Integrating Data-Driven Approaches and High-Resolution Data for Enhanced Groundwater Management in Lemvig Municipality

This study addresses the escalating challenges associated with high near-surface groundwater levels in Lemvig Municipality, focusing on two distinct sites: an agricultural field and an urban area, both of which are experiencing issues with near-surface groundwater levels. The research aims to develop a comprehensive understanding of groundwater dynamics and their response to precipitation events in these areas.

A network of 82 IoT groundwater loggers, distributed across the two areas with distances between boreholes as close as 30 meters, monitors the near-surface groundwater levels at intervals down to every 15 minutes. The high-resolution data enables the calculation of weekly reconstructed groundwater tables and estimation of flow patterns for both locations, identifying regions at risk of flooding from high groundwater levels. The study also examines the areas response to rainfall events and hence their vulnerability to extreme precipitation. An estimation of the necessary data density required to perform the analyses will be provided, ensuring that the results are adequate for stakeholders to implement targeted climate adaptation and management of the near-surface groundwater.

Due to the high near-surface groundwater levels the water utility sewer-system in the two areas experiences at present an excessive water inflow, particularly during the winter months. Data from the areas have already been used to confirm infiltration into the sewer-network and to identify areas where sewers are likely situated below the groundwater table. This information is crucial for managing the water supply and mitigating the impacts of high groundwater levels. The data gathered in this study is thus already offering valuable insights into effective groundwater management and climate adaptation strategies.

The study will proceed with developing a process-based hydrological model and use data driven techniques to investigate the potential of enhancing prediction accuracy for different scenario calculations.