Land susceptibility to water and wind erosion risks in the East Africa region - Fenta, Ayele Almaw; Tsunekawa, Atsushi; Haregeweyn, Nigussie; Poesen, Jean; Tsubo, Mitsuru; Borrelli, Pasquale; Panagos, Panos; Vanmaercke, Matthias; Broeckx, Jente; Yasuda, Hiroshi; Kawai, Takayuki; Kurosaki, Yasunori (The Science of the total environment, 2020)
Water erosion factor layers: R, rainfall erosivity; K, soil erodibility; LS, slope length and steepness; C, cover management; and P, support practice. Wind erosion factor layers: CE, climatic erosivity; EF, wind-erodible fraction; SC, soil crust; VC, vegetation cover; and SR, surface roughness. [Display omitted] •We assessed spatial patterns of water and wind erosion risks in East Africa.•The spatially distributed RUSLE model was adopted to assess water erosion.•A wind erosion index was developed by integrating five factors using fuzzy logic.•Areas of moderate or elevated erosion risks cover 10% (water) and 25% (wind).•Cropland and bareland are most affected by water and wind erosion, respectively. Land degradation by water and wind erosion is a serious problem worldwide. Despite the significant amount of research on this topic, quantifying these processes at large- or regional-scale remains difficult. Furthermore, very few studies provide integrated assessments of land susceptibility to both water and wind erosion. Therefore, this study investigated the spatial patterns of water and wind erosion risks, first separately and then combined, in the drought-prone region of East Africa using the best available datasets. As to water erosion, we adopted the spatially distributed version of the Revised Universal Soil Loss Equation and compared our estimates with plot-scale measurements and watershed sediment yield (SY) data. The order of magnitude of our soil loss estimates by water erosion is within the range of measured plot-scale data. Moreover, despite the fact that SY integrates different soil erosion and sediment deposition processes within watersheds, we observed a strong correlation of SY with our estimated soil loss rates (r2 = 0.4). For wind erosion, we developed a wind erosion index by integrating five relevant factors using fuzzy logic technique. We compared this index with estimates of the frequency of dust storms, derived from long-term Sea-Viewing Wide Field-of-View Sensor Level-3 daily data. This comparison revealed an overall accuracy of 70%. According to our estimates, mean annual gross soil loss by water erosion amounts to 4 billion t, with a mean soil loss rate of 6.3 t ha−1 yr−1, of which ca. 50% was found to originate in Ethiopia. In terms of land cover, ca. 50% of the soil loss by water erosion originates from cropland (with a mean soil loss rate of 18.4 t ha−1 yr−1), which covers ca. 15% of the total area in the study region. Model results showed that nearly 10% of the East Africa region is subject to moderate or elevated water erosion risks (>10 t ha−1 yr−1). With respect to wind erosion, we estimated that around 25% of the study area is experiencing moderate or elevated wind erosion risks (equivalent to a frequency of dust storms >45 days yr−1), of which Sudan and Somalia (which are dominated by bare/sparse vegetation cover) have the largest share (ca. 90%). In total, an estimated 8 million ha is exposed to moderate or elevated risks of soil erosion by both water and wind. The results of this study provide new insights on the spatial patterns of water and wind erosion risks in East Africa and can be used to prioritize areas where further investigations are needed and where remedial actions should be implemented.