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Wind Erosion in Western Queensland Australia

Modelling Land Susceptibility to Wind Erosion in Western ... - Ninti One

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Chapter 6 – Field Assessments and Model Validationchanges <strong>in</strong> vegetation cover at the low end (0 – 15%) of the cover range. This means thatmodelled erodibility values <strong>in</strong>crease slowly through the ‘no’, ‘low’ and ‘moderate’ classes,then rapidly <strong>in</strong>to the ‘high’ erodibility class. The flat distribution of the data is expla<strong>in</strong>ed bythe variability <strong>in</strong> predicted values for the high erodibility classes, which <strong>in</strong>dicate that themodel has difficulty <strong>in</strong> represent<strong>in</strong>g land erodibility at the high end of the rank<strong>in</strong>g.Secondly, data scal<strong>in</strong>g issues affect the accuracies of the visual assessments and modelpredictions of land erodibility. Both are dependent on the arrangement of vegetation <strong>in</strong> thelandscape and are affected by its anisotropic distribution <strong>in</strong> semi-arid rangelands (Ok<strong>in</strong>,2005). Assum<strong>in</strong>g a uniform distribution of cover, the model has a tendency to under-estimateerodibility when <strong>in</strong>put pixel vegetation cover values are high. Conversely, visual assessmentsof erodibility may account for vegetation patch<strong>in</strong>ess, lead<strong>in</strong>g to relative over-estimates ofland erodibility. This issue is particularly relevant <strong>in</strong> the Mulga Lands (<strong>W<strong>in</strong>d</strong>orah, Quilpiescenes), which are characterised by small erodible patches with<strong>in</strong> a broader matrix of wellvegetatedland (Pickup, 1985). Future research to address this limitation should exam<strong>in</strong>e:implement<strong>in</strong>g the validation at multiple spatial scales; account<strong>in</strong>g for vegetation distribution<strong>in</strong> the visual assessments of land erodibility; and <strong>in</strong>clud<strong>in</strong>g measures of vegetationdistribution <strong>in</strong> the model simulations (Ok<strong>in</strong> and Gillette, 2001).6.5 ConclusionsThis chapter has demonstrated that acquir<strong>in</strong>g visual assessments of land erodibility over largedistances us<strong>in</strong>g long-range transects can be a useful approach for monitor<strong>in</strong>g landscapecondition and test<strong>in</strong>g the performance of regional scale (>10 4 km 2 ) land erodibility models.Results suggest that AUSLEM performs better <strong>in</strong> the western Mitchell Grass Downs andChannel Country (Mt Dot, Cadell, Bedourie scenes) than <strong>in</strong> the Mulga Lands (<strong>W<strong>in</strong>d</strong>orah,Quilpie scenes). To the knowledge of the author the long-range transect approach described<strong>in</strong> this chapter has not previously been applied to test the performance of a w<strong>in</strong>d erosionmodel. Further development and application of methods for assess<strong>in</strong>g land erodibility at thelandscape scale will improve our capacity for monitor<strong>in</strong>g and modell<strong>in</strong>g land degradationprocesses <strong>in</strong> remote desert environments.166

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