Equilibrium modeling of the beach profile on a macrotidal embayed low tide terrace beach

Lemos, Clara; Floc'H, France; Yates, Marissa; Dantec, Nicolas Le; Marieu, Vincent; Hamon, Klervi; Cuq, Véronique Madec; Suanez, Serge; Delacourt, Christophe

doi:10.1007/s10236-018-1185-1

Cited by 38 publications

(26 citation statements)

References 37 publications

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“…In general, models showed a lower performance during the Shorecast period than during the calibration period, and lower performance compared to previous studies in different sites where the models were first presented and tested against a data set [12][13][14][15][16][17][18] . The exact reason is difficult to determine but it is evident that, despite data uncertainties, true predictions of unseen shoreline data remains a difficult task.…”

Section: Discussioncontrasting

confidence: 71%

“…Equilibrium conditions have been defined in terms of shoreline position 10,12 or wave history 13 . These models and similar variants have been applied successfully when addressing seasonal to interannual variability at many sites [14][15][16][17] , however they may fail to simulate the shoreline evolution in environments where other processes such as alongshore sediment transport play an important role 3 .…”

Section: Model Classificationmentioning

confidence: 99%

“…This article summarizes the main outputs from a study where well-known models that have been broadly used in diverse study sites worldwide [12][13][14][15][16][17][18] combined with new approaches were compared and evaluated objectively and with no possibility of parameter tuning during the last three years of the study. As all the models are tested with the same input dataset, there is no bias associated with data sources (regardless of any inherent uncertainties in the data that was used) allowing us to objectively assess the predictive capability of the models.…”

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confidence: 99%

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Blind testing of shoreline evolution models

Montaño

Coco

Antolínez

et al. 2020

Sci Rep

117

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Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer timescales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for tairua beach, new Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. in general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999-2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014-2017), both approaches showed a decrease in models' capability to predict the shoreline position. this was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models. Quantitative prediction of beach erosion and recovery is essential to planning resilient coastal communities with robust strategies to adapt to erosion hazards. Over the last decades, research efforts to understand and predict shoreline evolution have intensified as coastal erosion is likely to be exacerbated by climatic changes 1-5. The social and economic burden of changes in shoreline position are vast, which has inspired development of a growing variety of models based on different approaches and techniques; yet current models can fail (e.g. predicting erosion in accreting conditions). The challenge for shoreline models is, therefore, to provide reliable, robust and realistic predictions of change, with a reasonable computational cost, applicability to a broad variety of systems, and some quantifiable assessment of the uncertainties.

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Section: Discussioncontrasting

confidence: 71%

Section: Model Classificationmentioning

confidence: 99%

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confidence: 99%

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Blind testing of shoreline evolution models

Montaño

Coco

Antolínez

et al. 2020

Sci Rep

117

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“…Extending the same dataset to 2018, Dodet et al (2019) showed that the recovery signature is site specific and multi-annual, with one studied beach fully recovered after 2 years, and the others only partially recovered after 4 years. On open cross-shore transport dominated coasts, simple semiempirical shoreline change models (e.g., Yates et al, 2009;Splinter et al, 2014;Lemos et al, 2018) accurately reproduce the erosion driven by the 2013/2014 winter and subsequent post-storm recovery, but they largely fail on more complex sites. This further strengthens the recent development of a new generation of reduced-complexity shoreline change models coupling cross-shore, longshore, and other processes such as sea level rise (Vitousek et al, 2017;Robinet et al, 2018), to be used to investigate, hindcast, and ultimately forecast the erosion and recovery process in more detail on a wide range of coasts.…”

Section: Some Key Scientific Resultsmentioning

confidence: 99%

Coastal Ocean and Nearshore Observation: A French Case Study

et al. 2019

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To understand and predict the physical, chemical, and biological processes at play in coastal and nearshore marine areas requires an integrated, interdisciplinary approach. The case study of the French structuration of coastal ocean and nearshore observing systems provides an original overview on a federative research infrastructure named ILICO. It is a notable example of national structuration and pan-institution efforts to investigate the forefront of knowledge on the processes at work within the critical coastal zone. ILICO comprises, in a pluridisciplinary approach, eight distributed networksystems of observation and data analysis that are accredited and financially supported by French research institutions and the French Ministry for Higher Education, Research, and Innovation. ILICO observation points are implemented along metropolitan and overseas French coasts, where coastline dynamics, sea level evolution, physical and biogeochemical water properties, coastal water dynamics, phytoplankton composition, and health of coral reefs are monitored in order to address a wide range of scientific questions. To give an overview of the diversity and potential of the observations carried out, this paper offers a detailed presentation of three constituting networks: Service Observation en Milieu LITtoral (SOMLIT), with homogeneous sampling strategies, DYNALIT, with heterogeneous sampling strategies adapted to different environments, and Mediterranean Ocean Observing System for the Environment (MOOSE), an integrated, pluri-disciplinary coastal/offshore regional observatory in the northwestern Mediterranean Sea. ILICO was conceived using a European framework. It addresses the great challenges of the next decade in terms of sustainability, cost-efficiency, interoperability, and innovation. This paper emphasizes the added-value of federating these systems, and highlights some recommendations for the future.

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“…Kaczkowski et al (2018) are presenting the dune growth at two sites after major beach nourishments and describe the additional value of this dune formation for coastal protection. Lemos et al (2018) use a decadal data set of monthly beach profiles at a macro-tidal beach in France, together with an empirical model to unravel the drivers behind the beach evolution. Arriaga et al (2018) focus on the formation events of shoreline sand waves on gravel beaches.…”

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confidence: 99%