Wind Erosion in Western Queensland Australia

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