Groundwater Level Mapping Tool: An open source web application for assessing groundwater sustainability. (September 2020)

• Record Type: Journal Article
• Authors: Evans, Steven W.; Jones, Norman L.; Williams, Gustavious P.; Ames, Daniel P.; Nelson, E. James
• Is Part Of: Environmental modelling & software. Volume 131(2020)
• Publication Date: 2020-09
• Subjects: Groundwater -- Aquifer storage -- Data imputation -- Time series -- Water resources management -- Sustainability; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104782
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Decision makers need an accurate understanding of aquifer storage trends to effectively manage groundwater resources. Groundwater is difficult to monitor and quantify since the data collected from monitoring wells are often available only at irregular and infrequent intervals. We present an open-source web application (app) to visualize groundwater data over time and automatically calculate changes in aquifer storage volume to help managers assess aquifer sustainability. This app uses a novel multi-linear regression (MLR) algorithm to impute missing data for infrequently sampled wells, using correlated data from other wells in the same aquifer. The app uses this MLR-imputed data to spatially interpolate water levels throughout an aquifer at user-specified time steps using GSLIB Kriging code. Based on our tests of unconfined aquifer systems, the imputed data increased the accuracy of the spatial interpolation over standard methods and resulted in estimates of aquifer storage change comparable to those of detailed USGS studies. Highlights: Open-source web app for groundwater level mapping and storage change analysis. Uses machine learning algorithm (multi-linear regression) for data imputation. Aquifer storage change estimates compare favorably to previous studies.