No abstract

The details of a large-scale laboratory experiment to study the turbulence generated by waves breaking on a fixed barred beach are presented. The data set includes comprehensive measurements of free surface displacement and fluid velocity for one random and one regular wave case. Observations of the time-averaged turbulent kinetic energy per unit mass, , show that the turbulence generated by wave breaking was greatest at the bar crest and did not fully dissipate prior to reaching the bed. This indicates that, even in a time-averaged sense, wave breaking turbulence may be important for near-bed processes. Onshore of the bar, turbulence was generally confined to the upper part of the water column and had dissipated once the waves reformed (approximately 1.5 wavelengths onshore of the bar crest). The turbulent structure was the same in the random and regular wave cases; however, the magnitude of was much less in the random wave case, despite similar offshore wave conditions. Additionally, three methods were used to separate the wave-induced and turbulent components of velocity:. ensemble averaging, high-pass filtering and a differencing method proposed by Trowbridge (1998 J. Atmos. Ocean. Technol. 15 290–8). The magnitude of varied by as much as a factor of 5 among these methods, but qualitatively, the cross-shore and vertical structure were independent of the method used. The differencing method agreed closely with ensemble averaging in terms of the magnitude and structure of time-averaged quantities and in the signature of the time-dependent turbulent kinetic energy. Given this agreement, the differencing method appears to be the most suitable for application to random waves, such as those observed in the field.

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...

[1] A shoreline change model incorporating both long-and short-term evolution is integrated into a data assimilation framework that uses sparse observations to generate an updated forecast of shoreline position and to estimate unobserved geophysical variables and model parameters. Application of the assimilation algorithm provides quantitative statistical estimates of combined model-data forecast uncertainty which is crucial for developing hazard vulnerability assessments, evaluation of prediction skill, and identifying future data collection needs. Significant attention is given to the estimation of four non-observable parameter values and separating two scales of shoreline evolution using only one observable morphological quantity (i.e. shoreline position).

Extreme storms drive change in coastal areas, including destruction of dune systems that protect coastal populations. Data from four extreme storms impacting four geomorphically diverse barrier islands are used to quantify dune elevation change. This change is compared to storm characteristics to identify variability in dune response, improve understanding of morphological interactions, and provide estimates of scaling parameters applicable for future prediction. Locations where total water levels did not exceed the dune crest experienced elevation change of less than 10%. Regions where wave‐induced water levels exceeded the dune crest exhibited a positive linear relationship between the height of water over the dune and the dune elevation change. In contrast, a negative relationship was observed when surge exceeded the dune crest. Results indicate that maximum dune elevation, and therefore future vulnerability, may be more impacted from lower total water levels where waves drive sediment over the dune rather than surge‐dominated flooding events.