Technical note: Removing dynamic sea-level influences from groundwater-level measurements

Haehnel, Patrick; Rasmussen, Todd C.; Rau, Gabriel C.

doi:10.5194/hess-2023-54

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Furthermore, Heudorfer et al (2019) 2023) for hydroclimatic time series, would be of interest. High-resolution time series are, for example, required for analyzing the interaction of groundwater with the sea (Haehnel et al, 2023). Accounting for SWI in GWL dynamics pattern analysis is best supported by pattern recognition or correction with groundwater chemistry data (Narvaez-Montoya et al, 2023;Rau et al, 2020).…”

Section: Hydrogeological Similarity and Scaling Effectmentioning

confidence: 99%

Disentangling coastal groundwater level dynamics on a global data set

Nolte,

Haaf,

Heudorfer

et al. 2023

Preprint

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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.

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Section: Hydrogeological Similarity and Scaling Effectmentioning

confidence: 99%

Disentangling coastal groundwater level dynamics on a global data set

Nolte,

Haaf,

Heudorfer

et al. 2023

Preprint

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Disentangling coastal groundwater level dynamics in a global dataset

Nolte,

Haaf,

Heudorfer

et al. 2024

Hydrol. Earth Syst. Sci.

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Abstract. Groundwater level (GWL) dynamics result from a complex interplay between groundwater systems and the Earth system. 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 groundwater in coastal regions. The most striking aspects of GWL dynamics and their controls were identified through a combination of statistical metrics, calculated from about 8000 groundwater hydrographs, pattern recognition using clustering algorithms, classification using random forest, and SHapley Additive exPlanations (SHAPs). Hydrogeological similarity was defined by four clusters representing distinct patterns of GWL dynamics. These clusters can be observed globally across different continents and climate zones but simultaneously vary regionally and locally, suggesting a complicated interplay of controlling factors. The main controls differentiating GWL dynamics were identified, but we also provide evidence for the currently limited ability to explain GWL dynamics on large spatial scales, which we attribute mainly to uncertainties in the explanatory data. Finally, this study provides guidance for systematic and holistic groundwater monitoring and modeling and motivates a consideration of the different aspects of GWL dynamics, for example, when predicting climate-induced GWL changes, and the use of explainable machine learning techniques to deal with GWL complexity – especially when information on potential controls is limited or needs to be verified.

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Technical note: Removing dynamic sea-level influences from groundwater-level measurements

Haehnel,

Rasmussen,

Rau

2024

Hydrol. Earth Syst. Sci.

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Abstract. The sustainability of limited freshwater resources in coastal settings requires an understanding of the processes that affect them. This is especially relevant for freshwater lenses of oceanic islands. Yet, these processes are often obscured by dynamic oceanic water levels that change over a range of timescales. We use regression deconvolution to estimate an oceanic response function (ORF) that accounts for how sea-level fluctuations affect measured groundwater levels, thus providing a clearer understanding of recharge and withdrawal processes. The method is demonstrated using sea-level and groundwater-level measurements on the island of Norderney in the North Sea (northwestern Germany). We expect that the method is suitable for any coastal groundwater system where it is important to understand processes that affect freshwater lenses or other coastal freshwater resources.

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Technical note: Removing dynamic sea-level influences from groundwater-level measurements

Cited by 3 publications

References 29 publications

Disentangling coastal groundwater level dynamics on a global data set

Disentangling coastal groundwater level dynamics on a global data set

Disentangling coastal groundwater level dynamics in a global dataset

Technical note: Removing dynamic sea-level influences from groundwater-level measurements

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