USGS Science⌕ Search

USGS · sir20125136

Simulation of streamflow, evapotranspiration, and groundwater recharge in the middle Nueces River watershed, south Texas, 1961-2008

Abstract

The U.S. Geological Survey—in cooperation with the U.S. Army Corps of Engineers, Fort Worth District; City of Corpus Christi; Guadalupe–Blanco River Authority; San Antonio River Authority; and San Antonio Water System— configured, calibrated, and tested a watershed model for a study area consisting of about 7,726 square miles of the middle Nueces River watershed in south Texas. The purpose of the model is to contribute to the understanding of watershed processes and hydrologic conditions in the middle Nueces River watershed. The model simulates streamflow, evapotranspiration, and groundwater recharge by using a numerical representation of physical characteristics of the landscape and meteorological and streamflow data. Model simulations of streamflow, evapotranspiration, and groundwater recharge were performed for various periods of record depending upon available gaged data for input and comparison, starting as early as 1961. Because of the large size of the study area, the middle Nueces River watershed was divided into eight subwatersheds, and separate Hydrological Simulation Program—FORTRAN models were developed for each subwatershed. Simulation of the overall study area involved running simulations in downstream order. Output from the model was summarized by subwatershed, point locations, stream and reservoir reaches, and the Carrizo– Wilcox aquifer outcrop area. Four long-term U.S. Geological Survey streamflow-gaging stations were used for streamflow model calibration and testing with data from 1990 to 2008. Monthly evaporation estimates from 2001 to 2008 and waterlevel data from 1961 to 2008 at Lake Corpus Christi also were used for model calibration. Additionally, evapotranspiration data for 2006–8 from a U.S. Geological Survey meteorological station in Medina County were used for calibration. Streamflow calibrations were considered poor to very good. The 2000–8 calibration results were characterized as good to very good for total flow volumes and for the volume of the highest 10 percent of daily flows. Calibration results for streamflow volumes of the lowest 50 percent of daily flows were considered poor. The daily streamflow calibration at U.S. Geological Survey streamflow-gaging station 08210000 Nueces River near Three Rivers, Tex., had the lowest (best) root mean square error, and U.S. Geological Survey streamflow-gaging station 08194500 Nueces River near Tilden, Tex., had the highest root mean square error expressed as a percentage of the mean flow rate. The mean daily reservoir volume during 1961–2008 was 182,000 acre-feet. Simulated mean daily reservoir volume was within 9 percent of this computed volume. Selected results of the model include streamflow yields for the subwatersheds and water-balance information for the Carrizo–Wilcox aquifer outcrop area. For the entire model domain, the area-weighted mean streamflow yield from 1961 to 2008 was 1.12 inches/year. The mean annual rainfall on the outcrop area during the 1961–2008 simulation period was 21.7 inches. Of this rainfall, an annual mean of 20.1 inches (about 93 percent) was simulated as evapotranspiration, 1.2 inches (about 6 percent) was simulated as groundwater recharge, and 0.5 inches (about 2 percent) was simulated as surface runoff.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 27.5° to 30.000833333333333° latitude; -100.5° to -97.5° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Benjamin J. Dietsch, Loren L. Wehmeyer. 2012. Simulation of streamflow, evapotranspiration, and groundwater recharge in the middle Nueces River watershed, south Texas, 1961-2008. https://doi.org/10.3133/sir20125136

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Karst hydrologic processes in carbonate fensters of the western Great Smoky Mountains, Tennessee, 2017–18

The geology of the Great Smoky Mountains in Tennessee and North Carolina is dominated by metamorphic strata. Along the western extent of the range, however, several carbonate fensters expose the Ordovician Jonesboro Limestone along the western edge of the Great Smoky Fault. The fensters (Cades Cove, Tuckaleechee Cove, and Wear Cove) create opportunities for allogenic recharge at the contact between the surrounding, relatively insoluble, strata and the underlying soluble formations, resulting in the development of karst systems. Karst environments have a high degree of connectivity between surface streams and groundwater systems; thus, karst aquifers are inherently vulnerable to contamination from surface activities. Karst environments also provide a specific environment for sensitive biota. Protection of the unique resources of karst environments requires an understanding of the behavior of karst aquifers, knowledge of karst recharge areas, and the mapping of groundwater flow paths. A study began in 2017 to characterize the hydrologic behavior of the karst systems in Great Smoky Mountains National Park through hydrologic monitoring, dye tracing, and seepage investigations. Hydrologic monitoring equipment was installed at several karst hydrogeologic features: two in-cave sites, two major springs, Abrams Creek in Cades Cove, and a karst depression wetland. Dye tracing was conducted to determine resurgences for the cave systems, to delineate spring recharge areas, and to estimate karst groundwater velocities. Seepage investigations along Abrams Creek helped determine if streams were sinking into the Jonesboro Limestone, either at the contact with insoluble strata or as streams flowed across the carbonate fenster. Study results indicate considerable differences in groundwater behavior between the conduit-dominated flow systems in Tuckaleechee Cove compared to the lower-gradient, colluvium-covered karst systems in Cades Cove. Study results also indicate that contaminants in the area of Bull sink and Whiteoak Sink could be rapidly transported through karst groundwater towards springs in Tuckaleechee Cove. Karst resources in Cades Cove are most likely to be affected by local events or activities taking place within the cove itself or in upper Abrams Creek upstream of the Great Smoky Fault. This study provides information on the vulnerability and uniqueness of the karst systems in the western Great Smoky Mountains, which are characterized by complex geology, high-gradient subterranean streams, deep caves, and carbonate fensters.

Tennessee↗

Analysis of groundwater geochemistry in the Lansing, Michigan, area

Groundwater resources in the Lansing, Michigan, area are protected by a wellhead protection plan, but previous studies have indicated that groundwater in this area may be susceptible to surface contamination. The U.S. Geological Survey, in cooperation with the Lansing Board of Water & Light, analyzed geochemical and aquifer characteristics to assess the potential for contamination of groundwater resources. Recharge age categories were determined from tritium concentrations for some wells, and by using ternary diagrams and principal component analysis for other wells. These age classifications, combined with hydrogeologic and geochemical data, were analyzed by using statistical methods to identify trends that may indicate areas of increased groundwater susceptibility. Wells with modern recharge tended to have higher sodium, chloride, and chloride:bromide ratios than did wells with mixed recharge, suggesting greater surface connectivity and potential influence from anthropogenic sources such as road salt. In contrast, wells in the mixed age category exhibited more stable geochemistry and lower constituent concentrations. Classification and regression tree models identified aquifer geometry—particularly the thickness of the uppermost bedrock unit and presence of confining layers—as key predictors of recharge age and geochemical indicators. The trends identified from these analyses could help to reveal locations in the Lansing area that are among the most susceptible to contamination.

Michigan↗

Evaluating the number of realizations (train-test splits) necessary to ensure robust estimates of conventional hydrothermal resource favorability using Monte Carlo cross-validation, the Great Basin, Western USA

Supervised machine learning approaches used to produce favorability maps in resource assessments commonly partition an input dataset into training and testing subsets (that is, through train-test splits). However, if a single split is not representative of underlying resource conditions, the resulting favorability map may be biased, particularly with sparse or heterogeneous datasets. To mitigate the potential for unrepresentative favorability maps, recent works by U.S. Geological Survey researchers have used Monte Carlo cross-validation to implement a large number ( n) of randomized train-test splits to produce n realizations. The realizations are then aggregated using cell-wise median predictions to produce the resulting final favorability map, recognizing that the median will remove extreme values that might have been estimated from a comparatively few unrepresentative realizations. Theoretically, the median prediction at every cell converges to a single estimate as n approaches infinity, and the question addressed herein is: How many realizations are necessary to get a robust estimate (that is, an estimate unaffected by additional realizations)? Therefore, convergence metrics are described to identify the number of train-test splits and realizations needed to make a reliable map using the data and supervised machine learning workflow for conventional hydrothermal resource assessment of the Great Basin of the Western United States. For this study, robust prediction occurs with n approximately equal to 600 realizations, and smaller ensembles (for example, n is approximately equal to 200 or even 100) may be suitable during preliminary or exploratory analyses. Although the resulting threshold numbers will vary depending on the data and modeling approach used, the convergence metrics proposed and applied herein are applicable to other supervised machine learning studies that use Monte Carlo cross-validation.

California, Idaho, Nevada, Oregon, Utah↗