Estimation of roughness lengths and flow separation over compound bedforms in a natural-tidal inlet

Lefebvre, Alice; Ernstsen, Verner Brandbyge

doi:10.1016/j.csr.2013.04.030

Cited by 35 publications

(32 citation statements)

References 37 publications

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“…They create additional bed roughness on top of the grain‐related roughness, which affects the vertical mixing in the flow (Smith and McLean, ; Nielsen, ; Lefebvre et al. , , ). Models that are used to estimate sediment transport, like Delft3D, TELEMAC and MIKE21, are dependent on roughness predictors to calculate sediment transport (Tonnon et al.…”

Section: Introductionmentioning

confidence: 99%

“…Bedforms like ripples affect both hydrodynamics and sediment transport. They create additional bed roughness on top of the grain-related roughness, which affects the vertical mixing in the flow (Smith and McLean, 1977;Nielsen, 1979;Lefebvre et al, 2013Lefebvre et al, , 2014. Models that are used to estimate sediment transport, like Delft3D, TELEMAC and MIKE21, are dependent on roughness predictors to calculate sediment transport (Tonnon et al, 2007;Elias and Hansen, 2013;Herrling and Winter, 2014;Lenstra et al, 2019;McCann et al, 2011;Deigaard et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

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Spatio‐temporal characteristics of small‐scale wave–current ripples on the Ameland ebb‐tidal delta

Brakenhoff

Kleinhans

Ruessink

et al. 2020

Earth Surf Processes Landf

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Ebb‐tidal deltas are highly dynamic environments affected by both waves and currents that approach the coast under various angles. Among other bedforms of various scales, these hydrodynamics create small‐scale bedforms (ripples), which increase the bed roughness and will therefore affect hydrodynamics and sediment transport. In morphodynamic models, sediment transport predictions depend on the roughness height, but the accuracy of these predictors has not been tested for field conditions with strongly mixed (wave–current dominated) forcing. In this study, small‐scale bedforms were observed in the field with a 3D Profiling Sonar at five locations on the Ameland ebb‐tidal delta, the Netherlands. Hydrodynamic conditions ranged from wave dominated to current dominated, but were mixed most of the time. Small‐scale ripples were found on all studied parts of the delta, superimposed on megaripples. Even though a large range of hydrodynamic conditions was encountered, the spatio‐temporal variations in small‐scale ripple dimensions were relatively small (height η≈ 0.015 m, length λ≈ 0.11 m). Also, the ripples were always highly three‐dimensional. These small dimensions are probably caused by the fact that the bed consists of relatively fine sediment. Five bedform height predictors were tested, but they all overestimated the ripple heights, partly because they were not created for small grain sizes. Furthermore, the predictors all have a strong dependence on wave‐ and current‐related velocities, whereas the ripple heights measured here were only related to the near‐bed orbital velocity. Therefore, ripple heights and lengths in wave–current‐dominated, fine‐grained coastal areas ( D50<0.25 mm) may be best estimated by constant values rather than values dependent on the hydrodynamics. In the case of the Ameland ebb‐tidal delta, these values were found to be η=0.015 m and λ=0.11 m. ©2019 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatio‐temporal characteristics of small‐scale wave–current ripples on the Ameland ebb‐tidal delta

Brakenhoff

Kleinhans

Ruessink

et al. 2020

Earth Surf Processes Landf

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“…The tides in the area are semi-diurnal, with a mean tidal range of 1.6 m, and the tidal prism is on the order of 175 × 10 6 m 3 (Pedersen and Bartholdy, 2006). The main channel is approximately 1 km wide and with an average water depth of approximately 15 m (Lefebvre et al, 2013). The study site is an elongated 3.2 km 2 (0.85 km × 4 km) section of the Knudedyb tidal inlet system (Fig.…”

Section: Study Areamentioning

confidence: 99%

Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment

Andersen

Gergely

Al-Hamdani

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The transition zone between land and water is difficult to map with conventional geophysical systems due to shallow water depth and often challenging environmental conditions. The emerging technology of airborne topobathymetric light detection and ranging (lidar) is capable of providing both topographic and bathymetric elevation information, using only a single green laser, resulting in a seamless coverage of the land-water transition zone. However, there is no transparent and reproducible method for processing green topobathymetric lidar data into a digital elevation model (DEM). The general processing steps involve data filtering, water surface detection and refraction correction. Specifically, the procedure of water surface detection and modelling, solely using green laser lidar data, has not previously been described in detail for tidal environments. The aim of this study was to fill this gap of knowledge by developing a step-by-step procedure for making a digital water surface model (DWSM) using the green laser lidar data. The detailed description of the processing procedure augments its reliability, makes it user-friendly and repeatable. A DEM was obtained from the processed topobathymetric lidar data collected in spring 2014 from the Knudedyb tidal inlet system in the Danish Wadden Sea. The vertical accuracy of the lidar data is determined to ±8 cm at a 95 % confidence level, and the horizontal accuracy is determined as the mean error to ±10 cm. The lidar technique is found capable of detecting features with a size of less than 1 m 2 . The derived high-resolution DEM was applied for detection and classification of geomorphometric and morphological features within the natural environment of the study area. Initially, the bathymetric position index (BPI) and the slope of the DEM were used to make a continuous classification of the geomorphometry. Subsequently, stage (or elevation in relation to tidal range) and a combination of statistical neighbourhood analyses (moving average and standard deviation) with varying window sizes, combined with the DEM slope, were used to classify the study area into six specific types of morphological features (i.e. subtidal channel, intertidal flat, intertidal creek, linear bar, swash bar and beach dune). The developed classification method is adapted and applied to a specific case, but it can also be implemented in other cases and environments.

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“…roughness (Fredsøe and Deigaard, 1992;Lefebvre et al, 2013). Accurately estimating the roughness of the seabed in the presence of bedforms is crucial to hydrodynamic numerical modeling efforts where bedforms may not be fully resolved but rather the roughness due to the bedforms is parameterized (Williams, 1995).…”

Section: Resultsmentioning

confidence: 99%

“…Further work on tidal ridges similarly hypothesized that offshore tidal ridges are made from the accretion of cross-beds formed by dunes (Lobo et al, 2010;Reynaud and Dalrymple, 2012). More recently, Lefebvre et al (2013) suggested that smaller bedforms may be influencing the dunes on which they reside. Laboratory studies of superimposed bedforms in unidirectional flows concluded that the sediment transport of the smaller features may influence the sediment transport of the larger bedforms, however, this hypothesis was not validated .…”

Section: Introductionmentioning

confidence: 99%

Measurements and dynamics of multiple scale bedforms in tidally energetic environments

Jones¹

2018

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The presence of superimposed bedforms, where smaller bedforms exist on larger bedforms, is ubiquitous to energetic tidal environments. Due to their wide range in scale, it is difficult to simultaneously observe these features over tidal timescales. This thesis examines the morphological response of superimposed bedforms to a tidally reversing flow using novel instrumentation and platform systems. A method is outlined in chapter 2 to expand the functionality of low-mounted sidescan sonars by utilizing sonar shadows to estimate bedform height and asymmetry. Empirical models are generated to account for realistic variability in the seabed and the method is validated with bathymetric observations of wave-orbital ripples and tidally reversing megaripples. Given the high temporal and spatial resolution of seafloor frame mounted rotary sidescan sonars, the dynamics and evolution of the bedforms over an approximately 40 m x 40 m area can be resolved. In chapter 3 the method is applied to data of superimposed bedforms at Wasque Shoals, an ebb delta off the southeast corner of Martha's Vineyard, MA. These data reveal the small, superimposed bedforms reversing their asymmetry with the flow while the larger bedforms on which they reside remain oriented in the direction of the dominant flow. Similar bedform dynamics are observed at Nauset Inlet, a dynamic inlet system, on Cape Cod, MA using an autonomous jet-powered kayak, the

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Estimation of roughness lengths and flow separation over compound bedforms in a natural-tidal inlet

Cited by 35 publications

References 37 publications

Spatio‐temporal characteristics of small‐scale wave–current ripples on the Ameland ebb‐tidal delta

Spatio‐temporal characteristics of small‐scale wave–current ripples on the Ameland ebb‐tidal delta

Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment

Measurements and dynamics of multiple scale bedforms in tidally energetic environments

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