Predicting sheetwash and rill erosion over the Australian continent

Abstract

Soil erosion is a major environmental issue in Australia. It reduces land productivity and has off-site effects of decreased water quality. Broad-scale spatially distributed soil erosion estimation is essential for prioritising erosion control programs and as a component of broader assessments of natural resource condition. This paper describes spatial modelling methods and results that predict sheetwash and rill erosion over the Australian continent using the revised universal soil loss equation (RUSLE) and spatial data layers for each of the contributing environmental factors. The RUSLE has been used before in this way but here we advance the quality of estimation. We use time series of remote sensing imagery and daily rainfall to incorporate the effects of seasonally varying cover and rainfall intensity, and use new digital maps of soil and terrain properties. The results are compared with a compilation of Australian erosion plot data, revealing an acceptable consistency between predictions and observations. The modelling results show that: (1) the northern part of Australia has greater erosion potential than the south; (2) erosion potential differs significantly between summer and winter; (3) the average erosion rate is 4.1 t/ha.year over the continent and about 2.9 × 109 tonnes of soil is moved annually which represents 3.9% of global soil erosion from 5% of world land area; and (4) the erosion rate has increased from 4 to 33 times on average for agricultural lands compared with most natural vegetated lands.

A review of the (Revised) Universal Soil Loss Equation (R/USLE): with a view to increasing its global applicability and improving soil loss estimates

“…Diodato (2004) in Italy and Kavian et al (2011) used the R/USLE method to calculate storm energy and summed these up per month 10 and season to obtain R-factors. Other studies used daily and monthly rainfall to calculate monthly R-factors and combine them for seasonal R-factors (Alexandridis et al, 2015;Kavian et al, 2011;López-Vicente et al, 2008;Lu et al, 2003;Panagos et al, 2015d;Shamshad et al, 2008). The results of these studies focused on identifying high and low periods of the landscape's vulnerability to soil erosion, depending on combinations of rainfall intensity and land cover.…”

“…Lu & Yu (2002) computed monthly R-factors in Australia, which was then used in a later study that computed C-30 factors based on satellite imagery and the NDVI, to produce monthly maps of soil erosion vulnerability over the entire Australian continent (Lu et al, 2003;Lu & Yu, 2002). The method of estimating C-factors using NDVI is popular due to the available of remotely-sensed imagery, and the capability of processing datasets with relative expedience compared to timeHydrol.…”

Catchment modelling of sediment, nitrogen and phosphorus nutrient loads with SedNet/ANNEX in the Tully - Murray basin

“…The K factor (soil erodibility) was recalculated by the method of Lu et al (2003) for 68 soil types using the analytical data from published soil surveys (Wilson and Baker 1990;Cannon et al 1992;Malcolm et al 1999). The recalculation reduced the K factors to ∼51% of the Hateley et al (2006) values.…”

Assessing temporal couplings in social–ecological island systems: historical deforestation and soil loss on Mauritius (Indian Ocean)

“…Timber plantations do not provide the same soil protection as native forests because the trees are harvested every 7-10 years, when considering fast-growing tree species. It is often considered that 3 years after harvesting, the protective cover of timber plantations is comparable to that of a native forest (Lu et al 2003). However, timber plantations have a much lower resistance than native forest to the heavy rains associated with cyclones that are common on Mauritius (Brouard 1963), increasing their erosion risk.…”