Probabilistic assessment of erosion and flooding risk in the northern Gulf of Mexico

Abstract

13-Sea-storm time series are simulated with a multivariate probabilistic model 14 -Erosion and flooding risk are assessed accurately with a joint probability approach 15 -Return water levels and impact hours could be larger than recently observed 16 Abstract 21We assess erosion and flooding risk in the northern Gulf of Mexico by identifying 22 interdependencies among oceanographic drivers and probabilistically modeling the resulting 23 potential for coastal change. Wave and water level observations are used to determine 24 relationships between six hydrodynamic parameters that influence total water level and 25 therefore erosion and flooding, through consideration of a wide range of univariate 26 distribution functions and multivariate elliptical copulas. Using these relationships, we 27 explore how different our interpretation of the present-day erosion/flooding risk could be if 28 we had seen more or fewer extreme realizations of individual and combinations of parameters 29 in the past by simulating 10,000 physically and statistically consistent sea-storm time series. 30 We find that seasonal total water levels associated with the 100-year return period could be up 31 to 3 m higher in summer and 0.6 m higher in winter relative to our best estimate based on the 32 observational records. Impact hours of collision and overwash -where total water levels 33 exceed the dune toe or dune crest elevations -could be on average 70% (collision) and 100% 34 (overwash) larger than inferred from the observations. Our model accounts for non-35 stationarity in a straightforward, non-parametric way that can be applied (with little 36 adjustments) to many other coastlines. The probabilistic model presented here, which 37 accounts for observational uncertainty, can be applied to other coastlines where short record 38 lengths limit the ability to identify the full range of possible wave and water level conditions 39 that coastal mangers and planners must consider to develop sustainable management 40 strategies. 41 Key words 42Multivariate sea-storm model; elliptical copulas; coastal erosion and flooding; northern Gulf 43 of Mexico 44 3 Introduction 45Erosion and flooding occur on sandy coastlines when the total water level (TWL) exceeds 46 critical thresholds of backshore features. Ruggiero [2013] defined TWL as the superposition 47 of astronomical tide (η A ), storm surge (or non-tidal residual; η NTR ), and the extreme wave 48 runup statistic (R2%; e.g., Stockdon et al. [2014]), all of which can be derived with various 49 numerical or empirical models. The impacts of extreme oceanographic events, in terms of 50 erosion of barrier islands and sandy beaches and flood damages in low-lying coastal areas, 51 also strongly depend on how long critical TWL thresholds are exceeded (i.e. the event 52duration is important). For long-term simulations of erosion, the duration of calm periods 53 between successive sea-storm events are also relevant since they determine how much the 54 beach or dune can recover before the next extreme eve...

Tidal Variability Related to Sea Level Variability in the Pacific Ocean

“…Coastal inundation is associated with peak water level, not mean sea level, and depends on the combined effects of tides, storm surge, sea level variability, inland precipitation, river flow, and other factors which may lead to increases in extreme water level exceedance probabilities. This has been shown for many regions worldwide, including the Australian coasts (Haigh et al, ; Pugh & Woodworth, ), the United Kingdom (Svensson & Jones, , ), the Netherlands (Klerk et al, ), China (Lian et al, ), and the United States (Wahl et al, ). A more detailed listing of other studies that focus on compound factor coastal flooding is given in Table SM.2 in the supporting information of Moftakhari et al ().…”

A Vine-Copula Model for Time Series of Significant Wave Heights and Mean Zero-Crossing Periods in the North Sea

“…In the bivariate case, many different copula families have proven to be useful (e.g., Salvadori et al 2014;Masina et al 2015, and references therein). For more than two oceanographic variables, nested (also called hierarchical) Archimedean copulas (Wahl et al 2012;Corbella and Stretch 2013;Lin-Ye et al 2016) and elliptical copulas, such as Gaussian or t, (Li et al 2014a;Wahl et al 2016;Rueda et al 2016) have been implemented and found valuable, but also dependence trees (Poelhekke et al 2016) and vines (De Michele et al 2007;Montes-Iturrizaga and Heredia-Zavoni 2016), which are a generalization thereof, have been proposed. Callaghan et al (2008) and Serafin and Ruggiero (2014) adopt two other approaches to model dependencies.…”

“…Storm sequences (i.e., time series of storm events) have been modeled as different types of renewal processes (De Michele et al 2007;Callaghan et al 2008;Corbella and Stretch 2013;Li et al 2014b;Wahl et al 2016) often approximated by a triangle (Fig. 1) whose size is determined by a peak value and a critical threshold value of, for example, wave height or water level together with the storm duration (e.g., Boccotti 2000).…”

A statistical simulation method for joint time series of non-stationary hourly wave parameters

“…Many simulation methods are based on renewal processes to model alternating sequences of storm and calm durations [8,9,10,11,12,13]. For the storm periods, high temporal resolution time series of the relevant metocean variables are then derived from an idealized 'storm shape'.…”

“…The above studies used different techniques to account for non-stationarities. The simplest approach has been to focus on the most important season [24] or to piecewise model seasons or months [26,12,4]. Other studies have used a superposition of linear or cyclic functions of time [17,19,21,9,7,27] and climate indices as co-variates [5,6,28,13] to represent trends or seasonal cycles on semiannual to decadal time scales.…”