Search USGS⌕ Search

USGS · sir20175051

Status and understanding of groundwater quality in the North San Francisco Bay Shallow Aquifer study unit, 2012; California GAMA Priority Basin Project (ver. 1.1, February 2018)

Abstract

Groundwater quality in the North San Francisco Bay Shallow Aquifer study unit (NSF-SA) was investigated as part of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in Marin, Mendocino, Napa, Solano, and Sonoma Counties and included two physiographic study areas: the Valleys and Plains area and the surrounding Highlands area. The NSF-SA focused on groundwater resources used for domestic drinking water supply, which generally correspond to shallower parts of aquifer systems than that of groundwater resources used for public drinking water supply in the same area. The assessments characterized the quality of untreated groundwater, not the quality of drinking water. This study included three components: (1) a status assessment , which characterized the status of the quality of the groundwater resources used for domestic supply for 2012; (2) an understanding assessment , which evaluated the natural and human factors potentially affecting water quality in those resources; and (3) a comparison between the groundwater resources used for domestic supply and those used for public supply. The status assessment was based on data collected from 71 sites sampled by the U.S. Geological Survey for the GAMA Priority Basin Project in 2012. To provide context, concentrations of constituents measured in groundwater were compared to U.S. Environmental Protection Agency (EPA) and California State Water Resources Control Board Division of Drinking Water regulatory and non-regulatory benchmarks for drinking-water quality. The status assessment used a grid-based method to estimate the proportion of the groundwater resources that has concentrations of water-quality constituents approaching or above benchmark concentrations. This method provides statistically unbiased results at the study-area scale and permits comparisons to other GAMA Priority Basin Project study areas. In the NSF-SA study unit as a whole, inorganic constituents with human-health benchmarks were detected at high relative concentrations (RCs) in 27 percent of the shallow aquifer system, and inorganic constituents with secondary maximum contaminant levels (SMCL) were detected at high RCs in 24 percent of the system. The inorganic constituents detected at high RCs were arsenic, boron, fluoride, manganese, nitrate, iron, sulfate, and total dissolved solids (TDS). Organic constituents with human-health benchmarks were detected at high RCs in 1 percent of the shallow aquifer system. Of the 148 organic constituents analyzed, 30 constituents were detected, although only 1, chloroform, had a detection frequency greater than 10 percent. Natural and anthropogenic factors that could affect the groundwater quality were evaluated by using results from statistical testing of associations between constituent concentrations and values of potential explanatory factors. Groundwater age class (modern, mixed, or pre-modern), redox class (oxic or anoxic), aquifer lithology class (metamorphic, sedimentary, or volcanic), and dissolved oxygen concentrations were the explanatory factors that explained distribution patterns of most of the inorganic constituents best. Groundwater classified primarily as pre-modern or mixed in age was associated with higher concentrations of arsenic and fluoride than waters classified as modern. Anoxic or mixed redox conditions were associated with higher concentrations of boron, fluoride, and manganese. Similar patterns of association with explanatory variables were seen for inorganic constituents with aesthetic-based benchmarks detected at high concentrations. Nitrate and perchlorate had higher concentrations in oxic than in the anoxic redox class and were positively correlated with urban land use. The NSF-SA water-quality results were compared to those of the GAMA North San Francisco Bay Public-Supply Aquifer study unit (NSF-PA). The NSF-PA was sampled in 2004 and covers much of the same area as the NSF-SA, but focused on the deeper public-supply aquifer system. The comparison of the NSF-PA to the NSF-SA showed that there were more differences between the Valleys and Plains study areas of the two study units than between the Highlands study areas of the two study units. As expected from the shallower depth of wells, the NSF-SA Valleys and Plains study area had a lesser proportion of pre-modern age groundwater and greater proportion of modern age groundwater than the NSF-PA Valleys and Plains study area. In contrast, well depths and groundwater ages were not significantly different between the two Highlands study areas. Arsenic, manganese, and nitrate were present at high RCs, and perchlorate was detected in greater proportions of the NSF-SA Valleys and Plains study area than the NSF-PA Valleys and Plains study area.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 38.06539235133249° to 38.97008658346543° latitude; -123.25012207031249° to -122.16522216796875° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

George L. Bennett. 2017-07-20. Status and understanding of groundwater quality in the North San Francisco Bay Shallow Aquifer study unit, 2012; California GAMA Priority Basin Project (ver. 1.1, February 2018). https://doi.org/10.3133/sir20175051

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

KEEP EXPLORING

Related USGS reports

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↗

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↗

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↗