Geomorphic Classification of Rivers: An Updated Review

Buffington, John M.; Montgomery, David R.

doi:10.1016/b978-0-12-818234-5.00077-8

Cited by 12 publications

(16 citation statements)

References 336 publications

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“…(2005) for

S<

0.01 and Pitlick et al. (2008) for

S\ge

0.01 following Buffington and Montgomery (2021):

{\tau }_{r}^{\ast }\,=\left\{\begin{array}{ll}0.021+2.18S\hfill & ,S< 0.01\hfill \\ 0.36{S}^{0.46}\hfill & ,S\ge 0.01\hfill \end{array}\right.

…”

Section: Resultsmentioning

confidence: 99%

“…(c) Total (

{\tau }_{r})

and effective reference shear stress (

{\tau }_{r}^{\prime })

. (d) Total reference Shields stress (

{\tau }_{r}^{\ast })

, estimated from data plotted relative to empirical data as a function of channel slope reported in Buffington and Montgomery (2021),

{\tau }_{r}^{\ast }=0.021+2.18S,\,S< 0.01;\,{\tau }_{r}^{\ast }=0.36{S}^{0.46},\,S\ge 0.01

.…”

Section: Resultsmentioning

confidence: 99%

“…From the median grain size, channel slope and flow depth,

{\tau }_{b}^{\prime }/{\tau }_{r}^{\prime }

can be approximated from typical values of

{\tau }_{b}^{\ast }/{\tau }_{r}^{\ast }

. Values of

{\tau }_{b}^{\ast }/{\tau }_{r}^{\ast }

reflect bed mobility (Bunte et al., 2013) and sediment supply (Pfeiffer et al., 2017) and are reported for specific channel morphologies (Buffington & Montgomery, 2021). For a given reach morphology,

{\tau }_{b}^{\prime }/{\tau }_{r}^{\prime }

thus provides context for hypothetical sediment supply or bed mobility conditions of the reach.…”

Section: Methodsmentioning

confidence: 99%

“…In the Sauk River watershed, it is equivalent to the 1.2 year flow. (Bunte et al, 2013) and sediment supply (Pfeiffer et al, 2017) and are reported for specific channel morphologies (Buffington & Montgomery, 2021) We calculated 𝐴𝐴 𝐴𝐴 ′ 𝑏𝑏 using a reduced-energy-slope approximation for effective shear stress (Rickenmann 2012):…”

Section: Nondimensional Bedload Equationmentioning

confidence: 99%

See 3 more Smart Citations

How Does Precipitation Variability Control Bedload Response Across a Mountainous Channel Network in a Maritime Climate?

Keck

Istanbulluoglu

Lundquist

et al. 2022

Water Resources Research

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Key to predicting how a river will respond to a given flow rate is predicting the channel conveyance at the time of the flood (Sturm, 2010). In mountainous watersheds, channel conveyance is maintained by the capacity of the channel to move bedload. If bedload transport capacity falls below the supply rate, the channel fills with sediment and channel conveyance decreases until consummate increases in channel slope balance transport and supply rates (Lane, 1955).At gaged locations, an estimate of bedload transport capacity under historic hydrologic conditions can be determined from a survey of the reach geometry, measurement of bed surface grain size and the hydrograph (Wilcock et al., 2009). For ungauged locations or future climate scenarios, a hydrograph must be approximated. If the hydrograph is modeled from precipitation derived from observation networks like the US National Weather Service Cooperative Observer Network (NWS, 2020) and Livneh et al. (2013) data set, or a future climate meteorology data set such as the Salathé et al. (2014) data set, the precipitation may largely be recorded as daily average values.The frequency of precipitation observations necessary to accurately model floods was described decades ago (Bras, 1979;Eagleson & Shack, 1966;Singh, 1997) but time series of daily average precipitation are still commonly used to model hydrologic response to climate change (Dan et al., 2012;Shrestha et al., 2012), with precipitation assumed to fall at a constant rate over the day for hourly hydrologic simulations. As the understanding of hydrologic processes has improved, detailed physical models have confirmed the necessity of accurate temporal representation of precipitation for flood prediction at the basin outlet (Paschalis et al., 2014). Furthermore, over geologic timescales, precipitation variability has been shown to control erosion rates and channel

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“…(2005) for

S<

0.01 and Pitlick et al. (2008) for

S\ge

0.01 following Buffington and Montgomery (2021):

{\tau }_{r}^{\ast }\,=\left\{\begin{array}{ll}0.021+2.18S\hfill & ,S< 0.01\hfill \\ 0.36{S}^{0.46}\hfill & ,S\ge 0.01\hfill \end{array}\right.

…”

Section: Resultsmentioning

confidence: 99%

“…(c) Total (

{\tau }_{r})

and effective reference shear stress (

{\tau }_{r}^{\prime })

. (d) Total reference Shields stress (

{\tau }_{r}^{\ast })

, estimated from data plotted relative to empirical data as a function of channel slope reported in Buffington and Montgomery (2021),

{\tau }_{r}^{\ast }=0.021+2.18S,\,S< 0.01;\,{\tau }_{r}^{\ast }=0.36{S}^{0.46},\,S\ge 0.01

.…”

Section: Resultsmentioning

confidence: 99%

“…From the median grain size, channel slope and flow depth,

{\tau }_{b}^{\prime }/{\tau }_{r}^{\prime }

can be approximated from typical values of

{\tau }_{b}^{\ast }/{\tau }_{r}^{\ast }

. Values of

{\tau }_{b}^{\ast }/{\tau }_{r}^{\ast }

reflect bed mobility (Bunte et al., 2013) and sediment supply (Pfeiffer et al., 2017) and are reported for specific channel morphologies (Buffington & Montgomery, 2021). For a given reach morphology,

{\tau }_{b}^{\prime }/{\tau }_{r}^{\prime }

thus provides context for hypothetical sediment supply or bed mobility conditions of the reach.…”

Section: Methodsmentioning

confidence: 99%

Section: Nondimensional Bedload Equationmentioning

confidence: 99%

See 2 more Smart Citations

How Does Precipitation Variability Control Bedload Response Across a Mountainous Channel Network in a Maritime Climate?

Keck

Istanbulluoglu

Lundquist

et al. 2022

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…River channel pattern is a function of a variety of factors including longitudinal gradient, stream power, transport capacity, and bank strength (Buffington & Montgomery, 2022;Church, 2006;Kondolf, Piégay, Schmitt, & Montgomery, 2016). Previous studies on river pattern classification (Demarchi, Bizzi, & Piegay, 2017;Ham & Church, 2012;Horacio, Ollero, & Perez-Alberti, 2017) have been typically approached from a temporally static perspective, focusing on categorising planform at low flow.…”

Section: Introductionmentioning

confidence: 99%

Seasonal and annual tropical river pattern change detection using machine learning

Li¹,

Williams²,

Hoey³

et al. 2023

Preprint

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Rivers in the tropics are more likely to exhibit seasonal changes in pattern than those in temperate regions because of strongly seasonal rainfall. However, such changes in seasonal tropical river patterns have not been widely investigated. Machine learning methods are used in this study with Sentinel-2 multispectral remote sensing images to classify active channel landforms (water; unvegetated bars; vegetated bars) of the Bislak, Laoag and Abra Rivers, north-west Luzon, the Philippines. River patterns are classified five or six times per year from 2016 to 2020. Spatial and temporal trends were investigated, in the context of the rivers’ active width, valley confinement, tectonic setting and precipitation. Results show a variety of relationships between each landform unit and active width, but a strong correlation was shown between active width and vegetation area in dry and wet seasons. Rivers were divided into sub-reaches based on observed patterns of water frequency and confinement; Ensemble Empirical Mode Decomposition (EEMD) was then used to decompose the landform time series and precipitation record. EEMD indicates that water and vegetated bars commonly show synchronised fluctuations with precipitation, while unvegetated bars have an anti-phase oscillation with precipitation. It also suggests that deviations from periodic consistency in river pattern may reflect the influence of extreme events and/or human disturbance. At the river system scale, faults perpendicular to the channel centreline were associated with an increase in vegetated bar stability. Overall, the interplay of faults, elevation, confinement and tributary locations impact landform stability. This investigation demonstrates that in tropical regions river pattern should be considered as a dynamic entity as characterising pattern from a single time period may misrepresent a river’s character. EEMD is also demonstrated to be an appropriate statistical technique in geomorphology to decompose datasets that are generated from contemporary applications of machine learning to remotely sensed imagery.

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Influence of channel geomorphic units on bedload transport and river morphology during low‐magnitude bed‐forming floods coupled with sediment augmentation

Mörtl

Schroff

Stähly³

et al. 2023

Earth Surf Processes Landf

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In this study, we map different types of channel geomorphic units in a sediment‐starved, residual‐flow reach before and after an artificial flood. Bedload particles of a previous sediment augmentation measure are tracked with passive integrated transponder tags, and water depth and flow velocity are recorded across ten cross‐sections. The analysis focuses on the influence of channel geomorphic units on bedload transport and river morphology. Our hypotheses are that during low‐magnitude bed‐forming floods with sediment pulse migration, (i) existing channel geomorphic units influence bedload transport and, more specifically, (ii) that their type influences the retention rate of bedload particles. Furthermore, we hypothesise that (iii) the density of channel geomorphic units is linked to the hydromorphological diversity of a river section. We provide evidence that supports the first two hypotheses and contradicts the third one. We consider our results to be transferable to similarly regulated reaches based on an analysis of site‐specific conditions and alternative explanatory factors. Further research is needed to transfer the results to varying flood and sediment supply conditions in unregulated reaches.

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Geomorphic Classification of Rivers: An Updated Review

Cited by 12 publications

References 336 publications

How Does Precipitation Variability Control Bedload Response Across a Mountainous Channel Network in a Maritime Climate?

How Does Precipitation Variability Control Bedload Response Across a Mountainous Channel Network in a Maritime Climate?

Seasonal and annual tropical river pattern change detection using machine learning

Influence of channel geomorphic units on bedload transport and river morphology during low‐magnitude bed‐forming floods coupled with sediment augmentation

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