Tracing the value of data for flood loss modelling

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