Wind Erosion in Western Queensland Australia

Chapter 5 – Land Erodibility Model Developmentdata decreasing land erodibility in heterogeneous landscapes where small erodible features(i.e. dune crests in the Simpson Desert region) are “lost” by sub-grid scale averaging; and atshort time scales model output must be interpreted relative to a reference wind velocity (18ms -1 - determined by the grass cover function), otherwise erodibility may be over- or underpredictedby the model.Using dust-event data for model validation following its use in model development did notallow for a completely independent test of model performance. The dust-event data was,however, the only data available for testing AUSLEM performance at comparable spatial andtemporal scales. The validation period (1980 – 1990) was selected to avoid using the sameevent data for model development and validation. Comparisons of model output withassessments of erosion hazard on long-range (> 100 km) transects have potential to be usefulfor validation (as per Hassett et al., 2000; described in Chapter 6). However, efforts by theauthors to develop such methods have been restricted by scaling issues (relating field basedobservations to coarse resolution model output) and the limited availability of higher spatialresolution model input data required for the comparison.Where possible, these limitations should be addressed in future research. As detailed inChapter 4, attention must be directed toward developing models to simulate temporal changesin soil erodibility. A comparison of model output with field assessments of wind erosionhazard and predictions from alternate models like the Integrated Wind Erosion ModellingSystem (Shao et al., 1996, Lu and Shao, 1999) should also be conducted to independently testmodel performance.5.7 ConclusionsThis chapter has reported on the development of a model to predict land susceptibility towind erosion in western Queensland, Australia. A rationale for model development has beenpresented, and the model has been validated by a comparison of annual output time serieswith dust-event frequencies and DSI over an 11 year period. The model output had strongcorrelations with dust-event frequencies at half of the validation stations. Poor correlations atthe other stations were linked to limitations of the modelling scheme, model input datacharacteristics and problems with testing model performance using dust-event data which in155