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- Title
Efficient Tropical Cyclone Scenario Selection Based on Cumulative Likelihood of Potential Impacts.
- Authors
Sohrabi, Meraj; Moftakhari, Hamed; Moradkhani, Hamid
- Abstract
The incidence of climate‐related disasters is on the rise, which makes it imperative to intensify anticipatory action. Tropical cyclones (TC) bring extreme precipitation and storm surge to coastal areas. This poses a compound flood risk to coastal communities due to the coincidence/concurrence of multiple flood drivers. In the absence of sufficient spatiotemporal coverage of historic TC data, appropriate characterization of compound flood risk mainly relies on running a large number of synthetic scenarios with the hope that it covers the wide range of potential threats posed to a coastal community. Such an approach requires huge computational resources that make it infeasible in many cases. Here, we propose a dependence‐informed sampling scheme that helps reduce the dimensionality of the problem and systematically select a handful of scenarios with the largest Cumulative Likelihood of Potential Impact (CLPI). The CLPI is a compound flood index that ranks the candidate storms with the potential to cause compound flooding based on the regional dependencies between forcing (wind and rainfall) and coastal flooding drivers (storm surge and runoff). The analysis of historic TC records near the coast of Texas, USA shows the usefulness of CLPI in improving the efficiency of hazard risk assessment and providing reliable information at a lower cost. The proposed CLPI successfully ranks candidate hurricane scenarios based on their potential impact and filters out the less relevant scenarios without the need for detailed hydrodynamic simulation. Plain Language Summary: In recent decades, there has been a notable increase in climate‐related disasters, highlighting the need for proactive measures. One specific concern is the potential for tropical cyclones (TCs) to create compound flood risks by triggering multiple drivers that lead to flooding. Typically, we rely on synthetic scenarios to assess this risk, but this might not be the most efficient approach, as such method demands significant computational resources and not all those scenarios are relevant for flood risk assessment at a specific point of interest along the coastline. To address this challenge, a novel dependence‐informed sampling scheme has been introduced that utilizes a metric called the Cumulative Likelihood of Potential Impact (CLPI). CLPI serves as an index to help identify TCs that are more likely to cause severe flooding at a point of interest. Importantly, CLPI can efficiently rank hurricane scenarios based on their potential impact without the need for complex hydrodynamic simulations. A validation study conducted along the Texas coast, USA, using historical TC data, underscores the practicality of the proposed dependence‐informed sampling scheme with CLPI. It enhances the accuracy of flood forecasting, providing reliable information at a reduced cost. This advancement improves the efficiency of analyzing flooding patterns associated with selected storms using CLPI. Furthermore, CLPI can play a crucial role in providing essential information to vulnerable communities, potentially saving lives and reducing the risk of damage to coastal infrastructure. Key Points: A dependence‐informed sampling scheme is presented to help reduce computational complexity of compound flood risk assessmentThe proposed scheme ranks tropical cyclones scenarios based on their Cumulative Likelihood of Potential ImpactsApplicability of the proposed scheme is demonstrated based on historic records around Galveston Bay, Texas, USA
- Subjects
TEXAS; GALVESTON Bay (Tex.); FLOOD warning systems; TROPICAL cyclones; STORM surges; FLOOD risk; HISTORICAL source material; RAINFALL; STORMS; FLOOD forecasting
- Publication
Earth's Future, 2023, Vol 11, Issue 10, p1
- ISSN
2328-4277
- Publication type
Article
- DOI
10.1029/2023EF003731