Probabilistic Numerical Modeling of Compound Flooding Caused by Tropical Storm Matthew Over a Data‐Scarce Coastal Environment

Abstract

The passage of a tropical storm, as the main driver of storm surge and high waves in many coastal regions, can also generate heavy rainfall and cause river overflow. The resulting combination of riverine, pluvial, and coastal flood hazard can result in catastrophic losses particularly in densely populated coastal environments. In this study, we characterize compound flooding caused by Tropical Storm Matthew and assess the significance and associated uncertainties of multiple contributing factors over a data-scarce coastal region. A hydrological model combined with a simplified two-dimensional hydrodynamic model are set up and validated to investigate the compounding effects of storm tide, wave runup, rainfall, and river overflow at the southern coast of Saint Lucia in the Caribbean Sea. Pléiades-1 and Sentinel-1 satellite imageries are used to determine the flood-impacted areas. The analyses are performed based on deterministic and probabilistic approaches and the effects of uncertain boundary conditions and model parameters are investigated. Results show that the individual analysis of flood hazards, in isolation, can lead to substantial underestimation of flood risks. Heavy rainfall and wave runup are the most significant contributors to compound flooding in Saint Lucia. In addition, the interactions between seawater and streamflow can exacerbate riverine flood hazards particularly upstream of the river mouth. Communities in western Vieux Fort, and the Hewanorra International Airport, have high exposure to compound flooding, which is projected to intensify under climate change.

Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods

“…• projecting the risk of compound extremes for different levels of future warming (Zscheischler et al, 2018;Wang et al, 2020); • evaluating the impacts of the compound extremes on natural and built environments (AghaKouchak et al, 2020;Zhang and Najafi, 2020); • developing adaptation measures to the changing risk of compound extremes (Weber et al, 2020;Clarke et al, 2021); • enhancing subseasonal-to-seasonal prediction of these extremes (Zamora et al, 2021;Zou, 2021); • improving the representation and evaluation of compound extremes in fully-coupled climate models (Ridder et al, 2021;Zscheischler et al, 2021) and developing multivariate bias correction for these models (Vezzoli et al, 2017;Zscheischler et al, 2019); • applying machine learning to understand these extremes Zou, 2021).…”

“…For example, a global river routing model forced by global hydrological models and bounded downstream by a global tide and surge model has been used to assess the effect of storm surge on riverine flood (Eilander et al, 2020). Hydrologic and hydrodynamic models are combined to assess compound flooding caused by the 2016 tropical storm Matthew (Zhang and Najafi, 2020). In addition, joint probabilities and copula have been widely used to examine the compounds (Czajkowski et al, 2013;Petroliagkis et al, 2016;Couasnon et al, 2018).…”

Disaster Management in Smart Cities

“…To improve the investment options in preparedness, researchers in [43] developed a restoration map and a suite of infrastructure asset damage that will consider the infrastructure interdependencies. Study [44] concluded that a small number of methods have been proposed to model interdependencies among substations (S/Ss) in electric power systems. For this reason, the authors proposed a solution that offers a practical tool for decision-makers to distinguish between S/Ss and their mutual associations, which enables them to identify the critical S/Ss.…”

Multivariate Analysis of Compound Flood Hazard Across Canada's Atlantic, Pacific and Great Lakes Coastal Areas

“…These analyses include characterizing the statistical interrelationships between drivers of flooding based on Bayesian networks (Couasnon et al., 2018; Sebastian et al., 2017), copula theory (Bevacqua et al., 2017; Gori et al., 2020; Moftakhari et al., 2017; Paprotny et al., 2018; Xu et al., 2014), bivariate extreme value distributions (Zheng et al., 2014), correlation and linear regression (Robins et al., 2021), bivariate logistic threshold‐excess model (Zheng et al., 2013) among others. Besides, recent studies have assessed the compound flood impacts and risks through process‐based modeling and hybrid statistical‐dynamical framework (Ganguli et al., 2020; Ganguli & Merz, 2019; Najafi et al., 2021; Wang et al., 2021; Zhang & Najafi, 2020).…”