A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels-An Application to Ho Chi Minh City, Vietnam

State-of-the-art flood hazard maps in coastal cities are often obtained from simulating coastal or pluvial events separately. This method does not account for the seasonality of flood drivers and their mutual dependence. In this article, we include the impact of these two factors in a computationally efficient probabilistic framework for flood risk calculation, using Ho Chi Minh City (HCMC) as a case study. HCMC can be flooded subannually by high tide, rainfall, and storm surge events or a combination thereof during the monsoon or tropical cyclones. Using long gauge observations, we stochastically model 10,000 years of rainfall and sea level events based on their monthly distributions, dependence structure and cooccurrence rate. The impact from each stochastic event is then obtained from a damage function built from selected rainfall and sea level combinations, leading to an expected annual damage (EAD) of $1.02 B (95th annual damage percentile of $2.15 B). We find no dependence for most months and large differences in expected damage across months ($36-166 M) driven by the seasonality of rainfall and sea levels. Excluding monthly variability leads to a serious underestimation of the EAD by 72-83%. This is because high-probability flood events, which can happen multiple times during the year and are properly captured by our framework, contribute the most to the EAD. This application illustrates the potential of our framework and advocates for the inclusion of flood drivers' dynamics in coastal risk assessments.

Automated summary

State-of-the-art flood hazard maps in coastal cities are often obtained from simulating coastal or pluvial events separately. In this article, the authors propose a computationally efficient probabilistic framework for flood risk calculation that includes the impact of seasonality and mutual dependence of flood drivers. The framework is applied to Ho Chi Minh City, showing that excluding monthly variability leads to a significant underestimation of flood risk. This research highlights the importance of considering the dynamics of flood drivers in coastal risk assessments.