Analysis of hydrogeological behavior of coastal aquifers based on clusters of groundwater hydrograph features and environmental drivers

Distinguishing between natural and anthropogenic impacts on groundwater systems at regional scale is not straightforward using current data-driven and traditional numerical groundwater models. This limits their benefit for groundwater level predictions and thus future-oriented groundwater resource management. We propose an approach to leverage the large amount of information and variability in the characteristics of groundwater hydrographs and environmental factors to obtain generalized insights into the influences of natural and anthropogenic factors on specific patterns in groundwater hydrographs using data-driven regionalization of groundwater level dynamics. In our approach, we focus on coastal regions that are often under water stress due to the water demands of growing coastal populations building on a data set containing several thousand wells in Europe, North America, South Africa, and Australia.

The approach comprises construction and comparison of multiple unsupervised machine learning cluster models based on a) groundwater level dynamics information, aggregated into groundwater hydrograph features, and b) selected environmental drivers that potentially influence natural groundwater recharge and discharge processes. Environmental descriptors were extracted at well locations from available global map products. We discuss the extent to which our selection of features can express the range of dynamics in representative groundwater hydrographs of clustered basins. Furthermore, we compare the similarity of anthropogenic factors within and between clusters in order to test our hypothesis that hydrograph patterns differ in response to natural processes but irrespective of anthropogenic influences. This would contribute to our understanding of natural processes in coastal groundwater systems.