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From global circulation to local flood loss: Coupling models across the scales

Felder, Guido, Gómez-Navarro, Juan José, Zischg, Andreas Paul, Raible, Christoph C., Röthlisberger, Veronika, Bozhinova, Denica, Martius, Olivia, Weingartner, Rolf
The Science of the total environment 2018 v.635 pp. 1225-1239
atmospheric circulation, hydrologic models, industry, insurance, risk, runoff, watersheds
Comprehensive flood risk modeling is crucial for understanding, assessing, and mitigating flood risk. Modeling extreme events is a well-established practice in the atmospheric and hydrological sciences and in the insurance industry. Several specialized models are used to research extreme events including atmospheric circulation models, hydrological models, hydrodynamic models, and damage and loss models. Although these model types are well established, and coupling two to three of these models has been successful, no assessment of a full and comprehensive model chain from the atmospheric to local scale flood loss models has been conducted. The present study introduces a model chain setup incorporating a GCM/RCM to model atmospheric processes, a hydrological model to estimate the catchment's runoff reaction to precipitation inputs, a hydrodynamic model to identify flood-affected areas, and a damage and loss model to estimate flood losses. Such coupling requires building interfaces between the individual models that are coherent in terms of spatial and temporal resolution and therefore calls for several pre- and post-processing steps for the individual models as well as for a computationally efficient strategy to identify and model extreme events. The results show that a coupled model chain allows for good representation of runoff for both long-term runoff characteristics and extreme events, provided a bias correction on precipitation input is applied. While the presented approach for deriving loss estimations for particular extreme events leads to reasonable results, two issues have been identified that need to be considered in further applications: (i) the identification of extreme events in long-term GCM simulations for downscaling and (ii) the representativeness of the vulnerability functions for local conditions.