Wind Erosion in Western Queensland Australia

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Chapter 3 – Modelling Land Erodibility Review z zmax z zmin5maxTOPO =(3.29)where TOPO is the erodibility factor, z is the elevation of the relevant grid point, and z max andz min are the highest and lowest points in the surrounding area (10° x 10° lat./long.). For thegeomorphic factor, erodibility was considered proportional to the upstream area from whichdust source area sediments accumulate. The other two erodibility factors were based on linearand non-linear ratios of the surface reflectance at a grid cell to the global maximum surfacereflectance (recorded in the Sahara). Surface reflectance data were acquired from theModerate Resolution Imaging Spectroradiometer (MODIS) satellite sensors. The studydemonstrated how continental dust emissions are strongly influenced by source areaerodibility characterisations. A comparison of the model output with measured dust loadingsindicated that the erodibility factors performed well in representing spatial patterns in dustsource strengths (Grini et al., 2005).For validation, DEAD was implemented for an analysis period from 1990 to 1999 as acomponent of the Model for Atmospheric Chemistry and Transport (MATCH) ChemicalTransport Model (CTM). Meteorological inputs for the simulations were sourced fromobservational rather than simulated data (Zender et al., 2003a). Simulations of dust loadingand dry and wet deposition were compared with predictions from Duce et al. (1991) andProspero (1996), and the Global Ozone Chemistry Aerosol Radiation and Transport(GOCART) model (Prospero et al., 2002). DEAD dust emission predictions were found to beconsistent with the International Panel on Climate Change (IPCC) estimate ranges foremissions (Penner et al., 2001). DEAD predictions of Aerosol Optical Depth (AOD) werealso compared with measurements from the Advanced Very High Resolution Radiometer(AVHRR) and TOMS observations. Finally, DEAD dust concentrations were compared withmeasured concentrations from 18 stations with long-term surface dust concentration records.Good agreement was found between predicted and observed records (Zender et al., 2003a).Limitations of the model are consistent with those of other dust emission models. The mostsignificant of these is that the model does not account for temporal variations in soilerodibility.90
Chapter 3 – Modelling Land Erodibility Review3.4.3 Other Global Dust ModelsA number of global dust emission models have been developed, and there are similarities intheir erosion and dust emission schemes. In general, three approaches have been taken tomodel wind erosion (Zender et al., 2003a). The dependence of the model emission schemeson factors controlling land erodibility varies by the nature of their formulations.The first type parameterise mobilisation in terms of the third or fourth power of the windspeed or friction velocity, then impose size distribution factors on the emitted dust (e.g.Tegen and Fung, 1995; Mahowald et al., 1999; Perlwitz et al., 2001). These models arereliant on assumptions about the general characteristics of dust source areas and do notaccount for micro-physical entrainment processes (Zender et al., 2003a).The second type use a microphysical specification of the land surface to predict size-resolvedsaltation mass flux and dust emission (e.g. Marticorena and Bergametti, 1995; Gillette andPassi, 1988; Shao, 2001). Due to the complexity of inputs required for these models, thismodel type has typically been applied in regional scale modelling where spatial datarequirements are often better met and inherent assumptions built into the model are less likelyto violate the ranges of conditions seen in global dust source areas.The third type represents those models employing micro-physical parameterisations with anumber of simplifying assumptions to account for global dust source characteristics. Ingeneral these models are not able to accommodate fine scale soil erodibility factors like thefield to regional scale models. These models include the Community Aerosol and RadiationModel for Atmospheres (CARMA), the Global Ozone Chemistry Aerosol Radiation andTransport (GOCART) and DEAD (Ginoux et al., 2001; Woodward, 2001; Luo et al., 2003;Zender et al., 2003a, 2003b; Barnum et al., 2004).Recent advances in the application of remote sensing to detect atmospheric aerosols andglobal source areas have enabled significant improvements to be made to global dustemission models. An example of this is in the DEAD model, where global characterisationsof dust source areas by a range of geomorphic, topographic and hydrological factors haveenabled more advanced dust source parameterisations to be employed. These source area91
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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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