Abstract. In light of the increasing losses from flash floods intensified by climate change, there is a critical need for improved loss models. Loss assessments predominantly focus on fluvial flood processes, leaving a significant gap in understanding the key drivers of flash floods and the effect of emergency response on losses. To address these gaps, we introduce FLEMOflash – a novel multivariate probabilistic Flood Loss Estimation Model compilation for flash floods. The models are developed for companies and households based on survey data collected after flash flood events in 2002, 2016, and 2021 in Germany. FLEMOflash employs a data-driven feature selection approach, combining machine learning techniques (Elastic Net, Random Forest, XGBoost) to select key drivers influencing flash flood losses and Bayesian networks to model probabilistic loss estimates, including uncertainty. Model-based findings show that in extreme hazard scenarios, successful implementation of emergency measures can reduce building losses by up to 47 % for large companies. Households who knew exactly what to do during high water depth were able to reduce their building losses by 77 % and contents losses by 55 %. Thus, FLEMOflash can support risk communication and management by providing reliable estimation of flash flood losses.
Singh et al. (Tue,) studied this question.