Wind Erosion in Western Queensland Australia

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Chapter 7 – Land Erodibility Dynamics 1980-20067.3 Model DescriptionLand susceptibility to wind erosion, i.e. land erodibility, was modelled with the AustralianLand Erodibility Model (AUSLEM). AUSLEM was developed by Webb et al. (2009), and adescription of the model formulation, input parameters and performance characteristics arepresented in Chapter 5. For the current analysis the model was run at a 5 x 5 km spatialresolution on a daily time-step. In the absence of an operational model to predict temporalchanges in soil erodibility (Chapter 4), soil textural effects, E tx (tx), were considered static(after Lu and Shao, 2001) at time scales less than one month. In doing this the analysis ofmodel output was restricted to scales ~10 4 km 2 at which the input conditions were found tobest model land erodibility (Webb et al., 2009; Chapter 5, Section 5.5.3).7.4 Model Simulation and Analysis Methods7.4.1 Spatial PatternsAUSLEM was run to assess land erodibility on a daily time-step from January 1980 toDecember 2006. Output was then aggregated into monthly and annual means. Patterns in thedistribution of erodible land were examined by assessing the frequency at which modeloutput was recorded in four land erodibility classes. Arbitrary class boundaries were definedfor land that was highly erodible (1–0.15); moderately erodible (0.15–0.0375); had lowerodibility (0.0375–0.0091); and was not erodible (0.0091–0). The class boundaries werenecessarily non-linear to account for the exponential increase in land erodibility withdecreasing vegetation cover and soil moisture. Mean monthly model output was classifiedinto the four erodibility groups, and the percentage of years (out of 27) in which landoccurred in each class was computed on a per-pixel basis.7.4.2 Seasonal VariabilitySeasonal variations in land erodibility were examined by analysis of point time-series data.Mean monthly model output values were extracted from areas with 50 km radius aroundmeteorological stations at Birdsville, Boulia, Charleville, Longreach, Thargomindah, Quilpie,Urandangie and Windorah (Figure 7.1). Summary statistics of the time-series data were170
Chapter 7 – Land Erodibility Dynamics 1980-2006computed to provide information on mean monthly land erodibility conditions and the rangesof monthly variations. Trajectories of change in land erodibility within the four study areabioregions (Chapter 1, Section 1.6) were then examined by computing the proportionalabundance (percentage cover) of each land erodibility class within the bioregions. Theanalysis was conducted at an annual time scale to provide an indicator of the response of thebioregions to inter-annual climate variability.7.4.3 Inter-Annual VariabilityThe effects of drought on wind erosion activity have been examined from observational dataon atmospheric aerosol concentrations and dust-storm frequencies in Africa, the Middle East,North America, China and Australia (McTainsh et al., 1989; Goudie and Middleton, 1992;Brooks and Legrand, 2000; Okin and Reheis, 2002; Prospero and Lamb, 2003; Zender andKwon, 2005). Advances in modelling dust emissions at the global scale have enabled links tobe established between atmospheric dust concentrations and climate forcing mechanisms atmultiple temporal scales, including the North Atlantic Oscillation (NAO), El Niño/SouthernOscillation (ENSO), Pacific (inter-) Decadal Oscillation (PDO), and others (Moulin et al.,1997; Mahowald et al., 2003b; Ginoux et al., 2004; Hara et al., 2006). At regional scales theeffects of these global teleconnections may be manifested through changes in precipitationand windiness, and have been shown to influence the onset, intensity and duration of droughtand periods of enhanced rainfall (e.g. Allan, 1988). It is feasible to hypothesise that landerodibility in western Queensland will also be dynamic and respond to regional to globalscale climate variability.Pittock (1975) demonstrated that annual rainfall is correlated (r 2 = ~0.4) with the TroupSouthern Oscillation Index (SOI) over eastern Australia (east of 138° longitude). Thecorrelation between rainfall and the SOI varies seasonally, and is influenced by phaseinteractions of ENSO (3-7 year cycle) with the PDO (15-30 year cycle) (McBride andNicholls, 1983; Power et al., 1999; Crimp and Day, 2003). Consequently, episodes of pasturedegradation and recovery are linked to variations in Australian rainfall driven by ENSO-PDOinteractions (McKeon et al., 2004). The final phase of the model output analysis sought toquantify the relationships between dynamic changes in land erodibility, rainfall over westernQueensland, and these teleconnections.171
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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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Wind Erosion in Western Queensland Australia

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