<b>82.</b> MICORE: DUNE EROSION AND OVERWASH MODEL VALIDATION WITH DATA FROM NINE EUROPEAN FIELD SITES

Dongeren, Ap van; Bolle, Annelies; Vousdoukas, Michalis; Plomaritis, Theocharis A.; Eftimova, Petya; Williams, Jon J.; Armaroli, Clara; Idier, Déborah; Geer, Pieter van; Vries, Jaap van Thiel de; Haerens, Piet; Taborda, Rui; Benavente, Javier; Trifonova, E. P.; Ciavola, Paolo; Balouin, Yann; Roelvink, Dano

doi:10.1142/9789814282475_0084

Cited by 25 publications

(13 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many different paradigms of coastal evolution models exist to simulate behavior of certain processes and scales. Studies of coastal hazards and shoreline change due to extreme events often rely on detailed, computationally onerous, physics‐based numerical modeling efforts [e.g., Van Dongeren et al ., ; Barnard et al ., ] to resolve the hydrodynamic forcing and morphologic response. On the other hand, simplified process‐based or empirical models (detailed below) are often applied to predict chronic shoreline change.…”

Section: Introductionmentioning

confidence: 99%

A model integrating longshore and cross‐shore processes for predicting long‐term shoreline response to climate change

et al. 2017

View full text Add to dashboard Cite

We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS‐COAST (Coastal One‐line Assimilated Simulation Tool), is a transect‐based, one‐line model that predicts short‐term and long‐term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process‐based models of coastline evolution due to longshore and cross‐shore transport by waves and sea level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long‐term predictions. We apply CoSMoS‐COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995–2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model's predictive capability when applied to the forecast period (2010–2100) driven by GCM‐projected wave and sea level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea level rise scenarios of 0.93 to 2.0 m.

show abstract

Section: Introductionmentioning

confidence: 99%

A model integrating longshore and cross‐shore processes for predicting long‐term shoreline response to climate change

et al. 2017

View full text Add to dashboard Cite

show abstract

“…XBeach solves coupled equations for cross-and long-shore hydrodynamics and morphodynamics on the time scale of wave groups, including the generation of infragravity waves. The model has been calibrated and validated against 1-D flume experiments Van Thiel de Vries 2010) and some field studies McCall et al 2010;Van Thiel de Vries 2010;Vousdoukas et al 2011a;Van Dongeren et al 2009;Harley et al 2011). On the other hand, XBeach remains under development, and the cases considered for its validation cover a small range of the conditions found at coastal sites worldwide.…”

Section: Introductionmentioning

confidence: 99%

Toward reliable storm-hazard forecasts: XBeach calibration and its potential application in an operational early-warning system

et al. 2012

Self Cite

View full text Add to dashboard Cite

The study aims to calibrate/validate and apply the dune-erosion model, XBeach, in order to predict morphological response to storm events along a meso-tidal, steeply sloping beach. More than 10,000 XBeach calibration runs, including different model parameters and erosion events, were compared with measurements of beach-profile response to storm conditions. Off-shore wave and tidal measurements were used as input for a SWAN wave model, which was used to provide wave conditions to XBeach. The results indicate that using XBeach to predict beachprofile morphodynamic response during storm events on steeply sloping intermediate-to-reflective beaches may be more demanding than for dissipative beaches and that the default model setup can overestimate dune/beach-face erosion. The performance of the model after calibration was satisfactory, with Brier Skill Scores from 0.2 to 0.72. XBeach was found to be more sensitive to input parameters such as the beach-face slope and the surf similarity parameter ξ (especially for values ξ>0.6). The calibrated XBeach setup was used for simulations of storm scenarios with different return periods (5, 25, and 50 years), and the simulations highlighted the fragility of the dune field and the potential for storm-induced dune retreat, lowering, and overwash in the study area. Finally, the nested SWAN/ XBeach models were forced by an existing operational wave-forecast WAVEWATCH-III/SWAN model, operated by the Portuguese Hydrographic Institute to generate daily forecasts of storm impact and serve as a prototype-case for an early warning system for storm hazard mitigation.

show abstract

“…(), Van Dongeren et al . () and Brandenburg (). This model shows results with significant predictive skill under storm conditions (McCall et al ., ) but is computationally much more expensive, and therefore less suitable for application in combination with probabilistic methods as used in this study.…”

Section: Discussionmentioning

confidence: 97%

Fully probabilistic assessment of safety against flooding along the Dutch coast

Vuik

Balen

Vuren

2015

J Flood Risk Management

View full text Add to dashboard Cite

This paper describes a fully probabilistic safety assessment of the Dutch North Sea coast, in which stochastic properties of both hydraulic loads and strength of the flood defences have been taken into account. The study has led to an overview of failure probabilities along the coast with high spatial resolution. Both dikes and dunes have been considered. Failure probabilities at individual locations have been combined to flooding probabilities per dike ring area. The vast majority of the Dutch coastal defences is quite secure in terms of flooding. This study demonstrates that generally, the Dutch dunes provide a higher degree of safety than the sea dikes. When incorporating the consequences of flooding to the analysis, the calculated flooding probabilities can be used to determine flood risks. The probabilistic method, presented in this paper, enables accurate balancing between avoided flood risks and investments to reinforce the flood defences.

show abstract

82. MICORE: DUNE EROSION AND OVERWASH MODEL VALIDATION WITH DATA FROM NINE EUROPEAN FIELD SITES

Cited by 25 publications

References 0 publications

A model integrating longshore and cross‐shore processes for predicting long‐term shoreline response to climate change

A model integrating longshore and cross‐shore processes for predicting long‐term shoreline response to climate change

Toward reliable storm-hazard forecasts: XBeach calibration and its potential application in an operational early-warning system

Fully probabilistic assessment of safety against flooding along the Dutch coast

Contact Info

Product

Resources

About