Predicting Major Storm Surge Levels

Robert Mendelsohn

doi:10.3390/atmos12060756

Abstract

The National Atmospheric and Oceanic Administration (NOAA) calculates the surge probability distribution along the coast from their long-term tidal stations. This process is sufficient for predicting the surge from common storms but tends to underestimate large surges. Across 23 long-term tidal stations along the East Coast of the United States, 100-year surges were observed 49 times, although they should have occurred only 23 times. We hypothesize that these 100-year surges are not the tail outcome from common storms but are actually caused by major hurricanes. Matching these 100-year surges with major hurricanes revealed that major hurricanes caused 43 of the 49 surges. We consequently suggest a revised approach to estimating the surge probability distribution. We used tidal data to estimate the probability of common surges but analyzed major hurricane surges separately, using the return rate of major hurricanes and the observed surge from each major hurricane to predict hurricane surges. The revision reveals that expected coastal flooding damage is higher than we thought, especially in the southeast United States.

Highlights

In order to conduct careful analysis of expected flood damage, it is important to measure the probability distribution of storm surges in coastal areas
The surges’ cumulative probability distribution provides the cumulative probability of experiencing a surge of different heights. This is commonly expressed in terms of the return rate (1/cumulative probability) or average number of years it takes to see a surge of a specific height
When we look at the surges caused by major hurricanes, we find that the generalized extreme value (GEV)

Summary

Introduction

In order to conduct careful analysis of expected flood damage, it is important to measure the probability distribution of storm surges in coastal areas. The study relies on the maximum annual tides observed each year at each station [7] to calculate a flood probability distribution between the height of each surge versus its return rate (1/probability) for each site [8]. A generalized extreme value function is fit to the data in order to measure the flood probability distribution For common storms, this procedure accurately captures the relationship between the probability of a storm and the height of the storm surge. The return rate and height can be added to the results from common storms to measure expected flood damage This will lead to higher expected flood damage along the East Coast but especially in the southeast and Gulf states

Data and Methods

Results

Annual

Return

Discussion