Coastal sediment compartments, wave climate and centennial-scale sediment budget

Goodwin, Ian; Ribó, Marta; Mortlock, Thomas

doi:10.1016/b978-0-08-102927-5.00025-4

Cited by 8 publications

(7 citation statements)

References 62 publications

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“…3). These are identified as locations where updrift headland-attached shelf sediment bodies would have accumulated under an oblique wave climate, similar to the present-day wave climate 35 . We observed that the headland locations during SLM1 coincide with the modern outcrop of relict or ravined sand deposits over the inner shelf ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Shelf sand supply determined by glacial-age sea-level modes, submerged coastlines and wave climate

Ribó

Goodwin

O’Brien

et al. 2020

Sci Rep

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Submerged paloeshorelines preserved on the continental shelf indicate the depths of the most frequent (modal) low sea-levels within the glacial stages of the Late Quaternary. Here we have determined the south-east Australian shelf configuration when sea level was 40 m and 60 m below present-day sealevel (depths of the most persistent paleoshorelines within the last 120 ka), and we resolve the wave climate variations influencing the sediment transport pathways over this period. We present evidence demonstrating that the combination of shelf morphological evolution, changes in sea-level and variations in wave climate is responsible for latitudinal changes in sediment transport and deposition during the interglacial states. the paleoshoreline and shelf evolution is key to understanding the distribution of present-day shelf sand deposits and the contemporary sand budget response to future wave climate changes.wave climate throughout the glacial cycle, to resolve the connectivity and longshore sediment transport paths during the Late Quaternary glacial and interglacial cycles. The definition of paleo-sediment transport and relict sand bodies is essential data to apply to the projection of modern coastline response to net changes in wave climate, sediment transport and sea levels over the coming century. Scientific RepoRtS | (2020) 10:462 | https://doi.Figure 7. Conceptual model showing the shelf sand deposits evolution with changes in sea-level over shallow (a) and steep (b) slope gradients (oblique wave energy is assumed for both scenarios).Scientific RepoRtS | (2020) 10:462 | https://doi.

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Section: Resultsmentioning

confidence: 99%

Shelf sand supply determined by glacial-age sea-level modes, submerged coastlines and wave climate

Ribó

Goodwin

O’Brien

et al. 2020

Sci Rep

Self Cite

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“…The morphodynamic interdependence of nearshore morphology and the spatiotemporal variability of winds, surges, and waves represents an important area for future research and model development. Some insightful studies in eastern Australia have highlighted these interdependent relationships [36].…”

Section: Coastal Inundation and Climate Changementioning

confidence: 99%

Coastal Morphodynamics and Climate Change: A Review of Recent Advances

Wright,

Thom

2023

JMSE

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The shape of the coast and the processes that mold it change together as a complex system. There is constant feedback among the multiple components of the system, and when climate changes, all facets of the system change. Abrupt shifts to different states can also take place when certain tipping points are crossed. The coupling of rapid warming in the Arctic with melting sea ice is one example of positive feedback. Climate changes, particularly rising sea temperatures, are causing an increasing frequency of tropical storms and “compound events” such as storm surges combined with torrential rains. These events are superimposed on progressive rises in relative sea level and are anticipated to push many coastal morphodynamic systems to tipping points beyond which return to preexisting conditions is unlikely. Complex systems modeling results and long-term sets of observations from diverse cases help to anticipate future coastal threats. Innovative engineering solutions are needed to adapt to changes in coastal landscapes and environmental risks. New understandings of cascading climate-change-related physical, ecological, socioeconomic effects, and multi-faceted morphodynamic systems are continually contributing to the imperative search for resilience. Recent contributions, summarized here, are based on theory, observations, numerically modeled results, regional case studies, and global projections.

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“…Understanding streamflow patterns not only is essential for ecological health but also forms the basis for sedimentary budget forecasting in fluvial flows [4]. Streamflow modeling becomes a key tool in this regard, as it simulates hydrodynamic processes within river systems, including flow velocity, discharge, and channel morphology.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Methods for Streamflow Forecasting

Wei,

Hashim,

Lai

et al. 2024

IEEE Access

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Deep learning excels at managing spatial and temporal time series with variable patterns for streamflow forecasting, but traditional machine learning algorithms may struggle with complicated data, including non-linear and multidimensional complexity. Empirical heterogeneity within watersheds and limitations inherent to each estimation methodology pose challenges in effectively measuring and appraising hydrological statistical frameworks of spatial and temporal variables. This study emphasizes streamflow forecasting in the region of Johor, a coastal state in Peninsular Malaysia, utilizing a 28-year streamflow-pattern dataset from Malaysia's Department of Irrigation and Drainage for the Johor River and its tropical rainforest environment. For this dataset, wavelet transformation significantly improves the resolution of lag noise when historical streamflow data are used as lagged input variables, producing a 6% reduction in the root-mean-square error. A comparative analysis of convolutional neural networks and artificial neural networks reveals these models' distinct behavioral patterns. Convolutional neural networks exhibit lower stochasticity than artificial neural networks when dealing with complex time series data and with data transformed into a format suitable for modeling. However, convolutional neural networks may suffer from overfitting, particularly in cases in which the structure of the time series is overly simplified.Using Bayesian neural networks, we modeled network weights and biases as probability distributions to assess aleatoric and epistemic variability, employing Markov chain Monte Carlo and bootstrap resampling techniques. This modeling allowed us to quantify uncertainty, providing confidence intervals and metrics for a robust quantitative assessment of model prediction variability.

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Coastal sediment compartments, wave climate and centennial-scale sediment budget

Cited by 8 publications

References 62 publications

Shelf sand supply determined by glacial-age sea-level modes, submerged coastlines and wave climate

Shelf sand supply determined by glacial-age sea-level modes, submerged coastlines and wave climate

Coastal Morphodynamics and Climate Change: A Review of Recent Advances

Comparative Analysis of Artificial Intelligence Methods for Streamflow Forecasting

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