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
Chapter 5 – Land Erodibility Model Developmentwhere β is a regression coefficient (-0.236) denoting the sensitivity of local dust-eventfrequencies to soil moisture content. The relationship was found to be consistently strong andstatistically significant (r 2 = 0.94; p < 0.0001) for the stations and across the sandy to claytextured soils in western Queensland. The mean wind speed associated with the events was8.29 ms -1 . The large difference to that associated with the grass cover model (18 ms -1 ) is dueto: 1) the wind speeds used in the wind tunnel analysis by Leys (1991a) being more typical ofthose associated with dust storm as opposed to local dust events; and 2) local differencesexist between wind speeds measured at the meteorological stations and the actual dust sourceareas. An implication of combining the relationships is that the skill of the model will be verysensitive to the representativeness of Equation 5.2 and may in fact underestimate erodibilityin circumstances when wind speeds are
Chapter 5 – Land Erodibility Model DevelopmentChapter 4 of this thesis established a framework for modelling soil erodibility. Due to aninability to parameterise the framework, this factor is kept spatio-temporally constant inAUSLEM at short (daily to monthly) time scales. This creates a problem in circumstanceswhere vegetation cover and soil moisture are low, and where land erodibility is controlled bysoil erodibility, for example on a dry lake bed or playa. If a crust is present on the soil surfaceand the supply of saltation material is limited, soil erodibility will be considerably lower thanif the surface were disturbed (Houser and Nickling, 2001a; Langston and McKenna Neuman,2005). These effects vary depending on soil texture and crust characteristics. An a prioriassumption in modelling land erodibility with AUSLEM is that at seasonal to annual timescales regional variations in soil surface conditions in rangeland environments are reflected ingrass cover and soil moisture conditions. During wet periods when cover and moisture arehigh, soil aggregation and crust cover are likely to be higher than in dry seasons and periodsof drought (Chapter 4). At these times inter-particle binding by moisture will be low, andcrusts and soil aggregates are likely to be most disturbed by photo-degradation and tramplingby livestock (McTainsh and Strong, 2007). So, at seasonal to annual time scales regionaltrends in land erodibility modelled with vegetation cover and soil moisture are likely toreflect actual land erodibility. Interpretation of AUSLEM output should therefore be confinedto the examination of trends in output at time-scales longer than one month.A soil texture mask (E tx ) has been included in the current model formulation (Figure 5.3). Themask assigns a scaling factor (provisionally set to 3.0) to areas with soil clay content less than7.0 %. All other soils are assigned a factor of 1.0. The scaling factor forces sandy soils forwhich crusting and aggregation are likely to play a minor role in determining erodibility tohave a consistently higher susceptibility to mobilisation than soils with high clay content forwhich crusting and aggregation have greater potential effects on erodibility (Breuninger et al.,1989; Belnap and Gillette, 1997; Leys and Eldridge, 1998).Stone Cover EffectsThe effects of stone cover on land erodibility are considered by the addition of a mask thatassigns an erodibility value of 0 (not erodible) to areas with extensive stone cover. Theinclusion of the mask was considered important as significant areas (i.e. the Sturt StonyDesert) of the study region are covered by a dense stony pavement. Experimental wind tunnelresearch by Gillette et al. (1980) demonstrated that this type of dense stone cover is effectivein protecting surfaces from deflation. While wind erosion on stony floodplain and playa141
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Chapter 5 – Land Erodibility Model DevelopmentChapter 4 of this thesis established a framework for modell<strong>in</strong>g soil erodibility. Due to an<strong>in</strong>ability to parameterise the framework, this factor is kept spatio-temporally constant <strong>in</strong>AUSLEM at short (daily to monthly) time scales. This creates a problem <strong>in</strong> circumstanceswhere vegetation cover and soil moisture are low, and where land erodibility is controlled bysoil erodibility, for example on a dry lake bed or playa. If a crust is present on the soil surfaceand the supply of saltation material is limited, soil erodibility will be considerably lower thanif the surface were disturbed (Houser and Nickl<strong>in</strong>g, 2001a; Langston and McKenna Neuman,2005). These effects vary depend<strong>in</strong>g on soil texture and crust characteristics. An a prioriassumption <strong>in</strong> modell<strong>in</strong>g land erodibility with AUSLEM is that at seasonal to annual timescales regional variations <strong>in</strong> soil surface conditions <strong>in</strong> rangeland environments are reflected <strong>in</strong>grass cover and soil moisture conditions. Dur<strong>in</strong>g wet periods when cover and moisture arehigh, soil aggregation and crust cover are likely to be higher than <strong>in</strong> dry seasons and periodsof drought (Chapter 4). At these times <strong>in</strong>ter-particle b<strong>in</strong>d<strong>in</strong>g by moisture will be low, andcrusts and soil aggregates are likely to be most disturbed by photo-degradation and trampl<strong>in</strong>gby livestock (McTa<strong>in</strong>sh and Strong, 2007). So, at seasonal to annual time scales regionaltrends <strong>in</strong> land erodibility modelled with vegetation cover and soil moisture are likely toreflect actual land erodibility. Interpretation of AUSLEM output should therefore be conf<strong>in</strong>edto the exam<strong>in</strong>ation of trends <strong>in</strong> output at time-scales longer than one month.A soil texture mask (E tx ) has been <strong>in</strong>cluded <strong>in</strong> the current model formulation (Figure 5.3). Themask assigns a scal<strong>in</strong>g factor (provisionally set to 3.0) to areas with soil clay content less than7.0 %. All other soils are assigned a factor of 1.0. The scal<strong>in</strong>g factor forces sandy soils forwhich crust<strong>in</strong>g and aggregation are likely to play a m<strong>in</strong>or role <strong>in</strong> determ<strong>in</strong><strong>in</strong>g erodibility tohave a consistently higher susceptibility to mobilisation than soils with high clay content forwhich crust<strong>in</strong>g and aggregation have greater potential effects on erodibility (Breun<strong>in</strong>ger et al.,1989; Belnap and Gillette, 1997; Leys and Eldridge, 1998).Stone Cover EffectsThe effects of stone cover on land erodibility are considered by the addition of a mask thatassigns an erodibility value of 0 (not erodible) to areas with extensive stone cover. The<strong>in</strong>clusion of the mask was considered important as significant areas (i.e. the Sturt StonyDesert) of the study region are covered by a dense stony pavement. Experimental w<strong>in</strong>d tunnelresearch by Gillette et al. (1980) demonstrated that this type of dense stone cover is effective<strong>in</strong> protect<strong>in</strong>g surfaces from deflation. While w<strong>in</strong>d erosion on stony floodpla<strong>in</strong> and playa141