The effects of natural structure on estimated tropical cyclone surge extremes

Abstract

The past 12 years have seen significant steps forward in the science and practice of coastal flood analysis. This paper aims to recount and critically assess these advances, while helping identify next steps for the field. This paper then focuses on a key problem, connecting the probabilistic characterization of flood hazards to their physical mechanisms. Our investigation into the effects of natural structure on the probabilities of storm surges shows that several different types of spatial-, temporal-, and process-related organizations affect key assumptions made in many of the methods used to estimate these probabilities. Following a brief introduction to general historical methods, we analyze the two joint probability methods used in most tropical cyclone hazard and risk studies today: the surface response function and Bayesian quadrature. A major difference between these two methods is that the response function creates continuous surfaces, which can be interpolated or extrapolated on a fine scale if necessary, and the Bayesian quadrature optimizes a set of probability masses, which cannot be directly interpolated or extrapolated. Several examples are given here showing significant impacts related to natural structure that should not be neglected in hazard and risk assessment for tropical cyclones including: (1) differences between omnidirectional sampling and directional-dependent sampling of storms in near coastal areas; (2) the impact of surge probability discontinuities on the treatment of epistemic uncertainty; (3) the ability to reduce aleatory uncertainty when sampling over larger spatial domains; and (4) the need to quantify trade-offs between aleatory and epistemic uncertainties in long-term stochastic sampling.

An objective and efficient method for estimating probabilistic coastal inundation hazards

“…The differences of probabilistic water levels with 50-year and 100-year return periods are less than 9% between JPM-OS-AK and POT, and less than 13% between JPM-OS-AK and block maxima. The large differences of probabilistic water level with 500-year return period among the methods are likely due to the limited data used and parametric extrapolation by HSM, which could result in higher variability in the estimates, compared to JPM (Irish et al 2011;Resio et al 2017). Additionally, the differences could be due to the fact that the simulated storms from FSU-COAPS model, rather than the historical storms, are used for JPM and JPM-OS in this study.…”

“…Detailed discussion on the development of these JPM-OS methods is given by Toro et al (2010a, b) (JPM-OS-Q and JPM-OS-RS), Condon and Sheng (2012) (JPM-OS-MARS) and Resio et al (2017) (JPM-OS-BQ and JPM-OS-SRF). Resio et al (2017) presented a comprehensive review of the methods (historical storm method, JPM, empirical simulation technique, stochastic-deterministic track method, JPM-OS-BQ, and JPM-OS-SRF) to quantify coastal storm surge, and discussed the aleatory and epistemic uncertainties of JPM and JPM-OS.…”

“…Although the radii of maximum wind are somewhat overestimated, it did not lead to significant difference in the resulting inundation map. Resio et al (2017) discussed the correlation between storm heading and central pressure deficit (intensity) and showed that including the correlation would affect the return period of central pressure. However, the low correlation coefficient (0.02) between storm heading and intensity indicates that they are independent in this study.…”

Physical Drivers of Changes in Probabilistic Surge Hazard Under Sea Level Rise

“…The inclusion of the uncertainty term in equation (3) is important because storms whose simulated surge is below a prescribed AEP surge can still contribute probability mass due to the uncertainty term (Resio et al, 2017). The inclusion of the uncertainty term in equation (3) is important because storms whose simulated surge is below a prescribed AEP surge can still contribute probability mass due to the uncertainty term (Resio et al, 2017).…”

“…Using C as the measure for selection criteria allows us to take into account not only the surge magnitude produced by a storm but also the probability for that storm to happen and the level of uncertainty about that surge. The inclusion of the uncertainty term in equation (3) is important because storms whose simulated surge is below a prescribed AEP surge can still contribute probability mass due to the uncertainty term (Resio et al, 2017). Two thresholds were utilized to subsample the storms based on C: one for C ij ( ) at any point j in the area of interest and a second for the total contribution ∑ C i ( ) over all points in the area of interest.…”