24 Aug 2023
 | 24 Aug 2023
Status: this preprint is currently under review for the journal HESS.

Disentangling coastal groundwater level dynamics on a global data set

Annika Nolte, Ezra Haaf, Benedikt Heudorfer, Steffen Bender, and Jens Hartmann

Abstract. 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.

Annika Nolte et al.

Status: open (until 19 Oct 2023)

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Annika Nolte et al.

Data sets

Disentangling coastal groundwater level dynamics on a global data set - data Annika Nolte

Annika Nolte et al.


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Short summary
This study analyzes about 8,000 groundwater level (GWL) time series from five continents to explore similarities in groundwater systems at different scales. By applying statistical metrics and machine learning, we identify common GWL dynamics and their controlling factors. We also highlight the opportunities and barriers of using these approaches to improve our understanding of groundwater recharge and discharge processes.