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Pennsylvania Water Science Center

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In 2012, catchments were generated in the Delaware River Basin for 8-digit HUCs in the areas underlain by the Marcellus Shale (all of 02040101, 02040102, 02040103, 02040104; and headwater areas of 02040106 and 02040203) based on the National Hydrography Dataset (NHD) Strahler first- and second-order streams. There were areas that did not have a catchment generated so another methodology needed to be used in an attempt to fill in the 'gap areas'. A 900-cell, flow accumulation raster generated for the Pennsylvania StreamStats application was used as a surrogate stream layer with the same Strahler ordering system applied to help fill in the 'gap areas'. Points were manually placed at the downstream end of the Strahler...
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The Stream Conditions of Chester County Biological Monitoring Network was established by the U.S. Geological Survey and the Chester County Water Resources Authority in 1969. Benthic-macroinvertebrate, habitat, and stream chemistry data were collected annually from 1998-2021 at 18 fixed location sites. Additionally, nine flexible location sites were selected and sampled annually from 1998-2021. Some of the flexible location sites were sampled more than once over the study period. All data were collected in the fall months (October-November) during baseflow conditions. The benthic-macroinvertebrate data collected was used to calculate six metrics and to establish an index of biotic integrity for each sample, while...
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Groundwater quality data for 472 domestic well-water samples were retrieved from the USGS National Water Information System (NWIS) database and combined with descriptive information on the sampled well locations. The NWIS data for a sample, collected on the selected date, were averaged into a single record (one per well), and rounded according to USGS protocols. For evaluation and reporting, the water-quality data were further combined with physical attributes and classified by lithology, topographic position, physiographic province, region, redox, and pH (as standalone matrix, one row per well). A second data matrix file incorporating minimum reporting levels for censored and low-reported values (set to 0.99X highest...
Categories: Data
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Statistical predictions of groundwater levels and related spatial diagnostics for the Mississippi River Valley alluvial aquifer from the mmlMRVAgen1 statistical machine-learning software
A multiple machine-learning model (Asquith and Killian, 2024) implementing Cubist and Random Forest regressions was used to predict monthly mean groundwater levels through time for the available years described in the metadata for the Mississippi River Valley alluvial aquifer (MRVA). The MRVA is the surficial aquifer of the Mississippi Alluvial Plain (MAP), located in the south-central United States. Employing two machine-learning techniques offered the opportunity to generate model and statistical error and covariance between them to estimate total uncertainty. Potentiometric surface predictions were made at the 1-kilometer grid scale using the National Hydrogeologic Grid (Clark and others, 2018). For a full description...
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