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A platform for research: civil engineering, architecture and urbanism
Building-level adaptation analysis under uncertain sea-level rise
Recent studies show climate-induced sea-level rise (SLR) will accelerate storm surge impacts in many coastal areas around the world. The decision-making of building-level adaptation strategies is a challenging task due to uncertain climate impacts. This study evaluates building-level adaptation strategies through a dynamic programming-based cost-benefit analysis approach to incorporate the latest information of SLR in adaptation decision-making. The adaptation outcomes are estimated by applying a Monte-Carlo method with stochastic flood damage of buildings under four SLR projections. Based on a case study in Bay County, Florida (USA), results indicate that single-family and multi-family buildings are the most vulnerable buildings in Bay County. Mobile homes have a lower flood risk, while they are more sensitive to SLR. The long-term flood damage shows SLR could exponentially increase the average annual flood damage in the community from $17.7 million to $204 million. Investing in adaptive measures can substantially mitigate building-level flood risk, where the adapted average annual damage ranges from $9.57 million to $38.2 million in the county. The proposed adaptation method could facilitate more effective risk communications between the public and private sectors and improvise community adaptation planning under uncertain SLR.
Building-level adaptation analysis under uncertain sea-level rise
Recent studies show climate-induced sea-level rise (SLR) will accelerate storm surge impacts in many coastal areas around the world. The decision-making of building-level adaptation strategies is a challenging task due to uncertain climate impacts. This study evaluates building-level adaptation strategies through a dynamic programming-based cost-benefit analysis approach to incorporate the latest information of SLR in adaptation decision-making. The adaptation outcomes are estimated by applying a Monte-Carlo method with stochastic flood damage of buildings under four SLR projections. Based on a case study in Bay County, Florida (USA), results indicate that single-family and multi-family buildings are the most vulnerable buildings in Bay County. Mobile homes have a lower flood risk, while they are more sensitive to SLR. The long-term flood damage shows SLR could exponentially increase the average annual flood damage in the community from $17.7 million to $204 million. Investing in adaptive measures can substantially mitigate building-level flood risk, where the adapted average annual damage ranges from $9.57 million to $38.2 million in the county. The proposed adaptation method could facilitate more effective risk communications between the public and private sectors and improvise community adaptation planning under uncertain SLR.
Building-level adaptation analysis under uncertain sea-level rise
Yu Han (author) / Pallab Mozumder (author)
2021
Article (Journal)
Electronic Resource
Unknown
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