TY - JOUR
T1 - Use of Hydrological Models in Global Stochastic Flood Modelling
AU - Olcese, Gaia
AU - Bates, Paul D.
AU - Neal, Jeffrey C.
AU - Sampson, Christopher C.
AU - Wing, Oliver E. J.
AU - Quinn, Niall
N1 - 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.
PY - 2022/12/13
Y1 - 2022/12/13
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/686423
UR - https://onlinelibrary.wiley.com/doi/10.1029/2022WR032743
U2 - 10.1029/2022wr032743
DO - 10.1029/2022wr032743
M3 - Article
SN - 0043-1397
JO - Water Resources Research
JF - Water Resources Research
ER -