Integrating multi-source data to improve water erosion mapping in Tibet, China
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© 2018 Elsevier B.V. Quantitative estimation for soil erosion is necessary for protection of the environment, and to improve agricultural productivity. However, due to the large area, sparse and limited data in Tibet, soil erosion there is still poorly quantified. Here, we improved the factors of the Revised Universal Soil Loss Equation (RUSLE) and calculated water erosion in Tibet. Rainfall erosivity (R) was calculated with the 0.25°CPC Morphing technique (CMORPH) data and subsequently downscaled to 1-km spatial resolution using artificial neural network (ANN) based on environmental covariates; slope length and steepness (LS) was estimated using the 3 arc sec Shuttle Rader Topography Mission (SRTM) digital elevation model (DEM); cover management (C) and control practice (P) were assigned based on land cover and protection measurements; and soil erodibility (K) was calculated using the Environmental Policy Integrated Climate model (EPIC) with inputs of the contents of sand, silt, clay and organic carbon in soil samples from Tibet. We used the data-mining algorithm to model the K factor and the spatially referenced variables to generate a K factor map. The obtained factors were then used to calculate soil loss in Tibet at1-km resolution. Our study estimated the annual water erosion at5.43 t ha-1 y-1in Tibet, about5.44 × 108 t of soil lost yearly. The erosion rate increased from northwest to southeast, with most serious erosion occurring in the humid rain forest area in the southeast of Tibet. Our estimates of erosion area were generally consistent with previous national estimates. The largest differences were in the humid zone, Hengduan Mountain, and Yarlung Zangbo River basin, which are characterized by complex terrain and climate. Because of the applications of the best available data, we supply better, quantitative, finer spatial resolution estimates than previous studies. Our study is valuable for assessment of soil erosion in other data-scarce area suffering from soil loss by water erosion.
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