Rapidly developing high-performance computing has enabled RMS to create a high-resolution dynamic storm surge model that solves the fundamental challenges of modeling the flood impacts from catastrophic storm surge. Previously, there had not been a credible model to do so. Our North Atlantic Hurricane model is the only commercially available model that quantifies the risk from catastrophic hurricane-driven storm surge. The version 13 model, which incorporates still more scientifically advanced and high-resolution data, will be released on July 31, 2013.

Using advanced technology to improve storm surge modeling

In 2011, following several major surge events such as Katrina and Rita in 2005, and Ike in 2008, RMS incorporated one of the world's most advanced storm surge models, MIKE 21 from the DHI Group, into its North Atlantic Hurricane model suite. RMS remains the only catastrophe modeling company to use such a modern, fully time-stepping hydrodynamic storm surge model across a large geographic range. This fully probabilistic catastrophe model captures wave damage potential for offshore risks, as well as detailed onshore flood levels.

What makes the model unique is that it is dynamically linked with the hurricane wind field, so that the progress of the surge can be tracked throughout the entire lifecycle of the storm, not just at landfall.

It also models onshore surge dynamically, using a flexible mesh grid system that enables high-resolution modeling around complex coastlines like New Orleans or New York City. In addition, the onshore impacts of surge can be modeled using some of the highest resolution data possible, for features such as topography, land use, land cover, and coastal bathymetry.

The overall value of this model is its ability to capture the localized nature of flooding and damage, enabling users to assess damage and quantify loss to individual locations at resolutions as high as street level. This modeling technology has been thoroughly verified by our ability to capture Hurricane Sandy's extensive flooding: demonstrated through comparisons to more than 200 actual storm surge gauge recordings of flood heights.

Factors affecting loss-payouts

One key factor that influences insured storm surge loss payouts is the elevation of properties in high-risk coastal flood zones. For our version 13 of the North Atlantic Hurricane model, we have analysed and incorporated new and higher resolution versions of the Flood Emergency Management Agency (FEMA) Base Flood Elevation (BFE) data, and National Flood Insurance Program (NFIP) Flood Zone data to refine our modelling assumptions.

The line of business also has a significant impact on the loss payout. The loss payout dynamics for commercial properties are very different to residential, and cannot be treated with the same assumptions. For commercial and industrial properties, flood coverage is often included, but sub-limited in some way. These properties often have high-value contents located in the basements, and are vulnerable to business interruption (BI) and contingent BI impacts from wide-scale flooding and infrastructure outages.

Sandy demonstrated the real potential for industry storm surge losses for commercial and industrial lines of business: more than 65 per cent of the total insured loss for Sandy came from these lines of business, and mostly for storm surge driven loss. Learning from Sandy's industry losses, we have refined the loss calculations for commercial and industrial policies and vulnerability.

Residential and small commercial flood insurance is often not offered by the private market, and is provided by the NFIP scheme for properties only in high-risk zones. The private market does, however, still have a small amount of exposure to residential surge loss: through high-value homeowners policies, XS-NFIP policies, and claims for uninsured surge losses that are not covered by NFIP policies, but are being made under wind policies. Sandy has revealed that these issues are less of a concern now, because of improved claims handling practices and recent court rulings that reduce the potential for “coverage leakage”. We have conducted further research into this potential, and reflected the findings into the version 13 model, which will be released this month.

Uncertainty still present

When estimating storm surge losses, insurers and reinsurers must acknowledge that there are several uncertainties that continue to persist. For instance, the degree to which buildings comply with FEMA stipulated base flood elevations – BFEs in force since 1974 – as well as the presence of flood defenses, is still unclear.

Hurricane Sandy revealed that the industry does not know the overall proportion of commercial and industrial exposure that is insured for flood loss. There is also considerable uncertainty around the proportion of uninsured storm surge losses that may be paid out under residential wind-only policies following a major storm surge event. In light of recent court rulings and the industry's strengthened claims handling processes, the future potential could be quite different to payouts from past events.

The resolution of input exposure data also has a significant impact on the range of storm surge loss output. Generally, using coarser resolution input data, such as zip code level, to model a high-resolution hazard like storm surge, results in a higher degree of uncertainty because the localized surge impacts are not captured as accurately. Establishing more confidence around this type of information is critical to establishing credible insured loss estimates.

RMS continues to advance the development of loss estimates, by investigating actual building elevations in modeled locations, incorporating additional claims data from recent events – such as Hurricane Sandy – to assess flood coverage assumptions and vulnerability, and encouraging the industry to improve the resolution of input exposure data.

The RMS North Atlantic Hurricane model continues to be known as the industry's leading and most scientifically advanced hurricane model: our models enable the industry to understand its loss potential, with more accurate data and more research, and; reduce uncertainty in potential uninsured loss payouts and claims inflation.