An agent-based model for community flood adaptation under uncertain sea-level rise
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An agent-based model for community flood adaptation under uncertain sea-level rise Yu Han 1 & Kevin Ash 1 & Liang Mao 1 & Zhong-Ren Peng 1 Received: 22 October 2019 / Accepted: 19 July 2020/ # Springer Nature B.V. 2020
Abstract
Adaptation has become the major approach to reduce the adverse effects of storm surge and sea-level rise. However, maladaptation can happen when adaptation actions unintentionally increase community vulnerability. To evaluate the adequacy and efficacy of adaptation policies under uncertain sea-level rise, this study presents an agent-based model by integrating the random nature of storm surges, private adaptation decisions, and real estate market valuation. We evaluated the evolving flood damage of different adaptation strategies under two bounding cases of real estate market change. Our model results quantitatively illustrate the accelerating damages of storm surges under climateinduced sea-level rise. A reform in flood insurance to risk-based rates with a means-tested voucher program and a government-subsidized “twice and out” buyout program could both substantially improve coastal resilience. However, community adaptation with a public seawall may deliver false risk perception to high-risk property owners and result in maladaptation when sea-level rise rate is high. The modeling approach developed in this study can be used as a policy analysis tool to measure the impacts of sea-level rise and the effectiveness of adaptation strategies in coastal communities. Keywords Flood risk . Sea-level rise . Coastal adaptation . Agent-based model
1 Introduction Increases of global sea-level and floods are major threats to coastal properties and infrastructures in the world’s coastal zones (Heberger et al. 2011; Tschumi and Zscheischler 2019). For instance, the billion-dollar flooding disasters in the US have been increasing exponentially since 1980 (NOAA National Centers for Environmental Information (NCEI) 2020). The Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10584-02002802-6) contains supplementary material, which is available to authorized users.
* Zhong-Ren Peng [email protected]
1
University of Florida, Gainesville, FL, USA
Climatic Change
accelerating pace of sea-level rise (SLR) also indicates a substantial increase in storm surge frequency by the end of this century (Hinkel et al. 2012; Tebaldi et al. 2012). Building resilience under the uncertain climatic impacts has become a key aspect of climate adaptation goals (IPCC 2014). Nevertheless, a major concern of climate adaptation is how to avoid the “resilience trap,” which means adaptation policy favors short-term solutions, instead of reducing the cause of the vulnerability and increasing adaptive capacity (Malloy and Ashcraft 2020). Design of effective adaptation strategies under deep uncertainty of SLR is a challenging task due to unforeseen effects and interactions between human and environmental systems (Keller et al. 2008). To solve this issue, agent-based modeling (ABM) can be us
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