Towards large-scale compound flood risk modeling

In low-lying coastal areas floods occur from (combinations of) fluvial, pluvial, and coastal drivers. If these drivers co-occur, they can cause or exacerbate flooding, and are referred to as compound flood events. Furthermore, if these flood drivers are statistically dependent, their joint likelihood might be misrepresented if dependence is not accounted for. However, most large-scale flood risk models do not account for the hydrodynamic interactions and statistical dependence between flood drivers. We present a globally-applicable framework for compound flood risk assessments using combined hydrodynamic, impact and statistical modeling. The framework broadly consists of three steps. First, a large stochastic event set is derived from reanalysis data, taking into account co-occurrence of, and dependence between all annual maxima flood drivers. Then, both flood hazard and impact are simulated for different combinations of drivers at non-flood and flood conditions. Finally, the impact of each stochastic event is interpolated from the simulated events to derive a complete flood risk profile. The framework has been applied to a case study in Mozambique where we found that if dependence between flood drivers is not accounted for, the impact of especially rare events is underestimated. In this contribution we discuss findings from the case study as well as challenges faced when upscaling the framework to for large-scale compound flood risk assessments.