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Wind Erosion in Western Queensland Australia

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

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

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Chapter 5 – Land Erodibility Model DevelopmentCorrelations at Boulia and <strong>W<strong>in</strong>d</strong>orah 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 Thargom<strong>in</strong>dahcorrelations were weak, but strengthened toward the 100 km scale with DSI. Correlations atQuilpie are weak for all data comb<strong>in</strong>ations, but show evidence of strengthen<strong>in</strong>g 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 Thargom<strong>in</strong>dah while dust-eventfrequencies did not. At <strong>W<strong>in</strong>d</strong>orah, 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 w<strong>in</strong>d speeds were only found at Urandangie. As the correlations betweendust-event frequencies and modelled land erodibility were also strong (Table 5.2), this<strong>in</strong>dicates that dur<strong>in</strong>g the period 1980 to 1990 w<strong>in</strong>d erosion activity around Urandangie had amutual dependence on concurrently elevated w<strong>in</strong>d<strong>in</strong>ess and land erodibility. Results <strong>in</strong> Table5.3 suggest that for the rema<strong>in</strong><strong>in</strong>g stations annual variations <strong>in</strong> land erodibility are a strongerforc<strong>in</strong>g mechanism beh<strong>in</strong>d variations <strong>in</strong> 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 w<strong>in</strong>dspeeds (ms -1 ) and dust-event frequency groups and DSI for meteorological stations with<strong>in</strong> the studyarea. Correlations between mean 3 pm w<strong>in</strong>d 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 -0.16Thargom<strong>in</strong>dah -0.25 -0.23 -0.24 0.53Urandangie 0.79 0.81 0.78 0.60<strong>W<strong>in</strong>d</strong>orah 0.51 0.47 0.37 -0.10Longreach -0.32 0.03 0.38 -0.04Quilpie -0.05 -0.07 -0.16 0.17150

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