Modelling Morphological Response of Large Tidal Inlet Systems to Sea Level Rise

Dissanayake, Pushpa

doi:10.1201/b11559

Cited by 9 publications

(9 citation statements)

References 76 publications

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“…Along the Dutch coast, the tidal waves propagate from southwest to northeast and have a semi-diurnal character with a mean tidal range of approximately 2 m. The average significant height is about 1 m (Cheung, Gerritsen, and Cleveringa 2007) with the dominant wave direction being from northwest. A combined effect of tidal and waveinduced currents causes a complex interaction leading to huge sediment influxes (Jirka and Uijttewaal 2004) and a net easterly sediment transport between the barrier islands/ inlets systems (Dissanayake 2011). The approximate Secchi depths observed in this region ranges between 0.00 and 3.50 m.…”

Section: The Ameland Inletmentioning

confidence: 99%

Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery

Misra

Vojinović

Balaji

et al. 2018

International Journal of Remote Sensing

118

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Satellite imagery along with image processing techniques prove to be efficient tools for bathymetry retrieval as they provide time and cost-effective alternatives to traditional methods of water depth estimation. In this article, a nonlinear machine learning technique of Support Vector Machine (SVM) is used to derive shallow water bathymetry data along Sint Maarten Island and Ameland Inlet, The Netherlands, by combining echo-sounding measurements and the reflectance of blue, green, or red bands of Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+) and Landsat 8 Operational Land Imager (OLI) imagery with 30 m spatial resolution. In the analysis, 80% of data points of the echo-sounding measurements are used for training and the remaining 20% data points are used for testing. The model utilizes the radial basis kernel function (nonlinear) and the other training factors such as the smoothing parameter, penalty parameter C, and insensitivity zone ε are selected and tuned based on the learning (i.e. training) process. The overall errors during test phases for Sint Maarten Island (1-15 m) and Ameland Inlet (1.00-3.50 m) are 8.26% and 14.43%, respectively, reflecting that the model produces significant estimations for the shallow depths ranges, considered in this study. The results obtained are also compared statistically with those estimated from the widely used linear transform model and ratio transform model, which establish a linear relationship between the water depth and band reflectances. Based on the results, it is evident that SVM provides a comparable or better performance for shallow depth ranges and can be used effectively for deriving accurate and updated medium resolution bathymetric maps.

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Section: The Ameland Inletmentioning

confidence: 99%

Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery

Misra

Vojinović

Balaji

et al. 2018

International Journal of Remote Sensing

118

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“…This result confirms a previous sensitivity analysis performed with Delft3D [4], which showed that the factor for transverse bedload transport (which is analogous to the downslope parameter µ) controls the channel width to depth ratio. In Delft3D, channel widening could also be enhanced by increasing the dry-cell erosion factor [25]. This method is actually necessary in Delft3D to allow for widening of the inlet (Figure 9), whose banks cannot be eroded directly by bed shear stress (i.e., the term E).…”

Section: Morphodynamicsmentioning

confidence: 99%

A 2D Tide-Averaged Model for the Long-Term Evolution of an Idealized Tidal Basin-Inlet-Delta System

Mariotti

Musrhid

2018

JMSE

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We present a model for the morphodynamics of tidal basin-inlet-delta systems at the centennial time scales. Tidal flow is calculated through a friction dominated model, with a semi-empirical correction to account for the advection of momentum. Transport of non-cohesive sediment (sand) is simulated through tidal dispersion, i.e., without explicitly resolving sediment advection. Sediment is also transported downslope through a bed elevation diffusion process. The model is compared to a high-resolution tide-resolving model (Delft3D) with good agreement for different hydrodynamic and sedimentary settings. The model has low sensitivity with respect to temporal and spatial discretization. For the same spatial resolution, the model is about five orders of magnitude faster than tide-resolving models (e.g., Delft3D), and about three orders of magnitude faster than tide-resolving models that use a morphological acceleration factor. This numerical efficiency makes the model suitable to assess long-term changes of large coastal areas. The model's simplicity makes it suitable for coupling with other physical, ecological, and socio-economic dynamics.for tide-dominated transport, which also has a simple structure that will facilitate the inclusion of additional processes (i.e., river flow, waves, sand/mud dynamics, ecological processes) in the future.The bottleneck that increases the computation time of models for tidal morphodynamics is the need to solve the intra-tidal modulation, which generally requires time steps on the order of seconds for stability criterion. One approach to reduce this computation time is to only consider the net effect of a tidal cycle on the sediment transport without solving the intra-tidal variability. The basic idea of this approach is that the bidirectional tidal motion can be parameterized as a diffusion mechanism, here referred to as tidal dispersion [11,12]. This simplification is analogous to the use of a turbulent diffusion to simulate the bidirectional advective motion that takes place at spatial and temporal scales smaller than those simulated. The resulting coefficient for the tidal dispersion is very large, on the order of 100-1000 m 2 /s, which allows to transport large amounts of sediment and to effectively simulate channel network formation and evolution.The tidal dispersion model still requires to calculate a tide-averaged velocity from which to estimate the sediment resuspension and the dispersion coefficients. Di Silvio et al. [12] suggested to use a steady-state friction-dominated model for the tidal hydrodynamics. With this assumption the governing equations reduce to a Poisson problem. Thus, the combined hydrodynamic and sediment transport models constitute a robust system that can be solved efficiently [12]. The tidal dispersion model was initially proposed to simulate the evolution of a microtidal lagoon [12,13]. The model successfully reproduced the ontogeny of a realistic tidal network starting from a flat configuration. Unlike previous models, however, the model did not simu...

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“…Using the MORFAC approach, bed-level changes are multiplied by a morphological acceleration factor (MF) to enhance the morphological evolution. Extensive modeling studies for similar tidal systems show good results up to a MF of 400 (e.g., Braat et al, 2017;Dissanayake, 2011;Roelvink, 2006;van der Wegen & Roelvink, 2008), while wave action usually requires a lower MF.…”

Section: Model Descriptionmentioning

confidence: 99%

“…The α bn is a very important parameter for the channel-shoal morphology and is often used as a calibration parameter, while model results are practically insensitive to α bs . In estuarine and tidal basin models, especially when using the Van Rijn transport formulations, the α bn is often set much (an order of magnitude) larger than the experimental and default values (1.5;Ikeda, 1982) to prevent the formation of unrealistically steep banks, and narrow deep channels with sharp bends (e.g., Dissanayake, 2011;van der Wegen & Roelvink, 2012).…”

Section: Model Descriptionmentioning

confidence: 99%

Modeling the Morphodynamic Response of Estuarine Intertidal Shoals to Sea‐Level Rise

et al. 2022

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Intertidal shoals are key features of estuarine environments worldwide. Climate change poses questions regarding the sustainability of intertidal areas under sea‐level rise (SLR). Our work investigates the SLR impact on the long‐term morphological evolution of unvegetated intertidal sandy shoals in a constrained channel‐shoal system. Utilizing a process‐based model (Delft3D), we schematize a short tidal system in a rectangular (2.5 × 20 km) basin with a high‐resolution grid. An initial, mildly sloping, bathymetry is subjected to constant semidiurnal tidal forcing, sediment supply, and small wind‐generated waves modeled by SWAN. A positive morphodynamic feedback between hydrodynamics, sediment transport, and morphology causes the emergence of large‐scale channel‐shoal patterns. Over centuries, tide‐residual sediment transport gradually decreases leading to a state of low morphological activity balanced by tides, waves, and sediment supply. Tidal currents are the main driver of the SLR morphodynamic adaptation. Wave action leads to wider and lower shoals but does not fundamentally change the long‐term morphological evolution. SLR causes increased flood dominance which triggers sediment import into the system. Shoals accrete in response to SLR with a lag that increases as SLR accelerates, eventually causing intertidal shoals to drown. Seaward shoals near the open boundary sediment source have higher accretion rates compared to landward shoals. Similarly, on a shoal‐scale, the highest accretion rates occur at the shoal edges bounding the sediment suppling channels. A larger sediment supply enhances the SLR adaptation. Waves help distribute sediment supplied from channels across shoals. Adding mud fractions leads to faster, more uniform, accretion and muddier shoals under SLR.

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Modelling Morphological Response of Large Tidal Inlet Systems to Sea Level Rise

Cited by 9 publications

References 76 publications

Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery

Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery

A 2D Tide-Averaged Model for the Long-Term Evolution of an Idealized Tidal Basin-Inlet-Delta System

Modeling the Morphodynamic Response of Estuarine Intertidal Shoals to Sea‐Level Rise

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