Data-driven Estimation of Groundwater Level Time-Series Using Comparative Regional Analysis

Preprint, 2022

A new method is presented to efficiently estimate daily groundwater level time series at unmonitored sites by linking groundwater dynamics to local hydrogeological system controls. The presented approach is based on the concept of comparative regional analysis, an approach widely used in surface water hydrology, but uncommon in hydrogeology. The method uses regression analysis to estimate cumulative frequency distributions of groundwater levels (groundwater head duration curves (HDC)) at unmonitored locations using physiographic and climatic site descriptors. The HDC is then used to construct a groundwater hydrograph using time series from distance-weighted neighboring monitored (donor) locations. For estimating times series at unmonitored sites, in essence, spatio-temporal interpolation, stepwise multiple linear regression, extreme gradient boosting, and nearest neighbors are compared. The methods were applied to ten-year daily groundwater level time series at 157 sites in alluvial unconfined aquifers in Southern Germany. Models of HDCs were physically plausible and showed that physiographic and climatic controls on groundwater level fluctuations are nonlinear and dynamic, varying in significance from “wet” to “dry” aquifer conditions. Extreme gradient boosting yielded a significantly higher predictive skill than nearest neighbor and multiple linear regression. However, donor site selection is of key importance. The study presents a novel approach for regionalization and infilling of groundwater level time series that also aids conceptual understanding of controls on groundwater dynamics, both central tasks for water resources managers.