Empirical modelling of large scale patterns in river bed surface grain size

Snelder, Ton H.; Lamouroux, Nicolas; Pella, Hervé

doi:10.1016/j.geomorph.2010.12.015

Cited by 40 publications

(25 citation statements)

References 40 publications

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“…A second explanation is that some processes such as the interactions between groundwater and surface water and biotic influences on conductivity are not well taken into account by our eight predictors. More generally, comparable large-scale studies to predict grain-size distribution (Snelder et al, 2011) or the probability of intermittency (Snelder et al, 2013) across the French hydrographic network also revealed difficulties to obtain accurate reach-scale predictions. Overall, large-scale approaches such as ours are useful for identifying the drivers of observed hydraulic conductivity and their relative influence but cannot provide accurate predictions at the reach-scale.…”

Section: Discussionmentioning

confidence: 99%

Variation in reach-scale hydraulic conductivity of streambeds

Stewardson

Datry²,

Lamouroux³

et al. 2016

Geomorphology

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Streambed hydraulic conductivity is an important control on flow within the hyporheic zone, affecting hydrological, ecological, and biogeochemical processes essential to river ecosystem function. Despite many published field measurements, few empirical studies examine the drivers of spatial and temporal variations in streambed hydraulic conductivity. Reach-averaged hydraulic conductivity estimated for 119 surveys in 83 stream reaches across continental France, even of coarse bed streams, are shown to be characteristic of sand and finer sediments. This supports a model where processes leading to the accumulation of finer sediments within streambeds largely control hydraulic conductivity rather than the size of the coarse bed sediment fraction. After describing a conceptual model of relevant processes, we fit an empirical model relating hydraulic conductivity to candidate geomorphic and hydraulic drivers. The fitted model explains 72% of the deviance in hydraulic conductivity (and 30% using an external cross-validation). Reach hydraulic conductivity increases with the amplitude of bedforms within the reach, the bankfull channel width-depth ratio, stream power and upstream catchment erodibility but reduces with time since the last streambed disturbance. The correlation between hydraulic conductivity and time since a streambed mobilisation event is likely a consequence of clogging processes. Streams with a predominantly suspended load and less frequent streambed disturbances are expected to have a lower streambed hydraulic conductivity and reduced hyporheic fluxes. This study suggests a close link between streambed sediment transport dynamics and connectivity between surface water and the hyporheic zone.

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Section: Discussionmentioning

confidence: 99%

Variation in reach-scale hydraulic conductivity of streambeds

Stewardson

Datry²,

Lamouroux³

et al. 2016

Geomorphology

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“…Second, declining fi sh stocks have led to efforts to restore riverine habitat (e.g., NRC, 2004). Third, the exploding availability of highresolution remote-sensing data sets, both optical and light detection and ranging (LiDAR), has led to efforts to identify potential habitat in watersheds, either by estimating bed grain size (Buffi ngton et al, 2004;Carbonneau et al, 2005;Donaldson and Sklar , 2010;Gorman et al, 2011;Snelder et al, 2011;Wilkins and Snyder, 2011;Carbonneau et al, 2012) or via exploring relationships between fl uvial morphology and habitat use by fi sh (Coulombe-Pontbriand and Lapointe, 2004;Davey and Lapointe, 2007;Neeson et al, 2007;Kim and Lapointe, 2011;Whited et al, 2011). Here, we build on these previous studies by testing three processbased models that use inputs derived from digital elevation models (DEMs) and hydro logic monitoring to predict bed grain size.…”

Section: Introductionmentioning

confidence: 99%

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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“…Making site-specific predictions across large regions might appear challenging, but models based on readily available geographic predictors can now be developed easily and applied within a GIS framework to produce spatially explicit maps of expected nutrient conditions. Similar site-specific predictions have been made of streambed-surface grain sizes across France (Snelder et al 2011). As additional data describing the spatial and temporal factors affecting nutrient concentrations become available, models can be improved to set nutrient criteria that are ever more reliable and protective.…”

Section: ]mentioning

confidence: 68%