Wind Erosion in Western Queensland Australia

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Chapter 5 – Land Erodibility Model DevelopmentCorrelations at Boulia and Windorah strengthen at the 100 km scale, while correlations atCharleville and Urandangie strengthen towards the 25 km scale. At Birdsville significantstrong correlations were only found at the 150 km scale (max values). At Thargomindahcorrelations were weak, but strengthened toward the 100 km scale with DSI. Correlations atQuilpie are weak for all data combinations, but show evidence of strengthening toward the 25km scale. No match between modelled land erodibility and observed dust-event frequencieswas found at Longreach.Correlations between model output and DSI varied significantly between stations (Table 5.2).The DSI trajectory matched AUSLEM output trends at Thargomindah while dust-eventfrequencies did not. At Windorah, correlations with DSI strengthened toward the 25 km scalewhile at Urandangie they strengthened at the 100 km scale. At Boulia dust-event frequencytrends correlated with AUSLEM output, but DSI did not. No strong positive correlation wasfound between model output and DSI at Birdsville, Quilpie or Longreach.Table 5.3 shows that consistently strong correlations between dust-event frequencies andmean annual 3 pm wind speeds were only found at Urandangie. As the correlations betweendust-event frequencies and modelled land erodibility were also strong (Table 5.2), thisindicates that during the period 1980 to 1990 wind erosion activity around Urandangie had amutual dependence on concurrently elevated windiness and land erodibility. Results in Table5.3 suggest that for the remaining stations annual variations in land erodibility are a strongerforcing mechanism behind variations in dust-event frequencies, which is supported by thestronger correlations with model output (Table 5.2).Table 5.3 Correlation coefficients (r 2 ) for the cross-correlation analysis between mean 3 pm windspeeds (ms -1 ) and dust-event frequency groups and DSI for meteorological stations within the studyarea. Correlations between mean 3 pm wind speeds and dust-event frequencies that are statisticallysignificant (p < 0.05) are boldfacedDust Event ClassStation All NoHz NoHzWr DSIBirdsville 0.15 0.13 0.12 0.20Boulia 0.03 0.10 0.10 -0.20Charleville -0.33 -0.40 -0.25 -0.16Thargomindah -0.25 -0.23 -0.24 0.53Urandangie 0.79 0.81 0.78 0.60Windorah 0.51 0.47 0.37 -0.10Longreach -0.32 0.03 0.38 -0.04Quilpie -0.05 -0.07 -0.16 0.17150
Chapter 5 – Land Erodibility Model DevelopmentFigure 5.6 presents time series trajectories of mean AUSLEM output for 100 km circularAOIs and annual total dust-event frequencies (all events group).Figure 5.6 Time series trajectories of mean annual AUSLEM output for 100 km windows and annualtotal dust-event frequencies (all event types) for the eight meteorological stations used in modelvalidation. Solid lines represent AUSLEM output trajectories and dashed lines represent dust-eventfrequencies151
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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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Conference Presentations by the Aut
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2.2.5 Soil Moisture Effects........
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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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hand column) presents trajectories
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List of TablesChapter 1: Introducti
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Chapter 1 - Introduction• The abi
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Chapter 1 - Introductionchapter the
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Chapter 1 - Introductionevents yr -
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Chapter 1 - IntroductionFigure 1.2
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Chapter 1 - Introductionsusceptibil
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Chapter 1 - Introductiontemporal pa
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Chapter 1 - Introduction1.5 Researc
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Chapter 1 - IntroductionFigure 1.3
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Wind Erosion in Western Queensland Australia

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