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There is an urgent need for the climate community to translate their meteorological drivers into relevant hazard estimates. This is especially important for the climate attribution and climate projection communities as we seek to understand how anthropogenic climate change has, and will, impact our society. This can be particularly challenging because there are often multiple specialized steps to model the hazard. Current climate change assessments of flood risk typically neglect key processes, and instead of explicitly modeling flood inundation, they commonly use precipitation or river flow as proxies for flood hazard. Here, we lay out a clear methodology for taking meteorological drivers, e.g., from observations or climate models, through to high-resolution (~90 m) river flooding (fluvial) hazards. The meteorological inputs (precipitation and air temperature) are transformed through a series of modeling steps to yield, in turn, surface runoff, river flow, and flood inundation. We explore uncertainties at different modeling steps. The flood inundation estimates can then be directly related to impacts felt at community and household levels to determine exposure and risks from flood events. The approach uses global data-sets and thus can be applied anywhere in the world, but we use the Brahmaputra river in Bangladesh as a case study in order to demonstrate the necessary steps in our hazard framework.
|Journal||Geoscientific Model Development|
|Publication status||Submitted - 19 Aug 2020|