Validation of inundation prediction from JULES-CaMa-Flood global land surface simulations

Abstract:

Wetlands play a key role in hydrological and biogeochemical cycles, are home to a large part of global biodiversity and provide value to human society in the form of multiple ecosystem services. However, despite the importance of inundated areas, obtaining reliable data on the extent of global inundated areas and the magnitude of their contribution to global hydrological function and local water and carbon cycles remains problematic, and the uncertainties in available data sources remain broadly unquantified. We address these problems head-on in the Hydro-JULES project by taking a leading global data product on inundation extents (GIEMS) and matching against predictions from a leading global hydrodynamic model (CaMa-Flood) driven by runoff data generated from the JULES land surface model using high-resolution outputs from the eartH2Observe project. The reliability of the model and the data product are assessed in a number of case studies (inc. the Congo, the Amazon), which show that it performs well in the basins of large rivers, with a good match between corresponding seasonal cycles. Reducing uncertainty in inundation prediction has long been a key goal for policymakers concerned with implementing natural flood management plans or working in regions where water resources are under threat, but over the last decade this has additionally been recognised more widely in the scientific community in terms of predictions of climate change. This study provides timely data that can contribute to this effort, with positive impacts on our current ability to make such critical predictions at both national and global levels.