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
Worldwide, groundwater is a major source for agricultural irrigation, industrial processes, mining, and drinking water
We used the multi-linear regression (MLR) method of time series extension to make predictions at each well, and compared against a naïve prediction, where the groundwater was assumed to remain constant after 1995, and a linear least squares pre diction (Jackson et al, 2019; Roberts et al, 2018)
The objective of this work was to develop a technique to improve the accuracy of groundwater level mapping using a simple, low-cost approach
Summary
Groundwater is a major source for agricultural irrigation, industrial processes, mining, and drinking water. Fresh groundwater is often abundant, and extensively used, it is difficult and expensive to accurately quantify, compared to surface water re sources. The state of surface water resources is readily visible to the naked eye, viewable from satellites, can be measured and is straightforward to quantify. This is not the case for groundwater, which generally requires drilling a series of monitoring wells in order to measure the location of the phreatic surface and characterize aquifer properties. Groundwater levels are heavily influenced by climatic, geographic, lithological, and human factors For these reasons it is difficult to quantify and map aquifer water levels and storage volume changes
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