Use of hydrological models in global stochastic flood modelling

Abstract

Stochastic flood models can simulate synthetic flood events with a realistic spatial structure, unlike traditional flood models, which do not take into consideration the spatial dependency of flood events. This is particularly relevant to loss calculations at regional to continental scales. The development of large-scale stochastic flood models has been limited so far by the availability of gauge data, needed as a model input. Global hydrological models can provide simulated discharge hindcasts that have been used to drive stochastic flood modelling in data-rich areas. This research evaluates the use of discharge hindcasts from global hydrological models in building stochastic river flood models globally by simulating synthetic flood events in different regions of the world. The results (published in a recent paper in WRR) show a promising performance of the model-based approach, with errors comparable to those obtained over data-rich sites. This suggests that a network of synthetic gauge data derived from global hydrological models would allow the development of a stochastic flood model with detailed spatial dependency, generating realistic event sets in data-scarce regions and loss exceedance curves where exposure data are available. As part of this research, we are currently working on the development of a stochastic flood model of Southeast Asia using discharge data from global hydrological models.