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

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.

A Hybrid Framework for Rapidly Locating Transition Zones: A Comparison of Event‐ and Response‐Based Return Water Levels in the Suwannee River FL

“…Although the framework is transferable, it may not be applicable at every location due to a lack of data. For instance, the statistical modeling requires overlapping, in situ or numerical riverine and coastal water level records, potentially partitioned to account for seasonality (Couasnon et al., 2022). High resolution digital elevation data are a prerequisite for the numerical modeling and not available everywhere.…”

“…Data‐driven surrogate models empirically approximate the relationship between the inputs (and parameters) and the outputs of a complex model without attempting to emulate any of its internal parts (Razavi et al., 2012). Past applications of surrogate models in fluvial and coastal flooding studies range from conceptually simple look‐up tables (Apel et al., 2008) and empirical formulations (van Ormondt et al., 2021) to more complex approaches including Gaussian process models (Malde et al., 2016; Parker et al., 2019; Rohmer et al., 2022), kriging (Parker et al., 2019; Rohmer & Idier, 2012), 3D scatter interpolation (Serafin et al., 2019), bilinear interpolation (Couasnon et al., 2022), radial basis functions (Camus, Mendez, Medina, et al., 2011; Gouldby et al., 2014; Medellín et al., 2016; Rueda et al., 2016), support vector regression (Bermúdez et al., 2019; Chen et al.,. 2020; Jhong et al., 2017), random forests (Zahura & Goodall, 2022; Zahura et al., 2020), and artificial neural networks (Bermúdez et al., 2018; Peters et al., 2006; Santos et al., 2019).…”

Iso-severity flood hazard patterns in diverse relief and climatic regions to support flood management

“…Numerous studies have been conducted on oods and their spatial distribution patterns, including regionalization of ood estimation historical ood events (Barriendos and Rodrigo 2006;Arshia et al 2018); statistical distribution to analyze and estimate ood frequency with different return periods (Lin et al 2016); seasonal ood peak (Chen et al 2010;Fischer et al 2016; Bartiko et al 2019); ood seasonality and its temporal shifts (Ye et al 2017); seasonal distribution of ooding (Diakakis 2017); climate change impacts on seasonal oods (Muttarak and Dimitrova 2019); river ood timing and seasonality (Dhakal and Palmer 2020); ood frequency analysis (Engeland et al 2020); taking account of seasonality in a regional ood frequency (Ding and Arnaud 2022) and ood seasonality assessment (Ruiz et al 2014;Al eri et al 2020;Couasnon et al 2022).…”

Performance Indices for Operational Considerations of Coastal Hydraulic Structures

“…The complexity of these factors and their interactions lead to further challenges during operations. Moreover, co-occurrence or compound events, such as heavy precipitation during a high tide, can cause considerable damage (Veatch and Villarini, 2020;Deidda et al, 2021;Couasnon et al, 2022). The extent of the damage can be several miles upstream of the structure, depending on the water surface pro les.…”

“…The extent of the damage can be several miles upstream of the structure, depending on the water surface pro les. Veatch and Villarini (2020) and Couasnon et al (2022) emphasized the use of characterization of the multivariate joint distribution of precipitation, skew surge, and high tides. They developed an extensive framework to obtain ood risk, which captures the seasonality and dependence between precipitation and sea levels.…”