Field-derived relationships for flow velocity and resistance in high-gradient streams

Comiti, Francesco; Mao, Luca; Wilcox, Andrew C.; Wohl, Ellen; Lenzi, Mario Aristide

doi:10.1016/j.jhydrol.2007.03.021

Cited by 153 publications

(180 citation statements)

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“…In fact, wood presence and expansions and contractions of the channel banks can provide an important additional source of resistance [Bathurst, 1985;Dust and Wohl, 2012;Kean and Smith, 2006]. The Comiti et al [2007] equation, which employs a step-pool-specific steepness factor, produces a certain dispersion of data around the line of perfect agreement, abundantly underpredicting U in the gentlest TS reach and overpredicting U in the steepest TB reach. When the three reach types are analyzed separately, the (21); b in Figure 5), provides similar RMS values for the three reaches and differentiates the behavior of the two step-pool reaches excessively.…”

Section: 1002/2014wr016631mentioning

confidence: 99%

“…Techniques predicting flow resistance coefficients from field data exist for Chezy's C [e.g., ASCE Task Force, 1963], DarcyWeisbach's f [Bathurst, 1985;Kaufmann, 1987;Comiti et al, 2007;Kaufmann et al, 2008], and Manning's n [Jarrett, 1984;Rickenmann, 1994]. Furthermore, methods to directly estimate mean flow velocity do not consider the classical hydraulic resistances (C, f, n) in mountain rivers.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, methods to directly estimate mean flow velocity do not consider the classical hydraulic resistances (C, f, n) in mountain rivers. They have been developed using field data [Jarrett, 1984;Rickenmann, 1994;Ferguson, 2007;Comiti et al, 2007], laboratory data from self-formed steps [Aberle and Smart, 2003;Comiti et al, 2009;Zimmermann, 2010] and the combination of field data with flume experiments [Lee and Ferguson, 2002]. The mean flow velocity has proved to be inversely related to flow resistance [Limerinos, 1970;Bathurst, 1985;Lee and Ferguson, 2002;Wilcox and Wohl, 2007;Reid and Hickin, 2008;Ferguson, 2010], but its expeditious and precise quantification in boulder bed torrents still remains a challenge, particularly in terms of the overall kinematics of a series of step-pool sequences [David et al, 2010].…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, many researchers have calibrated experimental equations to directly predict U [e.g., Jarrett, 1984;Rickenmann, 1994;Ferguson, 2007;Comiti et al, 2007]. In a dimensionally correct form, the direct relationship can be written as follows [Rickenmann, 1991;Aberle and Smart, 2003]: where q is the unit discharge, D c is the reference grain diameter or reference geometric bed roughness, and g is the gravity acceleration.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, Aberle and Smart [2003] and Zimmermann [2010] adopted the standard deviation of the longitudinal bed profile (r z ), the results of which are more significant in streams with substantial bed forms. Comiti et al [2007] introduced the classical hydraulic geometry equation in a dimensionless form:…”

Section: Introductionmentioning

confidence: 99%

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On kinematics and flow velocity prediction in step‐pool channels

D’Agostino

Michelini

2015

Water Resources Research

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This paper verifies methods for the prediction of mean flow velocity at the reach scale in mountain streams, investigating the kinematics of a series of two small-scale artificial step-pool sequences and a transitional reach between plane-bed and step-pool under well-controlled hydraulic conditions, and improving the estimation of the energy expenditure between the step crest and the downstream pool. Experimental data were collected using three fish ladder reaches with slopes between 2.6 and 10%. Four types of field measurements were conducted: topographical surveys to extract the thalweg profiles and cross-sectional geometry of reference cross sections; grain size analyses of the bed surface; steady state runs with a given flow rate (0.005-0.234 m 3 /s), and surveying of the water profile in the most significant cross sections. The following main conclusions were reached: (i) the dominance of spill resistance at the lowest discharge (pool water depth-step height ratios of 0.4) causes primary dimensionless head losses of up to 80%, and these losses progressively decrease to approximately 40% when the water discharge and related pool water depth submerge the upstream step height. A specific predictive equation for the head loss was calibrated and then verified via data from the Rio Cordon. (ii) The verification of literature-sourced equations to predict the reach-averaged flow velocity provided suitable results for several of these equations indicating that the use of a specific step-pool equation does not appear to be crucial to achieving accurate predictions.

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Section: 1002/2014wr016631mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On kinematics and flow velocity prediction in step‐pool channels

D’Agostino

Michelini

2015

Water Resources Research

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Unraveling bed slope from relative roughness in initial sediment motion

Prancevic

Lamb

2015

JGR Earth Surface

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Understanding incipient sediment transport is crucial for predicting landscape evolution, mitigating flood hazards, and restoring riverine habitats. Observations show that the critical Shields stress increases with increasing channel bed slope, and proposed explanations for this counterintuitive finding include enhanced form drag from bed forms, particle interlocking across the channel width, and large bed sediment relative to flow depth (relative roughness). Here we use scaled flume experiments with variable channel widths, bed slopes, and particle densities to separate these effects which otherwise covary in natural streams. The critical Shields stress increased with bed slope for both natural gravel (ρ s = 2.65 g/cm 3 ) and acrylic particles (ρ s = 1.15 g/cm 3 ), and adjusting channel width had no significant effect. However, the lighter acrylic particles required a threefold higher critical Shields stress for mobilization relative to the natural gravel at a fixed slope, which is unexpected because particle density is accounted for directly in the definition of Shields stress. A comparison with model predictions indicates that changes in local velocity and turbulence associated with increasing relative roughness for lighter materials are responsible for increasing the critical Shields stress in our experiments. These changes lead to concurrent changes in the hydraulic resistance and a nearly constant critical stream power value at initial motion. Increased relative roughness can explain much of the observed heightened critical Shields stresses and reduced sediment transport rates in steep channels and also may bias paleohydraulic reconstructions in environments with exotic submerged densities such as iron ore, pumice, or ice clasts on Titan.

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Recent advances quantifying the large wood dynamics in river basins: New methods and remaining challenges

Ruíz-Villanueva

Piégay

Gurnell

et al. 2016

Reviews of Geophysics

197

194

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Large wood is an important physical component of woodland rivers and significantly influences river morphology. It is also a key component of stream ecosystems. However, large wood is also a source of risk for human activities as it may damage infrastructure, block river channels, and induce flooding. Therefore, the analysis and quantification of large wood and its mobility are crucial for understanding and managing wood in rivers. As the amount of large-wood-related studies by researchers, river managers, and stakeholders increases, documentation of commonly used and newly available techniques and their effectiveness has also become increasingly relevant as well. Important data and knowledge have been obtained from the application of very different approaches and have generated a significant body of valuable information representative of different environments. This review brings a comprehensive qualitative and quantitative summary of recent advances regarding the different processes involved in large wood dynamics in fluvial systems including wood budgeting and wood mechanics. First, some key definitions and concepts are introduced. Second, advances in quantifying large wood dynamics are reviewed; in particular, how measurements and modeling can be combined to integrate our understanding of how large wood moves through and is retained within river systems. Throughout, we present a quantitative and integrated meta-analysis compiled from different studies and geographical regions. Finally, we conclude by highlighting areas of particular research importance and their likely future trajectories, and we consider a particularly underresearched area so as to stress the future challenges for large wood research.

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Field-derived relationships for flow velocity and resistance in high-gradient streams

Cited by 153 publications

References 48 publications

On kinematics and flow velocity prediction in step‐pool channels

On kinematics and flow velocity prediction in step‐pool channels

Unraveling bed slope from relative roughness in initial sediment motion

Recent advances quantifying the large wood dynamics in river basins: New methods and remaining challenges

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