Streamflow and water-quality properties in the West Fork San Jacinto River Basin and regression models to estimate real-time suspended-sediment and total suspended-solids concentrations and loads in the West Fork San Jacinto River in the vicinity of Conroe, Texas, July 2008-August 2009

Lee J. Bodkin; Jeannette H. Oden

doi:10.3133/sir20105171

Home/Archive/USGS

USGS · sir20105171

Streamflow and water-quality properties in the West Fork San Jacinto River Basin and regression models to estimate real-time suspended-sediment and total suspended-solids concentrations and loads in the West Fork San Jacinto River in the vicinity of Conroe, Texas, July 2008-August 2009

Lee J. Bodkin·Jeannette H. Oden

View original source ↗Export metadata ↓

Abstract

To better understand the hydrology (streamflow and water quality) of the West Fork San Jacinto River Basin downstream from Lake Conroe near Conroe, Texas, including spatial and temporal variation in suspended-sediment (SS) and total suspended-solids (TSS) concentrations and loads, the U.S. Geological Survey, in cooperation with the Houston-Galveston Area Council and the Texas Commission on Environmental Quality, measured streamflow and collected continuous and discrete water-quality data during July 2008-August 2009 in the West Fork San Jacinto River Basin downstream from Lake Conroe. During July 2008-August 2009, discrete samples were collected and streamflow measurements were made over the range of flow conditions at two streamflow-gaging stations on the West Fork San Jacinto River: West Fork San Jacinto River below Lake Conroe near Conroe, Texas (station 08067650) and West Fork San Jacinto River near Conroe, Texas (station 08068000). In addition to samples collected at these two main monitoring sites, discrete sediment samples were also collected at five additional monitoring sites to help characterize water quality in the West Fork San Jacinto River Basin. Discrete samples were collected semimonthly, regardless of flow conditions, and during periods of high flow resulting from storms or releases from Lake Conroe. Because the period of data collection was relatively short (14 months) and low flow was prevalent during much of the study, relatively few samples collected were representative of the middle and upper ranges of historical daily mean streamflows. The largest streamflows tended to occur in response to large rainfall events and generally were associated with the largest SS and TSS concentrations. The maximum SS and TSS concentrations at station 08067650 (180 and 133 milligrams per liter [mg/L], respectively) were on April 19, 2009, when the instantaneous streamflow was the third largest associated with a discrete sample at the station. SS concentrations were 25 mg/L or less in 26 of 29 environmental samples and TSS concentrations were 25 mg/L or less in 25 of 28 environmental samples. Median SS and TSS concentrations were 7.0 and 7.6 mg/L, respectively. At station 08068000, the maximum SS concentration (1,270 mg/L) was on April 19, 2009, and the maximum TSS concentration (268 mg/L) was on September 18, 2008. SS concentrations were 25 mg/L or less in 16 of 27 of environmental samples and TSS concentrations were 25 mg/L or less in 18 of 26 environmental samples at the station. Median SS and TSS concentrations were 18.0 and 14.0 mg/L, respectively. The maximum SS and TSS concentrations for all five additional monitoring sites were 3,110 and 390 mg/L, respectively, and the minimum SS and TSS concentrations were 5.0 and 1.0 mg/L, respectively. Median concentrations ranged from 14.0 to 54.0 mg/L for SS and from 11.0 to 14.0 mg/L for TSS. Continuous measurements of streamflow and selected water-quality properties at stations 08067650 and 08068000 were evaluated as possible variables in regression equations developed to estimate SS and TSS concentrations and loads. Surrogate regression equations were developed to estimate SS and TSS loads by using real-time turbidity and streamflow data; turbidity and streamflow resulted in the best regression models for estimating near real-time SS and TSS concentrations for stations 08097650 and 08068000. Relatively large errors are associated with the regression-computed SS and TSS concentrations; the 90-percent prediction intervals for SS and TSS concentrations were (+/-)48.9 and (+/-)43.2 percent, respectively, for station 08067650 and (+/-)47.7 and (+/-)43.2 percent, respectively, for station 08068000. Regression-computed SS and TSS concentrations were corrected for bias before being used to compute SS and TSS loads. The total estimated SS and TSS loads during July 2008-August 2009 were about 3,540 and 1,900 tons, respectively, at station 08067650 and about 156,000 an

Explore related subjects

Scientific Investigations Report Report

Source-reported bounding extent: 29.9167° to 30.75° latitude; -95.9333° to -95.1° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Lee J. Bodkin, Jeannette H. Oden. 2010. Streamflow and water-quality properties in the West Fork San Jacinto River Basin and regression models to estimate real-time suspended-sediment and total suspended-solids concentrations and loads in the West Fork San Jacinto River in the vicinity of Conroe, Texas, July 2008-August 2009. https://doi.org/10.3133/sir20105171

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

KEEP EXPLORING

Related USGS reports

USGS2026

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.

Scientific Investigations Report↗

USGS2026

Groundwater budget for the Portneuf River Basin, southeastern Idaho

The U.S. Geological Survey, in cooperation with the Idaho Department of Water Resources, developed the first groundwater budget for the upper Portneuf River Basin in southeastern Idaho, a tributary of the Snake River. A groundwater budget for the Portneuf River Basin supports water resource and administrative management decisions within the basin and for downstream users of the eastern Snake River Plain aquifer. The analysis quantifies inflows, outflows, and residuals for an aquifer system defined by the Idaho Department of Water Resources under three recent precipitation conditions: an average year (2016), a dry year (2021), and a wet year (2023). The budget was calculated for the basin and for two subbasins delineated by the U.S. Geological Survey streamgage Portneuf River at Topaz, Idaho (13073000): the above Topaz subbasin, which includes the Portneuf-Gem valley, and the below Topaz subbasin, which includes Marsh Valley. Aquifer inflows include mountain-front recharge; areal recharge; recharge from applied water, including recharge from irrigation, canal seepage, and effluent returns; groundwater flow into the basin; and reservoir seepage. Outflows include groundwater pumping for agricultural, municipal, and domestic uses; subsurface outflow through the Portneuf gap; and discharge to streams as base flow. Estimated annual inflows for the full basin ranged from 116,880 acre-feet in 2021 to 154,880 acre-feet in 2023, and outflows ranged from 132,190 acre-feet in 2016 to 158,490 acre-feet in 2023. Basinwide residuals, the change in storage plus uncertainty, were −2,410 acre-feet in 2016, −18,410 acre-feet in 2021, and −3,610 acre-feet in 2023, and each subbasin had a mixture of negative and positive residuals. These basinwide results, interpreted as change in storage plus water-budget uncertainty, indicate minor net losses that increase with dry conditions, consistent with well depth-to-water measurements indicating gradual multi-decadal declines. Patterns in subbasin residuals are driven by irrigation practices and surface-water management; for example, the above Topaz subbasin relies more on groundwater for irrigation, resulting in more negative residuals during dry years. In contrast, the below Topaz subbasin depends more on surface water, producing positive residuals during dry years because of irrigation losses and canal seepage. The analyses for both subbasins indicate decades-long groundwater declines, which agree with the overall negative residuals calculated. The largest sources of uncertainty include climate inputs (precipitation and evapotranspiration) and poorly constrained components such as groundwater inflow and tributary stream infiltration. These findings provide insight into how climatic variables and irrigation practices affect groundwater inflows and outflows within the Portneuf River Basin.

Idaho↗

USGS2026

World minerals outlook—Antimony, copper, graphite, niobium, potash, silicon, silver, and tungsten through 2030

Demand for mineral commodities continues to increase owing to rapid technological advancements and global economic growth. To support this increase in demand, information about current and near-term supplies of these mineral commodities is essential. Such data help evaluate the health and stability of global supply chains and protect against potential supply risks. Current production and production capacity data coupled with information regarding near-term expected changes (such as facility closures, planned expansions, or the completion of new mineral production projects) can provide insight into expected future market developments and expected available supply of mineral commodities. This report within the “World Minerals Outlook” series analyzes data for antimony, copper, natural graphite, niobium, potash, silicon metal, silver, and tungsten—eight mineral commodities listed in the 2025 List of Critical Minerals. World production data from 2019 through 2025, as well as estimated world capacity data from 2023 through 2025 were analyzed. Using these data, near-term (2026–2030) expected world capacities were estimated. For antimony, copper, niobium, silicon metal, silver, and tungsten, modest increases in production capacity of 4 to 10 percent were expected between 2025 and 2030, whereas capacities for natural graphite and potash were expected to increase by about 18 to 20 percent.

Scientific Investigations Report↗