Methods to quality assure, plot, summarize, interpolate, and extend groundwater-level information—examples for the Mississippi River Valley alluvial aquifer. (December 2020) - Asquith, William H.; Seanor, Ronald C.; McGuire, Virginia L.; Kress, Wade H.
Abstract: Large-scale computational investigations of groundwater levels are proposed to accelerate science delivery through a workflow spanning database assembly, statistics, and information synthesis and packaging. A water-availability study of the Mississippi River alluvial plain, and particularly the Mississippi River Valley alluvial aquifer (MRVA), is ongoing. Software (visGWDBmrva) has been released as part of the study that demonstrates groundwater informatics for the aquifer. Considerable water-level data collected by multiple agencies over a seven-state area exist (18, 903 wells; 287, 272 measurements [April 22, 2019]). Data and metadata quality assurance methods, basic statistics, hydrograph visualization, outlier identification, hypothesis testing, and time-series modeling are described. Two approaches (generalized additive models [GAMs] and support vector machines [SVMs]) are used for data interpolation and extension to monthly water-level estimates. Numerical congruence between GAM and SVM estimates will be useful to limit inclusion of monthly estimates from subsequent science activities. Highlights: Informatics are reviewed for groundwater-level processing for the Mississippi River Valley alluvial aquifer (MRVA). visGWDBmrva software is demonstrated for seven MRVA wells through depiction of hydrographs and neighboring hydrographs. Generalized additive models (GAMs) and support vector machines (SVMs) used for joint estimation of monthly water levels. Numerical congruence of site-specific and neighborhood GAMs and SVMs could limit use of monthly estimates in other endeavors. GAMs and SVMs provide outlier detection and help allocate resources for follow-up investigations for enhancing data quality.