Measurement of Coarse Gravel and Cobble Transport Using Portable Bedload Traps

Bunte, Kristin; Abt, Steven R.; Potyondy, John P.; Ryan, Sandra E.

doi:10.1061/(asce)0733-9429(2004)130:9(879)

Cited by 156 publications

(140 citation statements)

References 31 publications

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“…Variations in measured bedload have been attributed to fluctuations occurring over several scales, including individual particle movement (Bunte 2004), the passing of bedforms others 1989, 1990), the presence of bedload sheets (Whiting and others 1988), and larger pulses or waves of stored sediment (Reid and Frostick 1986). As a result, rates of bedload transport can exhibit exceptionally high variability, often up to an order of magnitude or greater for a given discharge.…”

Section: Introductionmentioning

confidence: 99%

A tutorial on the piecewise regression approach applied to bedload transport data

Ryan¹,

Porth²

2007

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149

114

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This tutorial demonstrates the application of piecewise regression to bedload data to define a shift in phase of transport so that the reader may perform similar analyses on available data. The use of piecewise regression analysis implicitly recognizes different functions fit to bedload data over varying ranges of flow. The transition from primarily low rates of sand transport (Phase I) to higher rates of sand and coarse gravel transport (Phase II) is termed "breakpoint" and is defined as the flow where the fitted functions intersect. The form of the model used here fits linear segments to different ranges of data, though other types of functions may be used. Identifying the transition in phases is one approach used for defining flow regimes that are essential for self-maintenance of alluvial gravel bed channels. First, the statistical theory behind piecewise regression analysis and its procedural approaches are presented. The reader is then guided through an example procedure and the code for generating an analysis in SAS is outlined. The results from piecewise regression analysis from a number of additional bedload datasets are presented to help the reader understand the range of estimated values and confidence limits on the breakpoint that the analysis provides. The identification and resolution of problems encountered in bedload datasets are also discussed. Finally, recommendations on a minimal number of samples required for the analysis are proposed. Keywords: Piecewise linear regression, breakpoint, bedload transportYou may order additional copies of this publication by sending your mailing information in label form through one of the following media. Please specify the publication title and series number. Publishing Services

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Section: Introductionmentioning

confidence: 99%

A tutorial on the piecewise regression approach applied to bedload transport data

Ryan¹,

Porth²

2007

Self Cite

149

114

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“…Hence, the two methods should yield comparable estimates of bed-load transport at higher flows, which are the focus of this study. However, Bunte et al ͑2004͒ show that bed-load rating curves for Helley-Smith samples tend to have lower slopes than those of traps. Because effective discharge calculations are sensitive to rating-curve slopes, differences in sampling methods may yield different estimates of the "observed" effective discharge, and thus different results of equation performance when comparing observed versus predicted effective discharges.…”

Section: Potential Error In Observed Transport Datamentioning

confidence: 82%

“…However, based on a number of simplifying assumptions, Hubbell and Stevens ͑1986͒ suggest a maximum probable error of 40% when using a Helley-Smith sampler to measure bed-load transport. Field studies by Bunte et al ͑2004͒ indicate that Helley-Smith samples may overestimate transport rates by 3-4 orders of magnitude at lower flows ͑Ͻ50% bankfull͒, but with transport rates converging toward those measured by fixed traps at higher flows. Hence, the two methods should yield comparable estimates of bed-load transport at higher flows, which are the focus of this study.…”

Section: Potential Error In Observed Transport Datamentioning

confidence: 95%

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Performance of Bed-Load Transport Equations Relative to Geomorphic Significance: Predicting Effective Discharge and Its Transport Rate

et al. 2008

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Previous studies assessing the accuracy of bed-load transport equations have considered equation performance statistically based on paired observations of measured and predicted bed-load transport rates. However, transport measurements were typically taken during low flows, biasing the assessment of equation performance toward low discharges, and because equation performance can vary with discharge, it is unclear whether previous assessments of performance apply to higher, geomorphically significant flows ͑e.g., the bankfull or effective discharges͒. Nor is it clear whether these equations can predict the effective discharge, which depends on the accuracy of the bed-load transport equation across a range of flows. Prediction of the effective discharge is particularly important in stream restoration projects, as it is frequently used as an index value for scaling channel dimensions and for designing dynamically stable channels. In this study, we consider the geomorphic performance of five bed-load transport equations at 22 gravel-bed rivers in mountain basins of the western United States. Performance is assessed in terms of the accuracy with which the equations are able to predict the effective discharge and its bed-load transport rate. We find that the median error in predicting effective discharge is near zero for all equations, indicating that effective discharge predictions may not be particularly sensitive to one's choice of bed-load transport equation. However, the standard deviation of the prediction error differs between equations ͑ranging from 10% to 60%͒, as does their ability to predict the transport rate at the effective discharge ͑median errors of less than 1 to almost 2.5 orders of magnitude͒. A framework is presented for standardizing the transport equations to explain observed differences in performance and to explore sensitivity of effective discharge predictions.

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“…3). A similar technical solution (Colorado State University/Forest Service, CSU/FS bedload traps) was used by Bunte et al (2003Bunte et al ( , 2004, to determine bedload transport rate. According to Bunte and Abt (2009), samplers of the type (e.g.…”

Section: Methodsmentioning

confidence: 99%

Variability of Sediment Transport in the Scott River Catchment (Svalbard) During the Hydrologically Active Season of 2009

Kociuba

Janicki

Siwek

2014

Quaestiones Geographicae

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Investigations of fluvial transport in the glacial river catchment (Scott River, Spitsbergen) were conducted in the melt season of 2009. A special attention was given to dynamics and distribution of bedload transport − the major component of fluvial transport in a proglacial gravel-bed river. Bed-load transport rate was determined using the River Bedload Traps (RBT) constructed for the project’s need. The obtained results indicate high diversity of bedload transport, the amount of which reached up to 220 kg m-1 day-1 for twenty-four hours in particular measurement sites. The results confirmed also great variability of local intensity fluvial processes in polar zone.

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Measurement of Coarse Gravel and Cobble Transport Using Portable Bedload Traps

Cited by 156 publications

References 31 publications

A tutorial on the piecewise regression approach applied to bedload transport data

A tutorial on the piecewise regression approach applied to bedload transport data

Performance of Bed-Load Transport Equations Relative to Geomorphic Significance: Predicting Effective Discharge and Its Transport Rate

Variability of Sediment Transport in the Scott River Catchment (Svalbard) During the Hydrologically Active Season of 2009

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