Purpose and Scope

This report documents a hydrologic investigation of the Salt Fork Arkansas River and Chikaskia River alluvial aquifers in northern Oklahoma, featuring (1) an updated summary of the hydrogeologic system, with a definition of the hydrogeologic framework (including an updated spatial extent) of the aquifers, as well as the hydrologic units, hydraulic properties, and surface-water and groundwater flow characteristics of these aquifers; (2) a discussion of the development of conceptual and calibrated numerical groundwater-flow models for the aquifers representing the 1980–2020 study period; and (3) results of simulations of groundwater availability scenarios.

The construction, calibration, and use of the numerical groundwater-flow model are described to provide estimates of the response of the aquifer to transient stresses and various groundwater withdrawals and drought scenarios. The groundwater-availability scenarios use the calibrated numerical groundwater-flow model to (1) estimate the EPS groundwater withdrawal rate that could result in a minimum 20-, 40-, and 50-year life of the Salt Fork Arkansas River alluvial aquifer for which a saturated thickness of at least 5 ft remains at the end of each period; (2) quantify the potential effects of projected well withdrawals on groundwater storage in the Salt Fork Arkansas River alluvial aquifer over a 50-year period; and (3) simulate the potential effects of a hypothetical 10-year drought on groundwater storage in the Salt Fork Arkansas River alluvial aquifer. This work is based on the current understanding of the Salt Fork Arkansas River and Chikaskia River alluvial aquifer, and a comprehensive hydrologic investigation of the aquifers could improve the development of a groundwater-flow model. The calibrated numerical groundwater-flow model and groundwater-availability scenarios were archived and released in a USGS data release (Smith and Gammill, 2025).

The geographic scope of this report is the Oklahoma part of (1) the Salt Fork Arkansas River alluvial aquifer, which ends at the confluence of the Salt Fork Arkansas and Arkansas Rivers, and (2) the Chikaskia River alluvial aquifer (fig. 1). However, the Salt Fork Arkansas River alluvial aquifer was the primary focus for the collection of new data, summarization of existing data, and model simulations described in this report. For this report, the Salt Fork Arkansas River and Chikaskia River alluvial aquifers include selected alluvium, terrace, and dune deposits adjacent to major tributaries of the Salt Fork Arkansas and Chikaskia Rivers in Oklahoma (fig. 1). Selected sections in this report are modified from Ellis and others (2017; 2020), Smith and others (2021), and Rogers and others (2023). Although the study areas and aquifers of interest differ, the organization and wording of this report are largely based on Rogers and others (2023).

Description of Study Area

The Salt Fork Arkansas River and Chikaskia River alluvial aquifers are long, narrow, connected, unconfined aquifers that consist mostly of unconsolidated alluvial, terrace, and dune deposits in Alfalfa, Garfield, Grant, Kay, Noble, and Woods Counties in northern Oklahoma (fig. 1). In the eastern part of the study area, the Salt Fork Arkansas and Chikaskia Rivers are perennial except during extreme droughts, as documented by historical USGS streamgage records for each stream obtained from the USGS National Water Information System (NWIS) database (USGS, 2024; table 1). In the western part of the study area, the Salt Fork Arkansas River typically has no flow (defined herein as daily discharge less than 1 cubic foot per second) for several days per year. The Salt Fork Arkansas River generally flows west to east for about 175 miles (mi) (Horizon Systems Corporation, 2015) in Oklahoma. In the study area, the Chikaskia River generally flows north to south for about 50 mi (Horizon Systems Corporation, 2015), joining the Salt Fork Arkansas River east of Tonkawa, Okla.

Construction of the Great Salt Plains Reservoir was authorized in 1936 for the purposes of flood control and wildlife conservation in the approximately 32,000-acre Salt Plains National Wildlife Refuge, which mostly surrounds the reservoir (USACE, 2023a; U.S. Fish and Wildlife Service, 2023). In 1941, the Great Salt Plains Reservoir was impounded on the Salt Fork Arkansas River about 70 river mi from the Kansas State line, along the eastern edge of a feature known locally, and referred to herein, as the “Great Salt plains” in northern Oklahoma (Horizon Systems Corporation, 2015). Because saline groundwater wells up to the surface from local “salt seeps,” the reservoir was not designed for use as a water supply. At a conservation-pool altitude of about 1,125 ft above the North American Vertical Datum of 1988 (NAVD 88), the reservoir is relatively shallow, with a maximum depth of about 16 ft (OWRB, 2023) and storage of about 26,000 acre-feet (acre-ft) (USACE, 2023b). The 310-ft-wide primary spillway is uncontrolled (ungated), so releases from the reservoir are continuous except during times of drought (USACE, 2023a). Unlike nearby drainage basins in Oklahoma (Ellis and others, 2020; Rogers and others, 2023), the drainage basins of the Salt Fork Arkansas and Chikaskia Rivers contain relatively few Natural Resources Conservation Service floodwater-retarding structures (fig. 2) or other large dams (associated with labeled reservoirs in fig. 2) that alter the surface-water hydrology.

Land-cover data for the Salt Fork Arkansas River and Chikaskia River alluvial aquifers study area were obtained from the CropScape database (National Agricultural Statistics Service, 2022) (fig. 3), which includes land-cover characteristics at 30-meter (m) resolution for the 2010–21 period. During this period, land-cover consisted of cropland (46.8 percent), grass or pasture (36.1 percent), developed (4.5 percent), forest or shrubland (2.7 percent), and other (9.9 percent), the last of which includes open water, wetland, and barren cover primarily in the areas near the Great Salt Plains Reservoir. Winter wheat was the major crop-cover type in the study area, accounting for 62.1 percent of cropland by area. Soybeans (12.2 percent) and alfalfa or hay (6.2 percent) were the next largest crop-cover types by area. Corn and sorghum accounted for 5.4 and 3.7 percent of cropland by area, respectively. Canola (1.3 percent), rye (1.1 percent), and fallow or idle cropland (4.6 percent) were the only other crop-cover types that accounted for more than 1 percent of cropland by area (fig. 3). Crop-cover types can change in response to economic conditions and hydrologic factors, but the percentages of total cropland cover and individual crop types did not change substantially during the 2010–21 period (National Agricultural Statistics Service, 2022).

Climate Characteristics

Historical daily data from selected climate stations in northern Oklahoma (fig. 1) were obtained from the National Centers for Environmental Information (2022) (fig. 4A, table 2). These data, which extend back to the 1890s at some locations, were summarized to tabulate and graph annual and monthly temperature and precipitation statistics for the study area (fig. 4A, table 3). A locally weighted scatterplot smoothing function line (Cleveland, 1979) with a smoothing factor of about 0.04, chosen to mimic a 5-year moving mean (5 divided by 126 annual values), was used to delineate periods of below- and above-mean annual temperature and precipitation. The mean annual precipitation during 1895–2020 was 28.9 inches (in.) (fig. 4A, table 3), and the mean annual precipitation increases by about 10.0 in. from west to east across the study area (Oklahoma Climatological Survey, 2022a, b). The mean annual precipitation for the 1980–2020 study period was 33.1 in. (fig. 4A, table 3). Within the available record, 1981–2010 was an unprecedented wet period; above-mean precipitation was recorded in 22 of these 30 years (73 percent). This wet period contributed to a higher mean precipitation during the study period compared to the mean for the period of record (fig. 4A). May is typically the wettest month, and January is typically the driest month (fig. 5A). The mean annual snowfall is about 11 in. across the western part of the study area and 8 in. across the eastern part (Oklahoma Climatological Survey, 2023). The mean annual temperature during 1895–2020 was 58.3 degrees Fahrenheit (°F) (fig. 4B, table 3), and increases by about 1 °F from east to west across the study area (Oklahoma Climatological Survey, 2022a, b; Oklahoma Mesonet, 2019).

Multiyear to decadal droughts are common in Oklahoma (Moreland, 1993). The 1929–41 (“Dust Bowl,” Egan, 2006) and 1952–56 drought periods were among the most severe in Oklahoma in the 20th century; two shorter, less severe drought periods also occurred later in the 20th century, during 1961–72 and 1976–81 (fig. 4A) (Tortorelli, 2008; Shivers and Andrews, 2013). The most severe droughts on record developed from extended periods of below-mean precipitation paired with above-mean temperature.

Streamflow Characteristics and Trends

Daily streamflow data, with varying and sometimes interrupted periods of record, were recorded at selected USGS streamgages in the study area (fig. 1) and summarized for the 1980–2020 study period (tables 4, 5). Daily streamflow data were also collected at one OWRB streamgage in the study area, OWRB streamgage 621010010160-001AT near White Eagle, Okla. (hereinafter referred to as the “OWRB White Eagle streamgage”). OWRB White Eagle streamgage data were provided as furnished record (Derrick Wagner, Technical Studies Manager, Oklahoma Water Resources Board, 2023) and are published in the companion USGS data release (Smith and Gammill, 2025). Streamflow measured at streamgages is the sum of runoff and base flow originating upstream; base flow is the component of streamflow supplied by the discharge of groundwater to streams (Barlow and Leake, 2012). For this report, streamflow-hydrograph data obtained from the NWIS database (USGS, 2024) were separated into runoff and base-flow components by using the standard Base-Flow Index code (Wahl and Wahl, 1995) in the USGS Groundwater Toolbox (Barlow and others, 2015). The Base-Flow Index code uses the minimum streamflow in a moving n-day window as a basis for hydrograph separation; for consistency, a 5-day window was used for all streamgages listed in this report. The 5-day window was selected by testing multiple n-day windows for the study period at USGS streamgage 07151000 Salt Fork Arkansas River at Tonkawa, Okla. (hereinafter referred to as the “Tonkawa streamgage”) and plotting the resulting mean base-flow percentage against n; a slope change was evident at 5 days. Base flow, as computed by using this method, generally accounts for about 15–60 percent of annual streamflow for the periods of record at the Tonkawa streamgage (fig. 6C) and at USGS streamgage 07152000 Chikaskia River near Blackwell, Okla. (hereinafter referred to as the “Blackwell streamgage”) (fig. 6E). This percentage, known as the base-flow index (BFI) (Wahl and Wahl, 1995; Barlow and others, 2015), generally increased annually over the period of record through 2021 at streamgages on the Salt Fork Arkansas, Medicine Lodge, and Chikaskia Rivers (fig. 6A–E).

Annual BFI, base-flow, and streamflow trends described in this report were analyzed by using the Kendall tau test (Kendall, 1938) (kendalltau, SciPy version 1.7.1 [Virtanen and others, 2020]) with the Theil-Sen slope estimator (Sen, 1968) (theilslopes, SciPy version 1.7.1 [Virtanen and others, 2020]) and specifying an alpha value of 0.05 as the significance level. Statistically significant upward or downward trends are indicated when the probability value (p-value) is less than the alpha value (Helsel and others, 2020). For the full period of record, the upward trends in the BFI were found to be statistically significant (p-value = 0.0002) at USGS streamgage 07148400 Salt Fork Arkansas River near Alva, Okla. (hereinafter referred to as the “Alva streamgage”), statistically significant (p-value < 0.0001) at USGS streamgage 07149000 Medicine Lodge River near Kiowa, Kan. (hereinafter referred to as the “Kiowa streamgage”), and statistically significant (p-value = 0.0001) at the Tonkawa streamgage. The upward trends in the BFI were found to be not statistically significant (p-values = 0.2135 and 0.0856, respectively) at USGS streamgages on the Chikaskia River, which included USGS streamgage 07151500 Chikaskia River near Corbin, Kan. (hereinafter referred to as the “Corbin streamgage”) and the Blackwell streamgage. Statistically significant upward trends in base flow were detected in the streamflow records at all streamgages, but upward trends in the BFI were not always associated with upward trends in base flow. For the full period of record, the upward trends in base flow were statistically significant at all stations except the Alva streamgage, where the p-value was 0.0592. However, a 29-year data gap in the Alva streamgage record (fig. 6A) complicates the evaluation of trends at this site.

For the full period of record, upward patterns in streamflow were evident in the streamflow records for all streamgages except for the Alva streamgage; however, statistically significant upward trends in streamflow were detected only at the Tonkawa streamgage (p-value = 0.0186) and the Blackwell streamgage (p-value = 0.0069). Greater amounts of precipitation during the 1980–2020 study period (relative to the preceding period of record) were likely the primary cause of upward patterns and statistically significant trends in streamflow.

No statistically significant trends were found in BFI, base flow, or streamflow for the 1980–2020 study period. This lack of significant trends in BFI, base flow, and streamflow over the study period (paired with the presence of significant trends in BFI, base flow, and streamflow over the full period of record) indicates that the hydrologic changes responsible for the expression of trends likely occurred prior to the study period and did not continue into the study period.

Groundwater Use

The OWRB permits and regulates groundwater withdrawals of 5 acre-feet per year (acre-ft/yr) or more used for domestic and agricultural purposes and groundwater withdrawals used for irrigating more than 3 acres of land for growing gardens, orchards, or lawns (Oklahoma Statute §82–1020.1 [Oklahoma State Legislature, 2021a, OWRB, 2014]; Oklahoma Statute §82–1020.3 [Oklahoma State Legislature, 2021c]). Groundwater-use data since 1980 are self-reported annually to the OWRB by permitted users; OWRB staff compiled and reviewed groundwater-use data described in this report to ensure the quality and completeness of the data (Smith and Gammill, 2025). For the purposes of this study, all groundwater use was assumed to be consumptive use (That is, none of the groundwater withdrawn returns to the aquifer or streams.)

Most groundwater-use permits for the Salt Fork Arkansas River alluvial aquifer were allocated for irrigation or public supply, but some were allocated for other uses, including commercial, industrial, mining (including oil and gas), recreation, fish, and wildlife (fig. 7) (Smith and others, 2021; OWRB, 2022d). The Salt Fork Arkansas River alluvial aquifer can yield about 100–200 gallons per minute (OWRB, 2012a). In 2020, about 120 long-term temporary (regular) groundwater-use permits and about 80 prior-right groundwater-use permits were active for the Salt Fork Arkansas River alluvial aquifer, and 3 long-term temporary (regular) groundwater-use permits and 3 prior-right groundwater-use permits were active for the Chikaskia River alluvial aquifer (OWRB, 2022d). Each permit is tied to a land area and well location (or locations) designated for a single groundwater-use type (fig. 7).

Since 1980, the OWRB has required irrigation permit holders to report annual groundwater use in terms of the number of applications and number of inches of water applied per application (OWRB, 2024). Prior to 1980, however, the number of inches of water applied during irrigation was not required to be reported. As a result, the OWRB adopted rules to estimate the number of inches of water applied for pre-1980 data based on the number of water applications (OWRB, 2014). This change in estimation methods results in what appears to be a step-change decrease in irrigation groundwater use after 1980 (fig. 8).

Groundwater use for domestic supply (self-supplied directly to a residence by a private well) was assumed to be a negligible part of the total groundwater use. The study area is mostly rural, with a small, widely dispersed population that relies on private wells; most of the population is concentrated in cities, where the water is supplied by municipal or rural water districts rather than by private wells.

OWRB reported groundwater use data were compiled for the 1967–2020 period of record (fig. 8; tables 6, 7) and summarized for the 1980–2020 study period (OWRB, 2024; Smith and Gammill, 2025). During the 1980–2020 study period, annual reported groundwater use in the Salt Fork Arkansas River alluvial aquifer upgradient from the Great Salt Plains Reservoir was primarily for irrigation (45.5 percent) and public supply (42.2 percent), with secondary groundwater use for recreation, fish, and wildlife (7.8 percent) and mining (4.0 percent) (fig. 8A). Annual reported groundwater use in the Salt Fork Arkansas River alluvial aquifer downgradient from the Great Salt Plains Reservoir was primarily for irrigation (50.1 percent) and public supply (43.7 percent) with secondary groundwater use for industrial (5.9 percent) purposes (fig. 8B). For the entire Salt Fork Arkansas River alluvial aquifer, annual reported groundwater use for the study period was about 5,550 acre-ft/yr (tables 6, 7), of which about 44.3 percent was for irrigation, 47.8 percent was for public supply, 3.2 percent was for industrial, 2.9 percent was for recreation, fish, and wildlife, and 1.6 percent was for mining use (fig. 8C). Annual reported groundwater use in the Salt Fork Arkansas River alluvial aquifer decreased upgradient from, and increased downgradient from, the Great Salt Plains Reservoir over the 1980–2020 study period (fig. 8A–B). The decreased groundwater use upgradient from the Great Salt Plains Reservoir over the 1980–2020 study period was caused by a combination of decreased irrigation and public-supply use, whereas the increased groundwater use downgradient was mostly caused by increased irrigation use. Annual reported groundwater use in the Chikaskia River alluvial aquifer was almost exclusively for public supply by the City of Tonkawa (fig. 8D), which reported annual groundwater use from 0 to approximately 1,100 acre-ft/yr during the 1980–2020 study period (tables 6, 7). Some of the reported groundwater-use values of 0 acre-ft/yr may be due to a lack of reporting rather than an actual estimate of how much groundwater was used. Groundwater use from the Salt Fork Arkansas River and Chikaskia River alluvial aquifers (in the Upper Arkansas planning region [OWRB, 2012a]) is projected to increase by 42 percent from 2010 to 2060; the greatest growth in projected groundwater use is expected to be for municipal (public supply), industrial, oil and gas (mining), and crop irrigation uses (OWRB, 2012a).

Alluvium and Terrace Deposits

The Quaternary deposits that contain the Salt Fork Arkansas River and Chikaskia River alluvial aquifers consist primarily of alluvium and terrace deposits, gypsum, and windblown dune sands (now covered by vegetation). These Quaternary deposits are mostly composed of gravel, sand, silt, and clay and overlie the Permian geologic units (figs. 10–11). In western Grant County and northeastern Alfalfa County, thick and broad deposits of dune sands (fig. 11B) extend from southeast of the Salt Plains National Wildlife Refuge northward across the Kansas border (Eckenstein, 1995). Quaternary deposits are thickest around the Salt Plains National Wildlife Refuge and thin to the east along the Salt Fork Arkansas River, ranging from 1 to 20 mi wide throughout.

The salt flats are a featureless, unvegetated, gypsite-salt-encrusted surface covering about 25 square miles in central Alfalfa County inside the Salt Plains National Wildlife Refuge (figs. 1, 9). This area consists of loose Quaternary deposits of fluvial and lacustrine origin that are saturated with a natural brine that seeps up from the underlying Permian bedrock (USACE, 1978). This brine contains elevated concentrations of sodium chloride and calcium sulfate dissolved from evaporite deposits in the underlying Permian bedrock (Johnson, 1972). When this brine evaporates, it precipitates salt crusts on the surface and selenite gypsum crystals just below the surface (USACE, 1978).

During the Permian, an inland sea deposited layers of interbedded halite and gypsum salts (Jordan and Vosburg, 1963). There is salt dissolution across the Permian Hennessey Group, with some brine migrating upward upon reaching the artesian zones of sandstone and siltstones (Davis, 1968). The water originating from the Hennessey Group has chloride concentrations that range from about 150,000 to 250,000 milligrams per liter (mg/L) (Johnson, 1972). For a frame of reference, the total salinity of seawater is only about 35,000 mg/L. The Great Salt Plains Reservoir has a lower salinity because of the dilution effect of surface water, having about 15,000 mg/L of dissolved salts in water, which is about half that of seawater. The salinity of the reservoir varies, but the amount of salt that flows out of the reservoir through the Salt Fork Arkansas River averages about 3,000 tons per day (Johnson, 1972). Attempting to mitigate the adverse effects of salinity migration downstream, the USACE has tried to control the large concentrations of chloride by creating brine pools and constructing the Great Salt Plains Reservoir (USACE, 1978).

Bedrock Units

The Ogallala Formation of Tertiary age and Whitehorse Group of Permian age are present in the far western part of the study area (fig. 9) but do not underlie the Salt Fork Arkansas River and Chikaskia River alluvial aquifers in Oklahoma. These units, therefore, have little effect on groundwater flow and quality in the Salt Fork Arkansas River and Chikaskia River alluvial aquifers.

The bedrock units that underlie the Salt Fork Arkansas River and Chikaskia River alluvial aquifers are of Permian age and primarily composed of shale, siltstone, and fine-grained sandstone (Bingham and Bergman, 1980; Morton, 1980). The Permian El Reno Group, which includes the Dog Creek Shale, Blaine Formation, and Flower-pot Shale, is composed of red, brown, and green gypsiferous shales as well as several beds of siltstone, sandstone, and dolomite (fig. 10). Because the siltstones and sandstones are locally transmissive enough to support low-yielding groundwater production wells, the El Reno Group is considered a minor aquifer in the study area (OWRB, 2012a). The Hennessey Group consists of fine to coarse grained sandstone, red-brownish siltstone, gray to red brown shale, and salt and calcium carbonate layers (Jordan and Vosburg, 1963). The Permian Garber Sandstone of the Sumner Group is composed of red-brown fine-grained sandstone that grades northward into shale and siltstone (Bingham and Bergman, 1980; Morton, 1980). Geologic mapping by Stanley and Miller (2007) indicated that this unit thins northward and pinches out before reaching the Salt Fork Arkansas River. The Permian Wellington Formation of the Sumner Group consists mainly of gray, green, and maroon silty shale, and also includes salt, anhydrite, silty limestone, and dolomite. The Garber Sandstone and the Wellington Formation collectively contain the North-central Oklahoma minor bedrock aquifer (Bingham and Bergman, 1980; Morton, 1980). The Permian Chase Group is the oldest bedrock unit in the study area and consists of red and brown to grayish shale with arkosic sandstone and limestone conglomerates (Bingham and Bergman, 1980). Rocks of the Chase Group only underlie a small part of the Salt Fork Arkansas River alluvial aquifer.

The bedrock units that underlie the Salt Fork Arkansas River and Chikaskia River alluvial aquifers dip south and southwest in the western half of the study area (Bingham and Bergman, 1980; Morton, 1980). In the eastern part of the study area, the bedrock units dip to the west and southwest, and the regional dip is about 40 feet per mile (Bingham and Bergman, 1980; Morton, 1980). Intermittent bedrock layers of evaporites composed of halite (sodium chloride) and gypsum/anhydrite (calcium sulfate) occur in the bedrock layers of the Hennessey Group in Woods and Alfalfa Counties; when exposed to water, these evaporites dissolve to form brines that discharge at and near the land surface in Alfalfa County (USACE, 1969; Morton, 1980). The subsequent evaporation of brines that discharge at the land surface forms halite and selenite gypsum crystals in an area of about 25 square miles within the Salt Plains National Wildlife Refuge (fig. 1) (USACE, 1978; Morton, 1980). Evaporite layers are absent in bedrock layers east of Alfalfa County (Jordan and Vosburg, 1963, plate 1; USACE, 1969). In the study area, erosional processes have exposed parts of the bedrock units at land surface, forming gently rolling hills broken up by escarpments capped by resistant sandstones and limestones and valleys of shale (USACE,1969). Most of the deposition of the study area took place in restricted, saline marine environments, which are defined as having two or more entrance channels or inlets and sufficient water circulation because of tidal currents and wind effects. These types of depositional environments are responsible for the highly soluble constituents, such as halite, gypsum, and dolomite, present in the study area (USACE, 1978).

Groundwater and Surface-Water Quality

The Salt Fork Arkansas River alluvial aquifer is a freshwater aquifer with areas of saline groundwater that locally may limit its use for public supply and other selected uses. Seepage from the Salt Fork Arkansas River and the localized upward flow of saline groundwater from the underlying bedrock are the primary sources of TDS that contribute to the salinity of the aquifer. The highest TDS concentrations in the Salt Fork Arkansas River alluvial aquifer (3,770 mg/L) were measured near the Great Salt Plains Reservoir, where groundwater brines from the underlying bedrock unit, the Hennessey Group, discharge at the surface. The least saline groundwater (80 mg/L TDS) was contained in windblown dune sands northeast of the Great Salt Plains Reservoir.

Complete major-ion groundwater-quality data are useful for evaluating the salinity of water but were not available for the Chikaskia River alluvial aquifer (all analyses were lacking bicarbonate concentrations). Partial major-ion groundwater-quality data (Bingham and Bergman, 1980) indicate the groundwater in the Chikaskia River alluvial aquifer may be more saline in some locations than groundwater in the Salt Fork Arkansas River alluvial aquifer.

The salinity of surface water in the Salt Fork Arkansas River in the study area varies depending on location and flow conditions. In general, the surface water is slightly to moderately saline, with salinity concentrations of 1,000–3,000 mg/L and 3,000–10,000 mg/L, respectively (Winslow and Kister, 1956). Mean TDS concentrations of the Salt Fork Arkansas River range from about 1,500 mg/L at the Alva streamgage in Woods County to about 6,000 mg/L at USGS streamgage 07150500 Salt Fork Arkansas River near Jet, Okla. (hereinafter referred to as the “Jet streamgage”) in Alfalfa County (fig. 1) (USGS, 2024). Water released from the Great Salt Plains Reservoir is the primary source of salinity to the Salt Fork Arkansas River and the Salt Fork Arkansas River alluvial aquifer in Grant and Kay Counties (Eckenstein, 1994). Salinity of the Salt Fork Arkansas River decreases with distance downstream from the Great Salt Plains Reservoir because of dilution by freshwater tributary inflows in Grant and Kay Counties (Eckenstein, 1994).

A combination of recently collected and historical groundwater quality data and historical surface-water quality data were used in this analysis. Groundwater-quality data for the Salt Fork Arkansas River alluvial aquifer were collected by the OWRB as part of the Groundwater Monitoring and Assessment Program (GMAP) (OWRB, 2018). Groundwater samples were collected from 30 wells during July–August 2014 (fig. 12) and analyzed for physicochemical properties (dissolved oxygen, pH, specific conductance, and temperature), major ions, nitrate plus nitrite (as nitrogen), and selected trace metals. To describe groundwater quality in parts of the aquifer where GMAP wells were absent, historical data collected from 11 wells were used in this analysis. The historical groundwater quality data were retrieved from the USGS National Water Information System (NWIS) database (USGS, 2024) and consisted of major ions and nitrate (as nitrogen) concentrations measured in groundwater samples collected during 1948–72. Historical water-quality data measured in surface-water quality samples collected from seven selected sites on the Salt Fork Arkansas River during 1948–72 were used to help identity mixtures of fresh water and saline water in the aquifer; the surface-water quality data were also retrieved from the NWIS database (fig. 12).

A statistical summary of 30 OWRB samples from the GMAP program (OWRB, 2018) was analyzed for pH, TDS, hardness constituents, sulfate, and chloride concentrations. The GMAP report indicates that groundwater from the Salt Fork Arkansas River alluvial aquifer tends to have a neutral pH, with a median pH value of 7.1 standard units. Of 30 samples, 1 was below the secondary maximum contaminant level (SMCL) for pH (6.5 SU) in finished drinking water (U.S. Environmental Protection Agency [EPA], 2017, 2020a). TDS concentrations ranged from 86.3 to 1,470 mg/L, with a median of 552 mg/L. Of 30 samples, 18 exceeded the SMCL of 500 mg/L for TDS, 5 of which would qualify as slightly saline (1,000–3,000 mg/L, as defined in Winslow and Kister [1956]) with TDS concentrations ranging from 1,220 to 1,470 mg/L (OWRB, 2018). Hardness as calcium carbonate ranged from 41.0 to 872 mg/L, with a median concentration of 348 mg/L. Of 30 samples, 27 had TDS concentrations exceeding 225 mg/L and are classified as hard, with hardness as calcium carbonate values exceeding 180 mg/L (Hem, 1985; OWRB, 2018). Concentrations of sulfate ranged from less than 10.0 to 508 mg/L with a median of 66.1 mg/L. Of 30 samples, 4 exceeded the SMCL of 250 mg/L for sulfate in finished drinking water (EPA, 2020a). Concentrations of chloride ranged from less than 10 to 398 mg/L, with a median of 55.3 mg/L. Of 30 samples, 5 exceeded the SMCL of 250 mg/L for chloride (EPA, 2020a).

Three Federally regulated water-quality constituents (nitrate plus nitrite measured as nitrogen, arsenic, and uranium) were measured in 30 samples collected by OWRB and USGS at concentrations exceeding their respective EPA maximum contaminant levels (MCL) for finished drinking water (EPA, 2017, 2020b). The water-quality data for these 30 samples are stored in the USGS NWIS database (USGS, 2024). Concentrations of nitrate plus nitrite ranged from less than 0.05 to 20.0 mg/L, with a median concentration of 4.14 mg/L; in the 30 samples collected by OWRB (2018), 5 of which exceeded the MCL of 10 mg/L. Concentrations of nitrate (as nitrogen) in the 12 USGS groundwater samples ranged from 0.05 to 36 mg/L, and 4 of the 12 samples exceeded the MCL (OWRB, 2018).

Concentrations of dissolved arsenic and uranium measured in samples collected by OWRB were evaluated and compared to their respective MCLs. Concentrations of dissolved arsenic exceeded the MCL of 10.0 micrograms per liter (µg/L) in 1 of the 30 OWRB samples; in 4 samples, the dissolved arsenic concentrations were less than the laboratory reporting level of 1.0 µg/L (OWRB, 2018). The median dissolved arsenic concentration was 2.0 µg/L. Concentrations of dissolved uranium exceeded the MCL of 30.0 µg/L in one of the 30 OWRB samples at 30.9 µg/L; the dissolved uranium concentration was less than the laboratory reporting level of 1 µg/L in 2 of the samples (OWRB, 2018). The median concentration of dissolved uranium in 30 samples was 4.7 µg/L. Uranium and arsenic were not analyzed in conjunction with the 12 USGS groundwater samples.

Cation-anion balances were used to determine water types for the groundwater samples collected at the 28 OWRB and 11 USGS groundwater-quality sites, and at 7 USGS surface-water-quality sites on the Salt Fork Arkansas River. These analyses were determined by first converting major-ion concentrations to milliequivalents per liter. When the milliequivalent concentrations of cations and anions balance within acceptable limits, they can be used to determine the water type of a given sample (Hem, 1985). For this study, cation-anion balances within 7 percent were considered acceptable for determining water types (Hem, 1985). The cation-anion balances of the major-ion concentrations were within 7.0 percent in 26 of the 28 OWRB groundwater samples, and of 11 USGS water-quality samples, 3 were above 7.0 percent (one of which was a groundwater sample and the other two surface water not sampled in the aquifer). The USGS surface and groundwater-quality sites were represented as the median major-ion concentrations from available samples.

Stiff diagrams and Piper diagrams were used to better understand variations in water types and the mixing of saline and fresh water. Groundwater- and surface-water-quality data were plotted on Stiff (1951) diagrams for visual comparisons (fig. 12). Stiff diagrams (Stiff, 1951) are visual representations of major-ion concentrations showing the dominant water type. These diagrams were made by using the Python WQChartPy package (Yang and others, 2022) to compare and interpret the water-quality data. The dominant water type in the Salt Fork Arkansas River varies based on geographic location and whether the sample was from a surface or groundwater source. Water-quality data were also plotted on a Piper (1944) diagram for visualization of groundwater types and the mixing of saline water and fresh groundwater (fig. 13). When describing water type, cations and anions were considered predominant when composing 50 percent or more of the total cation or anion concentration expressed in milliequivalents per liter (Hem, 1985). The term “mixed” was used when no cation or anion concentrations were predominant.

The water types of the 37 groundwater samples analyzed for this study (26 samples collected by OWRB, 11 collected by USGS) ranged from bicarbonate as the dominant anion (bicarbonate water type) to chloride as the dominant ion (chloride water type), or a mixture of anions (mixed anion water type) (figs. 12–13). TDS concentrations measured in the groundwater samples were generally lower in the bicarbonate-water type than in the other water types. Of the 37 groundwater samples, 29 were of the bicarbonate water type with calcium, magnesium, or sodium as the dominant cations or characterized by a mixture of cations (mixed cation water type). TDS concentrations of the bicarbonate water type groundwater samples ranged from 86 to 885 mg/L, with a median TDS concentration of 495 mg/L. Groundwater samples from the part of the Salt Fork Arkansas River alluvial aquifer within 3 mi of Pond Creek in Grant County were also of the bicarbonate water type, with major-ion concentrations similar to groundwater samples from most wells along Little Sandy Creek and Sand Creek in Alfalfa County. Bicarbonate water-type samples plot within quadrants A and B of the Piper diagram’s upper diamond on figure 13A. These quadrants represent freshwater types with bicarbonate as the dominant anion and the cations calcium, magnesium, and sodium (plus potassium) representing various percentages of the total ion concentrations. The TDS concentrations were generally the samples representing the highest chloride and mixed-anion water types, which are considered water types indicative of freshwater saline-water mixtures. Chloride or mixed-anion water type samples plot within quadrants C and D of the Piper diagram’s upper diamond (fig. 13). Of 37 groundwater samples, 16 samples were chloride or mixed-anion water types with TDS concentrations ranging from 735 to 1,470 mg/L, and a median TDS concentration of 1,220 mg/L. Major-ion water quality of the Salt Fork Arkansas River at the three USGS surface-water sites was plotted on the Piper diagram as saline end members representing calcium-sulfate and sodium-chloride water types. Shifting the discussion from individual anions and cations to the freshwater and saline water mixtures in the upper diamond, the groundwater-quality samples show generalized mixing from freshwater types in quadrants A and B to the saline end members in quadrants C and D (fig. 13). Quadrants C and D represent groundwater mixtures of different proportions along this mixing region. In general, TDS is higher in groundwater-quality samples approximating saline-end members than in other samples. The triangular anion (ternary) part of the Piper diagram (fig. 13) also illustrates the mixing between freshwater types and saline end-members. Groundwater-quality samples with larger proportions of sulfate and chloride plot closer to the saline-water end members (OWRB, 2018; USGS, 2024).

Surface-water types in the river basin show a distinct difference between sites located upgradient and downgradient from the Great Salt Plains Reservoir (fig. 12). Concentrations of calcium and sulfate were higher upstream from the salt flats, with sodium chloride water types present. Changes in the geology west of the salt flats, where halite and gypsum deposits associated with the Hennessey Group are widespread, create higher saline signatures in the surface-water samples; groundwater-surface interactions are the source of elevated sodium and chloride concentrations in surface water (fig. 9; USACE, 1978; Morton, 1980). At the Great Salt Plains Reservoir, sodium and chloride concentrations are substantially higher than in upstream surface-water samples, which is likely attributable to active exchanges between the salt flats and surface water. Further east, downgradient from the Great Salt Plains Reservoir, the sodium chloride signal decreases but is still much higher than the calcium sulfate signature (fig. 12). In all of the surface-water samples, the magnesium bicarbonate ions are the less dominant ions, as the water is higher in sodium and chloride than most other alluvial aquifers in the State (Dover, 1957; Davis, 1968; Eckenstein, 1994, 1995).

The groundwater well samples generally have less sodium chloride than all of the surface-water samples and higher ionic concentrations of bicarbonate, indicating fresher, less saline groundwater. The groundwater samples collected from wells completed in the alluvial and terrace deposits near the western part of the Great Salt Plains Reservoir were characterized by higher sodium and chloride concentrations and higher calcium and sulfate concentrations compared to groundwater samples collected from wells completed near the eastern part of the study area (fig. 12). In the central to eastern part of the study area, much lower sodium and chloride concentrations and higher bicarbonate concentrations were measured in groundwater samples collected in the alluvium compared to groundwater samples collected from west of the Great Salt Plains Reservoir, indicative of less saline groundwater in this region. In the dune sands north of the salt flats, there are even higher concentrations of bicarbonate than in the central to eastern part of the study area; this observation, combined with the fact that the lowest concentrations of calcium sulfate and sodium chloride measured in the entire aquifer, suggests that the groundwater in the dune sands is the least saline in the study area (fig. 12). The low salinity in the dune sands could be at least partially attributable to percolation of groundwater from the Permian bedrock through the dune sands in the area (Ward, 1961; Davis, 1968). Overall, the groundwater samples collected from wells completed in the alluvium had higher concentrations of sodium and chloride than those collected from wells completed in the terrace deposits. This may be due to the longer residence times the groundwater has with the sediments, possibly allowing for potential chemical interactions that will decrease the concentrations of sodium and chloride from the groundwater that was collected from the wells completed in the terrace deposits. Overall, most of the groundwater samples from the alluvium contain higher concentrations of sodium chloride compared to samples from the terrace deposits because of the longer residence times the groundwater has with the sediments, possibly allowing for potential chemical interactions decreasing the concentrations of sodium chloride in the terrace deposits (fig. 12).

Aquifer Extent

Previously published spatial aquifer extents for the Salt Fork Arkansas River and Chikaskia River alluvial aquifers were determined from 1:250,000-scale geologic maps (Stoeser and others, 2005). The spatial aquifer extents for this study were updated by using information from finer (1:100,000) scale geologic maps obtained from OWRB (2022a). The geographic extents of the Salt Fork Arkansas River and Chikaskia River alluvial aquifers (fig. 1) were updated from GMAP Aquifer Study Areas extents available from the OWRB Open Data Portal (OWRB, 2022a) by using information obtained from 1:100,000-scale geologic maps (Miller and Stanley, 2003; Stanley and others, 2003; Stanley and Miller, 2007, 2008). Compared to the coarser scale of the older 1:250,000-scale geologic map of the study area, the finer 1:100,000-scale geologic maps showed a narrower extent of the alluvium and terrace deposits that form the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, and therefore the updated spatial aquifer extents presented in this report are smaller compared to the previously published extents.

For modeling purposes, the updated spatial extents of the Salt Fork Arkansas River and Chikaskia River alluvial aquifers were reduced by removing small tributaries where the width of alluvial materials was less than 2,000 ft, because including these narrow tributaries would contribute negligibly to the characterization of the groundwater system. The updated spatial extents of the Salt Fork Arkansas River and Chikaskia River alluvial aquifers were extended in a few areas where groundwater permits and lithologic logs obtained from well-completion reports (specifically, lithologic logs of the physical characteristics of geologic units observed during the initial drilling of the well [OWRB, 2022a]) indicated that alluvial materials were present in sufficient thickness to allow production of groundwater at a steady rate and, thereby, serve as an economic resource. The largest increase in the spatial extents of the alluvial aquifers was northwest of the Great Salt Plains Reservoir and west of Sand Creek, in an area where the 1:250,000-scale geologic maps showed surficial terrace deposits but the 1:100,000-scale geologic maps did not.

Where present, the top of the Salt Fork Arkansas River alluvial aquifer was defined as the land-surface altitude obtained from a 10-m (horizontal resolution) digital elevation model (DEM) (USGS, 2015), and depressions in the DEM were filled by using the ArcGIS Fill tool (Esri, 2021a). The altitude of the base of the Salt Fork Arkansas River alluvial aquifer, which was equivalent to the bedrock altitude, was contoured at a 25-ft interval from bedrock depths obtained from drillers’ lithologic logs from well-completion reports (OWRB, 2022a), ambient-seismic-method depths (Smith and Gammill, 2025), and test-hole data (Engineering Enterprises, unpub. data, 2021) in addition to data obtained by the USGS from direct-push test holes (fig. 14; USGS, 2024). For each of these data sources, the altitude of the base of the Salt Fork Arkansas River alluvial aquifer was calculated by subtracting the measured bedrock depth from the land-surface altitude. For consistency, the land-surface altitude was obtained from the 10-m DEM, even when the data source provided a land-surface altitude.

Potentiometric Surface and Saturated Thickness

Potentiometric-surface maps show the altitude at which the water level would have stood in tightly cased wells at a specified time; the potentiometric surface is usually contoured or spatially interpolated from synoptic water-table-altitude measurements made in many wells across an aquifer extent. Potentiometric-surface maps are used to indicate the general directions of groundwater flow in an aquifer. Groundwater generally flows perpendicular to potentiometric contours in the direction of decreasing contour altitude (Freeze and Cherry, 1979).

During February 24–28, 2020, a total of 70 synoptic water-table altitudes were measured by using methods described in Cunningham and Schalk (2011) at selected wells in the study area. The wells were generally unused and constructed with steel or slotted polyvinyl chloride (PVC) well casings and a sand-filled annulus. The depth to water was measured, referenced to land surface, and subtracted from the land-surface altitude obtained from a 10-m DEM (USGS, 2015) to determine the water-table altitude referenced to the North American Vertical Datum of 1988. The DEM also was used to obtain water-surface altitudes at selected points along major streams for use as additional control data. A potentiometric surface raster was then interpolated from both sets of water-level altitude data by using inverse-distance-weighted interpolation methods (Esri, 2021b). Potentiometric-surface contours at 25-ft intervals (fig. 15) were derived from the interpolated raster, and the DEM was used to check that contour altitudes remained below land surface altitudes except in areas where flowing wells and wetlands were known to be present. The saturated thickness, as used in this study, is the difference between the water-table altitude and the bedrock (aquifer-base) altitude. The mean saturated thicknesses for the Salt Fork Arkansas River and Chikaskia River alluvial aquifers in February 2020 were about 34.0 and 27.9 ft, respectively. Local flow in the Salt Fork Arkansas River and Chikaskia River alluvial aquifers was generally from topographically high areas toward the Salt Fork Arkansas River and its major tributaries with regional flow in the aquifer generally from west to east (fig. 15). The most notable feature in the potentiometric surface is the potentiometric high associated with the dune sand area northeast of the Great Salt Plains Reservoir. That potentiometric high directs flow west toward Sandy Creek and flowing artesian wells in northeastern Alfalfa County and east toward Sand Creek in Grant County.

Textural and Hydraulic Properties

This section describes (1) textural and aquifer hydraulic properties, such as grain size, distribution, and percent coarse value that were estimated by using lithologic logs, and (2) the spatial distribution of lithologic units to characterize the alluvium and terrace deposits for the Salt Fork Arkansas River alluvial aquifer. The distribution and variability of textural and hydraulic properties of aquifer materials, especially the horizontal hydraulic conductivity, were assumed to be the primary controls on groundwater flow in the Salt Fork Arkansas River and Chikaskia River alluvial aquifers (Sudicky, 1986). Multiple methods were used to estimate the range and central tendency of horizontal hydraulic conductivity values in the aquifer. These methods included in-place estimation in test holes and summarizing data in drillers’ lithologic logs.

Hydraulic Conductivity Estimation Based on Lithologic Categorization

The hydraulic conductivity of the Salt Fork Arkansas River alluvial aquifer was determined by applying a percent coarse material multiplier (table 8) to lithology-specific depth intervals within each compiled lithologic log based on lithologic category. There are many variations in categories of lithologic logs, but most lithologic categories defined by drillers on the lithologic logs include some combination of gravel, sand, and clay. Most categories vary widely among each driller’s log depending upon the driller doing the drilling analysis. To standardize and simplify the lithologic logs, the lithologic descriptions were reclassified into five categories that were used to estimate the percentage of coarse material range of each depth interval. Lithology was categorized into clay and silt, fine sand, medium sand, coarse sand, and fine gravel. A percentage of coarse material values (ranging from 0 to 100 percent) based on the midpoint size range of each category was assigned to each lithologic-log depth interval based on the lithologic category assigned (table 8; Wentworth, 1922; Guy, 1969; Mashburn and others, 2018).

Table 8.    

Percentage of coarse-material multiplier values for the generalized lithologic categories used to obtain horizontal hydraulic conductivity values for the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, northern Oklahoma.

Table 8.    Percentage of coarse-material multiplier values for the generalized lithologic categories used to obtain horizontal hydraulic conductivity values for the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, northern Oklahoma.

Percent-coarse multiplier	Lithologic category1	Size range
10	Clay and silt	0.0625–0.125 millimeter (mm)
30	Fine sand	0.125–0.25 mm
50	Medium sand	0.25–0.50 mm
70	Coarse sand	0.50–2 mm
90	Fine gravel	2–4 mm

¹

Size categories for the different lithologic categories are described in Wentworth (1922) and Guy (1969).

The hydraulic properties of the Salt Fork Arkansas River and Chikaskia River alluvial aquifer materials were estimated using techniques to simplify the lithologic description as described in Mashburn and others (2018) The percentage of coarse material value for each lithologic log was then used to estimate and spatially interpolate the hydraulic properties of the Salt Fork Arkansas River alluvial aquifer materials.

A percentage of coarse material value of 0 percent represents silt and clay size material, whereas a value of 100 percent represents fine gravel size material. If there was

• • 0–20 percent coarse material, the given section of the lithologic log was classified as clay and silt and assigned a percent-coarse multiplier value of 10 (table 8);

• • 21–40 percent coarse material, the given section of the lithologic log was classified as fine sand and assigned a percent-coarse multiplier value of 30 (table 8);

• • 41–60 percent coarse material, the given section of the lithologic log was classified as medium sand and assigned a percent-coarse multiplier value of 50 (table 8);

• • 61–80 percent coarse material, the given section of the lithologic log was classified as coarse sand and assigned a percent-coarse multiplier value of 70 (table 8); and

• • 81–100 percent coarse material, the given section of the lithologic log was classified as fine gravel and assigned a percent-coarse multiplier value of 90 (table 8).

The percentage of coarse material value for each lithologic-log depth interval was determined as the thickness-weighted mean of the percentage of coarse material values assigned to the lithologic categories in each log.

A horizontal hydraulic conductivity of 0.1 foot per day (ft/d) was assigned to the clay and silt standardized category, and a horizontal conductivity of 100.1 ft/d was assigned to the fine gravel standardized category based on the thickness-weighted mean horizontal hydraulic conductivity values estimated for each lithologic category (Heath, 1983; Mashburn and others, 2018). This range of categories spans the expected grain sizes in the Salt Fork Arkansas River alluvium and terrace deposits. By assuming that a horizontal hydraulic conductivity of 0.1 ft/d and a percentage of coarse material multiplier of 10 represents the clay and silt standardized category and that a horizontal hydraulic conductivity of 100 ft/d and a percentage of coarse material multiplier of 90 represents the fine gravel standardized category, as well as that the relation between the two properties is linear, the mean horizontal hydraulic conductivity for the aquifer material was calculated by using the following equation modified from Ellis and others (2017):

K

_h

= (1.25×(Σ t×P

_s

)/Σ t) – 12.4,

(2)

where

K_h

is the thickness-weighted mean horizontal hydraulic conductivity, in feet per day;

t

is the thickness, in feet, of the lithologic log interval; and

P_s

is the percentage of coarse material value assigned to the lithologic log interval.

The frequency distribution of horizontal hydraulic conductivity (fig. 16) was determined by using equation 2 for lithologic logs with a minimum thickness of 15 ft, which was thought to be the minimum thickness that could support productive wells. Observations used to calculate hydraulic conductivities were obtained from 2,386 driller’s lithologic logs and 5 core samples. The mean hydraulic conductivity is 32.1 ft/d for the lithologic log samples (fig. 16). Figure 16A compares the hydraulic conductivity estimates derived from the core data versus the lithologic log data. The core samples indicated a mean hydraulic conductivity of about 32.1 ft/d. The core samples were not included in figure 16B because of the small sample size relative to the lithologic log sample size. Most hydraulic conductivity values estimated for the Salt Fork Arkansas River alluvial aquifer were in the range of 16–50 ft/d (fig. 16). The increased frequency of lithologic logs with horizontal hydraulic conductivity at 0–4 ft/d is mostly caused by an abundance of logs in which the lithology was fine throughout the vertical profile before terminating at bedrock at a relatively shallow depth (less than 25 ft).

Water-Budget Components

Water-budget components in the conceptual model summarize actual inflows and outflows of water across the hydrologic boundaries of the aquifer. Water-budget components that act as both inflows and outflows may be referred to as “net inflows” or “net outflows” depending on which flow component dominates.

Recharge

Recharge is the predominant source of water in the Salt Fork Arkansas River and Chikaskia River alluvial aquifers. Recharge is defined in this report as the amount of precipitation and applied irrigation water that infiltrates from the land surface through the unsaturated zone and reaches the groundwater level over a given time. This definition of recharge includes irrigation return flows to groundwater. Other processes, such as stream seepage or lateral groundwater flow from adjacent hydrogeologic units, are not considered recharge and are accounted for separately in the conceptual-model water budget. Recharge rates are controlled by many factors, including precipitation rate, land-surface gradient, soil and sediment permeability, evapotranspiration rates, and vegetation cover type (citation needed). Although recharge rates are difficult to measure because of high spatial and temporal variability, methods involving environmental tracers, physical measurements, streamflow-hydrograph techniques, and computer codes can be used to estimate recharge rates. For this study, a groundwater-hydrograph-based water-table-fluctuation (WTF) method (Healy and Cook, 2002) was used to estimate localized recharge rates for 1981–95 and 2019–21, and a code-based water-balance-estimation technique (Westenbroek and others, 2010) was used to estimate spatially distributed recharge rates for the 1980–2020 study period.

Groundwater Level Fluctuations

Historical and active groundwater well sites were used for monitoring; each well was instrumented with a vented pressure transducer and set to record at 1-hour or 30-minute intervals. Two historical continuous recorder groundwater wells were set to record depth-to-water observations at 1-hour intervals. The first historical continuous recorder groundwater well, Alva near Alva, Okla. (USGS station 365143098404201), was active from July 1980 to September 1995. The second historical continuous recorder groundwater well, Burl near Burl, Okla. (USGS station 365342098175301) was active from August 1983 to May 1990. Two continuous recorder groundwater wells monitoring the Salt Plains National Wildlife Refuge recorded depth-to-water observations at 30-minute intervals from October 2013 to present (2025): well GSP1 (USGS station 364821098144901) and well GSP2 (USGS station 364831098120201) (fig. 1, table 11).

Table 11.    

Groundwater well data-collection stations in and near the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, northern Oklahoma.

[U.S. Geological Survey (USGS, 2024) data can be accessed using the 15-digit station number or other identifier. M/D/Y, month/day/year; NAD 83, North American Datum of 1983; NAVD 88, North American Vertical Datum of 1988; --, unknown or not applicable, SFR2, Streamflow-Routing package; WTF, water-table-fluctuation method]

Table 11.    Groundwater well data-collection stations in and near the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, northern Oklahoma.

Station name	Station number	Other identifier (fig. 1)	Latitude (decimal degrees NAD 83)	Longitude, (decimal degrees NAD 83)	County	Period of record (may contain gaps) (M/D/Y)		Land-surface altitude, (feet above NAVD 88)	Well or hole depth (feet)	Use in numerical groundwater-flow model
						Begin	End
27N-11W-12 BCB 1 SFAR01A	365013098202902	W01	36.8369	−98.3414	Alfalfa	4/10/2019	3/7/2022	1,177	--	--
26N-09W-02 AAD 1 SFAR02	364555098074101	W02	36.7654	−98.1282	Alfalfa	4/11/2019	3/7/2022	1,128	33	Recharge (WTF)
26N-07W-11 CCD 1 SFAR03	364422097553901	W03	36.7395	−97.9275	Grant	4/11/2019	3/7/2022	1,092	75	Recharge (WTF)
26N-03W-33 DAA 1 SFAR04	364112097310101	W04	36.6867	−97.5171	Grant	4/12/2019	6/16/2021	1,000	39.4	Recharge (WTF)
25N-02E-15 AAD 1 SFAR05A	363856097040401	W05	36.6488	−97.0677	Kay	5/1/2019	3/7/2022	956	--	Recharge (WTF)
27N-09W-09 BBA 2 SFAR06	365025098104301	W06	36.8403	−98.1786	Alfalfa	5/2/2019	3/7/2022	1,226	29	--
27N-13W-11 CDB 1 SFAR07	364947098341401	W07	36.8297	−98.5705	Woods	5/3/2019	3/7/2022	1,273	18.3	Recharge (WTF)
28N-14W-20 CBA 1 SFAR08	365327098440001	W08	36.8908	−98.7333	Woods	5/1/2020	9/7/2021	1,368	30.3	--
28N-14W-35 BCC 1 Alva GW Well	365143098404201	Alva	36.8639	−98.6823	Woods	7/30/1980	9/29/1995	1,360	54	Recharge (WTF)
28N-11W-27 DAD 1 Burlington GW Well	365342098175301	Burl	36.8745	−98.3604	Alfalfa	8/25/1983	5/20/1990	1,185	36	Recharge (WTF)
27N-09W-19 AAD 1 GSP Refuge GW WELL 2	364831098120201	GSP2	36.8086	−98.2007	Alfalfa	10/1/2013	Present	1,136	22.5	--
27N-09W-24 AAC 1 GSP Refuge GW WELL 1	364821098144901	GSP1	36.8058	−98.2471	Alfalfa	10/1/2013	Present	1,143	29.2	--
27N-12W-12-BCB 1 (SFAR HPTCo 1)	365010098270001	TH01	36.836	−98.45	Alfalfa	--	--	--	21.1	Hydraulic properties and aquifer base
27N-10W-04-CCD 1 (SFAR HPTCo 2)	365025098170801	TH02	36.8405	−98.2857	Alfalfa	--	--	--	22.6	Hydraulic properties and aquifer base
26N-07W-28-DCC 1 (SFAR HPTCo 3)	364142097572401	TH03	36.6951	−97.9568	Grant	--	--	--	38.4	Hydraulic properties and aquifer base
26N-04W-08-BBB 1 (SFAR HPTCo 4)	364509097393601	TH04	36.7526	−97.6602	Grant	--	--	--	21.8	Hydraulic properties and aquifer base
25N-01E-32-CDC 1 (SFAR HPTCo 5)	363538097132001	TH05	36.594	−97.2225	Kay	--	--	--	39.6	Hydraulic properties and aquifer base
28N-14W-35 BCC 1 Alva GW Well	365143098404201	Alva	36.8639	−98.6823	Woods	1/5/1980	10/3/1995	1,361	54	Calibration
27N-11W-23 ABD 1	364837098205501	9006	36.8103	−98.349	Alfalfa	1/28/1980	1/14/2015	1,180	41	Calibration
26N-05W-31 ADA 1	364133097460901	9431	36.6925	−97.7695	Grant	1/31/1980	1/8/2018	1,040	51	Calibration
25N-01W-08 BBA 1	364001097200001	9506	36.6653	−97.3337	Kay	2/1/1980	2/25/1997	962	--	Calibration
29N-09W-18 CDD 1	365916098125001	9009	36.9878	−98.2142	Alfalfa	1/29/1980	8/27/1993	1,225	35	Calibration
27N-09W-19 AAD 1 GSP Refuge GW WELL 2	364831098120201	GSP2	36.8086	−98.2007	Alfalfa	9/9/2013	6/15/2020	1,136	22.5	Calibration

Eight additional groundwater wells completed in the Salt Fork Arkansas River alluvial aquifer were selected on the basis of their distance from streams, varied geologic setting, and availability for use and were instrumented with Level TROLL 500 vented pressure transducers (In-Situ, Inc., 2023) set to continuously record the depth to water every 30 minutes. Depth-to-water observations were recorded during the following periods:

• • April 2019 to March 2022 at wells W01 (USGS station 365013098202902), W02 (USGS station 364555098074101), and W03 (USGS station 364422097553901).

• • April 2019 to June 2021 at well W04 (USGS station 364112097310101).

• • May 2019 to March 2022 at wells W05 (USGS station 363856097040401), W06 (USGS station 365025098104301), and W07 (USGS station 364947098341401).

• • May 2020 to September 2021 at well W08 (USGS station 365327098440001); the continuous recorder installed at this well was discontinued after only 17 months of operation because of frequent equipment damage by wildlife.

Hydrologic stressors can cause groundwater-level fluctuations on a spatial and temporal basis (Freeze and Cherry, 1979). The primary hydrologic stressors that affect groundwater-level fluctuations in an alluvial aquifer include precipitation, groundwater/surface-water interactions, groundwater withdrawals, evapotranspiration, and streamflow. Similar groundwater-level fluctuations were observed at the eight instrumented groundwater wells in response to precipitation and because of groundwater/surface-water interactions. During spring and summer, declining groundwater levels are primarily associated with seasonally higher rates of groundwater withdrawals for irrigation during the growing season from April through October; groundwater-level declines caused by evapotranspiration are also highest in spring and summer. Variations in streamflow in response to periods of stormwater runoff and periods of drought can cause groundwater levels to fluctuate appreciably in alluvial aquifers (Freeze and Cherry, 1979).

Water-Table-Fluctuation Method

The WTF method (Healy and Cook, 2002) was the primary method used to estimate recharge to the Salt Fork Arkansas River and Chikaskia River alluvial aquifers. The WTF method assumes that rises in groundwater levels in unconfined aquifers that occurred during a relatively short period (hours to a few days) can be attributed to recharge arriving at the saturated zone following a period of precipitation. The WTF method is most appropriately applied to groundwater wells in areas where the groundwater level is shallow, and hydrographs show sharp increases in groundwater levels after precipitation (Healy and Cook, 2002). The WTF method cannot account for a steady rate of recharge or recharge from sources other than precipitation. Annual recharge (R), in inches per year, was estimated by using the following equation:

R

=

Sy

×Σ(

Δh

/

Δt

),

(3)

where

Sy

is the specific yield (dimensionless);

Δh

is the rise in groundwater-level altitude, in inches; and

Δt

is the change in time, in years.

Water-level hydrographs from seven USGS continuous-recorder wells (Alva, Burl, W02, W03, W04, W05, and W07; USGS [2024], fig. 1, table 11) in the Salt Fork Arkansas River alluvial aquifer were used to estimate annual recharge for 1981–95 and 2019–21. Wells Alva, Burl, and W07 (fig. 18A–C) were upgradient from the Great Salt Plains Reservoir, whereas wells W02, W03, W04, and W05 (fig. 18D–G) were downgradient from the Great Salt Plains Reservoir (fig. 1, table 11). The water-level hydrographs from other continuous-recorder wells in the Salt Fork Arkansas River alluvial aquifer were not analyzed because they were affected by surface-water seepage (wells W01, W06, and GSP1, fig. 18H–J), did not show sharp water-level rises (well W08, fig. 18K), or were affected by nearby groundwater withdrawals (well GSP2, fig. 18L). Daily precipitation data were obtained from the climate station with at least 96 percent precipitation-data coverage nearest to each continuously monitored well (fig. 2; National Centers for Environmental Information, 2022); those precipitation data were summed for each year of the analysis period (table 3). A specific yield of 0.12 for wells upgradient from the Great Salt Plains Reservoir and 0.065 for wells downgradient from the reservoir were assumed from aquifer tests in the nearby Cimarron Terrace aquifer (Reed and others, 1952); a larger specific yield (near the upper end of those measured by Reed and others [1952]) was used for the wells upgradient from the Great Salt Plains Reservoir because of the predominance of windblown (presumably well-sorted) dune sand (fig. 9) in the upgradient part of the aquifer (table 12). The annual recharge rate, in inches per year, was calculated as the product of the specific yield and the sum of annual water-level rises, in inches. The annual recharge estimates ranged from 0.0 in. (0.0 percent of the station’s annual precipitation) to 12.1 in. (26.5 percent of the station’s annual precipitation) (table 12).

Table 12.    

Summary of recharge amounts estimated using the water-table-fluctuation method for the Salt Fork Arkansas River alluvial aquifer, northern Oklahoma, 1981–95 and 2019–21.

[NCEI, National Centers for Environmental Information. Dates in month, day, year format. Continuous water-level recorder wells W01, W06, W08, GSP1, and GSP2 were not suitable for analysis with the water-table-fluctuation method. E, base-10 exponent (for example, 6.5E-02 equals 6.5×10⁻²); --, not quantified or not applicable]

Table 12.    Summary of recharge amounts estimated using the water-table-fluctuation method for the Salt Fork Arkansas River alluvial aquifer, northern Oklahoma, 1981–95 and 2019–21.

Descriptor	U.S. Geological Survey continuous water-level-recorder well (fig. 1; table 11)
	Upgradient from Great Salt Plains Reservoir			Downgradient from Great Salt Plains Reservoir
	Alva	Burl	W07	W02	W03	W04	W05
Mean annual precipitation 1980–2020, in inches, northern Oklahoma (NCEI, 2022; table 3)	31.4	31.4	31.4	35.1	35.1	35.1	35.1
Climate station (fig. 2, table 2)	ALE, CHE	HEL	ALW	LAM	LAM	LAM	PON
Specific yield (dimensionless; values obtained from Reed and others [1952, table 12])	1.2E-01	1.2E-01	1.2E-01	6.5E-02	6.5E-02	6.5E-02	6.5E-02
Year 1, beginning date	1/1/1981	1/1/1984	1/1/2020	1/1/2020	1/1/2020	4/12/2019	1/1/2020
Station annual precipitation, in inches	30.2	21.9	30.7	31.4	31.4	45.6	29.7
Sum of water-level rises, in feet	2.9	4.0	4.9	7.1	9.9	15.5	7.8
Recharge, in inches per year	4.2	5.8	7.1	5.5	7.7	12.1	6.1
Recharge, percent of annual precipitation	13.8	26.3	23.0	17.7	24.6	26.5	20.5
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	4.3	8.3	7.2	6.2	8.6	9.3	7.2
Year 2, beginning date	11/1/1982	1/1/1985	1/1/2021	1/1/2021	1/1/2021	4/12/2020	1/1/2021
Station annual precipitation, in inches	25.6	30.2	23.2	23.3	23.3	30.9	25.6
Sum of water-level rises, in feet	3.9	3.4	2.5	4.2	7.2	8.1	10.7
Recharge, in inches per year	5.6	4.9	3.6	3.3	5.6	6.3	8.3
Recharge, percent of annual precipitation	21.9	16.2	15.5	14.1	24.1	20.5	32.6
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	6.9	5.1	4.9	4.9	8.5	7.2	11.4
Year 3, beginning date	11/1/1983	1/1/1986	--	--	--	--	--
Station annual precipitation, in inches	31.9	35.3	--	--	--	--	--
Sum of water-level rises, in feet	2.5	4.3	--	--	--	--	--
Recharge, in inches per year	3.6	6.2	--	--	--	--	--
Recharge, percent of annual precipitation	11.3	17.5	--	--	--	--	--
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	3.5	5.5	--	--	--	--	--
Year 4, beginning date	1/1/1984	1/1/1987	--	--	--	--	--
Station annual precipitation, in inches	21.8	36.0	--	--	--	--	--
Sum of water-level rises, in feet	1.5	4.1	--	--	--	--	--
Recharge, in inches per year	2.2	5.9	--	--	--	--	--
Recharge, percent of annual precipitation	9.9	16.4	--	--	--	--	--
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	3.1	5.1	--	--	--	--	--
Year 5, beginning date	1/1/1985	1/1/1988	--	--	--	--	--
Station annual precipitation, in inches	38.6	29.7	--	--	--	--	--
Sum of water-level rises, in feet	4.9	2.2	--	--	--	--	--
Recharge, in inches per year	7.1	3.2	--	--	--	--	--
Recharge, percent of annual precipitation	18.3	10.7	--	--	--	--	--
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	5.7	3.3	--	--	--	--	--
Year 6, beginning date	1/1/1986	1/1/1989	--	--	--	--	--
Station annual precipitation, in inches	34.8	31.7	--	--	--	--	--
Sum of water-level rises, in feet	3.4	4.1	--	--	--	--	--
Recharge, in inches per year	4.9	5.9	--	--	--	--	--
Recharge, percent of annual precipitation	14.1	18.6	--	--	--	--	--
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	4.4	5.8	--	--	--	--	--
Year 7, beginning date	1/1/1987	--	--	--	--	--	--
Station annual precipitation, in inches	33.0	--	--	--	--	--	--
Sum of water-level rises, in feet	3.3	--	--	--	--	--	--
Recharge, in inches per year	4.8	--	--	--	--	--	--
Recharge, percent of annual precipitation	14.4	--	--	--	--	--	--
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	4.5	--	--	--	--	--	--
Year 8, beginning date	1/1/1988	--	--	--	--	--	--
Station annual precipitation, in inches	19.3	--	--	--	--	--	--
Sum of water-level rises, in feet	2.0	--	--	--	--	--	--
Recharge, in inches per year	2.9	--	--	--	--	--	--
Recharge, percent of annual precipitation	14.9	--	--	--	--	--	--
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	4.7	--	--	--	--	--	--
Year 9, beginning date	1/1/1989	--	--	--	--	--	--
Station annual precipitation, in inches	44.2	--	--	--	--	--	--
Sum of water-level rises, in feet	1.5	--	--	--	--	--	--
Recharge, in inches per year	2.2	--	--	--	--	--	--
Recharge, percent of annual precipitation	4.9	--	--	--	--	--	--
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	1.5	--	--	--	--	--	--
Year 10, beginning date	1/1/1990	--	--	--	--	--	--
Station annual precipitation, in inches	30.9	--	--	--	--	--	--
Sum of water-level rises, in feet	0.5	--	--	--	--	--	--
Recharge, in inches per year	0.7	--	--	--	--	--	--
Recharge, percent of annual precipitation	2.3	--	--	--	--	--	--
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	0.7	--	--	--	--	--	--
Year 11, beginning date	1/1/1991	--	--	--	--	--	--
Station annual precipitation, in inches	26.9	--	--	--	--	--	--
Sum of water-level rises, in feet	1.2	--	--	--	--	--	--
Recharge, in inches per year	1.7	--	--	--	--	--	--
Recharge, percent of annual precipitation	6.4	--	--	--	--	--	--
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	2.0	--	--	--	--	--	--
Year 12, beginning date	1/1/1992	--	--	--	--	--	--
Station annual precipitation, in inches	35.0	--	--	--	--	--	--
Sum of water-level rises, in feet	3.3	--	--	--	--	--	--
Recharge, in inches per year	4.8	--	--	--	--	--	--
Recharge, percent of annual precipitation	13.6	--	--	--	--	--	--
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	4.3	--	--	--	--	--	--
Year 13, beginning date	1/1/1993	--	--	--	--	--	--
Station annual precipitation, in inches	38.6	--	--	--	--	--	--
Sum of water-level rises, in feet	8.0	--	--	--	--	--	--
Recharge, in inches per year	11.5	--	--	--	--	--	--
Recharge, percent of annual precipitation	29.9	--	--	--	--	--	--
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	9.4	--	--	--	--	--	--
Year 14, beginning date	1/1/1994	--	--	--	--	--	--
Station annual precipitation, in inches	33.4	--	--	--	--	--	--
Sum of water-level rises, in feet	0.0	--	--	--	--	--	--
Recharge, in inches per year	0.0	--	--	--	--	--	--
Recharge, percent of annual precipitation	0.0	--	--	--	--	--	--
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	0.0	--	--	--	--	--	--
Year 15, beginning date	11/1/1995	--	--	--	--	--	--
Station annual precipitation, in inches	37.8	--	--	--	--	--	--
Sum of water-level rises, in feet	1.8	--	--	--	--	--	--
Recharge, in inches per year	2.6	--	--	--	--	--	--
Recharge, percent of annual precipitation	6.8	--	--	--	--	--	--
Recharge, in inches per year, normalized to mean annual precipitation 1980–2020	2.2	--	--	--	--	--	--
Mean annual recharge 1980–2020, in inches per year, normalized to mean annual precipitation 1980–2020	--	--	--	--	--	--	5.4
Mean annual recharge 1980–2020, in percent, normalized to mean annual precipitation 1980–2020	--	--	--	--	--	--	16.3

¹

Years with incomplete water-level data.

The periods of record for the selected continuous water-level recorder wells did not sufficiently overlap to allow separate WTF-calculations of mean annual recharge for parts of the aquifer upgradient and downgradient from the Great Salt Plains Reservoir; the upgradient period of record was biased to the 1980s and the downgradient period of record was biased to 2020–21. Therefore, the annual recharge estimates upgradient and downgradient from the Great Salt Plains Reservoir were averaged to obtain one mean annual recharge value for the Salt Fork Arkansas River alluvial aquifer. That mean annual recharge value was also applied to the Chikaskia River alluvial aquifer in cases where no continuous-recorder wells were available. When all of the annual recharge estimates are normalized by the mean annual precipitation for the 1980–2020 study period (31.4 in. upgradient from and 35.1 in. downgradient from the Great Salt Plains Reservoir), the resulting mean annual recharge value for the period of record is 5.4 in. (table 12), or about 16.3 percent of mean annual precipitation for the period of record, 1895–2020 (33.1 in., table 3, fig. 4). Multiplied by the 512,808-acre total aquifer area and unit converted, the WTF-calculated mean annual recharge for both aquifers was 230,764 acre-ft; this value was used for the conceptual model recharge during 1980–2020 (table 8).

Soil-Water-Balance Code

The Soil-Water-Balance (SWB) code (Westenbroek and others, 2010) was used to estimate the amount and spatial distribution of daily groundwater recharge to the Salt Fork Arkansas and Chikaskia River alluvial aquifers for each month of the 1980–2020 study period. A modified Thornthwaite and Mather (1957) soil-water-balance method based on a gridded data structure is used in the SWB code to compute the daily amount of recharge as infiltrating precipitation that exceeds the storage capacity of the plant root zone and the transpiration demand from plants. The soil-water-balance equation (modified from Westenbroek and others, 2010) has the form of the following equation:

R

= (

P

+

S

+

R_i

) – (

Int

+

R_o

+

P_et

) –

ΔSm

,

(4)

where

R

is recharge, in inches per day;

P

is precipitation, in inches per day;

S

is snowmelt, in inches per day;

R_i

is surface runoff inflow, in inches per day;

Int

is plant interception, in inches per day;

R_o

is surface runoff outflow, in inches per day;

P_et

is potential evapotranspiration, in inches per day; and

ΔSm

is the change in soil moisture, in inches per day.

Use of the SWB code requires climate and landscape characteristic data inputs, including precipitation, temperature, soil-water storage capacity, hydrologic soil group, land-surface flow direction, and land-cover type (Westenbroek and others, 2010). Each of these inputs was assigned to a user-specified grid of 300 by 700 cells, where the dimension of each cell was 800 by 800 ft. The landscape inputs were assumed to remain constant during the study period, but climate data inputs varied daily. Daily climate data, including precipitation (P) and minimum and maximum air temperature, were obtained from the Daymet database (version 4; Thornton and others, 2022) and resampled with bilinear interpolation to match the 800-ft cell size. Accumulated snowmelt (S) was derived in the SWB code based on the daily minimum and maximum air temperatures. Interception (Int) was calculated over a 204-day growing season (April–October [Oklahoma Climatological Survey, 2015; National Agricultural Statistics Service, 2020]). Potential evapotranspiration (P_et) was calculated by using the Hargreaves and Samani (1985) method for a reference latitude range of 36.6–37.1 degrees. Surface runoff (R_i and R_o) was routed downslope by using a flow-direction grid derived from a 10-m DEM (USGS, 2015). As explained in the “Aquifer Extent” section of this report, depressions in the DEM were filled by using the ArcGIS Fill tool (Esri, 2021a); depressions were filled to ensure correct routing of surface runoff and to eliminate areas of internal drainage that can result in unrealistically high rates of recharge. Soil properties (soil–water storage capacity and hydrologic soil group) were derived from the Soil Survey Geographic database (SSURGO; Natural Resources Conservation Service, 2022), which is an inventory of generalized soil characteristics. Land-cover types (Fry and others, 2011; Multi-Resolution Land Characteristics Consortium, 2011; fig. 3) were used in conjunction with hydrologic soil group to partition daily precipitation into interception (Int) and surface runoff (R_i and R_o) components and assign plant root-zone depths. The root-zone depths for grass/pasture and crops (the dominant land-cover types overlying the aquifer; fig. 3) varied with soil texture but ranged from about 1.0 to 1.8 ft (50 percent of the values used by Westenbroek and others [2010] for permeable glacial deposits in Wisconsin). The soil-water storage capacity, analogous to specific yield in the saturated zone, multiplied by the root-zone depth, indicates the maximum volume of water available in the plant root zone. Changes in soil moisture (ΔSm) exceeding the soil-water storage capacity were assumed to be recharge (R) to the saturated zone. Larger root-zone depths resulted in increased evapotranspiration of water from the plant root zone and decreased recharge; smaller root-zone depths resulted in decreased evapotranspiration of water from the plant root zone and increased recharge. Recharge from irrigation was not simulated by using the SWB code but was assumed to be negligible given the relatively small amount (about 2,378.9 acre-ft/yr, tables 6, 7) of irrigation groundwater use in the study area. The resulting SWB model for the Salt Fork Arkansas River aquifer study area is included in the companion USGS model archive data release (Smith and Gammill, 2025).

The SWB-estimated mean annual recharge values for the 1980–2020 study period were 5.13 in. (about 15.5 percent of the mean annual precipitation of 33.1 in. during 1980–2020) for the Salt Fork Arkansas River alluvial aquifer and 3.57 in. (about 10.8 percent of the mean annual precipitation of 33.1 in. during 1980–2020) for the Chikaskia River alluvial aquifer (fig. 19A, C). The minimum and maximum SWB-estimated annual recharge values for the Salt Fork Arkansas River alluvial aquifer were 0.99 in. for 2014 and 10.06 in. for 2007, respectively. The minimum and maximum SWB-estimated annual recharge values for the Chikaskia River alluvial aquifer were 0.85 in. for 2006 and 7.05 in. for 2007, respectively. In both aquifers, recharge efficiency (monthly mean recharge as a percentage of monthly mean precipitation) was greatest during November–March, when evapotranspiration was at a minimum, and lowest during July–September, when evapotranspiration was at a maximum (fig. 19B, D). Spatially, mean annual recharge for the study period was greatest in windblown dune sands northwest of the Great Salt Plains Reservoir and in areas of active alluvium near the Salt Fork Arkansas River and tributaries (figs. 9, 20). SWB-estimated recharge and recharge efficiency were 35.9 percent greater in the Salt Fork Arkansas River alluvial aquifer than in the Chikaskia River alluvial aquifer (fig. 19).

Mean annual recharge rates estimated from SWB were compared to published estimates of mean annual recharge near the study area. Reed and others (1952) estimated a mean annual recharge rate of 4.2 in., or about 14.6 percent of the mean annual precipitation of 28.8 in. during a 1.5-year study period (July 1950–December 1951) in the Cimarron Terrace aquifer, which is mostly composed of dune sands in the southwest part of the study area (fig. 9). The authors cautioned that the recharge estimate may be biased low because the partial year 1950 was unusually dry. Belden (1997) estimated a mean annual recharge rate of 4.5 in. for the Chikaskia River alluvial aquifer, but that estimate was based largely on the estimate of Reed and others (1952). When calculated as a percentage of the mean annual precipitation for the study period 1980–2020, the mean annual recharge rate of 5.4 in. (16.3 percent of mean annual precipitation) that was estimated by using the WTF method for this study area is slightly higher than the published estimates of recharge for comparable aquifers in the study area.

The SWB code is a model used for simulation purposes, and SWB-estimated recharge must, therefore, be checked against and calibrated to field-based measurements or estimates of recharge such as those estimated using the WTF method. In other studies (Smith and others, 2017, 2021; Ellis, 2018; Ellis and others, 2020; Rogers and others, 2023), SWB-estimated recharge was calibrated by decreasing root-zone depths until the SWB-estimated mean annual recharge approximated the conceptual-model recharge. That method was also applied in this study, and the SWB-estimated recharge values reported above reflect those calibration methods. Additional fine-scale adjustment of SWB-estimated recharge was accomplished during calibration of the numerical model, as detailed in the “Numerical Groundwater-Flow Model” section of this report.

Streambed Seepage

Base flow can be measured directly in streams when the runoff component of streamflow is at or near zero (Garner and Bills, 2012). When base-flow measurements are made at multiple locations over a short period, they are commonly referred to as “synoptic base-flow” (seepage-run) measurements. These measurements can be used to calculate net streambed seepage and classify stream reaches as gaining or losing at a point in time. Gaining reaches exhibit an increase in base flow between the upstream and downstream endpoints of the reach, whereas losing reaches exhibit a decrease in base flow between the upstream and downstream endpoints. Streambed-seepage rates provided in this report were calculated as the difference in measured base flows (adjusted for tributary inflows) divided by the stream-reach length between measurement locations.

On March 2, 2020, synoptic base-flow measurements were collected by using the methods of Rantz and others (1982) during a period of minimal runoff and used in net streambed seepage computations. These synoptic base-flow measurements, along with streamflow data obtained from the active USGS streamgages listed in table 1 and a synoptic streamflow measurement made by the USGS at the OWRB White Eagle streamgage, served to delineate tributary inflow and base-flow conditions across the Salt Fork Arkansas River and Chikaskia River alluvial aquifers at a given point in time. These data, however, were not ideal for making conclusions about net streambed seepage, computed as the average of base-flow gains and losses over the study period. Excluding the Medicine Lodge and Chikaskia Rivers, tributaries contributed measured base flows ranging from 0.21 cubic feet per second (ft³/s) on an unnamed stream in Noble County to 95.3 ft³/s on Sandy Creek in Alfalfa County (fig. 21). Minor tributaries with unmeasured base flows were assumed to contribute no base flows and were not accounted for in computations of base flows in streams flowing across the Salt Fork Arkansas River and Chikaskia River alluvial aquifers. The base-flow measurements on the Salt Fork Arkansas River main stem generally increased downstream; the only exception was the reach immediately downstream from the Great Salt Plains Reservoir, which had a base-flow loss of 2.3 cubic feet per second per mile. The greatest base-flow gain (6.4 cubic feet per second per mile) occurred in the furthest downstream reach of the Salt Fork Arkansas River near Tonkawa, Okla.

Net streambed-seepage terms for the conceptual model were assumed to be net outflows from the Salt Fork Arkansas River and Chikaskia River alluvial aquifers and were estimated primarily from mean annual base flows computed by using the BFI (Wahl and Wahl, 1995; Barlow and others, 2015) at selected streamgages in the study area. Because few streamgages had complete record during the 1980–2020 study period, the full period of record was used to compute mean annual base flows. Net streambed seepage in the reach upgradient from the Great Salt Plains Reservoir (70,571 acre-ft/yr, table 10) was roughly estimated as the mean annual base flow at USGS streamgage 07149520 Salt Fork Arkansas River at State Highway 11 near Cherokee, Okla. (hereinafter referred to as the “State Highway 11 streamgage”) multiplied by a drainage-area ratio of 1.3 (to account for ungaged drainage areas downstream) minus the mean annual base flows at the Kiowa streamgage and USGS streamgage 07148350 Salt Fork Arkansas River near Winchester, Okla. (hereinafter referred to as the “Winchester streamgage”) (fig. 1, table 4; tributary base flows are summarized in Smith and Gammill [2025]). Net streambed seepage in the reach downgradient from the Great Salt Plains Reservoir (89,774 acre-ft/yr, table 10) was estimated as the mean annual base flow at the Tonkawa streamgage multiplied by a drainage-area ratio of 1.1 (to account for ungaged drainage areas downstream) minus the mean annual base flow just downstream from the Great Salt Plains Reservoir (fig. 1, table 5) multiplied by 0.43 (the mean annual base-flow index for the period of record at the Jet streamgage). The tributary base flows are summarized in Smith and Gammill (2025), which were computed as the average of the mean annual base flow at the Jet streamgage and the mean annual reservoir releases (which include runoff and base-flow components; U.S. Army Corps of Engineers, 2023a). Net streambed seepage in the Chikaskia River reach (18,084 acre-ft/yr, table 10) was roughly estimated as the mean annual base flow at the Blackwell streamgage multiplied by a drainage-area ratio of 1.1 (to account for ungaged drainage areas downstream) minus the mean annual base flow at the Corbin streamgage multiplied by a drainage-area ratio of 1.4 (to account for ungaged drainage areas upstream) (fig. 1, table 4; tributary base flows are summarized in Smith and Gammill [2025]). The total net streambed seepage estimated for all reaches was 178,430 acre-ft/yr (table 10) and accounted for 77.3 percent of the total outflows in the conceptual-model water budget.

Conceptual-Model Water Budget

The conceptual-model water budget (table 10) summarizes mean water flows exchanged between each hydrologic boundary and the Salt Fork Arkansas River and Chikaskia River alluvial aquifers for the study period 1980–2020. The components of the water budget were estimated from analyses of available data or assumed on the basis of published analogs as described in the “Water-Budget Components” section of this report. Recharge accounts for 100.0 percent of the conceptual-model inflows to the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, and net streambed seepage accounts for 77.3 percent of the outflows from the Salt Fork Arkansas River and Chikaskia River alluvial aquifers. Saturated-zone evapotranspiration (composing 15.1 percent of outflows) was the only other component estimated to be greater than 5 percent of inflows or outflows. Well withdrawals accounted for 2.3 percent of conceptual-model water-budget outflows, and net lateral groundwater flows accounted for 2.8 percent of conceptual-model water-budget inflows.

Spatial and Temporal Discretization

The model domain (fig. 22) of the Salt Fork Arkansas River and Chikaskia River alluvial aquifers was spatially discretized into 300 rows, 700 columns, 34,903 active cells (31,570 and 3,333 active cells for the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, respectively) measuring 800 by 800 ft each, and a single convertible layer based on the hydrogeologic framework described in this report. Because the model consists of one layer, it was used to simulate only 2-dimensional (horizontal) flow between cells. The cell size was chosen to minimize model-processing time while still representing the variability of properties being simulated. The chosen cell size also ensured that the narrowest parts of the aquifers were represented by no fewer than three cells; model instability and groundwater-level volatility often occur in narrow parts of an aquifer area where groundwater flow is focused into fewer cells. The single convertible layer represented the undivided Quaternary age deposits with variable thickness determined from the hydrogeologic framework; the underlying bedrock was not represented as a layer. The altitude of the top of the aquifer, which was determined from a DEM, was multiplied by 1.01 in the numerical model to prevent confined aquifer conditions that occur as a side effect of model discretization when the simulated water-table altitude exceeds the altitude of the top of the aquifer. This multiplier, which adds about 10 ft to the altitude of the top of the aquifer, has no effect on most parts of the model where the simulated water-table altitude is below the altitude of the top of the aquifer. However, some simulated saturated-zone evapotranspiration and streambed seepage flows could be affected where the simulated water-table altitude is above the altitude of the top of the aquifer, which mostly occurs along simulated streams.

The active modeled area (fig. 22) was initially derived from the extents (modified from OWRB [2022b]) as defined in the hydrogeologic framework of the Salt Fork Arkansas River and Chikaskia River alluvial aquifers. The active modeled area was expanded or contracted in some areas to remove isolated cells and ensure that each active cell was in connection with at least one other active cell. To improve model stability, the active modeled area was further modified by inactivating a relatively small number of model cells, mostly in thin, disconnected terrace lobes and in narrow areas along selected Salt Fork Arkansas River tributaries. These inactivated cells are shown as unmodeled aquifer area in figure 22.

The numerical model was temporally discretized into 492 monthly transient stress periods (each with two time steps to improve model stability) representing the 1980–2020 study period. The numerical-modeling period, hereinafter referred to as the “modeling period,” coincides with the 1980–2020 study period. An initial steady-state stress period represented mean annual inflows to, and outflows from, the aquifer. The steady-state solution was used as the initial condition for subsequent transient stress periods, as well as some groundwater-availability scenarios. The numerical model was constructed by using length and time units of feet and days, respectively.

Simulation of Hydrologic Boundaries and Hydraulic Properties

Hydrologic boundaries in the numerical model (fig. 22) define where and how water may enter or leave the model and include specified-flux and head-dependent boundaries (Harbaugh and others, 2000). Specified-flux boundaries were used to simulate recharge and well withdrawals. Head-dependent boundaries were used to simulate streambed seepage, spring seepage, and saturated-zone evapotranspiration. Lateral groundwater flows exchanged with surrounding and underlying bedrock units were not simulated because they were a small component of the conceptual model. Lateral groundwater flows between the Salt Fork Arkansas River and Chikaskia River alluvial aquifers were simulated in the numerical model, however. When available, hydrologic data, along with data-based assumptions and analogues, were used to estimate or constrain precalibration model inputs for each hydrologic boundary.

Well Withdrawals

Well withdrawals for the 1980–2020 study period were simulated by using the Well package (Harbaugh and others, 2000). Annual reported groundwater use for each permit was evenly distributed among all well locations recorded for that permit (OWRB, 2022d). In the case of permits for which there were no recorded wells, a single well was placed in the center of the land parcel recorded for the permit. Annual reported groundwater use was temporally split into model stress periods by using the mean monthly water demand distribution (fig. 23; OWRB, 2012a) from Oklahoma Comprehensive Water Plan water-management planning basins 67–70 (fig. 2; OWRB, 2022c). The monthly water demand for irrigation was greatest in the summer months; July–September accounted for about 91 percent of irrigation groundwater use (fig. 23). The monthly water demand for public supply also was greatest in the summer months; however, July–September only accounted for about 34 percent of public-supply groundwater use (fig. 23). The Well package file contains some withdrawals for wells outside of the active modeled area. These withdrawals were not simulated in the model but were included in the Well package file in case the active modeled area is expanded in future versions of the model.

Storage and Hydraulic Properties

The Upstream Weighting package of MODFLOW-2005 (Niswonger and others, 2011) was used to represent storage and hydraulic properties of the Salt Fork Arkansas River alluvial aquifer. Initial storage and hydraulic property values were assumed to be similar to those used in numerical models for other alluvial aquifers in Oklahoma (Ellis and others, 2017, 2020; Smith and others, 2017, 2021; Rogers and others, 2023). The storage and hydraulic property values were adjusted during model calibration but were held spatially uniform and temporally constant through all stress periods in all simulations. The specific yield (sy1, sy2, and sy3, table 13) for the Salt Fork Arkansas River and Chikaskia River alluvial aquifers was initially set to 0.065 (dimensionless) and was allowed to vary in the range of 0.02–0.10, based on values from aquifer tests in the nearby Cimarron Terrace aquifer (Reed and others, 1952). The specific storage (ss, table 13) was initially set to 1×10^–4 ft^–1 based on Fetter and Kreamer (2021) and was allowed to vary plus or minus one order of magnitude (from 1×10^–3 to 1×10^–5 ft^–1). Horizontal hydraulic conductivity (hkpp001–hkpp788, table 13) was represented with 788 pilot points (1–788, fig. 24; Doherty, 2010) placed at 8,000-ft intervals (one at every 10th column and row) within an 11,400-ft buffer (the approximate diagonal distance between pilot points) of the aquifer boundary. Pilot points are regularly spaced control points used to support an interpolation of cell-based horizontal hydraulic conductivity values across an active modeled area. The 788 pilot points were assigned an initial horizontal hydraulic conductivity value of 32.1 ft/d and were allowed to vary independently between 10 and 150 ft/d (Smith and Gammill, 2025) during calibration; the lower bound was from Rogers and others (2023), but the upper bound was adjusted upward from a value of 100 ft/d in Rogers and others (2023) because of improved calibration outcomes. Areas between pilot points were interpolated by kriging the pilot-point values before each model run to create the horizontal hydraulic conductivity array read by the numerical model (Doherty, 2010). Pilot-point kriging factors are outlined in model input files and model-preprocessing scripts in Smith and Gammill (2025).

Solver Settings and Budget Percentage Discrepancies

Most of the settings for the Newton solver were unchanged from suggested input values (Winston, 2018), which were converted to model units of feet and days. The head tolerance was increased to 0.075 ft, and the flux tolerance was increased to 5,000 cubic feet per day. These settings improved model stability while keeping solution budget percentage discrepancies under 1.0 percent for all 493 stress periods; the largest budget percentage discrepancy (by absolute value) was 0.49 percent in stress period 382 (Smith and Gammill, 2025).

Calibration

Model calibration is the process of systematically changing selected model input values (parameters) within predetermined limits to improve the fit between model-simulated data and observed data (calibration targets). The preferred calibration results (1) minimize the differences (residuals) between simulated and observed data and (2) conform to the predetermined conceptual model. The calibration process for the numerical model included both manual and automated adjustments of parameters. The manual calibration approach primarily focused on aligning the numerical-model water budget (particularly the recharge and streambed seepage components) to the conceptual-model water budget. The manual calibration also involved selected structural elements of the model, such as boundary altitudes, that are not easily adjusted by automated calibration methods. The automated calibration approach focused solely on minimizing residuals and used the PEST++ iterative ensemble smoother (White, 2018) to reduce run times associated with the calibration of highly parameterized models.

Calibration Targets

The suite of calibrated parameter values was evaluated based on the minimization of an objective function. The objective function was calculated as the sum of squared weighted residuals for calibration targets in seven observation groups: water-table-altitude observations, base-flow observations at the Alva streamgage, base-flow observations at the State Highway 11 streamgage, base-flow observations at the Tonkawa streamgage, base-flow observations at the Blackwell streamgage, base-flow observations (estimated) at the OWRB White Eagle streamgage, and selected conceptual-model (recharge and streambed-seepage) flows (tables 14, 15). The streamgages used for base-flow calibration had observations (or synthesized observations) during the study period and were not used to define base-flow inflows to the active modeled area (fig. 25).

Table 14.    

Components of the objective function for the automated precalibration of the numerical groundwater-flow model of the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, 1980–2020.

[The objective function is calculated as the sum of squared weighted residuals. Table 1 provides the full station names for streamgages listed in the source column. NAVD 88, North American Vertical Datum of 1988; SS, steady-state simulation; TR, transient simulation; min, minimum; max, maximum; RMSE, root-mean-square error; OWRB, Oklahoma Water Resources Board; USGS, U.S. Geological Survey; E, base-10 exponent (for example, 1.7E-07 equals 1.7×10⁻⁷); ft, foot; ft³/s, cubic foot per second; acre-ft/yr, acre foot per year]

Table 14.    Components of the objective function for the automated precalibration of the numerical groundwater-flow model of the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, 1980–2020.

Observation group	Source	Number of observations		Observation weight		Precalibration residuals (feet unless specified otherwise)
		SS	TR	SS	TR	Min	Mean	Max	Inter-quartile range	RMSE	Objective function component
											(square feet)	(percent)
Groundwater-level altitude (feet above NAVD 88) (headobs)	Water-table-altitude observations (OWRB, 2022a; USGS, 2024)	68	1,261	7.30E−01	7.30E−01	−35	−2.9	36.6	9.7	7.1	66,695	33.7
Base flow, ft3/s (gageobs1)	Alva streamgage (07148400; USGS, 2024)	1	165	3.40E−05	1.70E−07	−48.2	1.8	66.4	11.5	0.3	38	0
Base flow, ft3/s (gageobs2)	State Highway 11 streamgage (07149520; USGS, 2024)	1	86	2.00E−05	1.00E−07	−666.7	−139.4	163.3	161.1	1.1	616	0.3
Base flow, ft3/s (gageobs3)	Tonkawa streamgage (07151000; USGS, 2024)	1	492	4.00E−05	2.00E−08	−1,678.70	73.6	4,318.60	163.3	11.4	64,370	32.5
Base flow, ft3/s (gageobs4)	Blackwell streamgage (07152000; USGS, 2024)	1	483	2.00E−05	1.00E−08	−314.4	45.5	1,008.30	71.9	3.8	7,274	3.7
Base flow, ft3/s (gageobs5)	OWRB White Eagle Streamgage (621010010160-001AT; estimated as sum of the base flows at the Tonkawa and Blackwell streamgages plus 10 percent)	1	483	1.00E−05	5.00E−09	−1,515.70	150.5	6,123.80	272.9	6.2	18,998	9.6
Conceptual model (recharge and stream-seepage) flows, acre-ft/yr (budgetobs)	Conceptual model (table 10)	5	5	3.00E−03	3.00E−03	−31,398.60	−3,059.70	20,571.80	36,679.50	63.3	40,115.80	20.2
Total	--	78	2,975	--	--	--	--	--	--	--	--	--
Objective function	--	--	--	--	--	--	--	--	--	--	198,108	--

Table 15.    

Components of the objective function for the automated precalibration of the numerical groundwater-flow model of the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, 1980–2020.

[The objective function is calculated as the sum of squared weighted residuals. Table 1 provides the full station names for streamgages listed in the source column. NAVD 88, North American Vertical Datum of 1988; SS, steady-state simulation; TR, transient simulation; min, minimum; max, maximum; RMSE, root-mean-square error; OWRB, Oklahoma Water Resources Board; USGS, U.S. Geological Survey; E, base-10 exponent (for example, 1.7E-07 equals 1.7×10⁻⁷); ft, foot; ft³/s, cubic foot per second; acre-ft/yr, acre foot per year]

Table 15.    Components of the objective function for the automated precalibration of the numerical groundwater-flow model of the Salt Fork Arkansas River and Chikaskia River alluvial aquifers, 1980–2020.

Observation group	Source	Number of observations		Observation weight		Calibrated residuals
						(feet unless specified otherwise)
		SS	TR	SS	TR	Min	Mean	Max	Inter-quartile range	RMSE
Groundwater-level altitude (feet above NAVD 88) (headobs)	Water-table-altitude observations (OWRB, 2022a; USGS, 2024)	68	1,261	7.30E−01	7.30E−01	−28.7	−3	30	7.3	5.7
Base flow, ft3/s (gageobs1)	Alva streamgage (07148400; USGS, 2024)	1	165	3.40E−05	1.70E−07	−57.1	0.6	64.2	11.6	0.1
Base flow, ft3/s (gageobs2)	State Highway 11 streamgage (07149520; USGS, 2024)	1	86	2.00E−05	1.00E−07	−678.7	−150.9	122.3	163.5	2.1
Base flow, ft3/s (gageobs3)	Tonkawa streamgage (07151000; USGS, 2024)	1	492	4.00E−05	2.00E−08	−1,713.30	39.4	4,258.10	153.5	5.6
Base flow, ft3/s (gageobs4)	Blackwell streamgage (07152000; USGS, 2024)	1	483	2.00E−05	1.00E−08	−311.6	42.4	1,006.30	70.6	3.6
Base flow, ft3/s (gageobs5)	OWRB White Eagle streamgage (621010010160-001AT; estimated as sum of Tonkawa and Blackwell streamgages plus 10 percent)	1	483	1.00E−05	5.00E−09	−1,547.00	108.1	6,052.60	264.5	4.4
Conceptual model (recharge and stream-seepage) flows, acre-ft/yr (budgetobs)	Conceptual model (table 10)	5	5	3.00E−03	3.00E−03	−10,188.70	−1,357.50	7,102.80	8,669.10	17.4
Total	--	78	2,975	--	--	--	--	--	--	--
Objective function	--	--	--	--	--	--	--	--	--	--

Base-flow observation groups accounted for nearly half (46.1 percent) of the precalibration objective function; the precalibration weighting scheme reflects an assumption that base-flow observations are more important than groundwater-level altitude observations for the purpose of determining the parameter values that are most influential in estimating water availability (the primary subject of this report). Base flows are generally more sensitive to changes in specific yield, which is a primary factor determining the volume of water in the aquifer, whereas groundwater-level altitudes are generally more sensitive to changes in horizontal hydraulic conductivity, which is a primary factor determining the rates of groundwater flow in the aquifer (Arnold and others, 2000). Also, because streamgages monitor base flows originating from all areas upstream, these base-flow observations are more likely to summarize regional (rather than localized) conditions in the aquifer than local well observations.

Groundwater-Level Altitude Observations

The Head Observation (HOB) package (Hill and others, 2000) was used to compare simulated groundwater-level altitudes to observed groundwater-level altitudes in the Salt Fork Arkansas River and Chikaskia River alluvial aquifers for the modeling period 1980–2020. Observed groundwater-level altitudes were calculated by subtracting the depth-to-water measurements from the land-surface altitude obtained from a 10-m DEM (USGS, 2015). For consistency, the land-surface altitude was obtained from the DEM, even when the data source provided a land-surface altitude. Groundwater-level altitude observations were filtered such that only one observation per cell per stress period was included in the HOB package. Only 1,261 groundwater-level-altitude observations (OWRB, 2022a; USGS, 2024) from 68 wells were included for the transient simulation in the HOB package (fig. 25A, tables 14, 15). Groundwater-level-altitude observations from the transient simulation were averaged by well to derive 68 calibration targets (one for each well) for the steady-state simulation because few groundwater-level-altitude observations were available at the beginning of the modeling period (1980; fig. 25A, tables 14, 15).

Base-Flow Observations

Base-flow observations were available for 492 months (the full modeling period) at the Tonkawa streamgage on the Salt Fork Arkansas River (07151000, fig. 25B), 86 months at the State Highway 11 streamgage on the Salt Fork Arkansas River (07149500), and 483 months at the Blackwell streamgage on the Chikaskia River (07152000). Base-flow observations were available for 491 months at the Alva streamgage on the Salt Fork Arkansas River (07148400), but only the first 165 months (through September 1993) at the Alva streamgage were used as calibration targets because the rest were used to formulate Salt Fork Arkansas River inflows. Base-flow records also were available for the 2017–20 period at the OWRB White Eagle streamgage near the downgradient end of the active modeled area (fig. 2; streamgage data available in Smith and Gammill [2025]). Base-flow records at the OWRB White Eagle streamgage were used to calculate a scaling factor of 1.1 by which monthly base flows for 483 months could be estimated from summed base-flow observations at the Tonkawa streamgage on the Salt Fork Arkansas River and the Blackwell streamgage on the Chikaskia River (fig. 25). The monthly mean base flows determined for five streamgages (the Alva, State Highway 11, Tonkawa, and OWRB White Eagle streamgages on the Arkansas River, and the Blackwell streamgage on the Chikaskia River) (tables 14, 15) were used as calibration targets for the transient simulation. The mean annual base flows for these five streamgages were also used as calibration targets for the steady-state simulation. Base-flow observations from other selected streamgages were not used as calibration targets because they (1) were previously used to define inflows for SFR2 or (2) had periods of record that were less than 10 years, and thus too short to be representative of streamflow conditions during the modeling period.

Conceptual-Model Flow Observations

Conceptual-model flow observations consisted of five steady-state observations and five transient observations (tables 14, 15) derived from the conceptual-model water budget (table 10); both sets of observations were identical and were compared to the respective steady-state and transient budgets. Each set of observations included three recharge observations, one for each subarea, and two streambed seepage observations, upgradient and downgradient from the Great Salt Plains Reservoir dam. These conceptual model recharge and streambed seepage flows were initially used as observations to ensure that the numerical model budget honored the conceptual model. During manual calibration of the numerical model, however, the simulated base flows at the streamgages furthest downstream were always underestimated compared to the base-flow observations. During automated calibration, the conceptual-model recharge and streambed-seepage flow observations were increased by about 10 percent to increase simulated base flows and improve the calibration outcomes.

Calibration Results

Calibration results were evaluated based on the reduction of the objective function and the general fit of the calibrated numerical-model water budget to the predetermined (adjusted) conceptual-model water budget. Automated calibration reduced the objective function total by about 60 percent from precalibration inputs (tables 14, 15). Most of that reduction resulted from reducing conceptual-model-flow residuals from 20.2 to 3.8 percent of the objective function and base-flow residuals at the Tonkawa streamgage from 32.5 to 19.2 percent of the objective function. Some observation groups, most notably groundwater-level-altitude observations, increased in the percent contribution to the objective function after calibration. However, the objective function component decreased for all observation groups except for base-flow observations at the State Highway 11 streamgage (gageobs2).

Observation-Sensitivity Analysis

As part of the calibration process, an observation-sensitivity analysis was performed with the iterative parameter estimation software package PEST (Doherty, 2010) to identify which parameters were most effective, and most ineffective, at reducing the objective function. PEST measures the changes in calibration-target residuals resulting from 1-percent changes in each parameter and records those changes in a Jacobian matrix with dimensions equal to the number of observations by the number of parameters (Doherty, 2010). Parameters with the greatest effects on calibration-target residuals have the greatest observation sensitivities. Observation sensitivity values and ranks for individual parameters are available in the companion USGS model archive data release (Smith and Gammill, 2025) that accompanies this report.

Parameters closely linked, either spatially or temporally, to observations typically had the greatest observation sensitivities in the numerical model. Parameters with the greatest observation sensitivities were the saturated-zone evapotranspiration reference altitude multiplier (evttop) and extinction depth (evtextd), the steady-state recharge-rate multiplier (rch001), the steady-state saturated-zone evapotranspiration-rate multiplier (evtavg), and the streambed depth for SFR2 zone 10 (sbd10) (Smith and Gammill, 2025; table 13). Horizontal hydraulic conductivity pilot points 145, 203, 204, 218, and 282 (hkpp145, hkpp203, hkpp204, hkpp218, and hkpp282) were the only horizontal hydraulic conductivity pilot points with sensitivities ranking in the top 20 of the 1,516 calibration parameters. The horizontal hydraulic conductivity pilot points with the highest-ranking sensitivities were in parts of the aquifer with high spatial and temporal density of groundwater-level-altitude observations. The specific yield for reach 1 (sy1) and the specific yield for reach 2 (sy2) ranked 8 and 14, respectively, of the 1,516 calibration parameters. The steady-state recharge-rate multiplier parameter (rch001) determined how much recharge was applied in the steady-state simulation, which included many of the groundwater-level altitude observations (Smith and Gammill, 2025). The specific yield parameters sy1 and sy2 controlled the volume of water released from storage as streambed seepage to streams across the entire aquifer, which in turn partially controlled how much base flow was simulated in the Salt Fork Arkansas River.

To simplify and graphically summarize the observation-sensitivity analysis, parameters were placed into six groups (table 13), and observation-group sensitivities were calculated as the sum of the Jacobian matrix output for each parameter group (fig. 26). Sensitivities less than 1×10⁻⁵ with respect to the drain parameter group indicate no sensitivity to those parameters. All observation groups were most sensitive to changes in saturated-zone-evapotranspiration (evt) parameters. Base-flow observations also were sensitive to changes in recharge (rch) parameters, and to a lesser degree, hydraulic conductivity (hyd) and streamflow-routing (sfr) parameters; groundwater-level-altitude and conceptual-model flow observations were roughly equally sensitive to recharge (rch) and hydraulic conductivity (hyd) parameters. The storage (sto) parameter group, which included specific yield and specific storage parameters, and the drain (drn) parameter group had the least observation-group sensitivities. Base-flow observations in gageobs1, gageobs2, and gageobs4 (fig. 26B, C, E) are all far upgradient from and, therefore, unaffected by, drains in the model where groundwater flows out of the modeled area.

Calibrated Parameter Values

The calibrated parameter values (Smith and Gammill, 2025) selected for the numerical model were the combined result of manual and automated calibration approaches. Many of the calibrated parameter values were at the minimum or maximum bounds specified in the PEST control file (Smith and Gammill, 2025), indicating that the objective function could likely be further reduced by expanding those bounds; doing so, however, would divert the numerical model further from the conceptual model or increase model instability. For the recharge parameter group (rch, table 13), 319 of 493 calibrated recharge-rate multiplier values were assigned to either the minimum or maximum bounds; 166 of those values were at the minimum bound (0.50), and 153 were at the maximum bound (2.00 for the transient multiplier values or 1.20 for the steady-state multiplier value) (Smith and Gammill, 2025). For the horizontal hydraulic conductivity parameter group (hyd, table 13), 523 of 788 calibrated parameter (pilot point) values were assigned to either the minimum or maximum bounds; 243 of those values were at the minimum bound (10.0 ft/d), and 280 were at the maximum bound (150 ft/d) (Smith and Gammill, 2025). Several of the calibrated parameter values in the evapotranspiration group (evt, table 13) were at bounds, including the steady-state saturated-zone evapotranspiration-rate multiplier, and 7 of the 12 transient (monthly) saturated-zone evapotranspiration-rate multipliers. For the streamflow-routing parameter group (sfr, table 13), all but 15 of the streambed hydraulic conductivity zone calibrated parameter values (sbk01–sbk53), all but 15 of the streambed thickness zone calibrated parameter values (sbb01–sbb53), and all but 23 of the streambed depth zone calibrated parameter values (sbd01–sbd53) were at bounds; only five of the streambed width zone calibrated parameter values were at bounds. All four storage parameter values (sy1, sy2, sy3, and ss) were at bounds; given the absence of field data on specific yield and the relative importance of that parameter in storage volume estimates, however, a uniform specific-yield value was used in the calibrated numerical model.

Comparison of Simulated and Observed Calibration Targets

Conforming to MODFLOW convention (Harbaugh, 2005), calibration-target residuals in this report were calculated as observed minus simulated values; positive residuals indicate lower simulated than observed values, and negative residuals indicate higher simulated than observed values. The mean calibrated residual for groundwater-level-altitude observations was –3.0 ft (tables 14, 15), indicating that, on average, simulated water levels were slightly higher than observed water levels. The combined (steady-state and transient simulation) groundwater-level altitude root-mean-square error was 7.8 ft, and the interquartile range was 7.3 ft (tables 14, 15; fig. 27B). When averaged by well, the mean groundwater-level altitude residuals with the greatest magnitudes mostly were associated with wells north and east of the Great Salt Plains Reservoir (fig. 28). Several wells completed in the Salt Fork Arkansas River alluvial aquifer had multiple groundwater-level altitude observations spanning multiple years of the modeling period; simulated and observed hydrographs for six of those wells (Alva; GSP2; OWRB wells 9006, 9009, 9431, and 9506) are shown in figure 27C–E (table 11, fig. 1; OWRB, 2022a; USGS, 2024).

The mean calibrated residuals for base-flow observations at the Tonkawa streamgage (on the Salt Fork Arkansas River), Blackwell streamgage (on the Chikaskia River), and OWRB White Eagle streamgage (on the Salt Fork Arkansas River) indicated that, on average, simulated base flows were 39.4, 42.4, and 108.1 ft³/s (tables 14, 15) lower, respectively, than the observed base flows of 412.2 of 192.2 ft³/s (tables 4, 5) and 664.8 ft³/s ([synthesized as 110 percent of the sum of 412.2 and 192.2 ft³/s], respectively). Although the model was able to match lower base flows well (fig. 29A–D), it was unable to match the highest observed base flows at these streamgages (fig. 29B–D). This mismatch is likely caused by a combination of factors, including (1) overestimation of observed base flows obtained from the BFI method, especially at the Tonkawa streamgage, which receives uncontrolled releases from the Great Salt Plains Reservoir, (2) underestimation of base-flow inflows GSPR and CHIK (fig. 22), and (3) overestimation of the scaling factor used to estimate base-flow observations at the OWRB White Eagle streamgage. The inability to simulate extreme values in the observed data is typical of groundwater-flow models (Ellis and others, 2017; Smith and others, 2017, 2021; Rogers and others, 2023) and may result from numerical-model discretization, or the necessary simplification of spatially and temporally variable hydrologic processes that occur in the physical world (Mandelbrot, 1983).