Erosion threshold of saturated natural cohesive sediments: Modeling and experiments

Ternat, Fabien; Boyer, Patrick; Anselmet, Fabien; Amielh, Muriel

doi:10.1029/2007wr006537

Cited by 41 publications

(47 citation statements)

References 24 publications

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“…Rather than parameterizing bank resistance to erosion through a single parameter, here we explicitly model the effect of cohesive sediments on the bank erosion threshold, τ crit . We utilize the model of Ternat et al () to predict τ crit as a function of grain size within the banks ( d bank ).…”

Section: Model: Single‐thread Rivers Without Plantsmentioning

confidence: 99%

“…These assumptions are most valid for water‐saturated materials that are not fully consolidated, as is expected in the active surface layer of riverbanks (see also Text S2). Whereas banks are modeled with a single effective grain size to limit the number of variables in this analysis, the model formulation has the capability to determine cohesion for a mixture of different grain sizes (Ternat et al, ). In dimensionless form,

τ_{crit}^{*} = \frac{τ_{crit}}{((), ρ_{s} - ρ) g d_{bank}} = τ_{0}^{*} ((), 1 + \frac{F_{normalc}}{F_{normalw}}),

…”

Section: Model: Single‐thread Rivers Without Plantsmentioning

confidence: 99%

“…with n the porosity and n c and n max are the fully compacted and maximum porosities, respectively (Ternat et al, ). Ternat et al () do not define ϕ as a friction angle in the Mohr‐Coulomb sense (i.e.,

\tan ϕ \neq \frac{τ - c}{σ_{normaln}}

where τ and σ n are the shear and normal stresses, respectively, and cohesion, c , is accounted for in the numerator) but rather as the ratio of driving to stabilizing forces (i.e.,

\tan ϕ = \frac{F_{normald}}{F_{w} - F_{l} + F_{c}}

where F d and F l are drag and lift forces, respectively, and where the cohesive force, F c , is accounted for in the denominator); hence, it has a higher value than those typically reported for granular materials in geotechnical studies. Rewriting

τ_{crit}^{*}

as a function of Re p,bank using equations –, we express the model of Ternat et al () as

τ_{crit}^{*} = τ_{0}^{*} ((), {Re}_{p, bank}) ([], 1 + Ω {Re}_{normalp, bank}^{- 2}),

…”

Section: Model: Single‐thread Rivers Without Plantsmentioning

confidence: 99%

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Model for the Formation of Single‐Thread Rivers in Barren Landscapes and Implications for Pre‐Silurian and Martian Fluvial Deposits

et al. 2019

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Flume experiments and field observations show that bank vegetation promotes the formation of narrow and deep single‐thread channels by strengthening riverbanks. Consistent with this idea, the pre‐Silurian fluvial record generally consists of wide monotonous sand bodies often interpreted as deposits of shallow braided rivers, whereas single‐thread rivers with muddy floodplains become more recognizable in Silurian and younger rocks. This shift in the architecture of fluvial deposits has been interpreted as reflecting the rise of single‐thread rivers enabled by plant life. The deposits of some single‐thread rivers, however, have been recognized in pre‐Silurian rocks, and recent field studies have identified meandering rivers in modern unvegetated environments. Furthermore, single‐thread‐river deposits have been identified on Mars, where macroscopic plants most likely never evolved. Here we seek to understand the formation of those rarely recognized and poorly characterized single‐thread rivers in unvegetated landscapes. Specifically, we quantitatively explore the hypothesis that cohesive muddy banks alone may enable the formation of single‐thread rivers in the absence of plants. We combine open‐channel hydraulics and a physics‐based erosion model applicable to a variety of bank sediments to predict the formation of unvegetated single‐thread rivers. Consistent with recent flume experiments and field observations, results indicate that single‐thread rivers may form readily within muddy banks. Our model has direct implications for the quantification of riverbank strengthening by vegetation, understanding the hydraulic geometry of modern and ancient unvegetated rivers, interpreting pre‐Silurian fluvial deposits, and unraveling the hydrologic and climate history of Mars.

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Section: Model: Single‐thread Rivers Without Plantsmentioning

confidence: 99%

τ_{crit}^{*} = \frac{τ_{crit}}{((), ρ_{s} - ρ) g d_{bank}} = τ_{0}^{*} ((), 1 + \frac{F_{normalc}}{F_{normalw}}),

…”

Section: Model: Single‐thread Rivers Without Plantsmentioning

confidence: 99%

\tan ϕ \neq \frac{τ - c}{σ_{normaln}}

where τ and σ n are the shear and normal stresses, respectively, and cohesion, c , is accounted for in the numerator) but rather as the ratio of driving to stabilizing forces (i.e.,

\tan ϕ = \frac{F_{normald}}{F_{w} - F_{l} + F_{c}}

τ_{crit}^{*}

as a function of Re p,bank using equations –, we express the model of Ternat et al () as

τ_{crit}^{*} = τ_{0}^{*} ((), {Re}_{p, bank}) ([], 1 + Ω {Re}_{normalp, bank}^{- 2}),

…”

Section: Model: Single‐thread Rivers Without Plantsmentioning

confidence: 99%

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Model for the Formation of Single‐Thread Rivers in Barren Landscapes and Implications for Pre‐Silurian and Martian Fluvial Deposits

et al. 2019

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“…The interactions of antecedent wetness, rainfall intensity and depth with surface and subsurface structures are deemed as first order controls in each of these cases. Likewise, particle detachment and soil erosion is clearly a threshold process (Hicks et al, 2000;Salles et al, 2000;Hairsine et al, 2002;Shao et al, 2005;Maerker et al, 2008;Scherer, 2008;Ternat et al, 2008), which is controlled by rainfall intensity, shear stress due to overland flow and soil stability. Infiltration, vertical flow and transport of contaminants in field soils may be observed in two qualitatively different modes, namely in preferential pathways or in a slow form in the soil matrix continuum (Bouma, 1981;Beven and Germann, 1982;Edwards et al, 1989;Flury et al, 1994Flury et al, , 1996Stamm et al, 1998;Zehe and Flühler, 2001a, b;Vogel et al, 2005;McGrath et al, 2007).…”

Section: Examples Of Threshold Behaviour In Hydrology and Earth Systementioning

confidence: 99%

Threshold behaviour in hydrological systems as (human) geo-ecosystems: manifestations, controls, implications

Zehe¹,

Sivapalan

2009

Hydrol. Earth Syst. Sci.

196

139

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Abstract. In this paper we review threshold behaviour in environmental systems, which are often associated with the onset of floods, contamination and erosion events, and other degenerative processes. Key objectives of this review are to a) suggest indicators for detecting threshold behavior, b) discuss their implications for predictability, c) distinguish different forms of threshold behavior and their underlying controls, and d) hypothesise on possible reasons for why threshold behaviour might occur. Threshold behaviour involves a fast qualitative change of either a single process or the response of a system. For elementary phenomena this switch occurs when boundary conditions (e.g., energy inputs) or system states as expressed by dimensionless quantities (e.g. the Reynolds number) exceed threshold values. Mixing, water movement or depletion of thermodynamic gradients becomes much more efficient as a result. Intermittency is a very good indicator for detecting event scale threshold behavior in hydrological systems. Predictability of intermittent processes/system responses is inherently low for combinations of systems states and/or boundary conditions that push the system close to a threshold. Post hoc identification of "cause-effect relations" to explain when the system became critical is inherently difficult because of our limited ability to perform observations under controlled identical experimental conditions. In this review, we distinguish three forms of threshold behavior. The first one is threshold behavior at the Correspondence to: E. Zehe (e.zehe@bv.tum.de) process level that is controlled by the interplay of local soil characteristics and states, vegetation and the rainfall forcing. Overland flow formation, particle detachment and preferential flow are examples of this. The second form of threshold behaviour is the response of systems of intermediate complexity -e.g., catchment runoff response and sediment yield -governed by the redistribution of water and sediments in space and time. These are controlled by the topological architecture of the catchments that interacts with system states and the boundary conditions. Crossing the response thresholds means to establish connectedness of surface or subsurface flow paths to the catchment outlet. Subsurface stormflow in humid areas, overland flow and erosion in semi-arid and arid areas are examples, and explain that crossing local process thresholds is necessary but not sufficient to trigger a system response threshold. The third form of threshold behaviour involves changes in the "architecture" of human geoecosystems, which experience various disturbances. As a result substantial change in hydrological functioning of a system is induced, when the disturbances exceed the resilience of the geo-ecosystem. We present examples from savannah ecosystems, humid agricultural systems, mining activities affecting rainfall runoff in forested areas, badlands formation in Spain, and the restoration of the Upper Rhine river basin as examples of this phenomenon. This fun...

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“…For example, subsurface storm flow is a special case occurring after a threshold of precipitation depth is exceeded; or Hortonian overland flow that occurs when rainfall intensities exceed a threshold when the ability of the soil to infiltrate water is exceeded (Zehe and Sivapalan 2009). Release of soil and sediment particles and erosion also exhibit threshold-like behavior (Sichingabula 1998;Ternat et al 2008;Hicks et al 2000). Traditionally, univariate mathematical formulations of the relationship between concentration and discharge are used for the calculation of export coefficients and sediment yield (Walling 1977;Horowitz 2003;Schleppi et al 2006;Ide et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Dynamics of storm-driven suspended sediments in a headwater catchment described by multivariable modeling

Onderka

Krein²,

Wrede

et al. 2012

J Soils Sediments

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Purpose Previous research has shown that the rate at which suspended sediment is transported in watercourses depends primarily on discharge (Q) as the first-order control, but additional factors are thought to affect suspended sediment concentrations (SSC) as well. Among these, antecedent hydrological and meteorological conditions (e.g., rainfall depth and intensity, discharge prior to a runoff event and the duration of runoff events) may represent significant transport controlling mechanisms. Univariate models using Q-SSC rating curves often produce large scatter and nonlinearity, because many of the hydrological and biotic processes affecting the dynamics of sediment are non-linear and exhibit threshold behavior. The simulation of such highly non-linear processes is therefore an elusive task requiring consideration of several interrelated controlling variables. The aim of this study was to identify the major hydrological and meteorological controls determining the dynamics of SSC during storm-runoff events and the magnitude of SSC in a headwater catchment in Luxembourg. Materials and methods A parsimonious data-driven model (M5′ modular trees) was used to simulate SSC in response to the identified controlling variables. Antecedent hydrometeorological variables (e.g., antecedent precipitation depths, antecedent precipitation indices, and a suit of hydrological data) were used as input variables. Results and discussion Twenty-four-hour antecedent runoff volumes were determined as the major control explaining sediment depletion effects during high-flow periods, and a gradual decline of SSC as a runoff event progresses. The modeling results obtained by M5′ trees were then compared to conventional power-law rating curves. The M5′ model outperformed the rating-curve by being successful in describing the shape and magnitude of the analyzed sedigraphs. Therefore, we propose that incorporating antecedent hydrometeorological data into SSC prediction models may strongly enhance the accuracy of export coefficients. Two splitting criteria identified by the M5′ model tree (Q and antecedent runoff volume) were found and are discussed as possible thresholds responsible for the greatest nonlinearity in the Q-SSC relationship. Conclusions Our study highlights the dominant antecedent hydro-meteorological conditions acting as the major controls on the magnitude of SSC during episodic events in the headwater Huewelerbach catchment in Luxembourg. For future application, it would be interesting to extend and test the data-mining approach presented in this paper to other catchments, where other controls on sediment transport may be identified.

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Erosion threshold of saturated natural cohesive sediments: Modeling and experiments

Cited by 41 publications

References 24 publications

Model for the Formation of Single‐Thread Rivers in Barren Landscapes and Implications for Pre‐Silurian and Martian Fluvial Deposits

Model for the Formation of Single‐Thread Rivers in Barren Landscapes and Implications for Pre‐Silurian and Martian Fluvial Deposits

Threshold behaviour in hydrological systems as (human) geo-ecosystems: manifestations, controls, implications

Dynamics of storm-driven suspended sediments in a headwater catchment described by multivariable modeling

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