preprints.org > environmental and earth sciences > water science and technology > doi: 10.20944/preprints202306.0133.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 1 June 2023 / Approved: 2 June 2023 / Online: 2 June 2023 (05:44:34 CEST)

Coastal areas situated at lower elevations are becoming more vulnerable to flooding as a result of the accelerating global sea level rise. As the sea level rises, so does the groundwater. Barriers designed to shield against marine flooding do not provide protection against flooding caused by rising groundwater. Despite the increasing threat of groundwater flooding, there is limited knowledge about the relationship between sea level rise and groundwater fluctuations. This hinders the ability to adequately consider sea level rise induced groundwater flooding in adaptation initiatives. This study aims to investigate how local groundwater in Juelsminde, Denmark, responds to changes in sea level and to evaluate the predictability of these changes using a machine learning model. The influence of the sea on the shallow groundwater level has been investigated using six groundwater loggers located between 45 and 210 m from the coast. An initial manual analysis of the data revealed a systematic delay in the rise of water levels from the coast to inland areas, with a delay of approximately 15–17 hours per 50 m of distance. Subsequently, a support vector regression model was used to predict groundwater levels 24 hours into the future. This study shows how the groundwater level in Juelsminde is affected by sea level fluctuations. Results suggest a need for increased emphasis on this topic.

Global sea level rise; Groundwater fluctuations; Machine learning model; Predictability; Loggers

Environmental and Earth Sciences, Water Science and Technology

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