Disentangling coastal groundwater level dynamics on a global data set

Abstract

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.

Comment on hess-2024-111

“…Comparing the performance of different models can help both practitioners and developers to improve existing models by learning from other modeling concepts (Kollet et al, 2017) or calibration approaches (e.g., Freyberg, 1988). This is commonly mentioned as a reason why hydrologists should be interested in machine learning models (e.g., Haaf et al, 2023;Nolte et al, 2023;Kratzert et al, 2019), as they may result in new knowledge that in turn may be used to improve empirical and process-based groundwater models. Several studies have compared models to simulate head time series (e.g., Sahoo and Jha, 2013;Shapoori et al, 2015;Wunsch et al, 2021;Zarafshan et al, 2023;Vonk et al, 2024).…”

Comment on hess-2024-111

“…Comparing the performance of different models can help both practitioners and developers to improve existing models by learning from other modeling concepts (Kollet et al, 2017) or calibration approaches (e.g., Freyberg, 1988). This is commonly mentioned as a reason why hydrologists should be interested in machine learning models (e.g., Haaf et al, 2023;Nolte et al, 2023;Kratzert et al, 2019), as they may result in new knowledge that in turn may be used to improve empirical and process-based groundwater models. Several studies have compared models to simulate head time series (e.g., Sahoo and Jha, 2013;Shapoori et al, 2015;Wunsch et al, 2021;Zarafshan et al, 2023;Vonk et al, 2024).…”

Comment on hess-2024-111

“…Comparing the performance of different models can help both practitioners and developers to improve existing models by learning from other modeling concepts (Kollet et al, 2017) or calibration approaches (e.g., Freyberg, 1988). This is commonly mentioned as a reason why hydrologists should be interested in machine learning models (e.g., Haaf et al, 2023;Nolte et al, 2023;Kratzert et al, 2019), as they may result in new knowledge that in turn may be used to improve empirical and process-based groundwater models. Several studies have compared models to simulate head time series (e.g., Sahoo and Jha, 2013;Shapoori et al, 2015;Wunsch et al, 2021;Zarafshan et al, 2023;Vonk et al, 2024).…”