10.08.2015 Views

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

SHOW MORE
SHOW LESS

You also want an ePaper? Increase the reach of your titles

YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.

Chapter 5 – Land Erodibility Model DevelopmentAUSLEM was run to predict land erodibility on a daily time-step for the period 1980 to 1990.Output was then aggregated <strong>in</strong>to monthly and annual means to match the temporal resolutionof dust-event frequencies. A caveat of us<strong>in</strong>g observational dust-event data is the <strong>in</strong>herentdifficulty <strong>in</strong> identify<strong>in</strong>g source areas of non-local dust events (i.e. dust hazes, and duststorms). AUSLEM output was compared with the station data at multiple spatial scales todeterm<strong>in</strong>e if an optimal scale exists for this validation approach. Firstly, circular source areasof <strong>in</strong>terest (AOIs) were del<strong>in</strong>eated around each of the eight stations to act as potential dustsource regions with radii of 25 km, 50 km, 100 km, and 150 km. The AOI size range wasdeterm<strong>in</strong>ed based on the ranges of maximum visibility around the stations with<strong>in</strong> which dustevents could be observed, and the requirement to test AUSLEM performance across multiplescales. A second half-circle AOI from (25 to 150 km radii) was then used, with AOIs alignedto the north, south, east and west around the stations. The purpose of this second directionaltest was to determ<strong>in</strong>e whether a better comparison between model output and dust-eventfrequencies could be made if only dom<strong>in</strong>ant dust sources upw<strong>in</strong>d of the stations wereconsidered. Summary statistics (mean, m<strong>in</strong>, max, standard deviation, mode) of mean annualAUSLEM output were computed for each AOI at each station. Cross-correlation analysis wasthen used to compare temporal trends <strong>in</strong> model output statistics with the measures of dusteventfrequencies.5.5.2 Results - Annual Land Erodibility PredictionsFigure 5.4 presents mean annual land erodibility predictions for western <strong>Queensland</strong> from1980 to 1990. Results show that regions experienc<strong>in</strong>g high susceptibility to w<strong>in</strong>d erosionoccur <strong>in</strong> the south-western corners of the state <strong>in</strong> the Mulga Lands, Simpson DesertDunefields, and western Channel Country. The Mitchell Grass Downs <strong>in</strong> the north-east of thestudy region tend to have consistently low land erodibility. The regions of high landerodibility <strong>in</strong> the south-west lie <strong>in</strong> the most arid section of the study area (< 250 mm ra<strong>in</strong>fallper annum), while the low erodibility lands to the north-east are consistent with that areareceiv<strong>in</strong>g higher mean annual ra<strong>in</strong>fall (~450 mm). The distribution of modelled landerodibility is <strong>in</strong> agreement with the general spatial pattern of w<strong>in</strong>d erosion activity acrosswestern <strong>Queensland</strong> (Middleton, 1984; Burgess et al., 1989; McTa<strong>in</strong>sh et al., 1990).144

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!