An equilibrium-based shoreline rotation model

Jaramillo, Camilo; González, Mauricio; Medina, Raúl; Turki, Imen

doi:10.1016/j.coastaleng.2020.103789

Cited by 11 publications

(6 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other researchers have proposed equilibrium approaches to model the planform of embayed beaches (e.g., Turki et al, 2013;Jaramillo et al, 2021). Much like their sister crossshore equilibrium models described above, they rely on data to develop simplified relationships between the existing alongshore distribution of the shoreline and the prevailing wave forcing with the underlying physical assumption that beaches will tend to align with the prevailing wave climate direction.…”

Section: Alongshore Modelsmentioning

confidence: 99%

Challenges and Opportunities in Coastal Shoreline Prediction

Splinter

Coco

2021

Front. Mar. Sci.

View full text Add to dashboard Cite

Sandy beaches comprise approximately 31% of the world's ice-free coasts. Sandy coastlines around the world are continuously adjusting in response to changing waves and water levels at both short (storm) and long (climate-driven, from El-Nino Southern Oscillation to sea level rise) timescales. Managing this critical zone requires robust, advanced tools that represent our best understanding of how to abstract and integrate coastal processes. However, this has been hindered by (1) a lack of long-term, large-scale coastal monitoring of sandy beaches and (2) a robust understanding of the key physical processes that drive shoreline change over multiple timescales. This perspectives article aims to summarize the current state of shoreline modeling at the sub-century timescale and provides an outlook on future challenges and opportunities ahead.

show abstract

Section: Alongshore Modelsmentioning

confidence: 99%

Challenges and Opportunities in Coastal Shoreline Prediction

Splinter

Coco

2021

Front. Mar. Sci.

View full text Add to dashboard Cite

show abstract

“…Shoreline rotation is a key process within some embayments that is perhaps lacking in previous equilibrium models. Turki et al (2013) and Jaramillo et al (2021) both present an equilibrium model for shoreline rotation, utilising the foundations provided by Miller and Dean (2004) and further developed by Yates et al (2009).…”

Section: Semi-empirical Modelsmentioning

confidence: 99%

Shoreline modelling on timescales of days to decades

Hunt¹,

Davidson²,

Steele³

et al. 2023

Camb. prisms Coast. futures

View full text Add to dashboard Cite

In the context of increased probability of coastal erosion and flooding associated with climate change, there is a pressing need to predict future shorelines at both short-(daily) and mediumterm (decadal) timescales. Such predictions are essential for the assessment of the climateresilience of the world's coastlines and the delivery of effective, economic and data-informed coastal management. Coastal managers currently lack these predictions and there are many different modelling approaches to inform where increased coastal protection, adaption measures or future infrastructure developments should be focussed. Promising modelling advances have recently been made, particularly in the context of reduced complexity models. This paper reviews various numerical modelling approaches to predicting shoreline and coastal morphological change, comments on some of the most promising methods used to-date, provides some guidance on model selection, and highlights important future research directions and challenges to progress.

show abstract

“…The data from the study site are freely available and have been used previously in other studies (e.g. Jaramillo et al, 2021;Vitousek et al, 2021;Lim et al, 2022).…”

Section: Datamentioning

confidence: 99%

On the use of convolutional deep learning to predict shoreline change

Gomez-de la Peña,

Coco,

Whittaker

et al. 2023

Earth Surf. Dynam.

View full text Add to dashboard Cite

Abstract. The process of shoreline change is inherently complex, and reliable predictions of shoreline position remain a key challenge in coastal research. Predicting shoreline evolution could potentially benefit from deep learning (DL), which is a recently developed and widely successful data-driven methodology. However, so far its implementation for shoreline time series data has been limited. The aim of this contribution is to investigate the potential of DL algorithms to predict interannual shoreline position derived from camera system observations at a New Zealand study site. We investigate the application of convolutional neural networks (CNNs) and hybrid CNN-LSTM (Long Short-Term Memory) networks. We compare our results with two established models: a shoreline equilibrium model and a model that addresses timescales in shoreline drivers. Using a systematic search and different measures of fitness, we found DL models that outperformed the reference models when simulating the variability and distribution of the observations. Overall, these results indicate that DL models have potential to improve accuracy and reliability over current models.

show abstract

An equilibrium-based shoreline rotation model

Cited by 11 publications

References 66 publications

Challenges and Opportunities in Coastal Shoreline Prediction

Challenges and Opportunities in Coastal Shoreline Prediction

Shoreline modelling on timescales of days to decades

On the use of convolutional deep learning to predict shoreline change

Contact Info

Product

Resources

About