Wind Erosion in Western Queensland Australia

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Chapter 3 – Modelling Land Erodibility Reviewin WEPS, either do not perform well outside the regions in which they were developed, orsuitable input data for the models are not available with regional to global scale coverage.The major implication of this is that, without schemes to account for temporal variations insoil erodibility, the models cannot capture the true nature of temporal variations in landerodibilty. They are reliant on representing land susceptibility to wind erosion throughvegetation, soil moisture and soil textural controls. This limitation is particularly important inmodelling wind erosion in dryland environments that are devoid of vegetation and soilmoisture, for example over playas. In these circumstances land erodibility is controlled by theerodibility of the soil surface (Chapter 2, Section 2.4), and the models may significantly overorunder-estimate wind erosion activity based solely on soil textural characteristics.3.5.2 Data AvailabilityThe development of process-based models has created a requirement for high spatial and hightemporal resolution model input data. The models also often have a requirement for hard-tomeasureinputs such as soil textural characteristics, soil moisture content, and in particularvegetation characteristics like cover, height, density and frontal area. Where these data areavailable with regional to global coverage, their spatial and temporal resolutions are often toocoarse to resolve local scale (e.g. ~10 2 km 2 ) heterogeneity. This data availability issue hasundoubtedly contributed to the lack of model application at moderate to high spatialresolutions and at the landscape scale.Raupach and Lu (2004) describe methods for dealing with the lack of data required as inputto the process-based models. Commonly used methods include: 1) simplifying the modelssuch they can be run with available input data; and 2) constraining spatial patterns in thehard-to-measure parameters using correlated parameters for which spatial data is readilyavailable. Developments in remote sensing of soil moisture content (e.g. Loew, 2008) andvegetation structural attributes (e.g. McGlynn and Okin, 2006) will undoubtedly improve ourability to account for these factors at high spatial and temporal resolutions. In the interim,however, the ability of the models to represent local-scale heterogeneity in dust source areasremains weak. Capturing this heterogeneity is important if models are to accurately representdust emitting hot spots within dryland environments (Gillette, 1999; Okin, 2005).94
Chapter 3 – Modelling Land Erodibility Review3.5.3 Up-scaling Models and Sub-Grid Scale HeterogeneityModels that account for the effects of conditions such as soil texture, soil moisture andvegetation cover are based on plot-scale wind tunnel experimentation. The relationshipsdriving the models are therefore functional at these scales. Accuracy issues arise when therelationships are applied to coarse resolution spatial data and are used to examine winderosion processes at the landscape to regional scales. The spatial data used as input to themodels represents gridded averages of land surface conditions, including vegetation coverand soil moisture. These conditions are rarely homogeneous (Okin and Gillette, 2001). Thisissue links back to the nature of the model functions, which themselves may not adequatelyaccount for the non-uniform distribution of inputs (Raupach and Lu, 2004). Complicating theissue is the fact that the relationships between factors controlling land susceptibility to winderosion are non-linear and display threshold-like behaviour (Gillette, 1999).Shao (2000) reported on three methods for dealing with sub-grid scale variations in spatialmodelling. These include: averaging surface properties, i.e. treating grid cells ashomogeneous areas; representing sub-grid scale heterogeneity through multiple smallerhomogeneous sub-grids; and using probability density functions to represent sub-grid scaleheterogeneity. The latter approach has been adopted in a number of wind erosion models,including IWEMS and DEAD, to account for local variations in both land surface andmeteorological conditions (Lu and Shao, 2001; Zender et al., 2003a). The approach has alsobeen used to account for spatial variations in wind shear stress in a model to predict surfaceroughness effects by Okin (2008) (Chapter 2).The implications of not accounting for sub-grid scale heterogeneity are that models are likelyto significantly underestimate predictions of both land erodibility and dust emissions (Okinand Gillette, 2004; Okin, 2005). The issue may also complicate model validation as pointobservations of wind erosion activity that are typically used to validate wind erosion modelsdo not necessarily reflect spatially averaged predictions.3.5.4 Validation of Regional to Global Scale ModelsWind erosion models are most often validated by comparisons of output with pointmeasurements of wind erosion (Shao et al., 1996; Fryrear et al., 1998; Gregory and Darwish,95
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Modelling Land Susceptibility toWin
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who joined me on many trips, shared
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Conference Presentations by the Aut
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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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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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Wind Erosion in Western Queensland Australia

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