Evin J. Fetkovich Amy S. Morris Isaac A. Dale Chloe Codner Ethan A. Kirby Colin A. Baciocco Ian M.J. Rogers Derrick L. Wagner Zachary D. Tomlinson Eric G. Fiorentino 20250226 soil-water-balance model Denver, Colo. U.S. Geological Survey https://doi.org/10.5066/P14C6QFS Evin J. Fetkovich Amy S. Morris Isaac A. Dale Chloe Codner Ethan A. Kirby Colin. A. Baciocco Ian M.J. Rogers Derrick L. Wagner Zachary D. Tomlinson 2025 publication Scientific Investigations Report 2025-5013 Reston, Va. U.S. Geological Survey https://doi.org/10.3033/sir20255013 This archive contains the data used to (1) summarize the hydrogeology and update the hydrogeologic framework of the Antlers aquifer in southeastern Oklahoma, and (2) develop a conceptual groundwater-flow model and water budget to estimate recharge to the Antlers aquifer during 1980–2022 as part of a hydrologic investigation (the model documentation report is available at https://doi.org/10.3133/sir20255013). The U.S. Geological Survey (USGS), in cooperation with the Oklahoma Water Resources Board (OWRB), constructed a soil-water-balance (SWB) model of the Antlers aquifer in southeastern Oklahoma. The soil-water balance (SWB) model was used to simulate groundwater flow and estimate recharge to the Antlers aquifer. The input data and resulting output recharge data files are contained herein. Input data for the SWB model are precipitation, air temperature, soil-water storage capacity, hydrologic soil group, surface-water flow direction, and land-cover type. Data used in a multiple well aquifer pumping test, slug tests, groundwater-quality analysis, base-flow index estimation, and an analysis of groundwater use in the Antlers aquifer are provided. All associated data files are included in the ancillary folder of this data release. The data in this data release were used to develop a conceptual soil-water balance model to better understand groundwater flow in the Antlers aquifer in southeastern Oklahoma. Two key components for simulating a groundwater-flow system are the conceptual groundwater flow model and the resulting water budget. The soil-water-balance model was developed by using the data contained herein to estimate recharge rates from a water budget for the Antlers aquifer in southeastern Oklahoma. The SWB model was used to estimate the amount of recharge entering unconfined part of the Antlers aquifer between January 1980 and December 2022. 19670101 20221231 ground condition In work None planned -97.5297 -94.4670 34.3659 33.3512 ISO 19115 Topic Category geoscientificInformation environment inlandWaters USGS Thesaurus groundwater groundwater and surface-water interaction hydrology water budget modeling None None Soil-water-balance model Python Aquifer test Slug test Base-flow index Groundwater quality usgssoilwaterbalancemodel SWB Groundwater use Hydrogeologic framework Groundwater recharge Streambed seepage USGS Metadata Identifier USGS:6628008ad34ea70bd5f03b77 Stratum Antlers Aquifer Trinity Aquifer Geographic Names Information System (GNIS) Oklahoma Antlers Atoka County Bryan County Carter County Choctaw County Johnston County Love County Marshall County McCurtain County Pushmataha County Red River Blue River Kiamichi River Little River Clear Boggy Creek Muddy Boggy Creek Washita River Texas Arkansas Murray County Montague County Cooke County Grayson County Fannin County Lamar County Red River County Wise County Denton County Collin County Hunt County Delta County Hopkins County Franklin County Titus County Bowie County None. Acknowledgement of the USGS would be appreciated in products derived from this data release. None. Data contained in this archive, including the model input and output files, are provided to support the analyses documented in the associated model documentation report (https://doi.org/10.3133/sir20255013). Although the information contained in the provided data files may be useful for other purposes, it is incumbent on the user to understand the purpose, construction, and limitations of this soil-water-balance model. Data have been checked to ensure consistency with the accompanying report. If any errors are detected, please notify the originating office. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. U.S. Geological Survey Oklahoma-Texas Water Science Center Public Information Officer mailing and physical

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Austin Texas 78754 USA (512) 927-3500 otpublicinfo@usgs.gov Oklahoma Water Resources Board ESRI ArcGIS Pro Version 3.0.3 (Build N/A) Service Pack N/A; HydroSOLVE AQTESOLV for Windows Version 4.50.002 Professional (Build N/A) Service Pack N/A; Python Version 2.7.14 (Build N/A) Service Pack (N/A); Python Version 3.9.7 (Build N/A) Service Pack (N/A); Soil-water-balance code 64-bit Version 1.0.1 (Build N/A) Service Pack (N/A). Westenbroek, S.M. Kelson, V.A. Dripps, W.R. Hunt, R.J. Bradbury, K.R. 2010 publication Technique and Methods 6-A31 Reston, Va. U.S. Geological Survey Detailed descriptions of the soil-water-balance code input and output files included in this data release can be found in this report. https://doi.org/10.3133/tm6A31 J. J. Butler 1998 publication Lewis Publishers, Boca Raton, Florida, 252 p. R.G. Agarwal 1980 publication SPE Paper 9289 presented at the 55th SPE Annual Technical Conference and Exhibition, Dallas, TX, Sept. 21-24, 1980. V. Batu 1998 publication John Wiley & Sons, Inc., New York, 727 p. K.L. Wahl T.L. Wahl 1995 publication A component conference of the First International Conference on Water Resources Engineering, San Antonio, Texas, August 16–17, 1995 [Proceedings]: American Society of Civil Engineers, p. 77–86. P.M. Barlow W.L. Cunningham T. Zhai M. Gray 2015 publication U.S. Geological Survey Techniques and Methods, book 3, chap. B10, 27 p https://doi.org/10.3133/tm3B10 C.W. Thornthwaite J.R. Mather 1957 publication Drexel Institute of Technology, Laboratory of Climatology, Publications in Climatology, v. 10, no. 3, p. 185–311 https://www.wrc.udel.edu/wp-content/publications/ThornthwaiteandMather1957Instructions_Tables_ComputingPotentialEvapotranspiration_Water%20Balance.pdf M.M. Thornton R. Shresha Y. Pei P.E. Thornton S. Kao B.E. Wilson 2020 publication Version 4, ORNL DAAC, Oak Ridge, Tennessee https://doi.org/10.3334/ORNLDAAC/1840 U.S. Department of Agriculture 2021 publication n/a US Department of Agriculture Natural Resources Conservation Service https://gdg.sc.egov.usda.gov/ Multi-Resolution Land Characteristics Consortium 2023 publication https://www.mrlc.gov/data/nlcd-2016-land-cover-conus U.S. Geological Survey 2015 publication https://apps.nationalmap.gov/downloader/#/10/34. 77438352431586/-97.66967773437258/usgs_topo/elevation-products-three-dep/ G.H. Hargreaves Z.A. Samani 1985 publication Applied Engineering in Agriculture, v. 1, no. 2, p. 96–99 https://doi.org/10.13031/2013.26773 R.B. Morton 1992 publication U.S. Geological Survey Water-Resources Investigations Report 88–4208 https://doi.org/10.3133/wri884208 Oklahoma Climatological Survey 2021 publication Oklahoma Climatological Survey webpage https://climate.ok.gov/index.php/climate/map/normal_annual_precipitation National Centers for Environmental Information 2023 publication National Oceanic and Atmospheric Administration database https://ncei.noaa.gov/maps/daily/ Oklahoma Mesonet 2023 publication Oklahoma Mesonet web page https://www.mesonet.org/index.php/past_data/daily_data_retrieval Oklahoma Water Resources Board 2023 publication Oklahoma City, Okla. Oklahoma Water Resources Board https://www.owrb.ok.gov/maps/PMG/owrbdata_WR.html M.J. Hvorslev 1951 publication U.S. Army Corps of Engineers Waterways Experiment Station Bulletin 36 Vicksburg, Ms. U.S. Army Corps of Engineers https://apps.dtic.mil/sti/trecms/pdf/ADA950075.pdf Herman Bouwer R.C. Rice 20100709 publication Water Resources Research vol. 12, no. 3 n/a American Geophysical Union (AGU) p. 423–428 https://doi.org/10.1029/WR012i003p00423 Zafar Hyder James J. Butler Carl D. McElwee Wenzhi Liu 20100709 publication Water Resources Research vol. 30, no. 1 n/a American Geophysical Union (AGU) p. 2945–2957 https://doi.org/10.1029/94WR01670 No formal attribute accuracy tests were conducted. No formal logical accuracy tests were conducted. Dataset is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully (https://doi.org/10.5066/P14C6QFS) for additional details. No formal horizontal positional accuracy tests were conducted. No formal vertical positional accuracy tests were conducted. For the 1980–2022 study period, the amount and spatial distribution of daily groundwater recharge was used to estimate the mean annual recharge to the Antlers aquifer for the study period. Recharge was estimated by using the Soil-Water-Balance code (Soil-water-balance code version 1; Westenbroek and others, 2010). The soil-water-balance code uses a modified Thornthwaite-Mather (Thornthwaite and Mather, 1957) soil-water-balance method on a gridded data structure to compute the daily amount of infiltration, accounting for losses, that exceeds the storage capacity of the plant root zone. Input data required to estimate recharge using the soil-water-balance code includes precipitation, air temperature, soil-water storage capacity, hydrologic soil group, surface-water flow direction, and land-cover type (Westenbroek and others, 2010). The input data files and output recharge data files were included in this data release. The soil-water-balance code uses the following equation (modified from Westenbroek and others, 2010): R=(P+S+Ri)−(Int+ R0+Pet)− ΔSm R=P+S+Ri−Int+ R0+Pet− ∆Sm (3) where R is recharge, in inches per day (in/d); P is precipitation, in/d; S is snowmelt, in/d; Ri is surface runoff, in/d; Int is plant interception, in/d; R0 is surface runoff outflow, in/d; Pet is potential evapotranspiration, in/d; and ΔSm is the change in soil moisture, in/d. 2024 Input data needed for the soil-water-balance code were assigned to a user-specified grid that consisted of 917 columns by 372 rows of cells that were each 1,000 ft by 1,000 ft. Climate data inputs (daily precipitation, minimum temperature, and maximum temperature grids for 1980–2022) were obtained from the Daymet database (version 4; Thornton and others, 2020). Soil properties (soil-water storage capacity and hydrologic soil group) were obtained from the Gridded Soil Survey Geographic database (U.S. Department of Agriculture, 2023). Land-cover types were obtained from the National Land Cover Database (Multi-Resolution Land Characteristics Consortium, 2023) and resampled to the soil-water-balance grid resolution by using the most common land-cover type as a percentage of the total coverage within each cell. Flow direction was derived by calculating the land-surface gradient by using the D8 method from a 10-m digital elevation model (USGS, 2015); any depressions were filled by using the ArcGIS Fill tool after the digital elevation model was resampled to the soil-water-balance grid. Filling depressions in the digital elevation model ensures correct routing of surface runoff and eliminates isolated areas that could result in unrealistically high amounts of recharge. 2024 Potential evapotranspiration was calculated by using the Hargreaves and Samani (1985) method for a reference latitude range of 34.5–35.4 degrees. Land-cover types (Multi-Resolution Land Characteristics Consortium, 2023) were used in conjunction with hydrologic soil groups to partition daily precipitation into plant interception (Int) and surface runoff (Ri and Ro) components and assign plant root-zone depths. The root-zone depths for grass/pasture and forest/shrubland (the dominant land-cover types for land overlying the aquifer) varied with soil texture but ranged from about 0.7 to 1.5 ft after being scaled to 40 percent of the values used by Westenbroek and others (2010) which were in permeable glacial deposits in Wisconsin. The root-zone depths were scaled by 40 percent to account for the difference in root zones between the study area and Wisconsin (where Westenbroek and others, [2010] estimated the initial root zone depths). The maximum volume of water available in the root zone is calculated by multiplying the soil-water storage capacity by the root-zone depth. Changes in soil moisture (ΔSm) exceeding the soil-water storage capacity were assumed to be recharge (R) to the saturated zone. Smaller root-zone depths resulted in increased recharge and decreased evapotranspiration of water from the root zone, and larger root-zone depths resulted in decreased recharge and increased evapotranspiration of water from the root zone. Recharge from irrigation was not simulated by the soil-water-balance code but was assumed to be negligible given the relatively small amount of irrigation groundwater use in the study area. 2024 Recharge was assumed to only occur in the unconfined part of the Antlers aquifer, therefore the output data from the soil-water-balance model were summarized within the unconfined part of the Antlers aquifer. Recharge over large lakes was considered to be zero because in the soil-water-balance calculation it is assumed that open bodies of water do not contribute to recharge. The spatially distributed mean annual soil-water-balance-estimated recharge rate for the 1980–2022 study period was 8.58 in, or about 19 percent of the mean annual precipitation of 45.2 in. The annual soil-water-balance-estimated recharge rate ranged from 3.3 in in 2005 to 18.8 in in 2015, which respectively were years of much lower and higher precipitation compared to the mean annual precipitation. The ratio of the estimated monthly mean recharge to monthly mean precipitation is the recharge efficiency. 2024 The modeled soil-water-balance-estimated recharge rates were compared to published estimates of mean annual recharge rates for the Antlers aquifer. Morton (1992) estimated that recharge was between 0.32 inches per year (in/yr) and 0.96 in/yr. However, Morton (1992) estimated recharge as the net groundwater gain to the aquifer from stream seepage, which was calculated as gains or losses between stream-flow measurement locations along streams. This study used stream gain or loss measurements to calculate stream seepage similar to Morton (1992), but recharge was also estimated for spatial distribution of recharge across the Antlers aquifer by using the soil-water-balance method. 2024 Recharge for the Antlers aquifer was also estimated by using the water table fluctuation method. The water table fluctuation method is described in more detail in the accompanying larger work citation (Fetkovich and others, 2025 ). Using daily precipitation data from the nearest climate station and a specific yield of 0.1, the mean annual recharge estimate for the period during 2013–22 was 11.9 in or about 23 percent of the mean annual precipitation (51.7 in) normalized at each climate station for the period 1991–2020 (Oklahoma Climatological Survey, 2021; National Centers for Environmental Information, 2023). The data were normalized by the mean annual recharge estimate (11.9 in) and dividing by the mean annual precipitation during 1991–2022. The water table fluctuation estimated recharge rate of 11.9 in/yr was used for comparison with the soil-water-balance-estimated recharge rate of 8.58 in/yr. The higher recharge rate estimated from the water table fluctuation method compared to the soil-water-balance method can be explained by the location of the wells used for the water table fluctuation method. All wells used in the water table fluctuation method were completed in the eastern part of the aquifer, where precipitation is higher than the western part and higher than the overall mean precipitation rate for the Antlers aquifer (Oklahoma Mesonet, 2023; version 4; Thornton and others, 2020). 2024 The process used to develop and apply the soil-water-balance model is fully described in the model documentation report (https://doi.org/10.3133/sir20255013). 2024 Preprocessing: Python scripts were run to download climate data from Daymet (\ancillary\Pre_Processing\DaymetFileDownload.py) and grid it to the model grid ((\ancillary\Pre_Processing\MakeClimateGrids_DaymetToSWB_20220315.py). Data was also downloaded and processed as described in other processing steps. 2024 Running the soil-water-balance model: Within the "Model" folder, open the control file (recharge_40RZ.ctl), and ensure that the input file paths are correct for all needed inputs. Set the output file data and types to the desired settings. Once the control file is updated, open the command window and type "swb.exe recharge_40RZ.ctl," which will cause the command window to begin showing the progress of the soil-water-balance model. 2024 Post-processing: Post-processing Python scripts are in the "model" folder (\ancillary\Post_Processing\). There is one script for each output parameter used in the model (actual evapotranspiration, potential evapotranspiration, gross precipitation, and recharge). The scripts take the output ascii files from the soil-water-balance model outputs and computes the mean for the specified parameter for a specified time period (monthly, annual, and so forth). 2024 Raster Pixel Albers Conical Equal Area 29.5 45.5 -96.0 23.0 0.0 0.0 row and column 1000 1000 survey feet North_American_Datum_1983 Geodetic Reference System 1980 6378137.0 298.2572 North American Vertical Datum of 1988 0.3 feet Attribute values SWB_Model_Area.shp Polygon shapefile of soil-water-balance model area for the Antlers aquifer U.S. Geological Survey Area Identification number for the polygon in the shapefile representing the active soil-water-balance model area. Producer Defined 1 Identification number for the polygon. Producer defined Users are encouraged to review the associated model documentation report (https://doi.org/10.3133/sir20255013) to understand the purpose, construction, and limitations of this model. The model will run successfully only if the original directory structure is correctly restored. Instructions for reconstructing the original directory structure can be found in the readme.txt American Standard Code for Information Interchange (ASCII) text file in this data release. The readme.txt ASCII text file also contains instructions for running the model included in this data release and described in the model documentation report. Files in this data release include: -readme.txt: This ASCII text file describes the SWB model directory structure and data files. This file also includes instructions on how to run the model contained in this data release. -modelgeoref.txt: This ASCII text file defines the four corners of the model domain in the North American Datum of 1983, in decimal degrees, and in a custom Albers Equal-Area projection, in feet. Model data files are in the custom Albers Equal-Area projection. -ancillary.zip: This ZIP file contains ancillary data files that were used in the interpretation of model results in the associated model documentation report (https://doi.org/10.3133/sir20255013). The ancillary data files include data from a multiple-well aquifer pumping test, slug tests, groundwater quality data from the USGS and OWRB, and groundwater-use data from the OWRB. An expanded table of base-flow index data used to estimate stream seepage for the Antlers aquifer is also included. -bin.zip: This ZIP file contains the SWB 64-bit executable used to run the model documented in this data release and associated ASCII text file with instructions on how to run the executable. -SWB_Model_Area.zip: This ZIP file contains a polygon shapefile which defines the extent of the model domain. -model.zip: This ZIP file contains the input, control, and batch (run) files for the soil-water-balance model documented in this data release. Python scripts used for the creation of climate grids and summation of monthly and annual outputs and a projection file are also included. -output.zip: This ZIP file contains the output files for the SWB model documented in this data release. -source.zip: This ZIP file contains source files for standard codes of SWB version 1.0.1 used to run the model documented in this data release and Python 3.9.7. The Python 3.9.7 source code is a portable, packaged version of Python that does not require installation of Python on the user's system. https://doi.org/10.3133/sir20255013 U.S. Geological Survey - ScienceBase mailing address

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Denver CO 80225 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. The USGS or the U.S. Government shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Digital Data https://doi.org/10.5066/P14C6QFS None 20250226 Evin J. Fetkovich U.S. Geological Survey Hydrologist mailing and physical

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Oklahoma City Oklahoma 73116 United States of America 918-261-5449 efetkovich@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998