A generalized equilibrium model for predicting daily to interannual shoreline response

Abstract

Key Points:• Cross-shore equilibrium shoreline models show skill at shorter-term simulations • Model-free parameters are predictable and are used to derive parameterizations • Model skill of a parameterized form of the model remained high Abstract Coastal zone management requires the ability to predict coastline response to storms and longer-term seasonal to interannual variability in regional wave climate. Shoreline models typically rely on extensive historical observations to derive site-specific calibration. To circumvent the challenge that suitable data sets are rarely available, this contribution utilizes twelve 5+ year shoreline data sets from around the world to develop a generalized model for shoreline response. The shared dependency of model coefficients on local wave and sediment characteristics is investigated, enabling the model to be recast in terms of these more readily measurable quantities. Study sites range from microtidal to macrotidal coastlines, spanning moderate-to high-energy beaches. The equilibrium model adopted here includes time varying terms describing both the magnitude and direction of shoreline response as a result of onshore/offshore sediment transport between the surf zone and the beach face. The model contains two coefficients linked to wave-driven processes: (1) the response factor ( ) that describes the "memory" of a beach to antecedent conditions and (2) the rate parameter (c) that describes the efficiency with which sand is transported between the beach face and surf zone. Across all study sites these coefficients are shown to depend in a predictable manner on the dimensionless fall velocity (Ω), that in turn is a simple function of local wave conditions and sediment grain size. When tested on an unseen data set, the new equilibrium model with generalized forms of and c exhibited high skill (Brier Skills Score, BSS = 0.85).

Statistical modeling of interannual shoreline change driven by North Atlantic climate variability spanning 2000–2014 in the Bay of Biscay

“…This is addressed in this study. The topographic surveys were averaged alongshore to derive a mean beach profile (for more details, see Castelle et al 2014and Splinter et al 2014a. Figure 1c shows the superimposed mean profiles surveyed from 2005 to 2013 where the elevation is given with respect to the local mean sea level, highlighting the large vertical and cross-shore variability.…”

“…Shoreline evolution on timescales from hours (cf. storms) to years has recently been simulated with fair skill using wave-driven empirical equilibrium-based models (e.g., Davidson and Turner 2009;Yates et al 2009;Davidson et al 2013;Castelle et al 2014;Splinter et al 2014a). These models can also reproduce the interannual shoreline variability that sometimes exceeds the seasonal variability (e.g., Castelle et al 2014).…”

LX-Shore : Un nouveau modèle d’évolution du trait de côte pour les littoraux sableux dominés par l'action des vagues

“…Les vagues au déferlement sont déterminées avec le modèle de vagues simple car la côte est rectiligne et les isocontours bathymétriques relativement parallèles à la côte sur le domaine de calcul. La simulation est réalisée sur une période de 9 ans (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) LX-Shore parvient à reproduire une grande part de la variabilité du trait de côte observée sur des échelles allant de l'évènementiel à l'interannuel et avec des performances (r 2 = 0.67, RMSE = 7.74 m) similaires à celles obtenues dans d'autres études où ShoreFor est directement appliqué à la plage du Truc Vert sur la même période (SPLINTER et al, 2014). Des résultats très similaires sont obtenus lorsque les vagues sont propagées avec SWAN.…”

Shoreline Response to a Sequence of Typhoon and Monsoon Events

“…In Equation (3), i is the day prior to present, φ the number of past days where the decaying exponential function reaches 10% (more details in [2,14]). …”

“…Existing shoreline equilibrium models (among others: [2,13,14]) show appreciable skills in predicting shoreline location from wave energy at monthly or longer time-scales for mid-latitude, storm-dominated coasts. However, these skills may be at fault in a so-called low-energy environment as often encountered in the tropics.…”