Prediction of Shoreline Evolution. Reliability of a General Model for the Mixed Beach Case

Tomasicchio, Giuseppe Roberto; Francone, Antonio; Simmonds, David; D’Alessandro, Felice; Frega, Ferdinando

doi:10.3390/jmse8050361

Cited by 23 publications

(13 citation statements)

References 29 publications

(52 reference statements)

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“…A shoreline model (In-MPaS, Inje University-Mixture Particle Shoreline model) was built, which could consider single-particle-size (D 50 ) and multi-particle-size distributions for sediments by using a numerical model for practical shoreline-change prediction based on the one-line theory (Hanson, 1989;Tomasicchio et al 2020) [28,29].…”

Section: Building a Shoreline Model (In-mpas Model)mentioning

confidence: 99%

Prediction of Shoreline Change for the Calculation of the Integrated Littoral Sediment Budget

Kim

Yoon

2022

Water

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The severe coastal erosions are being accelerated along the east coast of South Korea owing to the intermittent erosions and depositions caused by the imbalance between the effective sediment volume supplied from coasts and rivers and the sediment transport rate. Consequently, many studies are being conducted to develop coastal-erosion reduction measures. To accurately determine the cause of coastal erosion, the causes of the erosion and deposition should be accurately diagnosed, and a comprehensive evaluation system for the sediment transport mechanism in the watershed and sea while considering regional characteristics is required. In particular, realizing the evaluation of the effective sediment volume that flows from the river to the sea through observations is a highly challenging task, and various research and developments are required to realize it, as it is still in the basic research stage. The purpose of this study was to systematically analyze the comprehensive sediment budget for coastal areas. First, an analytical system was developed. Then, a shoreline model was constructed by considering the size of the mixed particles. The parameters required for developing the model were determined using the observation data to improve the shoreline model. A sediment runoff model was applied to evaluate the effective sediment volume supplied from the river to the sea, and the applicability of this model was evaluated by comparing it with the sediment supply volume according to the soil and water assessment tool model. The representative wave and the input parameters of the model were set using the observation data of several years. It was found that the prediction performance of the shoreline change model improved when the effective sediment volume was considered, and the particles of the sediment on the shore were assumed to comprise multiple sizes. In particular, the prediction performance improved when the balance of the sediment budget was adjusted by applying a groin having a structurally similar performance to take into consideration the geographic features of the Deokbongsan (island) in front of the river mouth bar. The model demonstrated a good performance in reproducing long-term shoreline changes when the characteristics of the sea waves and the effective sediment volume were considered.

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Section: Building a Shoreline Model (In-mpas Model)mentioning

confidence: 99%

Prediction of Shoreline Change for the Calculation of the Integrated Littoral Sediment Budget

Kim

Yoon

2022

Water

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“…For a specific study area, the employed shoreline indicator is often related to the data availability. Common data sources used to monitor coastlines are historical land-based photographs, coastal maps and charts, aerial photographs [17], beach surveys [18], LiDAR [19], remote sensing techniques [20][21][22][23][24][25][26][27], video imaging systems [28][29][30][31], and GPS [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing-Based Automatic Detection of Shoreline Position: A Case Study in Apulia Region

Spinosa

Ziemba

Saponieri

et al. 2021

JMSE

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Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.

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“…Empirical data-driven models are increasingly being used for modeling shoreline changes on time scales from days to decades [1][2][3][4][5][6][7][8][9]. They are based on a set of parameters representing the contribution of complex physical processes that are evaluated on a calibration period using observed shoreline data.…”

Section: Introductionmentioning

confidence: 99%

“…A new vector-based model with more flexible shoreline cell shape is suggested by [23]. The GSb model [9,25] is based on the one-line approach to investigate the longshore drift at a coastal mound made up of non-cohesive sediments, e.g., sand, gravel, cobbles, shingle, and rock. The ShorelineS model in [24] is the latest model for long-term shoreline evolution and is capable of simulating rich coastal transformation behavior of complex shoreline features through its flexible grid.…”

Section: Introductionmentioning

confidence: 99%

Combined Longshore and Cross-Shore Modeling for Low-Energy Embayed Sandy Beaches

Tran

Marchesiello

Almar

et al. 2021

JMSE

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The present study focuses on the long-term multi-year evolution of the shoreline position of the Nha Trang sandy beach. To this end an empirical model which is a combination of longshore and cross-shore models, is used. The Nha Trang beach morphology is driven by a tropical wave climate dominated by seasonal variations and winter monsoon intra-seasonal pulses. The combined model accounts for seasonal shoreline evolution, which is primarily attributed to cross-shore dynamics but fails to represent accretion that occurs during the height of summer under low energy conditions. The reason is in the single equilibrium Dean number Ωeq of the ShoreFor model, one of the components of the combined model. This equilibrium Dean number cannot simultaneously account for the evolution of strong intra-seasonal events (i.e., winter monsoon pulses) and the annual recovery mechanisms associated with swash transport. By assigning a constant value to Ωeq, when the surf similarity parameter is higher than 3.3 (occurrence of small surging breakers in summer), we strongly improve the shoreline position prediction. This clearly points to the relevance of a multi-scale approach, although our modified Ωeq retains the advantage of simplicity.

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Prediction of Shoreline Evolution. Reliability of a General Model for the Mixed Beach Case

Cited by 23 publications

References 29 publications

Prediction of Shoreline Change for the Calculation of the Integrated Littoral Sediment Budget

Prediction of Shoreline Change for the Calculation of the Integrated Littoral Sediment Budget

Remote Sensing-Based Automatic Detection of Shoreline Position: A Case Study in Apulia Region

Combined Longshore and Cross-Shore Modeling for Low-Energy Embayed Sandy Beaches

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