Revealing controls on groundwater level patterns by backward variable elimination in the Baltic states

Groundwater level data are often spatially and temporary unevenly distributed, and the heterogeneity of hydrogeological systems limit simple extrapolation to data-scarce locations. Here, similarity of hydrogeological systems can be used to transfer dynamics of groundwater levels from monitored to unmonitored locations. Therefore, the major controls of hydrogeological systems need to be investigated. This study aims to uncover dominant patterns of the groundwater levels and to connect them with possible controls or driving features (such as the catchment and topography characteristics, aquifer properties, climate and land use) of hydrogeological systems in the Baltic states. Part of the spatial features were calculated for three different buffer sizes (300m, 3km and 5km). Gapless daily groundwater hydrographs (prepared by Bikše et al., 2023) were used to create groundwater level patterns by clustering approach. Then, random forest classification was performed using a backward variable elimination approach to find the most significant site-descriptive features for each cluster. The results show that the most significant tend to be geological features in the well cross-section, followed by topographic features within the vicinity of the well. We also found that anthropogenic impacts can play a significant role and should be considered when analyzing groundwater level patterns.

References:

Bikše, J., Retike, I., Haaf, E., Kalvāns, A., 2023. Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns. EarthArXiv. https://doi.org/10.31223/x5n94x

 

"The study has been funded by Iceland, Liechtenstein and Norway through the EEA and Norway Grants Fund for Regional Cooperation project No.2018-1-0137 “EU-WATERRES: EU-integrated management system of cross-border groundwater resources and anthropogenic hazards" and by the Latvian Council of Science, project “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”, project No. lzp-2019/1-0165.