Abstract
It is generally acknowledged that interdependent critical infrastructure in coastal urban areas is constantly threatened by storm-induced flooding. Due to changing climate effects, such as sea level rise (SLR), the occurrence of catastrophic events will be more frequent and may trigger an increased likelihood of severe hazards. Planning a protective measure or mitigation strategy is a complex problem given the constraints that it must fit within a prescribed and limited fiscal budget and be beneficial to the community it protects both socially and economically. This article proposes a methodology for optimizing protective measures and mitigation strategies for interdependent infrastructures subjected to storm-induced flooding and climate change impacts such as SLR. Optimality is defined in this methodology as a maximum reduction in overall expected losses within a prescribed budget (compared to the expected losses in the case of doing nothing for protection/mitigation). Protective measures can include seawalls, barriers, artificial dunes, restoration of wetlands, raising individual buildings, sealing parts of the infrastructure, strategic retreat, insurance, and many more. The optimal protective strategy can be a combination of several protective measures implemented over space and time. The optimization process starts with parameterizing the protective measures. Storm-induced flooding and SLR, and their corresponding consequences, are estimated using a GIS-based subdivision-redistribution methodology (GISSR) developed by the authors for finding a rough solution in the first brute-force iterations of the optimization loop. A storm surge computational model called GeoClaw is subsequently used to simulate ensembles of synthetic storms in order to fine-tune and achieve the optimal solution. Damage loss, including economic impacts, is quantified based on calculated flood estimates. The suitability of the potential optimal solution is examined and assessed with input from stakeholders' interviews. It should be mentioned that the results and conclusions provided in this work depend on the assumptions made about future sea level rise (SLR). The authors acknowledge that there are other, more severe predictions for sea level rise (SLR), than the one used in this paper.
Highlights
Communities in coastal regions are likely to face more severe catastrophic events such as storm-induced flooding in the future due to a changing climate, especially sea level rise (SLR) (Marcos et al, 2015; Marcos and Woodworth, 2017; Marsooli et al, 2019)
Once these protective measures are parameterized, inundated areas are analyzed with two types of flooding model tools: a Geographical Information Systems (GIS)-based subdivisionredistribution methodology (GISSR) (Miura, 2020) and the GEOCLAW model (Berger et al, 2011)
The GEOCLAW model is based on the finite volume method and solves the shallow water equations to capture flooding more accurately but is computationally more expensive compared to the GIS-based subdivision-redistribution methodology (GISSR) model
Summary
Communities in coastal regions are likely to face more severe catastrophic events such as storm-induced flooding in the future due to a changing climate, especially sea level rise (SLR) (Marcos et al, 2015; Marcos and Woodworth, 2017; Marsooli et al, 2019). Several models have been developed to assess global flood risk (Hirabayashi et al, 2013; Winsemius et al, 2013) but are limited to the circumstances of an absence of protective strategies These models do not account for various factors at the local level, which may be critical in finding the optimal solution. Other methodologies in related areas include the following: Longenecker et al (2020) introduced a rapid rivergauge-based methodology to support a decision-making process for first responders and communities at the community level in Yerington, Nevada It is a computationally efficient method, it lacks hydrological modeling for higher accuracy. The proposed optimization methodology incorporates accurate flood estimation models based on hydrological fluid dynamics, detailed building damage assessment, infrastructure inoperability loss analysis, and input from stakeholders’ firsthand knowledge. The focus of this study is NYC, the methodology is general enough to be applied to different regions when the required data (e.g., topography data, building data, hazard data) is available
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