A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels—An Application to Ho Chi Minh City, Vietnam. Issue 2 (1st February 2022) - Couasnon, A.; Scussolini, P.; Tran, T. V. T.; Eilander, D.; Muis, S.; Wang, H.; Keesom, J.; Dullaart, J.; Xuan, Y.; Nguyen, H. Q.; Winsemius, H. C.; Ward, P. J.
Abstract: 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. Plain Language Summary: In coastal cities, floods can result from different drivers such as intense rainfall and extreme sea levels. In order to estimate the expected annual damage (EAD) from flooding, it is important to correctly quantify the chance of these events happening and their impacts. Commonly, only the maximum value for a flood driver each year is taken, ignoring its seasonality; and these maxima are assumed to either never or always happen at the same time of the year. These assumptions become problematic when pluvial and coastal floods can occur at different times of the year and sometimes at the same time, such as in Ho Chi Minh City (HCMC). In this paper, we develop a framework to account for the seasonality of flood drivers and their mutual dependencies. Using statistical and hydrodynamic modeling, we generate the equivalent of 10, 000 years of rainfall and sea level events and estimate their impact. We find the EAD for HCMC to be 1.02 B. Neglecting the seasonality of rainfall and sea level leads to an underestimation of the EAD by 72–83%. This application illustrates the potential of our framework and advocates for the inclusion of flood drivers' dynamics in coastal risk assessments. Key Points: We develop a flood risk framework based on monthly time series that includes the seasonality and the dependence between flood drivers Not accounting for seasonality underestimates the expected annual damage (EAD) in Ho Chi Minh City by 72–83% Rainfall and storm surge are independent for most months but, under the assumption of full correlation, EAD would increase by 15%