complete record, SRK used the Daymet gridded data set(NASA 2023)to infill the missing data points
in the Shelby 2 NW data set. Daymet is an interpolated and extrapolated data set of daily records at
1-km grid spacing available for North America and Hawaii provided by NASA. A comparison of the
Shelby 2 NW and Daymet data sets indicated a difference of less than 1% in accumulated precipitation
over the 30-year period.
Monthly values of the 32-yr precipitation time series are presented in Appendix A.
The Water Balance model is configured so that the legacy time series may be used in sequence,
repeating the historical 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 (51h percentile)to extreme wet (951h percentile) conditions. The following climate
forcing years were produced:
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• 5th Percentile Year: Total Annual Precipitation = 33.71"
• 2511 Percentile Year: Total Annual Precipitation = 41.10"
• 50th Percentile Year: Total Annual Precipitation = 49.59"
• 751h Percentile Year: Total Annual Precipitation = 54.65"
• 951h Percentile Year: Total Annual Precipitation = 67.17"
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. However, the method suffers from the limitation of being
unable to simulate precipitation events that are not in the legacy record, and thus may not represent
the conditions that the site could experience in the future. For example, the 30-yr record produces a
maximum daily event of 5.8 inches,which is below the expected event according to AWA's site specific
Precipitation Frequency values, which suggest that, statistically speaking, the site should have
experienced a rainfall event of 6.6 inches in the 32-year record.
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
from open water bodies, such as ponds and reservoirs, using a lake evaporation term, which
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, 1998). SRK also calculated lake evaporation using the Hamon method
(Hamon, 1961), which requires daily temperatures. The calculated monthly ETo for the legacy period
is presented in Appendix A.
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.
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Table 3-2: Correction from Reference Evapotranspiration 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, SRK incorporated a stochastic synthetic
precipitation generator based on the WGEN climate generator model developed by the 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
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 2-
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.
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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 (N.L. 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. A summary of the probability of rain and Johnson SB distribution parameters is also included in
Appendix B.
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 in Figure
3-7).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.
Figure 3-8 presents a comparison of the annual maximum precipitation of the legacy record and
simulated climate against AWAs Annual Exceedance Probability(AEP) precipitation depths (provided
in Table 3-1) indicate that while the synthetic climate generator produces values very similar to the
legacy record, both values are 10-15% below AWAs 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-hr 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.
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 2-parameter gamma distribution to the daily
ETo, on a monthly basis, to produce stochastic ETo. The fitted gamma distribution parameters are
included in Appendix B. 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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Comparison of Simulated Climate vs Daily Precipitation for 1990-2022 Shelby 2 NW Record(lnfilled)
Xi t-W r series
P-ble Janus rebru Marl ApH M June JW A n somber O-be November December Annual . tiara Sun Xe Rio
S% Xirter I(So%) 2.11 LOB LS3 1 1.03 1 1.32 1 1.26 1 1.56 1 0.92 1 0.32 1 0.49 1 0.75 1 2.21 1 1 34.17 1 34.17 14.65 14.65
25Y Xi:toriol 25.OY 2.84 2.43 3.6a 266 2.d0 212 2.52 294 2.6a 1.89 1.66 2.78 43.15 43.15 20.82 20.82
50% Xe terical 5Q0% 389 320 4.39 3.56 3.97 3.85 3.77 443 3.66 3.63 3.46 415 50.36 50.36 26.71 26.71
75% Hi-ical 75.0% fi20 1 4.45 1 6.22 1 5.70 1 5.53 1 548 1 534 1 5.46 1 5.06 1 5.12 1 5.15 1 490 5515 1 55.65 3L60 31.60
95% Hi:toriol .0% 9.10 1 6.95 1 9.18 1 7.98 1 7.39 1 8.39 1 fi72 1 8.66 1 6.73 1 10.01 1 8.31 1 9.19 1 1 67.95 1 67.95 46.48 46.49
SinuYtion OYnete:100001 reaamtimtF
P-61. J---rV Pebrua Marl Apri Ma June MY A rt Spftmb,, October November December Amual A-9 Sum Xe Rain
SY S:nulated S.OY 1.34W4.U
1.57 1.10 I.OB 1.16 1.47 1.24 a59 0.51 0.80 1.30 37.93 37.93 a3l.403LO
ld
25% Simulatetl 25.0YN2941.30 2.56 2.57 2.57 2.90 2.68 19450% Simulated 50V% 4.46 3.88 3.94 3.88 413 4.05 337 3.42 356 4Ot 5026 5026 75% Simulated 5.0ll618 S.aS 5.57 5.47 5.85 5.76 5.16 5.37 5.30 5.68 55.65 55.65 Y Simu4ted 95.OY9.20 8.13 8.35 8.11 8.77 8.75 B.SS 8.87 8.a7 8.36 6a.23 6a.23
Comparison of Simulated Climate vs Daily Precipitation for 1990-2022 Shelby 2 NW Record (lnfilled)
al -----------
70
........... a
.---"'----
33
... � I•3W
8 Mw
60
Z ........... 6
20
13
�20
10
20
....................:.........
00 0
0
JaNV'( FtdW� Marta April Met loot Jul? inntq: He<i PtabM Sum NtlYr Aain
_-_--Metort.IR OttI �kbrakY llS O41 ��Kaolu�I50 Otl �unakal llS Otl -----..Ra/u119S.0\. _._..S...Ixa4!S OtI �S:mWtM IJS thI ��SimuYtN I50.d{I �SmW[e0 f15.0\I ----SmuWaC H6D1t)
Source:SRK,2023
Figure 3-7: Comparison of Simulated vs. Shelby 2 NW Climates
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14
12
10
m _
8
� C •
C O
Q �
E'tea •
E 6
g •
•
4
•
•
2 •
0 � —
0.000 0.200 0.400 0.600 0.800 1.000 1.200
Annual Exceedance Probability
• AWAAnnual Exeedance Probability —Simulated Precipitation ♦ Historical Precipitation
Source:SRK,2023, NOAA,2023
Figure 3-8: Comparison of Annual Maximum Daily Rainfall from Legacy, Simulated and NOAA
Atlas 14 Values
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 assumed to infiltrate into the material below. No physical
infiltration studies have been performed to date, so values were selected based on soil characteristics
and observed behavior for similar materials.A Green-Ampt(Green, 1911)simulation of the uncovered
tailings materials using a sandy clay loam soil texture and a hydraulic permeability of 2.52 mm/h
(7x10-5 cm/sec), similar to the soil properties of the tailings sample, 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 4.1.3 was performed to quantify the impact of these values have on the Model
results.
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Table 3-3: Surface Infiltration Parameters
Modeled Surface Soil Texture Infiltration as Percentage of Rainfall
TSF Clayey Silt 40%
Active Waste Rock Dump 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 in Figure 3-9,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 I S
Perc Pr
0.9 0.1
UH1 UH2
H
X4 2.X4
I I
Q9 Q1
Routing
store X3 R F(X2) /7 F(X2)
Qr Qd
Q
Source: Perrin,2002
Figure 3-9: GR4J Runoff Model Schematic
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.
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Using observed flows in Kings Creek and South Creek from the 2022 and 2023 streamflow monitoring
program, SRK iteratively developed the following 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 1045.5 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 Method (NRCS 2004)
to produce runoff from the surface in response to daily rainfall. The Curve Number(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 Equation 2:
S = 25.4("0 — 10) Equation 2
CN
Where:
• S = Maximum retention (mm)
• CN = Curve Number(1-100)
The runoff amount(Q) is then related to the rainfall with Equation 3:
Q = (P-0.2S)2 Equation 3
P+0.8s
Where:
• Q = Runoff(mm)
• P = Precipitation (mm)
SRK assigned the following CN values to surfaces within the water balance model. To assess the
impact of these assumptions, a sensitivity study, described in Section 4.1.3 was performed to quantify
the impact of these values have on the Model results.
Table 3-5: CN Values by Surface
Surface Curve Number
TSF 60
Active Waste Rock 40
Reclamation Cover 76
Perimeter and Haul Road 82
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3.3.4 Groundwater Inflows
Groundwater inflows to the model are used to define the seepage from the pit walls to the pit sump as
the existing pit bottom is located below the groundwater table and will be evacuated during active
placement in the TSF. SRK performed detailed studies as part of the hydrogeologic evaluation of the
Project site (SRK, 2024)that were incorporated into the water balance model.
The hydrogeological three-dimensional (3D) finite element modeling predicted groundwater inflows
and outflows from the open pit based on the period of mining, as well as the water levels within the
TSF. Groundwater inflows and outflows for different stages of pit activities were represented in the
water balance model, as follows:
• Groundwater inflows during the existing pit dewatering (pre-development).
• Groundwater inflows during the evacuation of the pit bottom during TSF development.
• Groundwater inflows and outflows during the closure/post-closure groundwater recover
period.
Groundwater inflows and outflows were provided on an average annual basis from the groundwater
model, as shown on Figure 3-10Figure 3-10. Later in the post-closure period, the Archdale TSF is
expected to act as a flow through system, so there will be both a groundwater inflow to the TSF and
an outflow to groundwater from the TSF. 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 have limited impact on the regional and internal groundwater levels in
the post-closure TSF, so no stochastic components were incorporated into the groundwater inflows.
Groundwater outflows from the TSF will include the addition of seepage produced by stochastically
generated precipitation, slightly increasing the groundwater flows leaving the TSF, similar to that of
natural recharge. Figure 3-10 shows the groundwater inflows and outflows predicted by the
Groundwater model.
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140
120
E
n
02 100
v
3
80
0
l7
E 60
0
0
0
0 40
LL
20
0
2030 2040 2050 2060 2070 2080 2090 2100
Time
Groundwater Inflows Groundwater Outflows
Source:SRK,2023
Figure 3-10: Groundwater Inflows and Outflows to the TSF
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. A top-level view of the model
structure is shown in the screen capture of the water balance in model in Figure 3-11.
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BalanceArchdale Site-Wide Water
SRE ENTRANCE Trucking_to_Archdale
t OCf'PERETER Ar j HIGH PONT
Rux.ACCESS ROW i — /
UGKT AENICLE ENTRANCE
ROCK FILL BeArKvt.'•.
Y '+W CRLSTOWElt. re
SEEPAGE INTERCEPTION DRAlr
TSF {
GERAETER ACCESS ROAD
WATER AND SEMER WAIN
UGNT AtHICAE ACCESSROAD
"TING CIRVERTS ITYP I
PROPOSEDcULv1ATRYP ®
\ )R""N
rTEI PAD
SEEPAGE COLLECTION T
IEARRTH/AlICE SHOP
Contac�t/\/vtater_Pono TO PASS PSRP
PROPpRV EOUNDARv /fl\�v'/..` ovEAEAD POWER '
�D t :/ CONTAcr WATER Pam
ur DOWN AREA
STOCRO>+lE(-"DMOf'N
TRUCK PARKING Environment
/ PARxiIQ
V
Source:SRK 2024
Figure 3-11: Top Level Screen Capture of the GoldSim Water Balance
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 in Figure
3-12. Key points of the typical layout shown in Figure 3-12 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 typically will be the only link to other modules in the Model (other linkages may
be utilized for reporting or verification).
Within the module, there will be additional inputs and calculations in the area between the boxes,
organized by additional levels of containment, if needed.
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Links © Kings Mtn Filtered Tailings Storage at Archdale site and Waste
Rock Perimeter
Module Switches
Module Inflows Module Outflows
/ sufxe_vaM ron°R m_asw2e
fx � -
,:m_�
flTOW...dw—.—
cob—
Source:SRK,2024
Figure 3-12: Screen Capture of Typical Water Balance Module
Water flows, water demands, ore and waste tonnages, and other information is passed between the
modules to determine the flows and accumulation of water in the system according to the conceptual
model. A detailed description of the facilities and key inputs used by the model is presented in the
following section.
3.4.1 Processing Plant Tailings Delivery and Filtered Tailings Stockpile
Dry filtered tailings are delivered to the Archdale TSF by highway trucks while mining activities and
mineral processing is underway at the Kings Mountain Lithium mine. Table 3-6 presents the delivery
rate of tailings to the TSF.
Table 3-6: Scheduled Rate of Tailings Delivery to the Archdale TSF
Dry Tailings
Date t d
1/1/2028 1,885
1/1/2029 2,615
1/1/2030 2,610
1/1/2031 2,610
1/1/2032 2,496
1/1/2033 2,617
1/1/2034 2,610
1/1/2035 2,610
1/1/2036 1,347
1/1/2037 0
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3.4.2 Filtered TSF
All tailings produced by the Kings Mountain mine process will be permanently deposited in the Filtered
TSF located at the Archdale Site.The TSF will be constructed using local borrowed material and waste
rock from the Kings Mountain Mine to form the berm, with appropriate geotechnical filters and
recompacted soil liners on the interior faces.
Prior to placement, tailings leaving the Processing Plant will be dewatered using filter presses and
excess water from the filter is returned to the Processing Plant. Filtered tailings will be placed in the
TSF in level lifts, always below the crest of the perimeter berm. The construction and phasing of the
TSF are described in detail in "Archdale TSF Design Report", SRK 2024.
A schematic of the flow components addressed in the TSF water balance model are shown in Figure
3-13.
The model assumes that waste rock from the Kings Mountain mine will be used to construct the TSF
Embankment. The bottom surface of the TSF will consist of the bottom of the existing Archdale pit,
recompacted native soils and a drainage collection system. The outer slopes of the perimeter berm
will be reclaimed as final grades are achieved.
Waste Rock Perimeter Filtered Tailings Interior Perimeter
Berm(20.5 ac) 57.7 acres Berm
Collection Sump to Contact
Water Management Pond(0) Net
Surface Sump to Contact Precipitation
Water Management Pond(S) (B)
Closed Tailings
Berm Crest Tailings Infiltration WRlnfiltration Op
Active Tailings Runoff(T)Op(C)/Closed(D) V/Closed(F)
Elev 960 — Runoff(S)
o Perm Storage
To/From Embankment
Transient Stora a Tailings(H)y
Tailings Tailings in7ailings(J)� eV O
take/Stor
WR Loss to Toe p into WR(G) Drainage to :::�:,:r '
Seepage(R) in WR(P) Drains(K)
^' '• Tailings Loss Regional Groundwater
411111111111111111 WR Drainage to .`�to GW(N) Inflow(L)
Drains(Z) 11,
> Tailings Seepage
Base of TSF Returned from
Elev 841 to 935 Groundwater storage in Saturated
WR Loss to (M) Tauimgs(V)/Volume
Gyy(0) Saturated Tai lings(X)
Tailings Seepage to
Storag n Saturated Regional
7Wa'ste,Fo�cl'
(W) Volume Groundwater(U)
turted Waste Rock(V)
Source:SRK,2024
Figure 3-13: Tailings Infiltration and Runoff Flow Schematic
All tailings placed in the TSF are assumed to be filtered and placed at breakthrough moisture content.
Thus, the model assumes that the filtered tailings are at the limit of the free draining condition prior to
placement and will not produce any water from the tailings solids pores without outside influence.
Under the same assumption, any portion of the precipitation that infiltrates into the TSF will displace
an equal amount of pore water from the tailings, and, after a propagation delay, report as seepage to
the base of the TSF. Most of this seepage will be collected by the over-liner drainage system and
report to the TSF Collection Sump at the bottom of the TSF where it will be pumped to the Contact
Water Management Pond (CWMP) (Section 3.1.3). Any water in excess of the collection sump pump
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capacity will be stored in the tailings voids and will be removed at the maximum rate of the sump
pumping system.
All runoff produced from the tailings surface will be collected in a low point on the surface of the TSF
and pumped to the CWMP to avoid ponding water on the TSF surface as much as possible.
Waste rock used to construct the TSF will be subject to runoff, infiltration, uptake, and percolation.
Any water percolating through the waste rock will be routed to the TSF drainage system and added to
the TSF Collection Sump before being pumped to the CWMP.
As described in 3.3.4, SRK's groundwater investigation (SRK, 2023a) determined that the bottom of
the TSF will have net inflow of groundwater during the deposition period. Groundwater is anticipated
to flow into the TSF and the waste rock berms throughout the deposition period and into closure at a
very stable rate, peaking. Groundwater inflows to the TSF will be added to the TSF sump and
transferred to the CWMP as contact water.
During operations, a surface collection sump will be maintained on the active placement surface of the
TSF to ensure all stormwater runoff from the exposed TSF is collected inside the TSF interior. To
address geotechnical concerns, a transfer pump was sized to remove the flood volume from the 100-
yr, 24-hr storm event on the TSF surface in less than two days, and the flood volume from the PMP
event in less than 2 weeks.
Due to the nature of the construction of the TSF perimeter, the areal extent of the tailings surface and
waste rock berm do not change over time, although the perimeter berm will rise as the tailings height
increases which will impact the time needed for infiltration to percolate vertically through the TSF.
A summary table of key parameters used to simulate the TSF is presented in Table 3-7.
Table 3-7: Key TSF Input Parameters
Parameter TSF Value
Active Tailings Footprint 57.7 ac
Perimeter Berm Footprint 20.5 ac
Geometry of Tailings Surface '/2 inverted cone, 5%slope to low point
Area of Exterior Berm 20.5 ac
Percolation velocity of filtered tailings 0.1984 ft/da
Percolation velocity of waste rock 2.835 ft.da
Uptake potential ofnewly placed waste rock 2.5% by weight
Surface Sump Pumping Capacity 1200 gpm
Collection Sump Pumping Capacity 500 gpm
The TSF perimeter configuration will prevent any off-site runoff from entering the TSF, routing all flows
from surrounding areas and the TSF Perimeter into a sediment control basin before being released to
Archdale Creek.
During closure,the model assumes that the entire exterior TSF perimeter berm will have been covered
with a reclamation cover, and the top of the tailings surface will also be graded to drain and covered
with the reclamation cover. Run-off from the reclaimed tailings surface will be collected at the TSF
Perimeter and added to the perimeter non-contact drainage network.
A seepage collection trench, installed along the downgradient side of the TSF will intercept post
closure groundwater outflows from the TSF into a sump for monitoring prior to release to Archdale
Creek.
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3.4.3 Contact Water Management Pond
The CWMP is located in the NPI pad area west of the TSF and is designed to collect and manage all
contact water produced by the TSF up to the PMP precipitation.Water will be transferred from the TSF
temporary surface water sumps as well as seepage collection sumps at the base of the TSF will be
detained in the CWMP and monitored prior to release to Archdale creek at a controlled rate. Archdale
Creek is a tributary of Dixon Branch, which joins Kings Creek south of Highway 85, approximately
2 miles south of the site.
Under normal operating conditions, sediment control of discharges from CWMP to Archdale Creek will
be maintained through the use of a float pond skimmer with a nominal discharge of 350 gpm to a low
level pipe outlet or pumping from a floating inlet structure without a low level outlet at a similar rate.
The model allowed a maximum outflow from the CWMP low level outlet of 585 gpm at the spillway
invert.
An emergency spillway is incorporated in the model as a 30-inch diameter spillway pipe. The Water
Balance model does not simulate the use of the emergency spillway, as it is only utilized during
extreme events up to and including the PMP storm events. A detailed stormwater analysis of the
spillway at a 1-minute time step was performed as part of the Storm Water Management Plan
developed for the Archdale site as a separate document(SRK 2024).
The water balance model includes calculations to determine the amount of daily flow discharged from
the CWMB during normal operations, as well as flow through the spillway under storm events including
in the probabilistic scenarios.
A summary table of key parameters used to simulate CWMP is presented in Table 3-8.
Table 3-8: Key Water Storage Basin 3 Input Parameters
Parameter Contact Water Pond Value
Pond Crest Height 12.5 ft
Pond Surface Area 56,000 ft2
Pond Capacity 3.84 M al
Operating Outlet Pump/Skimmer up to 585 gpnn
Spillway Invert Elevation 5.5 ft
Spillway Pipe Diameter 30 in
Source:SRK,2024
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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 2023 through operations and closure and extends
into post-closure.
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 2035, 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 2035, 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 2035, 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 2035, which is simulated using the 95th percentile climate year.
These climate scenarios were provided to the geochemistry team to provide flows and relative
contributions as inputs for the geochemical mixing model.
Figure 4-1 shows the annual total precipitation values.
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70
60
a
u 50
Q
40
30
2030 2040 2050 2060 2070 2080 2090 2100
Time
Annual Precipitation
Sth in 2035 25th in 2035 - 50th in 2035 - 75th in 2035 - 95th in 2035
Source:SRK,2024
Figure 4-1: Deterministic Climate Scenarios Annual Precipitation
The following subsections present key results of the model.
Discharge from the CWMP
The water balance model simulated discharge through the skimmer 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 deposition period and into closure. As each scenario is identical out to 2035, the
scenarios overlay each other. Different precipitation events from the climate forcing scenarios have
immediate affects on the pond volume, but the lagged percolation through the tailings solids can be
seen to have lasting impacts on the TSF outflows.
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Water to Environment
700
600
fl- 500
nn
b2
ra 400
U
Iq pq-r Pill 1-7
of 300- FT -F IF
YI
N
200
100
0
2024 2026 2028 2030 2032 2034 2036 2038 2040
Time
Water_to_Discharge
Sth in 2035 25th in 2035 50th in 2035 75th in 2035 95th in 2035
Source:SRK,2024
Figure 4-2: Outflow from WSB-3 Through the Skimmer under the Deterministic Climate
Scenarios
Figure 4-3 presents the pond volume for the deposition period and into closure.
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1.2
1.1
1.0
0.9
0.9
v 0.7
E
0.6
0.5
u 0.4
0.3
0.2
0.1
0.0 71�
2025 2027 2011 2031 2033 2035 2037 2031
Time
Contact Pond Volume
Sth in 2035 2Sth 1 n 2035 - 50th n 2035 - 75M in 203S 95th in 2035
Source:SRK,2024
Figure 4-3: Volume in the CWMP under the Deterministic Climate Scenarios
4.1.2 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 1,2024)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.
CWMP Volume
CWMP was implemented to collect contact water from the TSF sump at the surface and base of the
TSF. Figure 4-4 presents a probabilistic time series of the volume in the CWMP. Figure 4-5 shows a
corresponding graph of the water level elevation in CWMP, indicating the water level in the pond is
typically maintained at less than 1 ft of depth, providing storage to surge storm events or other upset
conditions to maintain a regular discharge rate from the CWMP.
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0.22
0.20
0.18
of0q 0.16
a 0.14
E
E
p 0.12
0.10
0
a
ti 0.08
o 0.06
U
0.04
0.02
0.00
2025 2027 2029 2031 2033 2035 2037 2039
Time
Statistics for Contact Pond Volume
5%..25%/75%..95% 25%..75i 50%
Source:SRK,2024
Figure 4-4: Probabilistic Time Series of CWMP Volume
1.1
1.0
0.9
0.8 ..I.s 0.7 V
0.6 �.,
a 0.5 �• � I' � di
c 0.4
eJu
U 0.3
0.2
0.1 hl�liwrdWWloiWi
0.0 j
2025 2027 2029 2031 2033 2035 2037 2039
Time
Statistics for Contact Pond Depth
5%..25r/75%..95r 25r..75r 50%
Source:SRK,2024
Figure 4-5: Probabilistic Time Series of CWMP Elevation
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4.1.3 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 excess water stored in the tailings during the period of deposition
was used as the representative model output; this provides a comparative measure of the amount of
excess water available in the Project during the deposition period.
The following inputs to the model were included in the sensitivity analysis.
Groundwater Inflow
The model simulations were made using groundwater inflow estimates from the hydrogeology model
(SRK, 2024). Groundwater flows into the TSF during operations and is managed in the collection
sump. The sensitivity analysis allowed the groundwater inflow rate to vary from 50% to 200% of the
estimate.
Tailings Percolation Velocity
The tailings material was simulated with an unsaturated vertical percolation rate of 1 x 10-' m/sec in
the simulations, based on saturated permeability testing of the tailings. Any seepage from the tailings
is managed in the collection sump. The sensitivity analysis allowed the percolation rate of the tailings
to vary from 1 x 10-1 to 1 x 10-6 m/sec.
Infiltration Percentage of Uncovered Tailing
The model simulations were made assuming 40% of precipitation falling on the uncovered tailings
would infiltrate into the tailings, creating seepage flows that report to the base of the TSF. The
sensitivity analysis allowed the infiltration rate for the tailings surface to vary from 30% to 50%.
Infiltration Percentage of Covered Tailing
The model simulations were made assuming 3% of precipitation falling on the covered tailings in
closure would infiltrate into the tailings, creating seepage flows that report to the base of the TSF. The
sensitivity analysis allowed the infiltration rate for the tailings surface to vary from 1% to 10%.
Waste Rock Percolation Velocity
The waste rock material used for the construction of the embankment berms was simulated with a
vertical percolation rate of 1 x 10-5 m/sec in the simulations, using typical values for coarse rock fill.
Any seepage from the tailings is managed in the collection sump. The sensitivity analysis allowed the
percolation rate of the waste rock to vary from 1 x 10-7 to 1 x 10-4 m/sec.
Sensitivity Analysis Results
Figure 4-6 provides a tornado chart presenting the results of independently varying the inputs
described above using a 50th percentile climate scenario during the pre-development through closure
periods (2023 through 2040). The horizontal bars indicate the range of total water collected in Sump
in the base of the TSF during the deposition period resulting from varying that parameter. The greater
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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.
Tornado Sensitivity Chart-Analyzed Result:Water in_Tailings_Sensitivity(m3)
3.Oe6 4.Oe6 5.Oe6 6.Oe6 7.Oe6 8.Oe6
I—GW—Sensitivity
a) I_Tails_Percolation_Velocity
C: I_FTSF_Pcnt_I nfi lt_contact
c
aN
Q
—
� I—FTSF Pcnt—Infilt—Cover
Percolate Rate WR
❑ Low N High
Source:SRK,2024
Figure 4-6: Sensitivity Study Tornado Chart on Total Water Released from the Tailings over the
Period of Deposition
The sensitivity analysis indicates that the groundwater inflow rate has the strongest impact on the
amount of tailings water collected at the base of the TSF. The next most impactful variable, tailings
percolation velocity, resulted in significantly less tailings water collected by the system. This indicates
that the amount of water collected at the base of the tailings is largely consistent of the groundwater
inflows, and the development of the infrastructure to remove this water should focus on groundwater
characterization as the first priority.
Overall, the model predicts that the site will collect water from the tailings collection sump under the
most reasonable ranges of inputs and updates to the groundwater predictions should be reflected in
the design of the seepage collection system.
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5 Interpretation and Conclusions
The water balance model for the Archdale TSF supporting 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 TSF, deposition activities, closure, and post-closure
activities. The model includes the time-dependent nature of the TSF, including tailings deposition
schedules and facility growth. 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 produce tailings contact water from the TSF
surface and bottom sumps that will need to be discharge offsite during operations and during the
closure and reclamation period. During the post-closure period, when active pumping ceases, the
groundwater entering the tailings will flow through the TSF to discharge into the groundwater system
downgradient of the TSF.
SRK sized the stormwater management ponds and pumping systems to manage the 100-yr, 24-hr
storm event with the pond system with a minimum time ponding on the TSF surface. The system was
designed to contain the PMP storm on the TSF surface or in the CWMP by controlled release through
the CWMP spillway.
A sensitivity analysis of the model indicates the model is relatively sensitive to the amount of
groundwater inflow entering the TSF. 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 percolation and infiltration parameters had even
less impact on the mine water balance.
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6 References
Allen, R., L.S. Pereira, D. Raes, and M. Smith. 1998. Crop evapotranspiration — Guidelines for
computing crop water requirements. FAO Irrigation and Drainage Paper No 56. Rome, Italy.
AWA, 2022. Site-Specific Probbable 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.
FHA 2021. HY-8 culvert analysis software, Federal Highway Administration. Version 7.70.10.0, June
2. 2021
GoldSim, 2021. GoldSim, Version 14.0. GoldSim Technology Group, November 2021.
GTG 2021. Model of Rural Engineering with 4 parameters Daily (GR4J) in GoldSim,
(https://sup port.golds im.com/hc/en-us/articles/360056312214-Model-of-Rural-Engi nee ring-
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. Estimating potential evapotranspiration J. Hydraul. Div. Proc. Am. Soc. Civ. Eng. 1961.
IPCC 2023, Assessment Report 6 Synthesis Report, International Panel on Climate Change March
19, 2023.
Johnson, N. L. 1949. Systems of Frequency Curves Generated by Methods of Translation.
Biometrika. 36 (1/2)
Kottek, M., J. Grieser, C. Beck, B. Rudolf, and F. Rubel, 2006: World Map of the Koppen-Geiger
climate classification updated. Meteorol. Z., 15, 259-263. DOI: 10.1127/0941-
2948/2006/0130.
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/l0.3334/ORNLDAAC/2129
NCEP 2023 Reanalysis data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from
their Web site at http://www.esrl.noaa.gov/psd/data/reanalysis/reanalysis.shtml
NOAA, 2023. National Center for Environmental Information, Station ID: GHCND:USC00317846
Shelby 2NW, from their website at www.ncei.noaa.gov
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.
DH/MS Archdale_WaterBalanceModeling_TR_USPR000576_Rev04.docx April 2024

SRK Consulting(U.S.), Inc.
2022 KM PFS Report-Surface Water:Water Balance Development Report Page 46
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. Archdale Water Quality Predictions Report, Kings Mountain Mining Project.
SRK, (2024). Archdale Filtered Tailings Storage Facility, Select Phase, Preliminary Engineering
Design Report Kings Mountain Mining Project.
USDA NRCS. (2023). Web Soil Survey. https://websoilsurvey.sc.egov.usda.gov/.
USDA Forest Service. (2023). Land Areas of the National Forest System.
https://www.fs.fed.us/land/staff/lar/LAR2017/FIA_data_products/LAR2017/FIA_data_product
All data used as source material plus the text, tables, figures, and attachments of this document have
been reviewed and prepared in accordance with generally accepted industry practices.
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2022 KM PFS Report-Surface Water:Water Balance Development Report Appendices
Appendices
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2022 KM PFS Report-Surface Water:Water Balance Development Report Appendices
Appendix A: Historical Climate Data
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Table A-1: Shelby 2 NW (infilled) Precipitation Monthly Time-Series
orn� :,gin 1U
70r 19902022 Shefty 2 NW
lanaA. Fe ErwrlPA-h / Mrc 1emDer CK1JDer nv.at'ecr De<anOer Orr.0 Tom,
Ye n n n n n n n n I.71 n
:99G :319 734 741 260 6.65 023 4 Z94 066 4.58 0U
199: E 36 250 4.42 793 3.L4 -30 8< 7 95 0 u - - 2.91 52 06
1992 3 29 5 03 832 3 16 7.29 7 7: 3 N 6 27 321 7.04 7977
1993 7 62 4 7G 9 66 t 17 0.00 2:2 :i7 5 95 2 73 4:3 31 A3
1993 6 36 329 7.32 319 1.23 .::3 390 634 315 - 3 N 156 37 23
997 883 6 0d 278 0 98 4.64 592 8.07 9,72 5.06 952 6 S8 Z.:D 70 A
:99E 6.53 2 52 7.25 3 37 4.41 436 3.93 3.24 6.56 0.76 317 430 5937
1997 490 366 114 805 3.70 463 7.18 016R3.55
7.12 326 4.90 3219
998 7.07 530 3.% 504 5.21 163 6.21 464 .49 272 4.03 5303
617 103 439 3 17 1.70 3.9E 1.77 143 4 6E 219 2.76 a1 73
2000 363 226 446 570 216 L39 399 1.79 0 a• 3 62 2 36 3673
200: 235 243 E22 0 68 3a1 710 534 098 :CC L07 3 06 41 L'20D2 a39 16 113 230 i07 5 V 303 3:2 470 7 30 473:2003 :76 442 9 33 10 0: 9.32 197 E63 570 :67 L39 280 65 35
Z004 :03 404 13E 356 3.47 940 156 193 0 99 876 7 09 3567
2005 331 3 20 436 383 3.97 733 6^ 101 394 399 433 3074
2006 357 0 88 L 11 2 43 1.75 5.35 126 190 3.83 4.E2 3 83 454 3929
zoo-,_ 2.79 197 190 Z 77 1.64 2.19 0.93 082 3.39 130 117 3 51 2436
200E 2.47 :84 405 Z 66 349 160 2.40 10.09 3.33 LE9 164 394 3910
zoos 2.B4 3 W 132 3 30 3.39 473 147 102 416 4.02 5 07 790 48 SB
z010 630 4 20 470 2 as 3.27 183 1.76 659 3.83 363 L 04 269 4632
ZOL' 3 30 2 X. 635 367 3.77 154 166 443 6.26 11 617 1 333 4989
2032 261 122 L71 2 74 7.33 129 7.86 i E 3 2.64 2.39 043 a 82 3964
2013 6.16 1 3 52 3.34 3 99 631 464 LL51 426 4.83 362 439 6,37 6633
2014 3137 2 90 433 4 97 2.43 7.39 2M A" 5.42 2.3E 457 L78 47 S2
2013 184 z 63 140 699 0.93 L76 1.90 L49 2.74 6 33 923 M19 3036
ZD16 124 339 L26 11D6 6.04 L64 3.38 468 1.61 090 J 65 L29 3034
2017 4.O Om 4.12 621 6.90 123 3.77 SK 4.17 4 42 093 2.32 4624
201E L33 4.43 439 6.74 6.03 L29 3.23 460 5.70 663 3-5 G62 63 47
2019 4.31 669 3.64 475 4.02 7.11 3.32 4-6 3.19 347 6.14 3263
2]S EYJ' 905 i12 7A1 553 33: 24J 542 3.00 S:1 3.33 6618
ZOZ: 3 M 5 53 9AB 206 2 46 Z:E 8 6:. L68 :97 43 17
2022 473 1 86 3.67 :34 2.70 4 A L .135
is
A 468 3 60 481 413 4A9 41E 4.y0 436 3.75 3<: 434 70-0
510 173 187 126 226 117 L60 2.6' 231 2.36
Min L03 080 L11 0.69 0.00 1 023 0.93 C.16 0.10 C CC L93 24>6
Ma L4.L9 9A9 9.66 1001 9.52 LL13 IL31 10.09 1LM :C'i LA19 b55
I t 40
14 70
1: C
Ef
6u 906
s �
4 4
r
` 30
a �
x
10
0
Source: NOAA,2023
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Table A-2: Penman-Monteith Reference Evapotranspiration Monthly Time-Series
Er w to.
rennin-6eonbeen ror MO-2=
Eeorue 444M� i Xt emwer Daober KR be. December 4mWTo_
rea. n n inn+ n n n ,n n n m
wk 200 216 3.84 526 629 754 7,56 702 546 369 2 90 :E3 33 w
-- :61 24: 3.90 508 656 707 756 637 340 ic 224 :86 340616
i�zz_ :93 2 48 3.71 3 10 3 80 6 32 7.84 6:7 4 83 3 E: 2 06 :i3 313,
:553 :72 2 09 3.25 502 643 719 &73 696 546 3 23 2 3: :.61 5509
.9% :w Z 32 3.99 530 6.Q 629 745 650 494 347 Z 37 -71 5294
:993 :.73 202 403 7Y 6.32 669 7.67 667 4.97 3.69 202 163 5310
:99E 161 243 ID 6.73 7.J 7.49 639 4.89 3.63 L% 168 R12
178 2 33 420 4 67 6.13 6:7 7.6E 7.:3 7.24 3.72 207 136 12 63
.-C L81 226 1!9 4� 6.30 7.4E 7.74 69: 5.49 3.% Z39 L84 3309
:995 2.08 2 46 3.39 520 6.54 648 7.56 7 23 3.27 343 Z 65 1-97 54 4E
ZOOID :.68 2 79 SSE 7.05 7:i 7 L' 679 469 3:i 220 129 5394
200: :63 141 3 41 3 i- 657 7 OS 6 3-17 09 4 82 329 1.94 51 1:
2002 2 07 3 2: 3 43 646 7££ E 6£8 494 322 :.79 33 w
2003 1.64 364 3.63 633 6 93 637 516 3 72 :.78 5079
2004 .95 2 L' 4901 526 7.01 639 7,42 629 463 343 :.74 32 47
2000 :53 226 162 3 cc 6.20 633 7.26 694 3.73 3 6: - :.69 73 39
200E 2 1 2 31 3.37 3 83 6.79 7.:9 7.72 689 4.93 3 M 2.07 33 75
2007 :.E9 224 433 721• 6.72 7.13 7.69 64/ 6.09 3:9 3 L% 386:
200E :73 257 3-93 a89 6.59 a18 7.77 673 4.90 365 222 164 5493
2009 :n 2 36 3-43 3 22 5.71 7.23 7.29 679 4.91 3 3- 2 30 146 5173
2010 :74 181 3,73 3 3£ 6.41 7.67 7.91 691 3.67 4 C4 Z41 139 371E
201.' 166 257 3.56 343 6-59 7A3 7.74 7.04 416 163 Z49 :.86 39"
2012 :94 233 43o 7 5 6.62 7.14 7.37 630 5.03 3.30 Z 39 1.72 }61
2013 L89 207 139 4:E 3.93 659 6.43 6,30 3.19 3.71 2.19 LZ 304E
2014 L70 133 334 6.77 689 6.93 637 4.7E 3.90 228 LA 31 r-
2013 L77 138 3.91 45C 7.03 7.46 8.13 7.03 4.96 3.4E Lw 1.9E 5463
201E L70 224 ♦19 6.14 7.49 8.01 6E3 771 4.06 Z79 L43 3609
2017 L98 L" 198 f 6.25 669 7.63 637 5.29 167 L40 L67 MAZ
2018 L23 2S 1m 4 C 6.12 7.37 7.42 693 3.27 3.92 L03 162 3330
2019 1.75 237 161 6.92 668 7.71 687 6.L3 4.02 2.16 186 33N
Mw L81 231 3.92 32L 5.K 689 711 6% 49- 178 X33 L70 MID
2021 L72 2m 191 332 8AZ 696 7.37 69: 7 2: 3.E1 2" U 213 30
2022 170 L" 4 3E 03 333 6A3 1.. 7. 671 7:3 3 63 :37 340E
l.e L91 7.D 179 3.1E 6.43 7.06 7.11 679 7:= 3-3 237 3393
St"
Deg 0.13 024 031 026 039 048 O.x 0403 C 24 024 C 2: L68
M I -60 131 I 123 40 5.60 60 6A3 6:7z 6Z 322 L% 1.29 M46
Ma I 2.0 297 4.30 333 7.05 818 3.73 83: 6 33 4:5 287 2-13 38164
1cm K
tam 70
Stam 10 6
p !
9 4m a B
b
6 4m w
7!
.m >e
:m m
om o
L1/1H•' �L':H; ��.a i,�'Jm: L'xl�
Source:SRK,2023
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Table A-3: Hamon Method Lake Evaporation vs Reference Evapotranspiration Monthly Time
Series
rm
Oak Preapdaton W 19W2022 S1 w 1b.2 NW-N"
feDnt• L40M i 3une ) 2 :ember October Aovernba Oecernber amwaTo_
n n In
:99C 0 97 1 le 186 2:: 3 89 3 07 3 73 3 13 126 092 33--
O E2 106 177 2 7: 431 IS. 3': :39 3 17 :99 110 0913 33 T
08: 106 L6J 232 333 3': ::: 3L9 .73 :13 077 33 9:
1993 0 r7 0!6 La6 225 396 6 1: 463 3 37 :83 103 0 72 32 G:
199i 066 as, L71 2 39 3.:2 504 3.13 :3E 2.91 :.77 SSE Ogg 30 63
1997 072 08'7 L77 233 3?9 G73 7A1 :23 3.01 2.00 393 C73 3123
199E 072 106 L38 2G: 4.01 GM 5.39 Gd[ 2.97 :.E7 101 C T- 309:
1997 080 10-1 185 1 212 3.31 4335.43 Gig 3AS :.77 093 C 3 299E
19% 0 E7 102 L47 Z x 3.9E 3.11 139 G 33 3.14 2.05 ]78 31 SE
19" OE2 091 L39 257 3.33 463 3.64 300 2.98 :81 124 - 313E
Z000 072 101 L74 216 3 93 3:E 3 26 G 63 2-9-6 :88 100 31 iG
207 073 100 Lai 260 3-- - :% :73 2.86 .-3 127 30 E6
2002 096 086 1.60 ZA2 3 3- ::3 5 6- -67 3.20 -93 395 - 3:94
am 063 011: L61 2-26 143 -39 31-, a.80 3.10 :EE 139 - 30
2004 C-6 Obi L76 233 4 40 G 92 3.+2 A38 3.07 2 0- 122 32 22
2003 C 92 093 1.73 Zt1 3.61 490 3.82 3.16 3.3i :0; 120 C 3277
2006 C 5.: O S.° L W 297 3.73 3.03 3.72 103 107 -- 1:6 C 53 32 80
2007 0.E6 199 247 3.67 126 5A8 183 3.60 2 3C 1:] C% 3936
2008 0.77 - 1-59 2a8 3.60 151 5.66 •72 3.20 CE3 322E
2005 0.76 O 93 161 247 3.91 51E 3.23 •% 3.13 -- C 6E 3192
Z010 0.69 0% Lab - 6.2E 3.i 3 6 19 3 21 3.39 0.38 3403
ZOL' 0.67 102 L60 Z 3.99 7 32 166 -22 3:3 :01 33:3
201-1 0.88 103 224 4.29 G 7E 1 E6 40 323 098 33
2013 0.91 034 L26 45 3.56 302 3:: G33 3 3 102 0$ 31 2:
Z01: 0.63 096 133 4i .16 3 2" 3 20 G40 336 4i ]97 0 V 3115.
ZOO 0.73 07: L63 Z_-f :.23 :65 59: G% 3:0 :5C 127 :.:3 3:22
2016 0.71 094 L% 3.7E 3.36 6.12 IZ2 372 119 0.E3 3434
2017 0.93 1 17 L62 3.37 300 7.91 472 3 19 112 0.80 33 32
ZOSE 0.67 127 L" 4.:2 154 5.66 493 3 K 399 0.82 3 X
Ni9 0.92 103 L47 z a.% 498 7.39 493 3- 2 3C ]- 0.8E 310E
202C 0.92 103 LB3 2 33 339 G% 6 00 497 13J 0.76 32 9:
ZOZ: 0.7B am L73 2 S 2 3 6: -97 3 - LOB 32:3
2022 0. 0.% L68 2 is :]i 6 13 G8a 3 L• 126 0.T 32 E2
A 0.79 0.% L6a 1 20 3.37 :C4 3.63 'Lao 3.23 L96 L30 0,83 3236
.1101 0.10 0-0 0.21 0.18 1 ]33 33 ]32 0.31 0.26 0.17 013 0.13 1-31
Mn 0.63 1 0.71 2-26 1 2.12 3 31 -33 :36 l39 2.86 L71 033 0.33 2998
MY 0.97 LV 2.24 1 237 :..:E - 183 3.99 L30 L39 L23 3456
um a1
um 70
S1m o0
a
g,am >o
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Source:SRK,2023
D H/M S Archdale_WaterBa IanceModel ing_TR_USPR000576_Rev04.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
DH/MS Archdale_WaterBalanceModeling_TR_USPR000576_Rev04.docx April 2024

SRK Consulting(U.S.), Inc.
2022 KM PFS Report-Surface Water:Water Balance Development Report Appendices
Table 13-1: Probability of Rain and Johnson SB Distribution Parameters for the WGEN Synthetic Climate Generator
Dailv Precipitation Pr bability of Rain
Number of Days Probability of Day
Number of Days with Rain with Rain FoW mng Probability of Day win, wdh Rain FollomV Probability of Day wnh
Month Number of Days Number of Rain Days Number of Dry Followtv Rain Days Dry Dap Rain Rain Day Rain Follow
January 1024 338 686 166 171 0.3301 0.4926 0.2493
February 932 295 637 134 161 0.3165 0.4558 0.2527
March 1023 339 684 160 179 0.3314 0.4734 0.2617
April 990 287 703 132 155 0.2899 0.4615 0.2205
May 1023 328 695 172 156 0.3206 0.52SO 0.2245
June 990 347 643 182 165 0.3505 0.5260 0.2566
J uty 1023 381 642 184 197 0.3724 0.4842 0.3069
Auousst 1023 360 663 181 179 0.3519 0.5042 0.2700
ember 990 253 737 125 128 0.2556 0.4960 0.1737
October 1023 256 767 125 131 0.2502 0.4902 0.1708
November 990 264 726 124 140 0.17 0.4715 0.1928
December 1023 332 691 170 162 0.3245 0.5136 0.2344
Annual 120541 3780 1 8274 1855 1 1924 1 0.3136 1 0.4909 0.2325
Daily Precipitation Johnson SB Probability Distribution Parameters for Precipitation Depth
Month Distribution Fitted Parameters for Selected Distributions
January Johnson SB Scale Gamma 2.0242 Scale Delta 0.8182 Scale Lambda 4.4092 Location Xi -0.07673
February Johnson SB Scale Gamma 3.1908 Scale Delta 0.9449 Scale Lambda 8.4564 Location Xi -0.03685
March Johnson SB Scale Gamma 3.1636 Scale Delta 0.9594 Scale Lambda 9.7596 Location Xi -0.06923
April Johnson SB Scale Gamma 1.6954 Scale Delta 0.7513 Scale Lambda 3.4780 Location Xi -0.04545
May Johnson SB Scale Gamma 2.0010 Scale Delta 0.7180 Scale Lambda 4.2443 Location Xi -0.02775
June Johnson SB Scale Gamma 1.7186 Scale Delta 0.5995 Scale Lambda 3.3286 Location Xi 0.00655
July Johnson SB Scale Gamma 3.0842 Scale Delta 0.8813 Scale Lambda 9.2951 Location Xi -0.06948
August Johnson SB Scale Gamma 2.9964 Scale Delta 0.8595 Scale Lambda 9.3422 Location Xi -0.07835
September Johnson SB Scale Gamma 3.2339 Scale Delta 0.9187 Sole Lambda 12.6210 Location Xi -0.10147
October Johnson SB Scale Gamma 1.8069 Scale Delta 0.6108 Scale Lambda 4.9435 Location Xi -0.03236
November Johnson SB Scale Gamma 2.9333 Scale Delta 0.9405 Scale Lambda 9.0768 Location Xi -0.09873
December Johnson SB Scale Gamma 2.7451 Scale Delta 0.9901 Scale Lambda 6.2177 Location Xi -0.09479
Source:SRK,2023
DH/MS Archdale_WaterBalanceModeling_TR_USPR000576_Rev04.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 1 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
DH/MS Archdale_WaterBalanceModeling_TR_USPR000576_Rev04.docx April 2024