Disentangling coastal groundwater level dynamics on a global data set
Preprint, 2023

This study aims to identify common hydrogeological patterns and to gain a deeper understanding of the underlying similarities and their link to physiographic, climatic, and anthropogenic controls of coastal groundwater. The most striking aspects of GWL dynamics and their controls were identified through a combination of statistical metrics, calculated from about 8,000 groundwater hydrographs, and pattern recognition, classification, and explanation using machine learning techniques and SHapley Additive exPlanations (SHAP). Overall, four different GWL dynamics patterns emerge, independent of the different seasons, time series lengths, and periods. We show in this study that similar GWL dynamics can be observed around the world with different combinations of site characteristics, but also that the main factors differentiating these patterns can be identified. Three of the identified patterns exhibit high short-term and interannual variability and are most common in regions with low terrain elevation and shallow groundwater depth. Climate and soil characteristics are most important in differentiating these patterns. This study provides new insights into the hydrogeological behavior of groundwater in coastal regions and guides systematic and holistic groundwater monitoring and modelling, motivating to consider various aspects of GWL dynamics when, for example, estimating climate-driven GWL changes – especially when information on potential controls is limited.

Author

Annika Nolte

Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon

Ezra Haaf

Chalmers, Architecture and Civil Engineering, Geology and Geotechnics

Benedikt Heudorfer

Steffen Bender

Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon

Jens Hartmann

University of Hamburg

Subject Categories

Ecology

Oceanography, Hydrology, Water Resources

DOI

10.5194/hess-2023-180

More information

Latest update

4/5/2024 8