Abstract
In many coastal communities, the risks driven by storm surges are motivating substantial investments in flood risk management. The design of adaptive risk management strategies, however, hinges on the ability to detect future changes in storm surge statistics. Previous studies have used observations to identify changes in past storm surge statistics. Here, we focus on the simple and decision-relevant question: How fast can we learn from past and potential future storm surge observations about changes in future statistics? Using Observing System Simulation Experiments, we quantify the time required to detect changes in the probability of extreme storm surge events. We estimate low probabilities of detection when substantial but gradual changes to the 100-year storm surge occur. As a result, policy makers may underestimate considerable increases in storm surge risk over the typically long lifespans of major infrastructure projects.
Highlights
Hurricanes Katrina and Sandy recently caused thousands of deaths and hundreds of billions in property damages (Kunz et al 2013)
We calculate how frequently we predict a statistically significant increase in the risk of the 100-year surge and are able to identify the appropriate method for detection based on the original generating function
Our analysis shows that for the considered observation system simulation experiment (OSSE) nature states, when changes in the 100year surge are caused by a changing μ, detection occurs earlier compared to the same shift in the 100-year surge caused by a changing σ
Summary
Hurricanes Katrina and Sandy recently caused thousands of deaths and hundreds of billions in property damages (Kunz et al 2013). We use Bnature state^ to mean a set of GEV parameters with prescribed changes over time, Bnature run^ to mean a simulation generated using a specific nature state, and Bsimulated observation^ to mean an estimated 100-year surge calculated from a nature run.
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