Critical dependence of morphodynamic models of fluvial and tidal systems on empirical downslope sediment transport

Baar, Anne; Albernaz, Márcio Boechat; Dijk, W. M. van; Kleinhans, Maarten G.

doi:10.1038/s41467-019-12753-x

Cited by 71 publications

(86 citation statements)

References 49 publications

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“…To be successful, they require sufficient quantitative understanding of key mechanisms that result in the different channel patterns. Although there is general consensus within the scientific community on which mechanisms affect the channel pattern, we are still far from quantitively incorporating these mechanisms into a mechanistic model (Baar et al, 2019; Kleinhans, 2010; Kleinhans et al, 2018; Parker et al, 2007; Tal and Paola, 2010). Hence, empirical discriminators are used more widely (e.g.…”

Section: Introductionmentioning

confidence: 99%

Predicting river channel pattern based on stream power, bed material and bank strength

Candel

Kleinhans

Makaske

et al. 2020

Progress in Physical Geography: Earth and Environment

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Rivers exhibit a wide variety of channel patterns, and predicting changes in channel pattern is important in order to foresee river responses to climate change and river restoration. Many discriminators have been developed to define approximate boundary conditions for different channel patterns, based on channel-pattern-controlling parameters such as discharge and valley gradient. However, presently available discriminators have two main shortcomings. First, they perform poorly for rivers with cohesive, relatively erosion-resistant banks. For this subset, discriminators tend to indicate an actively meandering channel pattern, whereas the river morphology and dynamics show that many of these rivers should be classified as laterally stable. Second, channel pattern discriminators are often used to predict channel patterns, which is only valid when parameters are used that are independent of actual channel pattern. This condition is often not met, as many discriminators use the channel slope or width–depth ratio of the channel as input. To resolve both shortcomings, we first propose an additional class of rivers with scroll bars and tortuous channel patterns, which have an inhibited mobility due to their self-formed cohesive deposits. Second, we compare frequently used empirical and mechanistic channel pattern discriminators, taking into account the success in predicting channel pattern and the independence of causal factors used. Thirdly, we present a novel channel pattern discriminator and predictor that includes the effect of a cohesive floodplain, using the average silt-plus-clay fraction of the river banks as proxy. We show that this new predictor outperforms previously used empirical and mechanistic approaches, and successfully predicts channel pattern for 87% of the rivers from a dataset of 70. This new predictor is widely applicable, as it is relatively simple and based on easily obtainable, and mostly independent, parameters.

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Section: Introductionmentioning

confidence: 99%

Predicting river channel pattern based on stream power, bed material and bank strength

Candel

Kleinhans

Makaske

et al. 2020

Progress in Physical Geography: Earth and Environment

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“…Firstly, the nondimensional mobility number allows for the comparison to natural systems (Kleinhans, 2010). Secondly, it provides vital insight into sediment transport fields and morphological activity, which is especially valuable for studying multi-channel systems and channel-bar margin interactions (De Vet et al, 2017;Van Dijk et al, 2018;Baar et al, 2019). For example, these data may be used to predict future locations of erosion and deposition ( Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Firstly, real material is used with its inherent laws and properties, as opposed to numerical models that require many parameters and approximations of laws for water flow, sediment transport (e.g. Meyer-Peter and Müller, 1948;Van Rijn, 2007;Baar et al, 2019) and life forms (e.g. Baptist et al, 2007;Van Oorschot et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, this practice differs from remodelling the morphological development of a scale experiment (e.g. Struiksma et al, 1985), which are subject to all the combined errors of sediment transport predictors (Baar et al, 2019). The extended integration of experimental data and a numerical model would not only expand the possibilities for data analyses of a series of experiment bed scans but would also open up fast methods of testing alternative experimental settings for either an experimental or idealised morphology.…”

Section: Introductionmentioning

confidence: 99%

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Complementing scale experiments of rivers and estuaries with numerically modelled hydrodynamics

et al. 2020

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Abstract. Physical scale experiments enhance our understanding of fluvial, tidal and coastal processes. However, it has proven challenging to acquire accurate and continuous data on water depth and flow velocity due to limitations of the measuring equipment and necessary simplifications during post-processing. A novel means to augment measurements is to numerically model flow over the experimental digital elevation models. We investigated to what extent the numerical hydrodynamic model Nays2D can reproduce unsteady, nonuniform shallow flow in scale experiments and under which conditions a model is preferred to measurements. To this end, we tested Nays2D for one tidal and two fluvial scale experiments and extended Nays2D to allow for flume tilting, which is necessary to steer tidal flow. The modelled water depth and flow velocity closely resembled the measured data for locations where the quality of the measured data was most reliable, and model results may be improved by applying a spatially varying roughness. The implication of the experimental data–model integration is that conducting experiments requires fewer measurements and less post-processing in a simple, affordable and labour-inexpensive manner that results in continuous spatio-temporal data of better overall quality. Also, this integration will aid experimental design.

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“…This is not surprising, since those reported exponents (dots in Figure 3c) result from far more complicated natural settings than the ideal configurations used in this study. Some of the variability in the reported exponents is possibly due to uncertainties in field observations and other factors that may affect the cross‐sectional shape, such as inhomogeneity of bed material (Leopold et al, 1964), lateral sediment transport (Baar et al, 2019), and bank strength (Eaton & Church, 2007; Eaton & Giles, 2009). The effects of these processes on the functional forms of the regime relationships deserve future studies.…”

Section: Discussionmentioning

confidence: 99%

A Universal Form of Power Law Relationships for River and Stream Channels

Coco

Zhou

et al. 2020

Geophysical Research Letters

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The description of the geomorphic characteristics in power law forms has been the subject of research, over the past 70 years, and has become the cornerstone of regime theory. However, just why power functions should represent such geomorphic relationships remains poorly understood. Hence, differences in the values of the regime exponents observed for different river systems remain largely unexplained. To address this issue, we derived generic forms of the power law relationships without postulating any power functions of the discharge. The theoretical approach accurately captures the systematic variations of the regime exponents shown by a number of large data sets from previous research. We also explain how frictional resistance is responsible for the systematic variability of regime exponents. Overall, our study provides a robust mechanism to describe the variations of the exponents, along with a deductive explanation of the power laws at the core of fluvial hydraulic geometry.

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Critical dependence of morphodynamic models of fluvial and tidal systems on empirical downslope sediment transport

Cited by 71 publications

References 49 publications

Predicting river channel pattern based on stream power, bed material and bank strength

Predicting river channel pattern based on stream power, bed material and bank strength

Complementing scale experiments of rivers and estuaries with numerically modelled hydrodynamics

A Universal Form of Power Law Relationships for River and Stream Channels

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