Efficient Modeling of Complex Sandy Coastal Evolution at Monthly to Century Time Scales

Roelvink, Dano; Huisman, B.J.A.; ELGHANDOUR, AHMED; Ghonim, Mohamed; Reyns, J.

doi:10.3389/fmars.2020.00535

Cited by 53 publications

(32 citation statements)

References 51 publications

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“…Shoreline data at high spatio-temporal resolution is also needed to study beach cusps, megacusps and shoreline sand waves, which are relevant not only due to their direct effect (the occurrence of erosional hot spots [4]) but also because they are a tool to investigate the poorly-understood sediment transport processes in the nearshore [5,6]. Measurements are essential both to perform experimental studies and to calibrate and validate morphodynamic models [7,8], an essential step before they can be used to predict the future coastal evolution under different climate scenarios. Despite the critical importance of quantifying the shoreline position, there is not yet a satisfactory solution that provides high-resolution accurate data.…”

Section: Introductionmentioning

confidence: 99%

Automatic Shoreline Detection from Video Images by Combining Information from Different Methods

et al. 2020

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Properly registering the time evolution of the shoreline—the coastal land-water interface—is a crucial issue in coastal management, among other disciplines. Video stations have shown to be powerful low-cost tools for continuous monitoring of the coast in the last 30 years. Despite the efforts of the scientific community to get algorithms able to properly track the shoreline position from video images without human supervision, there is not yet an algorithm that can be recognized as fully satisfactory. The present work introduces a methodology to combine the results from different shoreline detection algorithms so as to obtain a smooth and very much improved result when compared to the actual shoreline. The output of the introduced methodology, which is fully automatic, includes not only the shorelines at all available times but also a measure of the quality of the obtained shoreline at each point (called self-computed error). The results from the studied beaches—located in the region of Barcelona city (Spanish Mediterranean coast)—show that such self-computed errors are in general good proxies of the actual errors. Using a certain threshold for the self-computed errors, the final computed shorelines have RMSE (Root Mean Squared Errors) that are in general smaller than 2.5 m in the great majority of analysed images, when compared to the manually digitized shorelines by three expert users. The global RMSE for all dates and beaches is of 1.8 m, with a mean bias <1 m and percentage of retrieval success >95% of the points.

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Section: Introductionmentioning

confidence: 99%

Automatic Shoreline Detection from Video Images by Combining Information from Different Methods

et al. 2020

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“…Therefore, these coastline change projections only provide first-order approximations of the long-term evolution of the coastline at the case study sites. Coupling the projected sediment volumes with a coastline change model that provides a more realistic representation of the shape and orientation of coastlines, such as the Coastline Evolution Model (CEM) (Ashton and Murray, 2006), the Coastal One-line Vector Evolution Model (COVE) (Hurst et al, 2015), or ShorelineS (Roelvink et al, 2020) will significantly enhance the quality of coastline change projections.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic Application of an Integrated Catchment-Estuary-Coastal System Model to Assess the Evolution of Inlet-Interrupted Coasts Over the 21st Century

et al. 2020

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Inlet-interrupted sandy coasts are dynamic and complex coastal systems with continuously evolving geomorphological behaviors under the influences of both climate change and human activities. These coastal systems are of great importance to society (e.g., providing habitats, navigation, and recreational activities) and are affected by both oceanic and terrestrial processes. Therefore, the evolution of these inlet-interrupted coasts is better assessed by considering the entirety of the Catchment-Estuary-Coastal (CEC) systems, under plausible future scenarios for climate change and increasing pressures due to population growth and human activities. Such a holistic assessment of the long-term evolution of CEC systems can be achieved via reduced-complexity modeling techniques, which are also ably quantifying the uncertainties associated with the projections due to their lower simulation times. Here, we develop a novel probabilistic modeling framework to quantify the input-driven uncertainties associated with the evolution of CEC systems over the 21st century. In this new approach, probabilistic assessment of the evolution of inlet-interrupted coasts is achieved by (1) probabilistically computing the exchange sediment volume between the inlet-estuary system and its adjacent coast, and (2) distributing the computed sediment volumes along the inlet-interrupted coast. The model is applied at three case study sites: Alsea estuary (United States), Dyfi estuary (United Kingdom), and Kalutara inlet (Sri Lanka). Model results indicate that there are significant uncertainties in projected volume exchange at all the CEC systems (min-max range of 2.0 million cubic meters in 2100 for RCP 8.5), and the uncertainties in these projected volumes illustrate the need for probabilistic modeling approaches to evaluate the long-term evolution of CEC systems. A comparison of 50th percentile probabilistic projections with deterministic estimates shows that the deterministic approach overestimates the sediment volume exchange in 2100 by 15–30% at Alsea and Kalutara estuary systems. Projections of coastline change obtained for the case study sites show that accounting for all key processes governing coastline change along inlet-interrupted coasts in computing coastline change results in projections that are between 20 and 134% greater than the projections that would be obtained if only the Bruun effect were taken into account, underlining the inaccuracies associated with using the Bruun rule at inlet-interrupted coasts.

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“…Coastal morphodynamics and hydrodynamics are coupled processes, with each directly impacting the other (Roelvink et al, 2009). As such, climate change impacts including sea level rise and increases to tropical cyclone intensity and frequency will likely cause morphological changes to the U.S. coastline, with important implications for coastal flood risk (Passeri et al, 2015;Ozkan et al, 2020;Roelvink et al, 2020). Furthermore, land use is also changing with population growth and urban development (Lawler et al, 2014).…”

Section: Study Limitationsmentioning

confidence: 99%

Projected Climate Change Impacts on Hurricane Storm Surge Inundation in the Coastal United States

Camelo

Mayo

Gutmann

2020

Front. Built Environ.

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The properties of hurricanes directly influence storm surges; however, the implications of projected changes to the climate are unclear. Here, we simulate the storm surges of historical storms under present day and end of century climate scenarios to assess the impact of climate change on storm surge inundation. We simulate 21 storms that impacted the Gulf of Mexico and Atlantic Coasts of the continental U.S. from 2000 to 2013. We find that the volume of inundation increases for 14 storms and the average change for all storms is +36%. The extent of inundation increases for 13 storms, and the average change for all storms is +25%. Notable increases in inundation occur near Texas, Louisiana, Mississippi, the west coast of Florida, the Carolinas, and New Jersey. Our calculations of inundation volume and extent suggest that at the end of the century, we can expect hurricanes to produce larger storm surge magnitudes in concentrated areas, as opposed to surges with lower magnitudes that are widespread. We examine changes in maximum wind speed, minimum central pressure, translation speed, and radius of the 33 ms−1 wind to assess the impacts of individual storm characteristics on storm surge. We find that there is no single storm characteristic that directly relates to storm surge inundation or its climate induced changes. Even when all the characteristics are considered together, the resulting influences are difficult to anticipate. This is likely due to the complexity of the hydrodynamics and interactions with local geography. This illustrates that even as climate change research advances and more is known about projected impacts to hurricanes, implications for storm surge will be difficult to predict without explicit numerical simulation.

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Efficient Modeling of Complex Sandy Coastal Evolution at Monthly to Century Time Scales

Cited by 53 publications

References 51 publications

Automatic Shoreline Detection from Video Images by Combining Information from Different Methods

Automatic Shoreline Detection from Video Images by Combining Information from Different Methods

Probabilistic Application of an Integrated Catchment-Estuary-Coastal System Model to Assess the Evolution of Inlet-Interrupted Coasts Over the 21st Century

Projected Climate Change Impacts on Hurricane Storm Surge Inundation in the Coastal United States

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