Third party funded individual grant
Acronym: GrARBeKo
Start date : 19.04.2025
End date : 18.10.2026
As climate change drives more frequent extreme weather events, groundwater flooding poses an increasing threat to infrastructure and public safety. The GrARBeKo project (Grundwasserüberschwemmungen: Entwicklung eines Ansatzes zur Risikobewertung und -kommunikation) addresses this challenge by developing a robust, data-driven methodology for risk assessment and public engagement.
Led by Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), the project brings together key partners:
Using Garching as a pilot site, GrARBeKo combines groundwater modeling (MODFLOW) with machine learning techniques (ML) to generate rapid, high-resolution flood risk maps. By involving citizens in the installation of low-cost sensors and the collection of real-world data, such as water levels in basements, the project strengthens both the technical models and community awareness.
The project also aims to test innovative sensing technologies, improve uncertainty quantification using Bayesian inference, and produce guidelines that can be adapted by other municipalities. In this way, GrARBeKo improves flood preparedness locally while offering a scalable model for climate resilience planning.