Use of Hydrological Models in Global Stochastic Flood Modelling

Gaia Olcese, Paul D. Bates, Jeffrey C. Neal, Christopher C. Sampson, Oliver E. J. Wing, Niall Quinn

Research output: Contribution to journalArticlepeer-review


Typical flood models do not take into consideration the spatial structure of flood events, which can lead to errors in the estimation of flood risk at regional to continental scales. Large-scale stochastic flood models can simulate synthetic flood events with a realistic spatial structure, although this method is limited by the availability of gauge data. Simulated discharge from global hydrological models has been successfully used to drive stochastic modelling in data-rich areas. This research evaluates the use of discharge hindcasts from global hydrological models in building stochastic river flood models globally: synthetic flood events in different regions of the world (Australia, South Africa, South America, Malaysia and Thailand and Europe) are simulated using both gauged and modelled discharge. By comparing them, we analyse how a model-based approach can simulate spatial dependency in large-scale flood modelling. The results show a promising performance of the model-based approach, with errors comparable to those obtained over data-rich sites: a model-based approach simulates the joint occurrence of relative flow exceedances at two given locations similarly to when a gauge-based statistical model is used. 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.
Original languageEnglish (US)
JournalWater Resources Research
StatePublished - Dec 13 2022
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-12-15
Acknowledgements: The following organizations are thanked for providing observed streamflow data: the United States Geological Survey (USGS), the Global Runoff Data Centre (GRDC), the Brazilian Agência Nacional de Águas, EURO-FRIEND-Water, the Water Survey of Canada (WSC), the Australian Bureau of Meteorology (BoM), and the Chilean Chilean Center for Climate and Resilience Research (CR2). Gaia Olcese is being supported by the Engineering and Physical Sciences Research Council (EPSRC) and Fathom during her PhD. Paul Bates is supported by a Royal Society Wolfson Research Merit award. Jeffrey Neal is supported by UKRI NERC grants NE/S003061/1 and NE/S006079/1.

ASJC Scopus subject areas

  • Water Science and Technology


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