Dynamics of Coastal Systems

Dronkers, J.

doi:10.1142/5781

Cited by 74 publications

(81 citation statements)

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“…Regarding residual sediment transport, we propose that the total load transport is proportional to the current velocity by a power of 5 and a zero threshold velocity for initial sediment motion (in line with the Engelund and Hansen [] formula); thus, the tide‐averaged integration is followed as [ Dronkers , ; van de Kreeke and Robaczewska , ]

\begin{matrix} left \\ {true\int}_{0}^{normalT} U^{5} d t / T = {true\int}_{0}^{normalT} {[u_{o} (x) + u_{2} (x) \cos (w t - k x) + u_{4} (x) \cos (2 w t - 2 k x + φ)]}^{5} d t / T \\ = \underset{E N T}{\underset{u_{o}^{5} ((), x)}{true}} + \underset{E I A}{\underset{5 u_{o}^{3} ((), x) ([], u_{2}^{2} ((), x) + u_{4}^{2} ((), x)) + \frac{15}{8} u_{o} ((), x) ([], u_{2}^{4} ((), x) + u_{4}^{4} ((), x))}{true}} + \underset{T I A}{\underset{\frac{5}{4} u_{2}^{4} ((), x) u_{4} ((), x) \cos ((), φ)}{true}} \end{matrix}

…”

Section: Model Resultsmentioning

confidence: 96%

“…Stokes drift generates residual currents and transport. Caused by a phase lead or lag between tidal elevation and tidal currents, Stokes drift is present under progressive wave conditions [ Stokes , ; Dronkers , ; van Rijn , ]. Stokes drift causes a landward accumulation of water and momentum, resulting in a water level gradient (negative seaward).…”

Section: Introductionmentioning

confidence: 99%

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The role of river flow and tidal asymmetry on 1‐D estuarine morphodynamics

et al. 2014

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Numerous research efforts have been devoted to understanding estuarine morphodynamics under tidal forcing. However, the impact of river discharge on estuarine morphodynamics is insufficiently examined. Inspired by the Yangtze Estuary, this work explores the morphodynamic impact of river discharge in a 560 km long tidal basin based on a 1-D model (Delft3D). The model considers total load sediment transport and employs a morphodynamic updating scheme to achieve long-term morphodynamic evolution. We analyze the role of Stokes drift, tidal asymmetry, and river discharge in generating tidal residual sediment transport. Model results suggest that morphodynamic equilibrium is approached within millennia by vanishing spatial gradients of tidal residual sediment transport. We find that the interaction between ebb-directed Stokes return flow/river flow with tides is an important mechanism that flushes river-supplied sediment seaward. Increasing river discharge does not induce continuously eroded or accreted equilibrium bed profiles because of the balance between riverine sediment supply and sediment flushing to the sea. An intermediate threshold river discharge can be defined which leads to a deepest equilibrium bed profile. As a result, the shape (concavity or convexity) of the equilibrium bed profiles will adapt with the magnitude of river discharge. Overall, this study reveals the significant role of river discharge in controlling estuarine morphodynamics by supplying sediment and reinforcing ebb-directed residual sediment transport.

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\begin{matrix} left \\ {true\int}_{0}^{normalT} U^{5} d t / T = {true\int}_{0}^{normalT} {[u_{o} (x) + u_{2} (x) \cos (w t - k x) + u_{4} (x) \cos (2 w t - 2 k x + φ)]}^{5} d t / T \\ = \underset{E N T}{\underset{u_{o}^{5} ((), x)}{true}} + \underset{E I A}{\underset{5 u_{o}^{3} ((), x) ([], u_{2}^{2} ((), x) + u_{4}^{2} ((), x)) + \frac{15}{8} u_{o} ((), x) ([], u_{2}^{4} ((), x) + u_{4}^{4} ((), x))}{true}} + \underset{T I A}{\underset{\frac{5}{4} u_{2}^{4} ((), x) u_{4} ((), x) \cos ((), φ)}{true}} \end{matrix}

…”

Section: Model Resultsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

The role of river flow and tidal asymmetry on 1‐D estuarine morphodynamics

et al. 2014

View full text Add to dashboard Cite

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“…Our simulations indicate instead the possibility of what we define as “dynamic equilibrium” where gradients in sediment fluxes approach zero (and so do bed levels). Overall, there are many indicators to characterize an equilibrium state, e.g., empirical relationships [ Dronkers , ; Powell et al , ; Stive and Rakhorst , ], certain geometric characteristics of bottom profiles [ Kirby , ; Pritchard and Hogg , ; Toffolon and Lanzoni , ], tidal asymmetry characteristics [ Speer and Aubrey , ; Dronkers , ; Friedrichs and Aubrey , ], and energy entropy state [ Leopold and Langbein , ; Nield et al , ; van der Wegen and Roelvink , ]. In fact, all the above indicators converge toward a morphologically stable state which in our simulations is also characterized by almost null gradients in sediment fluxes.…”

Section: Discussionmentioning

confidence: 99%

A comparative study of physical and numerical modeling of tidal network ontogeny

et al. 2014

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We investigate the initiation and long-term evolution of tidal networks by comparing controlled laboratory experiments and their associated scaling laws with outputs from a numerical model. We conducted numerical experiments at both the experimental laboratory scale (ELS) and natural estuary scale (NES) and compared these simulations with experimental data and field observations. Sensitivity tests show that initial bathymetry, frictional parametrization, sediment transport, and bed slope terms play an important role in determining the morphodynamic evolution and the final landscape. Consistent with experimental observations, the morphodynamic feedbacks between flow, sediment transport, and bathymetry gradually lead the system to a less dynamic state, finally reaching a stable network configuration. In both the ELS and NES simulations, the initially planar lagoon with large intertidal areas is subject to erosion, indicating ebb-dominance. Based on quantitative analyses of the ELS and the NES simulations (e.g., geometric characteristics and relationship between modified tidal prism and cross-sectional area), we conclude that numerical simulations are consistent with laboratory experiments and show that both type of models provide a realistic, albeit simplified, representation of natural systems. The combination of laboratory and numerical experiments also allowed us to explore the possibility of reaching a long-term morphodynamic equilibrium. Both the physical and numerical models approach a dynamic equilibrium characterized by negligible gradients in sediment fluxes. The equilibrium configuration appears to be consistent with traditional relationships linking tidal prism and cross-sectional area of the inlet. Finally, this contribution highlights the significance of complementary research between experimental and numerical modeling in investigating long-term morphodynamics of tidal networks.

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“…Tracking the highest dune features and their translocation revealed that dune toe, dune crest, and barrier island area are predicted with high accuracy. Results for barrier island width were slightly worse, indicating that the relationship between barrier island width and oceanographic forcing may be too complex to be captured properly by the surrogate models used here (e.g., Dronkers, ). The weakest surrogate model performance was found for the prediction of the cross‐shore location of dune toe (xt) and dune crest (xc).…”

Section: Discussionmentioning

confidence: 92%

Combining Numerical and Statistical Models to Predict Storm‐Induced Dune Erosion

et al. 2019

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Dune erosion is an important aspect to consider when assessing coastal flood risk, as dune elevation loss makes the protected areas more susceptible to flooding. However, most advanced dune erosion numerical models are computationally expensive, which hinders their application in early-warning systems. Based on a combination of probabilistic and process-based numerical modeling, we develop an efficient statistical tool to predict dune erosion during storms. The analysis focuses on Dauphin Island, AL, in the northern Gulf of Mexico, where we combine synthetic sea storms with a calibrated and validated XBeach model to develop and test a range of different surrogate models for their ability to predict barrier island geometric parameters under storm conditions. Surrogate models are developed by combining the oceanographic forcing from 100 optimally sampled sea storm events covering the entire multivariate parameter space (used as XBeach input) and associated changes in the dune system (XBeach output). We test four surrogate models using a k-fold approach for validation. All models perform well in predicting changes in dune elevation, barrier island area, and width but are less accurate in predicting alterations in the cross-shore locations of dune morphological features. Multivariate adaptive regression splines is identified as the best surrogate model based on its fast development and good performance, attaining a modified Mielke index of 0.81 for dune crest height. As demonstrated at Dauphin Island, our approach shows potential to be used in an operational framework to predict dune response (in particular crest elevation change) when water level and wave forecasts are available. Key Points: • Surrogate models of XBeach are developed and tested, showing that multivariate adaptive regression splines exhibits the best performance • Carefully trained statistical and machine-learning models predict changes in barrier island geometric parameters at low computational cost • The highest performance is attained for the most extreme dune impact regimes, allowing fast and accurate dune height change predictions Correspondence to:Here, surrogate models are designed to predict coastal dune erosion by addressing issues related to computational performance and absence of sufficient forcing data. Our main goal is to understand complex morphological interactions through simulation of a wide range of realistic scenarios. To accomplish this, we develop and test surrogate models for XBeach that are trained with simulated, physically consistent oceanographic variables and associated dune response. This includes the following steps: (1) select a subset of synthetic multivariate sea storm events from Wahl et al. (2016) and consider time-dependent evolution of the oceanographic variables, (2) use XBeach to predict morphological response parameters for the selected sea storms, and (3) develop and test a range of different surrogate models to mimic XBeach at low computational cost. The different analysis steps are summarized in Figure 1. In...

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Dynamics of Coastal Systems

Cited by 74 publications

References 0 publications

The role of river flow and tidal asymmetry on 1‐D estuarine morphodynamics

The role of river flow and tidal asymmetry on 1‐D estuarine morphodynamics

A comparative study of physical and numerical modeling of tidal network ontogeny

Combining Numerical and Statistical Models to Predict Storm‐Induced Dune Erosion

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