A Fiscally Based Scale for Tropical Cyclone Storm Surge

Categorization of storm surge, particularly with the Saffir-Simpson Hurricane Scale (SSHS), had been a useful way to communicate potential impacts for decades. However, following Hurricane Katrina in 2005, storm surge was removed from the SSHS, resulting in no concise method to communicate storm surge risk despite its significant impacts to both life and property. This study seeks to create a new storm surge scale based on observed data for quick, simple, and clear communication. The proposed scale combines ideas from two well known scales: the SSHS and the Richter Scale. From the SSHS, the link to tangible impacts is used, but with a fiscal loss basis instead of a physical damage one. From the Richter Scale, the logarithmic nature is used to solve saturation issues at the higher end, its intuitive nature is used for ease of understanding, and the added decimal places are used for extra fiscal loss estimate precision. Advanced Circulation (ADCIRC) model simulation output data of storm surge height and velocity were obtained for four storms: Hurricane Katrina, Hurricane Gustav, Hurricane Ike, and Superstorm Sandy. Storm surge velocity has not been previously used in past scale attempts but is explored here. Four fiscal loss methods were explored on a countywide level. The first three methods used a combination of National Centers for Environmental Information (NCEI) Storm Events Database (SED) property damages and Bureau of Economic Analysis (BEA) population, per capita personal income, or total income. The fourth method used a combination of National Flood Insurance Program total coverage and paid claims. Correlations between the two proposed storm surge variables and each of the fiscal loss methods indicated that the mode of storm surge data above the 90th percentile had the best results. This method of storm surge representation was chosen for the proposed scale. Multiple linear regression then determined the choice of storm surge variables (height and velocity) and fiscal loss method (SED property damages divided by BEA population, or loss per capita). The proposed storm surge scale, named the Kuykendall or K-Scale, was calculated using the multiple linear regression analysis in the previous step. Comparison with the real data of the four storms shows good overall correlation to reported damage. This study demonstrates the Kuykendall Scale’s significant potential for future use in the operational and academic worlds.