Use of Hydrological Models in Global Stochastic Flood Modeling. Issue 12 (19th December 2022)

• Record Type: Journal Article
• Authors: Olcese, Gaia; Bates, Paul D.; Neal, Jeffrey C.; Sampson, Christopher C.; Wing, Oliver E. J.; Quinn, Niall; Beck, Hylke E.
• Is Part Of: Water resources research. Volume 58:Issue 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-19
• Subjects: stochastic modeling -- flood -- global flood modeling -- global hydrological models; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2022WR032743
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: 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 modeling 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, Thailand, and Europe) are simulated using both gauged and modeled discharge. By comparing them, we analyze how a model‐based approach can simulate spatial dependency in large‐scale flood modeling. 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. Key Points: Large scale stochastic flood models can simulate synthetic flood events with a realistic spatial structure even in data‐poor regions Using a model‐based multivariate extreme model provides more robust dependence estimates than empirical distance decay functions Modeled flow can be used in data poor‐regions to characterize dependence in large‐scale stochastic flood models and estimate flood risk