Changes in Channel Morphology Over Human Time Scales

Buffington, John M.

doi:10.1002/9781119952497.ch32

Cited by 63 publications

(99 citation statements)

References 286 publications

(287 reference statements)

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“…Climate-driven reductions in streamflow and transport capacity could further exacerbate such response, but may be offset to some degree by increased flow variability and more frequent occurrence of floods larger than the mean annual value, which should promote increased sediment transport and more dynamic channel conditions (Molnar, 2001;Andrews and Vincent, 2007;Buffington, in press). Data from the study area indicate that these mountain channels are currently supply limited, offering some resilience to increased sediment loads (Fig.…”

Section: Potential Effects Of Climate Change On Sediment Yields In Cementioning

confidence: 97%

Enhanced sediment delivery in a changing climate in semi-arid mountain basins: Implications for water resource management and aquatic habitat in the northern Rocky Mountains

2012

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Section: Potential Effects Of Climate Change On Sediment Yields In Cementioning

confidence: 97%

Enhanced sediment delivery in a changing climate in semi-arid mountain basins: Implications for water resource management and aquatic habitat in the northern Rocky Mountains

2012

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“…Riverbed grain size depends on channel form as well as the water, wood, and sediment delivered from the watershed (e.g., Church, 2006;Buffi ngton, 2012). Channel morphology evolves in response to changes in the watershed over time scales that vary widely.…”

Section: This Studymentioning

confidence: 99%

“…Channel morphology evolves in response to changes in the watershed over time scales that vary widely. For instance, in alluvial channels, width (w) and depth (h) can change more rapidly (on the order of 10 0 -10 3 yr) than S (except by changes in sinuosity; Parker et al, 2007;Buffi ngton, 2012). Fluvial channel type also varies.…”

Section: This Studymentioning

confidence: 99%

“…Fluvial channel type also varies. An important case, particularly with respect to anadromous fi sh habitat, is that of single-thread, coarse gravel-bedded rivers, which tend to evolve to a geometry where the bed sediment mobilizes (allowing for channel change) only around the discharge where the water begins to spill over the banks (Q bf , which is often approximated as Q 2 , the fl ow with recurrence interval, RI = 2 yr; e.g., Leopold et al, 1964;Parker et al, 2007;Gorman et al, 2011;Buffi ngton, 2012). Such systems are often termed "threshold channels," which Church (2006) defi ned as those with gravel-to-cobble beds where the competence is only exceeded by a modest amount during high-fl ow events, which in turn are characterized by size-selective transport (coarse grains may be immobile during some fl oods).…”

Section: This Studymentioning

confidence: 99%

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Predicting grain size in gravel-bedded rivers using digital elevation models: Application to three Maine watersheds

Snyder

Nesheim

Wilkins

et al. 2012

Geological Society of America Bulletin

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Riverbed grain size controls suitability of spawning habitat for threatened fi sh species. Motivated by this relationship, we developed a model that uses digital elevation models (DEMs) to predict bed grain size. We tested the accuracy of our model and two existing models with channel measurements from high-resolution airborne light detection and ranging (LiDAR) DEMs. All three models assume that bed grain size is a function of reach-average high-fl ow channel hydraulics (measured by shear stress or stream power). Our test data are fi eld measurements of median grain size (D 50 ) at 276 stations along four rivers in Maine. Pleistocene continental glaciation strongly infl uences the longitudinal profi les, which have alternating steep and gradual segments. We exploit the resulting variations in sediment supply to understand the controls on model success or failure in predicting bed grain size. Results show that all three models have ~70% success in predicting D 50 within a factor of two overall, and better where the rivers are coarse gravel bedded (~80% success where D 50 ≥ 16 mm). This similarity is unsurprising given that the models primarily rely on channel gradient (S) and drainage area as inputs. Measurements of S from LiDAR DEMs yield only a modest improvement in model success over those from topographic maps. We fi nd that our model works best in sediment-starved steep reaches. Model failures fall into two broad cate gories:(1) relatively fine-grained (D 50 < 16 mm) depo sitional reaches where our assumption of a constant, bankfull threshold for bed mobilization may be invalid; and (2) reaches where local variations in hydraulic roughness and/or sediment supply control D 50 . We argue that models based on airborne infrared LiDAR DEMs may reach a maximum around 80%-85% accuracy due to these sub-reach-scale factors, which cannot be easily measured from DEMs. The overall success of the models in predicting grain size indicates that the morphology of these channels has adjusted to the imposed S and sediment load during the ~15 k.y. since deglaciation and through the period of anthropogenic channel change over the past three centuries.

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“…Relationships between stream discharge and channel geometry are very well understood (Singh, 2003;Buffington, 2012;Gleason, 2015), and the empirical relationships we utilized (Castro and Jackson, 2001) have high coefficients of determination (r 2 ). Statistical regressions using data from three ecoregions produced r 2 equal to 0.76, 0.84, and 0.87 for Eq.…”

Section: Conveying Uncertaintymentioning

confidence: 99%