Statistical properties of hurricane surge along a coast

Abstract

[1] The validity and accuracy of approaches used to determine hurricane surge hazard risk received much attention following the hurricane seasons in mid-to late-2000, which caused record surge-related damage along the Gulf of Mexico coastline. Following Hurricane Katrina in 2005, research showed that most extreme-value statistics approaches underestimated the risk associated with this surge event. In this paper, two of the most popular methods for determining hurricane surge extreme-value statistics are reviewed: the historical surge population approach and the joint probability method. Here, it is demonstrated that both limited historical record length and random along-coast variability in hurricane landfall location can introduce significant errors into surge estimates. For example, the historical surge population approach gives errors of 9% to 17% for return periods between 50 and 1000 years when a surge record of 100 years is considered. In contrast, it is shown that the joint probability method yields significantly more reliable surge estimates, with errors of 2% to 3% for return periods between 50 and 1000 years when a storm record of 100 years is considered. Finally, we show that both methods remain robust when decadal-scale climate variability in the storm rate of occurrence is considered, so long as the hurricane history is long enough to capture the full decadal cycle. When used in conjunction with continuous surge response information, it can be concluded that the joint probability method is a practical and reliable approach for determining extreme-value hurricane surge statistics.

The effects of natural structure on estimated tropical cyclone surge extremes

“…Predictions based on this concept are consistent with the Monte Carlo simulation results presented in Irish et al (2011). This ratio of scales potentially depends on the annual exceedance probability (AEP), so the actual averaging for this application must be done numerically.…”

“…As shown by Monte Carlo simulations in Irish et al (2011), the HSM, based on surge hindcasts of historical storms, produces far more variability in the estimates than the JPM. A discussion of the source of this gain in information will be given in a subsequent section of this paper.…”

Tropical Cyclone Storm Surge Risk

“…Whereas, this may be adequate in that region, larger tidal ranges in other areas introduce sampling errors of the type described by Irish et al [34]. Other assumptions, such as (1) a single generalized distribution for storm sizes exists along the entire US East Coast [83] and (2) an omnidirectional basis, which assumes storm heading and storm intensity are independent, provides an optimal estimate for the geographic sample size [80], are also somewhat speculative.…”

“…As a result, since 2005, the USACE and FEMA adopted the JPM with some modifications to reduce the number of storms in the sample required for surge simulation, while maintaining good accuracy for planning purposes-sometimes termed the JPM-OS approach for Boptimal sampling^ [33,67,68,80]. Irish et al [34] showed the estimation power of the JPM significantly exceeds that of the HSM, and that the use of historical surge data alone for planning could introduce substantial local errors into design and planning decision-making. Although the impetus of Katrina provided momentum for change and helped advance the state of the art in quantifying flood hazards in coastal areas, a similar problem emerged in the estimation of risk for planning time scales as was noted in the section on synoptic forecasting.…”

“…Clearly, the assumption of independence between storm frequency and storm intensity is not supported in this region. The impact of climate variability on long-term storm surge hazard statistics is not yet fully quantified, but preliminary studies indicate that uncertainty introduced by climate variability into the probabilistic estimate is small with respect to other factors, so long as the trend is periodic and the record length for analysis captures this periodicity [34]. It is plausible, however, that this uncertainty may be significant in areas where these decadal and multi-decadal signals are more complex.…”