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
List of TablesChapter 1: IntroductionTable 1.1 Chronology of early surveys of land affected by wind erosion inAustralia (1900-1990) .........................................................................................4Chapter 2: Land Erodibility to Wind: Systems AnalysisTable 2.1 Summary of selected erodibility factors used in the Wind ErosionEquation (WEQ) model.....................................................................................27Table 2.2 Wind Erodibility Groups and Wind Erodibility Index for soils in theUnited States (after Skidmore et al., 1994). ......................................................28Chapter 3: Approaches to Modelling Land Erodibility to WindTable 3.1 Components of the Wind Erosion Equation (after Woodruff andSiddoway, 1965)................................................................................................72Table 3.2 Definitions of the WEPS sub-models used to simulate soil loss due towind erosion (after Hagen, 1991)......................................................................75Chapter 4: A Framework for Modelling Temporal Variations in SoilErodibilityTable 4.1 Summary of a selection of studies examining: (a) soil aggregationchanges in response to climate and management variability; (b) soilcrust disturbance effects on soil erodibility; and (c) soil crust responsesto trampling disturbance by livestock. ............................................................123Chapter 5: A Model to Predict Land Susceptibility to Wind Erosion inWestern Queensland, AustraliaTable 5.1 Dust-event frequencies at stations used for model validation. Dust eventclasses listed for each station include dust event frequencies for allevent types (All); events with hazes removed (NoHz); events with hazesand dust whirls removed (NoHzWr); and Dust Storm Index (DSI)values...............................................................................................................146xx
Table 5.2 Correlation coefficients (r 2 ) for the cross-correlation analysis betweenmean and maximum (max) AUSLEM output at multiple spatial scales(25 to 150 km) and dust-event frequencies for eight stations within thestudy area. Correlations between AUSLEM output and dust-eventfrequencies that are statistically significant (p > 0.05) are boldfaced.............149Table 5.3 Correlation coefficients (r 2 ) for the cross-correlation analysis betweenmean 3 pm wind speeds (ms -1 ) and dust-event frequency groups andDSI for meteorological stations within the study area. Correlationsbetween mean 3 pm wind speeds and dust-event frequencies that arestatistically significant (p < 0.05) are boldfaced .............................................150Chapter 6: Assessing Land Susceptibility to Wind Erosion: Validation ofthe Australian Land Erodibility ModelTable 6.1 Criteria used for the visual assessment of land susceptibility to winderosion. ............................................................................................................159Chapter 7: Simulations of the Spatio-Temporal Aspects of Land Erodibilityin the North-East Lake Eyre Basin, Australia, 1980-2006Table 7.1 Correlation (r 2 ) between mean annual rainfall, Troup SOI, PDO andmodelled land erodibility for the four study area bioregions, based onthe 27 year simulation. Significant correlations (p < 0.05) are boldfaced ......180xxi
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Table 5.2 Correlation coefficients (r 2 ) for the cross-correlation analysis betweenmean and maximum (max) AUSLEM output at multiple spatial scales(25 to 150 km) and dust-event frequencies for eight stations with<strong>in</strong> thestudy area. Correlations between AUSLEM output and dust-eventfrequencies that are statistically significant (p > 0.05) are boldfaced.............149Table 5.3 Correlation coefficients (r 2 ) for the cross-correlation analysis betweenmean 3 pm w<strong>in</strong>d speeds (ms -1 ) and dust-event frequency groups andDSI for meteorological stations with<strong>in</strong> the study area. Correlationsbetween mean 3 pm w<strong>in</strong>d speeds and dust-event frequencies that arestatistically significant (p < 0.05) are boldfaced .............................................150Chapter 6: Assess<strong>in</strong>g Land Susceptibility to <strong>W<strong>in</strong>d</strong> <strong>Erosion</strong>: Validation ofthe <strong>Australia</strong>n Land Erodibility ModelTable 6.1 Criteria used for the visual assessment of land susceptibility to w<strong>in</strong>derosion. ............................................................................................................159Chapter 7: Simulations of the Spatio-Temporal Aspects of Land Erodibility<strong>in</strong> the North-East Lake Eyre Bas<strong>in</strong>, <strong>Australia</strong>, 1980-2006Table 7.1 Correlation (r 2 ) between mean annual ra<strong>in</strong>fall, Troup SOI, PDO andmodelled land erodibility for the four study area bioregions, based onthe 27 year simulation. Significant correlations (p < 0.05) are boldfaced ......180xxi