Groundwater conditions in Georgia, 2015–16

Debbie W. Gordon; Jaime A. Painter

doi:10.3133/sir20175142

Home/Archive/USGS

USGS · sir20175142

Groundwater conditions in Georgia, 2015–16

Debbie W. Gordon·Jaime A. Painter

View original source ↗Read source document ↗Export metadata ↓

Abstract

The U.S. Geological Survey collects groundwater data and conducts studies to monitor hydrologic conditions, define groundwater resources, and address problems related to water supply, water use, and water quality. In Georgia, water levels were monitored continuously at 157 wells during calendar years 2015 and 2016. Because of missing data or short periods of record (less than 5 years) for several of these wells, data for 147 wells are presented in this report. These wells include 15 in the surficial aquifer system, 18 in the Brunswick aquifer system and equivalent sediments, 59 in the Upper Floridan aquifer, 13 in the Lower Floridan aquifer and underlying units, 9 in the Claiborne aquifer, 1 in the Gordon aquifer, 8 in the Clayton aquifer, 16 in the Cretaceous aquifer system, 2 in Paleozoic-rock aquifers, and 6 in crystalline-rock aquifers. Data from the well network indicate that water levels generally rose during the 10-year period from 2007 through 2016, with water levels rising in 105 wells and declining in 31 wells; insufficient data prevented determination of a 10-year trend in 11 wells. Water levels declined over the long-term period of record at 80 wells, increased at 62 wells, and remained relatively constant at 5 wells. In addition to continuous water-level data, periodic water-level data were collected and used to construct potentiometric-surface maps for the Upper Floridan aquifer in the Brunswick–Glynn County area during October 2015 and October 2016 and in the Albany–Dougherty County area during December 2015 and November and December 2016. Periodic water-level measurements were also collected and used to construct potentiometric-surface maps for the Cretaceous aquifer system in the Augusta–Richmond County area during July 2015 and June 2016. In general, water levels in the Upper Floridan aquifer were higher during 2015 than during 2016 in the Brunswick–Glynn County and Albany–Dougherty County areas due to higher precipitation during 2015. Water levels were lower, however, during 2015 than during 2016 in the Cretaceous aquifer system in the Augusta–Richmond County area. In the Brunswick area, maps showing the chloride concentration of water in the Upper Floridan aquifer constructed using data collected from 33 wells during October 2015 and from 30 wells during October 2016 indicate that chloride concentrations remained above the U.S. Environmental Protection Agency’s secondary drinking-water standard in an approximately 2-square-mile area. During calendar years 2015–16, chloride concentrations generally were similar to those measured during 2012–14; however, some wells did show an increase in chloride concentration, likely due to increases in pumping.

Explore related subjects

Scientific Investigations Report Report

Source-reported bounding extent: 30.357851° to 35.000768° latitude; -85.605165° to -80.840549° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Debbie W. Gordon, Jaime A. Painter. 2018-02-21. Groundwater conditions in Georgia, 2015–16. https://doi.org/10.3133/sir20175142

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

KEEP EXPLORING

Related USGS reports

USGS2026

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.

Scientific Investigations Report↗

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.

California, Idaho, Nevada, Oregon, Utah↗

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↗