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Filters: partyWithName: U.S. Geological Survey (X) > Types: Map Service (X) > partyWithName: Leon J Kauffman (X)

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A boosted regression tree (BRT) model was developed to predict pH conditions in three-dimensions throughout the glacial aquifer system (GLAC) of the contiguous United States using pH measurements in samples from 18,258 wells and predictor variables that represent aspects of the hydrogeologic setting. Model results indicate that the carbonate content of soils and aquifer materials strongly controls pH and when coupled with long flow paths, results in the most alkaline conditions. Conversely, in areas where glacial sediments are thin and carbonate-poor, pH conditions remain acidic. At depths typical of drinking-water supplies, predicted pH > 7.5 – which is associated with arsenic mobilization – occurs more frequently...
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A histrogram-based boosted regression tree (HBRT) method was used to predict the depth to the surficial aquifer water table (in feet) throughout the State of Wisconsin. This method used a combination of discrete groundwater levels from the U.S. Geological Survey National Water Information System, continuous groundwater levels from the National Groundwater Monitoring Network, the State of Wisconsin well-construction database, and NHDPlus version 2.1-derived points. The predicted water table depth utilized the HBRT model available through Scikit-learn in Python version 3.10.10. The HBRT model can predict the surficial water table depth for any latitude and longitude for Wisconsin. A total of 48 predictor variables...
The population using public supply drinking water was mapped in two ways: the census enhanced method (CEM) evenly distributes the population across the census block-group, and the urban land-use enhanced method (ULUEM) distributes the population only to certain urban land use designations in order to more precisely locate public supply users. This dataset consists of the estimated population using public supply surface water distributed across block-groups.
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Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large-scale numerical models can be resource intensive. With the data and computer codes in this data release users can (1) reconstruct and run 115 General Simulation Models (GSMs) of groundwater flow, (2) calculate groundwater age metrics at selected GSM cells, (3) train a boosted regression tree model using the provided data, (4) predict...
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A Groundwater Nitrate Decision Support Tool (GW-NDST) for wells in Wisconsin was developed to assist resource managers with assessing how legacy and possible future nitrate leaching rates, combined with groundwater lag times and potential denitrification, influence nitrate concentrations in wells (Juckem et al. 2024). Running and using the GW-NDST software involves downloading the software code (version 1.1.0) from the code repository (https://doi.org/10.5066/P13ETB4Q), downloading GIS data for the machine learning support models (child data release "GIS files required to run the Groundwater Nitrate Decision Support Tool for Wisconsin"), downloading the parameter uncertainty file (child data release "Parameter ensemble...
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The population using public supply drinking water was mapped in two ways: the census enhanced method (CEM) evenly distributes the population across the census block-group, and the urban land-use enhanced method (ULUEM) distributes the population only to certain urban land use designations in order to more precisely locate public supply users. This dataset consists of the estimated population using public supply groundwater distributed across census block-groups.
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An extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in shallow groundwater across the conterminous United States (CONUS). Nitrate was predicted at a 1-square-kilometer (km) resolution at a depth below the water table of 10 m. The model builds off a previous XGB machine learning model developed to predict nitrate at domestic and public supply groundwater zones (Ransom and others, 2022) by incorporating additional monitoring well samples and modifying and adding predictor variables. The shallow zone model included variables representing well characteristics, hydrologic conditions, soil type, geology, climate, oxidation/reduction, and nitrogen inputs. Predictor...
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This data release includes grids representing the depth and thickness of drinking-water withdrawal zones, polygons of hydrogeologic settings, an inventory of sources of well construction data, and summaries of data comparisons used to assess the depth of groundwater used for drinking-water supplies in the United States. Well construction data sources are documented in Table1_DataSources.xlsx. Data comparisons using the Mann-Whitney test to assess similarity between hydrogeologic settings were used to justify combining data where they were sparse (compare_neighbors_all_domestic.txt and compare_neighbors_all_public.txt). Water-supply-well depth varies geographically by water use and the type of well, which illustrates...
The population using public supply drinking water was mapped in two ways: the census enhanced method (CEM) evenly distributes the population across the census block-group, and the urban land-use enhanced method (ULUEM) distributes the population only to certain urban land use designations in order to more precisely locate public supply users. This dataset consists of the total estimated population using public supply surface water and groundwater combined, distributed using the urban land-use enhanced method.
The population using public supply drinking water was mapped in two ways: the census enhanced method (CEM) evenly distributes the population across the census block-group, and the urban land-use enhanced method (ULUEM) distributes the population only to certain urban land use designations in order to more precisely locate public supply users. This dataset consists of the total estimated population using public supply surface water and groundwater combined, distributed across block-groups.
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A seamless map of the major groundwater areas used by community public supply wells in the United States was needed in order to describe and compute the number of equivalent people using public supply water. This goal was met by the delineation of hydrogeologic mapping units (HMUs). An HMU is a mapped polygon, within which, all public supply wells have a common source of water. The source of water can be either a national Principal Aquifer (PA) as defined in USGS (2003) or a Secondary Hydrogeologic Region (SHR) as defined in Belitz et al. (2018). Collectively, both PAs and SHRs are referred to as Hydrogeologic Regions (HRs). The common source of water can be a single HR or multiple HRs, as HRs can overlap one another....
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This dataset provides the calculated proportion of people using publicly supplied groundwater (PSGF) for each county in the conterminous U.S. The county boundaries and the PSGF represent the year 2015.
The population using public supply drinking water was mapped in two ways: the census enhanced method (CEM) evenly distributes the population across the census block-group, and the urban land-use enhanced method (ULUEM) distributes the population only to certain urban land use designations in order to more precisely locate public supply users. This dataset consists of the estimated population using public supply surface water distributed using the urban land-use enhanced method.
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Green and others (2021) developed a gradient boosted regression tree model to predict the mean groundwater age, or travel time, for shallow wells across a portion of the Great Lakes basin in the United States. Their study applied machine learning methods to predict ages in wells using well construction, well chemistry, and landscape characteristics. For a dataset of age tracers in 961 water samples, the mean travel time from the land surface to the sample location (center of saturated open interval) was estimated for each sample using parametric functions. The mean travel times were then modeled using a gradient boosting machine algorithm with cross validation tuning of model hyperparameters. The model contained...
The population using public supply drinking water was mapped in two ways: the census enhanced method (CEM) evenly distributes the population across the census block-group, and the urban land-use enhanced method (ULUEM) distributes the population only to certain urban land use designations in order to more precisely locate public supply users. This dataset consists of the estimated population using public supply groundwater distributed using the urban land-use enhanced method.
    map background search result map search result map Data for depth of groundwater used for drinking-water supplies in the United States Data for three-dimensional distribution of groundwater residence time metrics in the glaciated United States using metamodels trained on general numerical simulation models Data for machine learning predictions of pH in the glacial aquifer system, northern USA Community public supply based Hydrogeologic Mapping Units Estimated equivalent population using public supply groundwater in the conterminous United States, CEM Data to support a Groundwater Nitrate Decision Support Tool for Wisconsin Python-HBRT model and groundwater levels used for estimating the static, shallow water table depth for the State of Wisconsin Public Supply Groundwater Fraction per County, 2015 Data for Machine Learning Predictions of Nitrate in Shallow Groundwater in the Conterminous United States Histogram-based gradient boosted regression tree model of mean ages of shallow well samples in the Great Lakes Basin, USA Python-HBRT model and groundwater levels used for estimating the static, shallow water table depth for the State of Wisconsin Data to support a Groundwater Nitrate Decision Support Tool for Wisconsin Histogram-based gradient boosted regression tree model of mean ages of shallow well samples in the Great Lakes Basin, USA Data for machine learning predictions of pH in the glacial aquifer system, northern USA Data for three-dimensional distribution of groundwater residence time metrics in the glaciated United States using metamodels trained on general numerical simulation models Data for depth of groundwater used for drinking-water supplies in the United States Data for Machine Learning Predictions of Nitrate in Shallow Groundwater in the Conterminous United States Estimated equivalent population using public supply groundwater in the conterminous United States, CEM Community public supply based Hydrogeologic Mapping Units Public Supply Groundwater Fraction per County, 2015