Wind Erosion in Western Queensland Australia

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Chapter 2 – Land Erodibility Controls2 = u *tRfor a surface with roughness elements (2.31) = 2su *tSfor a bare surfacewhere u *tR and u *tS are the threshold friction velocities for rough and smooth surfacesrespectively. The threshold friction velocity ratio (R t ) can then be computed as:u* tS ' s 1Rt = = =(2.32)u * tR( 1)( 1+ )where the factor (1 + βλ) -1/2 accounts for the reduction in surface stress by shelter fromroughness elements, and (1 - σλ) -1/2 accounts for the “amplification’ of the stress (τ’s) on theerodible surface over the difference between total stress and that acting on the roughnesselements (τ – τ R ) caused by the fraction of soil surface (σλ) occupied by the roughnesselements. This formulation was then modified to account for the fact that u *t is controlled bythe maximum stress (τ’’s) acting at any point on the surface, rather than spatially averagedstress (τ’s). The modification was thus designed to account for non-uniformity around nonerodibleroughness elements, and is implemented by the inclusion of the m parameter (wherem ≤ 1). Written for the computation of the threshold friction velocity over vegetated surfaces(u *tR ) as an adjustment to the threshold friction velocity of a smooth surface, we have:( 1m)( m )u* tR= u*tS1+(2.33)Key simplifications in the model are that: 1) surfaces are only described in terms of theroughness density, and the model does not account for differences in element shape; and 2)the model does not account for the presence of turbulent air flow close to the surface.Limitations identified in the drag partitioning scheme revolve around defining appropriatevalues for the m parameter, the applicability of the m parameter itself, and the roughnessdensity range for which the model is valid λ ≤ 0.1 (Okin and Gillette, 2004). The dragpartitioning scheme presented by Raupach (1992) and Raupach et al. (1993) was revised byYang and Shao (2005) to account for multiple drag partitioning (e.g. between roughnesslayers at different heights), and for roughness densities where λ ≥ 0.1.60
Chapter 2 – Land Erodibility ControlsOkin (2008) noted that the m parameter (Equation 2.33) accounts for the spatial variability ofshear stress acting on the land surface due to the distribution of vegetation. However, theparameter cannot be defined from a physical basis. While the model accounts for the amountof lateral cover on the surface (Equation 2.29), it does not account for the cover distribution.Vegetation cover in arid and semi-arid landscapes is rarely uniform. Rather, it is patchy andhas an anisotropic distribution. Okin (2008) proposed a new model that explicitly accountsfor the distribution of shear stress on the surface. The model considers the distribution ofvegetation using a factor describing the probability distribution of the spacing betweenvegetation elements. In doing this the model scale is specified to the plant-interspace/gapscale. Unlike the Raupach et al. (1993) scheme, this means that sediment flux predictions canbe more accurately scaled from single points to the landscape scale.Stone cover (reg) in gibber plains or deserts may be an effective erosion control. Dustemission from land areas with dense stony cover is generally limited (Pye, 1987). Whilestone cover may not be as effective as some vegetation in increasing the roughness lengthover a surface, it certainly provides armouring and protection of fine particles frompotentially erosive winds. As for vegetation, the effectiveness of stone cover in reducingwind erosion is dependent on the stone dimensions, cover and distribution.2.3 Anthropogenic Interactions with Wind Erosion ControlsAnthropogenic interactions with the environment affect wind erosion processes. Theseinteractions are greatest in cultivated and rangeland environments. Here, the way in whichlandscapes are managed affects the spatial and temporal dynamics of variations in winderosion controls, and therefore land erodibility. Tegen and Fung (1995) suggest thatapproximately half of the current atmospheric dust loading can be attributed to anthropogenicdisturbance of dust source areas. Understanding the implications of human-landscapeinteractions is therefore essential for monitoring and modelling land erodibility dynamics.Anthropogenic interactions with the landscape affect spatial and temporal patterns in winderosion by two main processes: 1) modification of vegetation cover and structure; and 2)modifications of soil surface condition. Gillette (1999) and Okin et al. (2006) note that thethreshold dependent nature of wind erosion has a magnifying effect on the importance of the61
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Modelling Land Susceptibility toWin
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AUSLEM was developed as a Geographi
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who joined me on many trips, shared
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Conference Presentations by the Aut
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2.2.5 Soil Moisture Effects........
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Chapter 6: Assessing Land Susceptib
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List of FiguresChapter 1: Introduct
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Figure 2.11 Conceptual model of lan
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hand column) presents trajectories
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Chapter 1 - Introduction• The abi
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Beadle, N.C.W., 1945. Dust storms.
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Bryan, R.B., Govers, G., Poesen, J.
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Chepil, W.S., 1965. Transport of so
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Fryrear, D.W., Sutherland, P.L., Da
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Gregory, J.M., 1984. Prediction of
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Hugenholtz, C.H., Wolfe, S.A., 2005
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Littleboy, M., McKeon, G.M., 1997.
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Marshall, J.K., 1973. Drought, land
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Assessment Report of the Intergover
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Penner, J.E., et al. , 2001. Climat
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Rickert, K.G., McKeon, G.M., 1982.
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Stokes, C., J., McAllister, R.R.J.,
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Wind Erosion in Western Queensland Australia

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