Abstract
The growing worldwide impact of flood events has motivated the development and application of global flood hazard models (GFHMs). These models have become useful tools for flood risk assessment and management, especially in regions where little local hazard information is available. One of the key uncertainties associated with GFHMs is the estimation of extreme flood magnitudes to generate flood hazard maps. In this study, the 1-in-100 year flood (Q100) magnitude was estimated using flow outputs from four global hydrological models (GHMs) and two global flood frequency analysis datasets for 1350 gauges across the conterminous US. The annual maximum flows of the observed and modelled timeseries of streamflow were bootstrapped to evaluate the sensitivity of the underlying data to extrapolation. Results show that there are clear spatial patterns of bias associated with each method. GHMs show a general tendency to overpredict Western US gauges and underpredict Eastern US gauges. The GloFAS and HYPE models underpredict Q100 by more than 25% in 68% and 52% of gauges, respectively. The PCR-GLOBWB and CaMa-Flood models overestimate Q100 by more than 25% at 60% and 65% of gauges in West and Central US, respectively. The global frequency analysis datasets have spatial variabilities that differ from the GHMs. We found that river basin area and topographic elevation explain some of the spatial variability in predictive performance found in this study. However, there is no single model or method that performs best everywhere, and therefore we recommend a weighted ensemble of predictions of extreme flood magnitudes should be used for large-scale flood hazard assessment.
Highlights
The vast worldwide impact of flood events, affecting 2.3 billion people and causing more than 662 US dollars in economic damage between 1995 and 2015 alone (CRED and UNISDR 2015), has motivated the development and application of global flood hazard models (GFHMs)
The aim of our study is to provide the global flood hazard modelling community with some insight into how they could advance their frameworks in the future, and we have included this dataset in our comparison as it is a potentially useful dataset of flood frequency estimates on the global scale
How well do regionalised flood frequency analysis (RFFA) and global hydrological model (GHM) reproduce observed extreme flood magnitudes? To compare how well GHMs and RFFA methods reproduce extreme flood magnitudes the %bias was calculated at each gauge between the Q100 estimate calculated from each model and the observed data
Summary
The vast worldwide impact of flood events, affecting 2.3 billion people and causing more than 662 US dollars in economic damage between 1995 and 2015 alone (CRED and UNISDR 2015), has motivated the development and application of global flood hazard models (GFHMs). GFHMs provide an understanding of the spatial extent and frequency of flood hazard at large spatial scales and can identify flood-prone areas in ungauged basins. They are increasingly being used for international disaster risk management and have been applied by the World Bank and the Global Facility for Disaster Reduction and Recovery (Ward et al2015). There are two key approaches for generating flood magnitudes (Trigg et al 2016) These have been developed to account for the global scale of analysis and the need for making predictions in regions of the world that are data limited:
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