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.

决策者需要对含水层存储趋势有准确的了解，才能有效地管理地下水资源。地下水很难监测和量化，因为从监测井收集的数据通常只能以不定期和不频繁的间隔获得。我们提供了一个开放源代码Web应用程序（app），以可视化方式显示一段时间内的地下水数据，并自动计算含水层存储量的变化，以帮助管理人员评估含水层的可持续性。该应用程序使用新颖的多线性回归（MLR）算法，使用来自同一含水层中其他井的相关数据，为不经常采样的井估算丢失的数据。该应用程序使用此MLR估算数据，使用GSLIB Kriging代码在用户指定的时间步长上对整个含水层中的水位进行空间插值。根据我们对无限制含水层系统的测试，