A Simplified Conceptual Model Using Global Open-Source Datasets to Provide Continental and Global Scale Fluvial Flood Risk.

Hydraulic modelling is used to accurately model extreme flood events but comes with high computational costs, significant data requirements, and long simulation times. Increasing computational resources and higher-resolution data with more spatial coverage means that global-scale flood risk modelling capabilities are constantly evolving. Taking a nested approach, we used the HAND-SRC methodology to develop flood risk data at a continent-scale level and identify areas that would benefit from hydraulic modelling at a more granular level.

Height Above Nearest Drainage (HAND) is a simplified method used for flood zoning and identifying areas at risk of flooding using a Digital Elevation Model (DEM), and drainage network – which can be derived from the DEM. The HAND-SRC method uses channel geometry estimates, obtained from the DEM, and the Manning’s equation, to develop synthetic rating curves (SRC) which allow the conversion of flood discharges to a water height. The flood height can then be combined with a HAND model to produce a flood map. Existing applications of HAND SRC include Central and Eastern Canada (Scriven et al. 2021), and rivers in Texas and North Carolina (Zheng et al. 2018), using national datasets.

 We applied the HAND-SRC methodology using Python and open-source global datasets, to create continental-scale flood risk maps for Europe and the US.  The use of open-source global datasets and Python means the method has the potential to be applied anywhere globally.