A global topography- and hydrography-based floodability index for the downscaling, analysis, and data-fusion of surface water

Abstract

Firstly, a new global floodability index with a resolution of 3 arc-second is built from topography-based information provided by the MERIT database, using a neural network approach. The topography and permanent water were defined in a coherent way, ensuring the coherency between the resulting floodability index and permanent water, which is unprecedented in previous versions. The evaluation of the floodability index is done with independent observation datasets on surface water and land cover, showing good performances in areas where surface water is naturally driven by topography conditions and limitation in human-affected areas and some specific environments like peatland. Secondly, some of the applications that the floodability index can serve are introduced, including downscaling low-resolution data, analyzing and comparing datasets at different resolution, and data fusion.