Abstract
Current estimates of global flood exposure are made using datasets that distribute population counts homogenously across large lowland floodplain areas. When intersected with simulated water depths, this results in a significant mis-estimation. Here, we use new highly resolved population information to show that, in reality, humans make more rational decisions about flood risk than current demographic data suggest. In the new data, populations are correctly represented as risk-averse, largely avoiding obvious flood zones. The results also show that existing demographic datasets struggle to represent concentrations of exposure, with the total exposed population being spread over larger areas. In this analysis we use flood hazard data from a ~90 m resolution hydrodynamic inundation model to demonstrate the impact of different population distributions on flood exposure calculations for 18 developing countries spread across Africa, Asia and Latin America. The results suggest that many published large-scale flood exposure estimates may require significant revision.
Highlights
Current estimates of global flood exposure are made using datasets that distribute population counts homogenously across large lowland floodplain areas
The reduction in exposed population when the High Resolution Settlement Layer (HRSL) data are used to define population distribution can be as much as ~60%; in Uganda, exposure totals reduce from ~4 M to 1.66 M when WorldPop and LandScanTM data are replaced by HRSL
The total population exposed to the 100 year flood in the 18 countries was calculated to be 134, 122, and 101 million when using the LandScanTM, WorldPop, and HRSL data, respectively
Summary
Current estimates of global flood exposure are made using datasets that distribute population counts homogenously across large lowland floodplain areas. The results show that existing demographic datasets struggle to represent concentrations of exposure, with the total exposed population being spread over larger areas In this analysis we use flood hazard data from a ~90 m resolution hydrodynamic inundation model to demonstrate the impact of different population distributions on flood exposure calculations for 18 developing countries spread across Africa, Asia and Latin America. A wide range of such data sets are available including GPW (Gridded Population of the World)[14], LandscanTM15, WorldPop[16], GHSL (Global Human Settlement)[17], GUF (Global Urban Footprint)[18], and HYDE (History Database of the Global Environment)[19] These products use similar input data to derive population densities, Calka et al.[12] note there is no standardised methodology for doing this. There can be large differences between the resolution at which the hydraulic computations are performed
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