Towards a new level of collaboration and communication between weather and flood forecasting: an overview of ongoing efforts in Germany

Challenges and risks related to flood prediction and warning, which became evident during and in the aftermath of devastating flood events in recent years in Germany, call for a new level of cooperation between weather and flood forecasting and their respective operational services. While these domains have historically been structurally quite separate in Germany, several collaborative projects are now laying the foundation for increased exchange and joint development.

The first phase of the project “Co-Design of Innovations between Weather and Flood Forecasting” (Italia–Deutschland science-4-services, IDEA-S4S), initiated directly in response to the lessons learned from the Ahrtal flood in 2021, addresses multiple topics. One focus is on improving mutual knowledge and understanding of forecast evaluation and verification, especially given the constantly increasing number of weather forecasting systems. Another emphasis has been placed on analyzing and training communication along the entire warning chain down to disaster management authorities, in particular regarding the handling of forecast uncertainty. The upcoming second phase of Co-Design aims to deepen this communication aspect by testing decision-making strategies that incorporate probabilistic criteria in multi-stage alert and readiness plans, in direct collaboration with emergency response units. Moreover, the second project phase will, among other topics, explore ML-based approaches to combine multiple ensemble models with different lead times into a best-guess ensemble as input for a hydrological model, thereby facilitating the use of the growing number of models in downstream flood applications.

The joint research project KI-HopE-De aims to test new machine-learning-based approaches and brings together meteorologists and hydrologists from both academia and operational services. First, a new high-resolution hourly hydrometeorological dataset (CAMELS-DE-1h) will be compiled and published for around 1,600 catchments across Germany. Second, various training strategies—from both hydrological and meteorological perspectives—for a regionally trained LSTM flood forecasting model will be developed to provide a benchmark model that is compared with the current operational models in Germany.

This contribution provides an overview of ongoing projects and highlights related presentations at the conference. We will share results and provide an outlook on upcoming work, and look forward to exchanging experiences with other initiatives at the intersection of meteorology and hydrology.