11 Apr 2022
11 Apr 2022

A globally-applicable framework for compound flood hazard modeling

Dirk Eilander1,2, Anaïs Couasnon1, Tim Leijnse2, Hiroaki Ikeuchi3, Dai Yamazaki4, Sanne Muis1,2, Job Dullaart1, Hessel C. Winsemius2, and Philip J. Ward1 Dirk Eilander et al.
  • 1Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
  • 2Deltares, Delft, The Netherlands
  • 3Ministry of Land, Infrastructure, Transport and Tourism, Tokyo, Japan
  • 4Institute of Industrial Sciences, the University of Tokyo, Tokyo, Japan

Abstract. Coastal river deltas are susceptible to flooding from pluvial, fluvial, and coastal flood drivers. Compound floods, which result from the co-occurrence of two or more of these drivers, typically exacerbate impacts compared to floods from a single driver. While several global flood models have been developed, these do not account for compound flooding. Local scale compound flood models provide state-of-the-art analyses but are hard to scale up as these typically are based on local datasets. Hence, there is a need for globally-applicable compound flood hazard modeling. We develop, validate and apply a framework for compound flood hazard modeling, which consists of the local high-resolution 2D hydrodynamic flood model SFINCS, which is automatically set up from global datasets and loosely coupled with a global hydrodynamic river routing and flood model, as well as a global surge and tide model to account for interactions between all drivers. To test the framework, we simulate two historical compound flood events, cyclones Idai and Eloise, in the Sofala province of Mozambique, and compare the flood extent to observations from remote sensing and to the global quasi 2D CaMa-Flood model. The results show that while the global and local model have similar skill in terms of the critical success index, they result in rather different flood maps. On the one hand, the local model has a higher hit ratio due to the representation of direct coastal and pluvial flooding (rain on grid) and a higher floodplain connectivity. It also shows a faster response to coastal drivers within the estuaries and more realistic flood depth maps. On the other hand, the local model has a higher false alarm ratio, which is partly explained by the inclusion of direct pluvial flooding without sufficient representation of small scale (subgrid) drainage capacity. To showcase a possible application of the framework, we also determine the dominant flood drivers and transition zones between flood drivers for both events. These vary significantly between both events because of differences in the magnitude of and time lag between the flood drivers. We argue that a wide range of plausible events should be investigated to get a robust understanding of compound flood interactions, which is important to understand for flood adaptation, preparedness, and response. As the model setup and coupling is automated, reproducible, and globally applicable, the presented framework is a promising step forward towards large scale compound flood hazard modeling.

Dirk Eilander et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-149', Anonymous Referee #1, 13 Jul 2022
  • RC2: 'Comment on egusphere-2022-149', Anonymous Referee #2, 15 Jul 2022

Dirk Eilander et al.

Dirk Eilander et al.


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Short summary
In coastal deltas flooding can occur from interactions between surge and waves, river discharge and precipitation, so-called compound flooding. Global flood models however ignore these interaction. We therefore present a framework to create a reproducible compound flood model anywhere at the globe and show how it can be used to better understand compound flooding. The framework is applied to two historical events tropical cyclone events in Mozambique with good results.