Now a team from the US and the UK has developed a technique to map and quantify inland flooding from cyclones and link it to the number of insurance claims. Gabriele Villarini of the University of Iowa and colleagues used data from stream gauging stations during Hurricane Ivan in 2004 to test their method. Around 67% of the residential damage claims resulting from Ivan were due to inland river flooding.

“While media attention and risk assessment efforts have mainly focused on coastal losses from storm surge…tropical cyclones have inflicted significant devastation inland as well,” Gabriele Villarini of the University of Iowa, US, told environmentalresearchweb. “Yet little is known about the relationship between tropical cyclone-related inland flooding and economic losses. Even if the loss assessment was focused inland, broadly accepted procedures for the regional characterization and quantification of the spatial structure of tropical cyclone flooding – essential for a proper assessment – are not available.”

To carry out the analysis, Villarini and colleagues compared the biggest flood peak caused by Hurricane Ivan to the 10-year flood peak value for 1989–2009 at each measurement station. A ratio of 1 meant Hurricane Ivan caused as much discharge as the 10-year flood, greater than 1 meant the Irene flooding was worse.

The team found a strong correlation between this flood ratio and the number of flood insurance claims, once they had accounted for areas that did not have insurance; more than 98% of inland flood insurance claims resulting from Ivan were from states with a flood ratio value of 1.4 or more in at least one census area.

The researchers believe that their results provide the foundation for tropical cyclone flood risk assessment across all impacted areas, not just coastal locations near landfall. “This new capacity will be of tremendous value to a number of public- and private-sector stakeholders dealing with disaster preparedness,” said Villarini. “For example, the flood peak ratio proxy could be calculated pre-landfall or relatively quickly thereafter and be used by emergency services, local, state and federal government agencies, and/or by insurers to forecast economic losses and better communicate inland flood risk.”

Ivan, which made landfall in Alabama and Florida, caused damage across 23 states. The storm resulted in 28,670 residential claims for damages totalling just under $1.5 billion – two-thirds of the total flood insurance payment made by the US federal government in 2004. The flooding was worst in western North Carolina, where the Appalachian Mountains enhanced rainfall. In Pennsylvania, Ivan interacted with another weather system and began its extratropical transition, also producing large floods. Both of these regions saw peak discharge more than four times higher than the 10-year flood peak.

“Through the utilization of empirical flood frequency methods, we avoid the complexities and limitations of implementing hydrologic models for flood hazard characterization over large regions,” said Villarini.

Now the team, who reported the results in Environmental Research Letters (ERL), would like to predict the cost, rather than the number, of cyclone damage claims using flood return data.

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