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Groundwater from the Mississippi River Valley alluvial aquifer (MRVA), coincident with the Mississippi Alluvial Plain (MAP), is a vital resource for agriculture and drinking-water supplies in the central United States. Water availability can be limited in some areas of the aquifer by high concentrations of salinity, measured as specific conductance. Boosted regression trees (BRT), a type of ensemble-tree machine-learning method, were used to predict specific conductance concentration at multiple depths throughout the MRVA and underlying aquifers. Two models were created to test the incorporation of datasets from a regional aerial electromagnetic (AEM) survey and evaluate model performance. Explanatory variables...
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The U.S. Geological Survey (USGS) in cooperation with the Pennsylvania Department of Conservation and Natural Resources, Bureau of Geological Survey, estimated groundwater recharge by automated streamflow-hydrograph methods for watersheds throughout Pennsylvania. These data serve as an update to previous estimates of groundwater recharge described in Risser and others (2005). The current analysis provides estimates of groundwater recharge from the recession-curve displacement method (RORA) and estimates of runoff and baseflow from the hydrograph-separation technique PART. Additional hydrograph separation methods, HySEP-Fixed, HySEP-LocMin, HySEP-Slide, BFI-Standard, BFI-Modified, DF-One Param, and DF-Two Param were...


    map background search result map search result map Machine-learning model predictions and rasters of groundwater salinity in the Mississippi Alluvial Plain Estimates of Baseflow, Runoff, and Groundwater Recharge Based on Streamflow-Hydrograph Methods: Pennsylvania Estimates of Baseflow, Runoff, and Groundwater Recharge Based on Streamflow-Hydrograph Methods: Pennsylvania Machine-learning model predictions and rasters of groundwater salinity in the Mississippi Alluvial Plain