Pattern and origin of stepped-bed morphology in high-gradient streams, Western Cascades, Oregon

Grant, Gordon E.; Swanson, Frederick J.; Wolman, M. Gordon

doi:10.1130/0016-7606(1990)102<0340:paoosb>2.3.co;2

Cited by 433 publications

(455 citation statements)

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“…Moreover, mountain streams having poorly sorted mixtures of grain sizes, where the largest grains (boulders) are only occasionally mobilized at high flows, may characteristically possess boulder clusters [Brayshaw et al, 1983] mingled within an irregular template of immobile boulders, for example, within reaches that have qualities similar to the "pool" and "riffle" units of Grant et al [1990] and to the "plane bed" reach type of Montgomery and Buffington [1997], although these bars do not necessarily possess the systematic spacing and geometry that is characteristic of self-formed alluvial channels. Like their lowland alluvial counterparts, moreover, the velocity fields and water surface topographies within streams like North Boulder Creek can exhibit systematic (albeit noisy) structures over distances of a few channel widths and longer, including the high-velocity locus whose smoothly sinuous trace arises in response to shoaling of flow over bed undulations [Furbish, 1993;Handel, 1996].…”

Section: Introductionmentioning

confidence: 99%

Irregular bed forms in steep, rough channels: 1. Stability analysis

Furbish¹

1998

Water Resources Research

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Abstract. A scaling analysis of the depth-integrated momentum equations tailored to the rough bed conditions of mountain streams suggests that certain velocity correlation terms that arise from depth integration, and which normally can be neglected in the case of smoother alluvial channels, can be a significant part of the momentum balance in these steep channels. By introducing the kinetic energy equation of the time-averaged motion to treat these correlation terms, which involve products of local deviations in velocity components about depth-averaged values, a flow model that suitably characterizes streamwise accelerations is obtained. A linear stability analysis using a flow model that retains the streamwise correlation terms suggests that their effect is to strengthen the initial selection of bed form wavelengths, as reflected by sharpened peaks in curves of growth rate versus bed form wavelength. Wavelengths with zero migration rate are close to wavelengths having the largest growth rate; thus selection of fixed bars is strong. Critical width-depth ratios necessary for bed form growth are significantly less than the critical ratios that are predicted when correlation terms are neglected. Moreover, a broader band of wavenumbers can be activated at a given width-depth ratio, and bed form modes representing midchannel bars can be activated in a narrower channel than would otherwise be predicted. Thus alternate bars can initially "compete" with midchannel bars, particularly at low sediment transport rates. This competition probably contributes to the complexity of bed topography that is typical of rough, mountain channels.

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Section: Introductionmentioning

confidence: 99%

Irregular bed forms in steep, rough channels: 1. Stability analysis

Furbish¹

1998

Water Resources Research

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“…The longitudinal structure has also been studied in terms of the 640 probability of the occurrence of geomorphic features (pool, cascades, rapids, 641 riffles, etc.) using the Markov chain (Grant et al, 1990). The aim of period-642 icity detection, different from ours explained in the introduction, is then to 643 identify the frequency of a given facies sequence (the pool-riffle sequence for 644 example) using a transitional probability matrix where each cell corresponds 645 to the probability that a facies can follow another one downstream.…”

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confidence: 99%

Comparison of statistical algorithms for detecting homogeneous river reaches along a longitudinal continuum

et al. 2012

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International audienceSeven methods designed to delineate homogeneous river segments, belonging to four families, namely -- tests of homogeneity, contrast enhancing, spatially constrained classification, and hidden Markov models -- are compared, firstly on their principles, then on a case study, and on theoretical templates. These templates contain patterns found in the case study but not considered in the standard assumptions of statistical methods, such as gradients and curvilinear structures. The influence of data resolution, noise and weak satisfaction of the assumptions underlying the methods is investigated. The control of the number of reaches obtained in order to achieve meaningful comparisons is discussed. No method is found that outperforms all the others on all trials. However, the methods with sequential algorithms (keeping at order n + 1 all breakpoints found at order n) fail more often than those running complete optimisation at any order. The Hubert-Kehagias method and Hidden Markov Models are the most successful at identifying subpatterns encapsulated within the templates. Ergodic Hidden Markov Models are, moreover, liable to exhibit transition areas

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“…There are several terms for discernible units of channel morphology at the ~ 1-10 W 61 scale, such as channel unit (e.g., Grant et al, 1990;Bisson et al, 1996), channel 62 geomorphic unit (e.g., Hawkins et al, 1993), geomorphic unit (e.g., Thomson et al,63 2001), morphological unit (e.g., Wadeson, 1994 DEM of a gravel-bed river and simulated a range of discharges using a two-dimensional 161 (2D) hydrodynamic model. They then used an algorithm to map six types of 162 mesohabitat regions within this range of discharges based on binned values of velocity, 163 depth, and shear stress.…”

Section: Mu Definition 60mentioning

confidence: 99%

“…A common 46 practice in fluvial geomorphology involves focusing on specific spatial scales at which 47 landforms have characteristic features (Grant et al, 1990;Rosgen, 1996; Thomson et 48 al., 2001). These scales are often thought of as dimensionless (i.e., exhibiting similarity 49 of forms and processes among systems of different absolute size) and proportional to 50 channel width (W), with common names such as catchment (entire watershed scale), 51 subcatchment, segment (~ 10 3 -10 4 W), reach (~ 10 2 -10 3 W), morphological (alternately 52 channel or geomorphic) unit (~ 10 0 -10 1 W), and hydraulic unit (~ 10 -1 -10 0 W) (Frissell et 53 al., 1986;Grant et al, 1990;Bisson et al, 1996;McDowell, 2001). This study presents a 54 new theory and methodology for delineating and mapping channel landforms at the 55 morphological-unit scale that eliminates in-field subjective decision making, adds full 56 transparency for map users, and enables future systemic alterations without having to 57 remap in the field.…”

Section: Introduction 42mentioning

confidence: 99%