• No results found

Figure 12 : Relationships as a function of flow depth (z) between mean flow velocity (v), mean

sediment velocity (u), mean particle activity ( – see equation 38) and mean sediment 3150

flux (q) for: a. flow dominated by coarse particles moving near to the substrate; b. 3151

99 flow dominated by fine particles moving throughout the flow column; and c. 3152

hyperconcentrated or débris flows where the velocities of the fluid and the sediment 3153

converge. In moving from condition a. to condition c., the upstream effects of flow 3154

variability cause both u(z) and (z to become more variable because of anisotropy in 3155

the turbulent flow. 3156

Figure 13: Spatial and temporal scales of measurement of sediment transport and their implicit

3157

or explicit uses. The area shaded in blue has been the one typically used to infer 3158

transport-capacity rates. Measurements at other scales and for other purposes have 3159

come to challenge the underlying concept of transport capacity. 3160

100

Tables

3161

Table I: A list of commonly used models that employ sediment-transport-capacity relations across 3162

different process domains. 3163

Model Transport-capacity equations Reference HEC-RAS (Hydrologic

Engineering Center’s River Analysis System)

Fluvial erosion:

Ackers and White (1973) England and Hansen (1967) Laursen (1968)

Meyer-Peter and Müller (1948) Toffaleti (1968)

Yang (1973, 1984) Wilcock (2001)

Brunner (2010)

ISIS: river and floodplain modelling

Fluvial transport:

Ackers and White (1973)

CH2MHILL (2015) DELFT3D: 3D modeling suite

to investigate hydrodynamics, sediment transport and morphology and water quality for fluvial, estuarine and coastal environments Wave erosion: Bijker (1971) Soulsby (1997) van Rijn (1993) Current erosion:

Ashida and Michiue (1974) Engelund and Hansen (1967) Meyer-Peter and Müller (1948) Wilcock and Crowe (2003)

Deltares (2014)

MIKE21: simulation of physical, chemical and biological processes in coastal and marine envrionments

Current erosion: van Rijn (1993)

Engelund and Fredsøe (1976) Engelund and Hansen (1967) Meyer-Peter and Müller (1948)

DHI (2013)

WEPP (Water Erosion Prediction Project)

Hillslope erosion:

Foster (1982) based on Yalin (1963)

USDA (1995)

TOPMODEL (TOPography based hydrological MODEL

Hillslope erosion: Kirkby (1993)

Kirkby (1997) EUROSEM (European Soil

Erosion Model)

Splash erosion: Poesen (1985) Govers (1991) Everaert (1992)

Poesen and Torri (1988) Rill erosion: Govers (1990) Interrill erosion: Everaert (1991) Fluvial erosion: Govers (1990) Morgan et al. (1998)

KINEROS (Kinematic Runoff and Erosion Model)

Hillslope and channel erosion: Ackers and White (1973) Engelund and Hansen (1967) Kilinc and Richardson (1973) Meyer and Wischmeier (1969) Yalin (1963)

Yang (1973)

101

Figures

3164

3165

Figure 1: Comparisons between observed and calculated bedload transport in Elbow

3166

River, Alberta, Canada data for different bedload formulae, illustrating each formula

3167

produces contrasting estimates. (HRS: a group of formulae developed by researchers

3168

associated with the United Kingdom Hydraulics Research) [from Gomez and Church,

3169

1989].

102 3171

3172

Figure 2: Measured submerged bedload transport rate, ib versus predicted values using 3173

equation 3. The data were compiled from Johnson (1943), Smart and Jaeggi (1983), Gomez 3174

and Church (1988), Recking (2006), and those compiled by Gao (2003). These data include 3175

all of the available experiments to date that transport bed load of homogeneous grains under 3176

the ideal condition and cover the full range of both the saltation and sheetflow régimes. 3177

103 3178

Figure 3: Schematic illustration of grain-size changes in the bed-surface (Ds50) and 3179

transported (D50) sediment. In (a), a feed flume, only the bed surface changes, while in a 3180

recirculating flume (b), the change is primarily in the transported sediment. In both cases (c), 3181

transport coarsens relative to the bed surface. Thus, the same ratio of D50/Ds50 may be caused 3182

by (1) a low transport rate with small D50 or (2) a high transport rate with big D50 (after 3183

Wilcock and DeTemple, 2005). 3184

104 3185

Figure 4: The modified two-phase model. The two solid curves represent equation 5 with

3186

c = 0.03 and 0.06, respectively. The two dashed lines denote the boundary between the two 3187

regimes for the same two c values. The areas between these curves and lines reflect the 3188

influence of the uncertainties in the determination of c values. The dots are the bedload data 3189

reported in Hayes (1999) from a gravel-bed river significantly affected by a recent volcanic 3190

eruption (the data that have values of B greater than 1 are not included). Régime I is the area 3191

below the horizontal zone that includes two parts, the narrow area bounded by the two solid 3192

curves and the one on the right representing bed load transported at and below capacities, 3193

respectively. In the below-capacity area, bedload transport rate is relatively low for a given 3194

flow meaning the transport efficiency is relatively low and the median size of bed load D50 is 3195

small comparing to that of the bed surface, Ds50 and substrate, Dsub50. In the at-capacity area, 3196

the transport rate is relatively high for the same flow suggesting the relatively high transport 3197

efficiency and D50 is between Ds50 and Dsub50. Régime II is the area above the dashed 3198

horizontal zone. It also has below-capacity and at-capacity areas. Flows in the former have a 3199

bed with an armour layer, while in the latter do not. D50 in the former is relatively small, 3200

while in the latter is equivalent to both Ds50 and Dsub50. 3201

105 3202

Figure 5: Plot of surface-based fractional transport rates (qbi/fi) against grain size fractions, 3203

D. Each curve represents a flow transporting bed load at capacity in one of four gravel-bed

3204

rivers in Idaho, USA. 3205

106 3206

3207

Figure 6: Schematic of the streamlines above a low amplitude undulation of a sand surface

3208

in an aeolian setting. The maximum u* is located at a distance upwind from the crest 3209

(maximum ) proportional to the wavelength . The sand flux maximum qsat is located at a 3210

distance Lsat downwind, which separates the zones of erosion and deposition (after Durán et 3211

al., 2011).

107 3213

Figure 7: Saturation length Lsat, rescaled by the drag length (𝐿𝑑𝑟𝑎𝑔 = 𝜌𝑝

𝜌𝑓 𝑑), as a function

3214

of the wind shear velocity u*a, rescaled by the threshold u*at. Direct measurements, 3215

performed in a wind tunnel () and in the field (△), are compared to those 3216

determined from the initial dune wavelength (storms: (☆) and slipfaceless dunes 3217

(○)) (after Andreotti et al., 2010). 3218

108 3219

Figure 8: Different equations used to predict sediment transport under longshore conditions,

3220

showing the wide range of potential values for a specific wave energy (after Komar, 1999). 3221

109 3222

3223

Figure 9: Conceptual model of soil erosion derived by Meyer and Wischmeier (1969) from

3224

Ellison (1947) and other sources. 3225

110 3226

Figure 10: Comparison of experimental data with derived transport capacity and detachment

3227

capacity of overland flow on bare soil without sediment input at the top of the slope, but with 3228

flow addition at the rates specified (after Schiettecatte et al., 2008). 3229

111 3230

Figure 11

: Schematic diagram of possible subglacial conditions, showing a plan view of the

3231

bed of an active ice sheet or glacier flowing from the top to the bottom of the page. The key 3232

sediment-transport mechanisms are illustrated. The thicknesses of the curves on the left 3233

represent the ice flux (linear scale), the water flux (logarithmic scale), and total flux of débris 3234

in the ice plus deforming or stream-transported sediment below the ice (logarithmic scale) 3235

(after Alley et al., 1997). 3236

112 3238

3239

Figure 12

: Relationships as a function of flow depth (z) between mean flow velocity (v),

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