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 Developmentitself is based on many generalisations at a range of spatial scales. The model agreement withdust-event frequencies was found to vary across spatial scales and was highly dependent onland type variability around the reference stations, and on the types of dust events used in thevalidation process. Validation of AUSLEM with field assessments of land erodibility,presented in Chapter 6, seeks to provide a further independent test of the model performance.Developing a land erodibility modelling capability with AUSLEM has potential tosignificantly contribute to our knowledge of wind erosion processes in Australia. Theresolution of its spatial data inputs (5 x 5 km resolution, daily time-step) and their availability(1890 to 3-month forecasts) set AUSLEM apart from other wind erosion models in terms ofits application potential. Further research, presented in Chapter 7, will provide an analysis ofspatio-temporal patterns in modelled land erodibility and relate these to landscapecharacteristics, climate variability and management practices within the study area. This willprovide a significant advancement over evaluations of static wind erosion hazard maps, andallow for quantitative assessments of potential land degradation by wind erosion to be madeat scales appropriate for land management.156
Chapter 6 – Field Assessments and Model ValidationChapter 6Assessing Land Susceptibility to Wind Erosion: Validationof the Australian Land Erodibility ModelThis chapter addresses Objectives 5 and 6. The chapter describes the development of amethod for visually assessing land erodibility at the landscape scale that can be used tomonitor spatial and temporal changes in landscape condition. The data are then used tovalidate the land erodibility model through a point (observation) to pixel (model output)comparison.6.1 IntroductionWind erosion models have been developed to assess dust emission and transport processes ata range of spatial (~1 km 2 - global) and temporal (minutes - years) scales. Calibration andvalidation of the models is essential if the output is to be used with confidence in researchand land management applications (Janssen and Heuberger, 1995). This has created arequirement for procedures to test and validate model performance across a variety ofapplication areas, including cultivated and rangeland environments. Historically, validationprocedures have relied on comparisons of model simulations with measurements of winderosion rates (soil loss per unit time), or of simulated and recorded dust concentration timeseries data from observation networks (Zobeck et al., 2003). Wind erosion is a dynamicprocess that displays considerable spatial and temporal variability (Gillette, 1999). Usingisolated point samples to validate spatial models is therefore problematic, as significantvariations in emissions and erodibility may occur at small scales (Okin, 2005). Accordingly,there is a requirement to develop methods for monitoring indicators of wind erosion at thelandscape scale (i.e. >10 3 km 2 ), and to integrate this data into the calibration and validation ofspatially explicit wind erosion models.157
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Chapter 6 – Field Assessments and Model ValidationChapter 6Assess<strong>in</strong>g Land Susceptibility to <strong>W<strong>in</strong>d</strong> <strong>Erosion</strong>: Validationof the <strong>Australia</strong>n Land Erodibility ModelThis chapter addresses Objectives 5 and 6. The chapter describes the development of amethod for visually assess<strong>in</strong>g land erodibility at the landscape scale that can be used tomonitor spatial and temporal changes <strong>in</strong> landscape condition. The data are then used tovalidate the land erodibility model through a po<strong>in</strong>t (observation) to pixel (model output)comparison.6.1 Introduction<strong>W<strong>in</strong>d</strong> erosion models have been developed to assess dust emission and transport processes ata range of spatial (~1 km 2 - global) and temporal (m<strong>in</strong>utes - years) scales. Calibration andvalidation of the models is essential if the output is to be used with confidence <strong>in</strong> researchand land management applications (Janssen and Heuberger, 1995). This has created arequirement for procedures to test and validate model performance across a variety ofapplication areas, <strong>in</strong>clud<strong>in</strong>g cultivated and rangeland environments. Historically, validationprocedures have relied on comparisons of model simulations with measurements of w<strong>in</strong>derosion rates (soil loss per unit time), or of simulated and recorded dust concentration timeseries data from observation networks (Zobeck et al., 2003). <strong>W<strong>in</strong>d</strong> erosion is a dynamicprocess that displays considerable spatial and temporal variability (Gillette, 1999). Us<strong>in</strong>gisolated po<strong>in</strong>t samples to validate spatial models is therefore problematic, as significantvariations <strong>in</strong> emissions and erodibility may occur at small scales (Ok<strong>in</strong>, 2005). Accord<strong>in</strong>gly,there is a requirement to develop methods for monitor<strong>in</strong>g <strong>in</strong>dicators of w<strong>in</strong>d erosion at thelandscape scale (i.e. >10 3 km 2 ), and to <strong>in</strong>tegrate this data <strong>in</strong>to the calibration and validation ofspatially explicit w<strong>in</strong>d erosion models.157