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Flood loss modelling is associated with considerable uncertainty. If prediction uncertainty of flood loss models is large, the reliability of model outcomes is questionable, and thus challenges the practical usefulness. A key problem in flood loss estimation is the transfer of models to geographical regions and to flood events that may differ from the ones used for model development. Variations in local characteristics and continuous system changes require regional adjustments and continuous updating with current evidence. However, acquiring data on damage influencing factors is usually very costly. Therefore, it is of relevance to assess the value of additional data in terms of model performance improvement. We use empirical flood loss data on direct damage to residential buildings available from computer aided telephone interviews that were compiled after major floods in Germany. This unique data base allows us to trace the changes in predictive model performance by incrementally extending the data base used to derive flood loss models. Two models are considered: a uni-variable stage damage function and RF-FLEMO, a multi-variable probabilistic model approach using Random Forests. Additional data are useful to improve model predictive performance and increase model reliability, however the gains also seem to depend on the model approach.

Large-scale flood risk assessments are needed for a number of purposes, such as national strategic planning or re-insurance purposes. However, large-scale assessments are typically limited to hazard assessment and/or they are pieced together from smaller-scale assessments, leading to spatially inconsistent hazard and risk estimates. We present the coupled model chain RFM (Regional Flood Model) which is able to derive spatially consistent hazard and risk estimates for large scales (several 100,000 km2). It consists of a hydrological model, a coupled 1D–2D hydrodynamic model and a flood loss model. This model chain can be driven by observed meteorology, output from regional climate models or a weather generator. In this application, we demonstrate for river basins in Germany that this approach is able to provide spatially consistent large-scale patterns of hazard and risk. A multi-site, multi-variate weather generator provides 10,000 years of spatially consistent weather at daily resolution which is used as input for the model chain. This approach allows deriving discharge, inundation and damage patterns which respect spatial interactions within and beyond catchment boundaries.