Evaluation of monthly-scale soil erosion spatio-temporal dynamics and identification of their driving factors in Northeast China

Soil water erosion has caused an enormous damage to agricultural production and ecosystems worldwide. Soil erosion exhibits certain seasonal variation patterns within year under the influence of multiple factors. In this study, we calculated the multi-year average monthly-scale soil erosion modulus from 2001 to 2019 to evaluate the soil erosion risk using the revised universal soil loss equation (RUSLE) in the black soil region of Northeast China. The results showed that the average annual soil erosion modulus in this region was 8.53 t center dot ha(- 1)center dot year(- 1), and the average monthly soil erosion modulus was 0.78 t center dot ha(-1)center dot month(-1). The months of April-July and October were identified as critical periods for erosion, with erosion modulus values of 1.17, 1.89, 1.61, 1.13, and 0.93 t center dot ha(- 1)center dot month(-1) during these five months, respectively. High soil erosion was mainly distributed in southern Liaoning province and western Inner Mongolia, and these high erosion areas were mostly distributed in the regions with complex landforms, in grasslands, and in the southern temperate climatic zone. Among multiple driving factors, the topographic factor LS (L = slope length, S = slope steepness) was one of the dominant factors determining the spatial distribution pattern of soil erosion with 30 % of the contribution. The rainfall erosivity (R) and cover management (C) were the major factors driving significantly the intra-annual variation of soil erosion in Northeast China with 27.2 % and 28.9 % of the contribution, respectively. The intra-annual erosion varied with the rainfall and the vegetation growth, and the period of high erosion occurs in the early part of the wet season when the rainfall intensity increases and the vegetation growth is insufficient to protect the soil. Our findings highlight the need to take dynamic factors into consideration in the RUSLE model and the importance for identifying the areas at high erosion risk so as to take soil conservation actions.