Wind Erosion in Western Queensland Australia

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Chapter 3 – Modelling Land Erodibility ReviewThe effects of water content are defined through an exponential relationship derived fromwind tunnel experiments by Shao et al. (1996):H( w) u ( )/u ( w) = exp( 22. w)= (3.21)* t0* t 7where w has the units m 3 m -3 . Roughness effects are computed by the drag partition model ofRaupach et al. (1993). The effects of surface roughness and cover are defined by:0.50.5( ) = ( 1) ( 1+ ) R (3.22)rm rrwhere β r is the ratio of the drag coefficient for isolated roughness elements over the dragcoefficient for the surface, σ r is the basal-to-frontal area ratio and m r is a parameter ≤ 1accounting for non-uniformity in the surface stress (Chapter 2, Section 2.2.8). The frontalarea index is estimated from fractional cover f c by the equation:( ) = c ln 1(3.23)f cwhere c λ is an empirical constant related to the distribution of roughness elements (a value of1 is assigned implying elements are uniformly distributed and isotropically oriented). Theeffect of dry, loose soil on u *t is computed by the scheme of Greeley and Iversen (1985):u( d ) A F( ) G( d )( g) 0. 5= (3.24)* t1Retdwhere g is the gravitational acceleration, σ is the particle-to-air density ratio, A 1 is anempirical coefficient, F(Re t ) and G(d) are empirical functions, and Re t is the thresholdparticle Reynolds number. The parameters R(λ), H(w) and Sc are estimated based on windtunnel experiments carried out in the study area (Shao et al., 1996). The model is linked to aGIS which contains input data on soils, vegetation, land management and climate across theMurray-Darling Basin.The M parameter (Equation 3.20) and particle size distributions are linked to the soil data inthe GIS database, with the M values being assigned to soil texture classes based ondescriptions of soil properties in the Australian Natural Resources Atlas. While WEAM does84
Chapter 3 – Modelling Land Erodibility Reviewnot directly account for soil surface crusting and aggregation, the M parameter values arebased on the likelihood of crust formation, hydraulic conductivity and the chemicalcomposition of the soils. The model does not, however, account for temporal changes in soilsurface conditions such as the effects of grazing on soil crusting (Shao and Leslie, 1997). Theavailability or supply of erodible material on the soil surface is not specified in the model, sothe predictions are unrestrained by temporal changes in soil erodibility. The vegetation dataused as input to the model are derived from satellite imagery of the Normalised DifferenceVegetation Index (NDVI), and do not represent actual cover measurements (%). Climate datafor the model are sourced from the Australian Government Bureau of Meteorology, and themodel uses a stochastic simulator to generate synthetic time-series meteorological inputs.Shao et al. (1996) described application of WEAM. A number of characteristics of WEAMaffect its performance in predicting dust emission. The limitations of this model are relatedto: 1) the absence of a scheme to account for spatial and temporal changes in surfaceerodibility, and 2) the fact that the model for vegetation cover effects and vegetation inputdata does not differentiate or account for the separate effects of both prostrate and standingcover. The frontal area index (λ) describes the effects of standing cover, but does not describethe effects of prostrate cover. Given the mix of cover types across the study region, theestimate of frontal area may have been too simplistic.Future development areas noted for WEAM were in the extension of the model to account fortemporal changes in soil erodibility. In achieving this, Shao et al. (1996) noted that researchis required to develop models that describe the soil aggregation state, formation andbreakdown of surface crusts, transitions between transport and source-limited situations, andthe effects of land management (including cultivation and grazing).3.3.3 Integrated Wind Erosion Modelling System (IWEMS)The Integrated Wind Erosion Modelling System (IWEMS) is an extension of WEAM (Luand Shao, 2001). IWEMS was developed by coupling WEAM with climate and land surfacesimulators within a GIS framework. The model was developed with the capacity to receiveinput data from a weather prediction model. The emission scheme and dust transport modelcan be applied at a national level across Australia. The input GIS database for land surfaceconditions has a spatial resolution of ~25 x 25 km at surface. The atmospheric component has85
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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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Chapter 8 - Conclusionsland use cha
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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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Williams, W.J., Eldridge, D.J., Alc
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Wind Erosion in Western Queensland Australia

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