Soil erosion is mainly affected by the rainfall characteristics and land cover conditions,and soil erosion modelling is important for evaluating land degradation status.The revised Universal Soil Loss Equation(RUSLE)h...Soil erosion is mainly affected by the rainfall characteristics and land cover conditions,and soil erosion modelling is important for evaluating land degradation status.The revised Universal Soil Loss Equation(RUSLE)have been widely used to simulate soil loss rate.Previous studies usually considered the general rainfall characteristics and direct effect of runoff with the event rainfall erosivity factor(R_(e))to produce event soil loss(A_(e)),whereas the fluctuation of rainfall intensity within the natural rainfall profile has rarely been considered.In this study,the relative amplitude of rainfall intensity(R_(am))was proposed to generalize the features of rainfall intensity fluctuation under natural rainfall,and it was incorporated in a new R_(e)(R_(e)=R_(am)EI_(30))to develop the RUSLE model considering the fluctuation of rainfall intensity(RUSLE-F).The simulation performance of RUSLE-F model was compared with RUSLE-M1 model(R_(e)=EI_(30))and RUSLE-M2 model(R_(e)=Q_(R)EI_(30))using observations in field plots of grassland,orchard and shrubland during 2011–2016 in a loess hilly catchment of China.The results indicated that the relationship between A_(e) and R_(am)EI_(30) was well described by a power function with higher R2 values(0.82–0.96)compared to Q_(R)EI_(30)(0.80–0.88)and EI_(30)(0.24–0.28).The RUSLE-F model much improved the accuracy in simulating A_(e) with higher NSE(0.55–0.79 vs−0.11∼0.54)and lower RMSE(0.82–1.67 vs 1.04–2.49)than RUSLE-M1 model.Furthermore,the RUSLE-F model had better simulation performance than RUSLE-M2 model under grassland and orchard,and more importantly the rainfall data in the RUSLE-F model can be easily obtained compared to the measurements or estimations of runoff data required by the RUSLE-M2 model.This study highlighted the paramount importance of rainfall intensity fluctuation in event soil loss prediction,and the RUSLE-F model contributed to the further development of USLE/RUSLE family of models.展开更多
Assessing spatiotemporal variation in global soil erosion is essential for identifying areas that require greater attention and management under the effects of anthropogenic activities and climate change.Soil erosion ...Assessing spatiotemporal variation in global soil erosion is essential for identifying areas that require greater attention and management under the effects of anthropogenic activities and climate change.Soil erosion can be modelled using the universal soil loss equation(USLE),which includes rainfall erosivity(R-factor),vegetation cover(C-factor),topography(LS-factor),soil erodibility(K-factor),and management practices(P-factor).However,global soil erosion modeling faces numerous challenges,including data acquisition,calculation processes,and parameter calibration under different climatic and topographic backgrounds.Thus,we presented an improved USLE-based model using highly distributed parameters.The R-,C-,and P-factors were modified by the climate zone,country,and topography.This distributed model was applied to estimate the intensity and variations in global soil erosion from 1992 to 2015.We validated the accuracy of this model by comparing simulations with measurements from 11,439 plot years of erosion data.The results showed that i)the average global erosion rate was 5.78 t ha^(-1)year^(-1),with an increase rate of 4.26×10^(-3)t ha^(-1)year^(-1);ii)areas with significantly increasing erosion accounted for 16%of the land with water erosion,whereas those with significantly decreasing erosion accounted for 7%;and iii)areas with severe erosion included the western Ghats,Abyssinian Plateau,Brazilian Plateau,south and east of the Himalayas,and western coast of South America.Intensified erosion occurred mainly on the Amazon Plain and the northern coast of the Mediterranean.This study provides an improved water erosion prediction model and accurate information for researchers and policymakers to identify the drivers underlying changes in water erosion in different regions.展开更多
基金supported by the National Natural Science Foundation of China(nos.U2243231,42041004 and 42201126)the Doctoral Foundation of Tianjin Normal University(no.52XB1910)the Youth Innovation Promotion Association CAS(no.Y202013)。
文摘Soil erosion is mainly affected by the rainfall characteristics and land cover conditions,and soil erosion modelling is important for evaluating land degradation status.The revised Universal Soil Loss Equation(RUSLE)have been widely used to simulate soil loss rate.Previous studies usually considered the general rainfall characteristics and direct effect of runoff with the event rainfall erosivity factor(R_(e))to produce event soil loss(A_(e)),whereas the fluctuation of rainfall intensity within the natural rainfall profile has rarely been considered.In this study,the relative amplitude of rainfall intensity(R_(am))was proposed to generalize the features of rainfall intensity fluctuation under natural rainfall,and it was incorporated in a new R_(e)(R_(e)=R_(am)EI_(30))to develop the RUSLE model considering the fluctuation of rainfall intensity(RUSLE-F).The simulation performance of RUSLE-F model was compared with RUSLE-M1 model(R_(e)=EI_(30))and RUSLE-M2 model(R_(e)=Q_(R)EI_(30))using observations in field plots of grassland,orchard and shrubland during 2011–2016 in a loess hilly catchment of China.The results indicated that the relationship between A_(e) and R_(am)EI_(30) was well described by a power function with higher R2 values(0.82–0.96)compared to Q_(R)EI_(30)(0.80–0.88)and EI_(30)(0.24–0.28).The RUSLE-F model much improved the accuracy in simulating A_(e) with higher NSE(0.55–0.79 vs−0.11∼0.54)and lower RMSE(0.82–1.67 vs 1.04–2.49)than RUSLE-M1 model.Furthermore,the RUSLE-F model had better simulation performance than RUSLE-M2 model under grassland and orchard,and more importantly the rainfall data in the RUSLE-F model can be easily obtained compared to the measurements or estimations of runoff data required by the RUSLE-M2 model.This study highlighted the paramount importance of rainfall intensity fluctuation in event soil loss prediction,and the RUSLE-F model contributed to the further development of USLE/RUSLE family of models.
基金This work was funded by the National Natural Science Foundation of China(U2102209).
文摘Assessing spatiotemporal variation in global soil erosion is essential for identifying areas that require greater attention and management under the effects of anthropogenic activities and climate change.Soil erosion can be modelled using the universal soil loss equation(USLE),which includes rainfall erosivity(R-factor),vegetation cover(C-factor),topography(LS-factor),soil erodibility(K-factor),and management practices(P-factor).However,global soil erosion modeling faces numerous challenges,including data acquisition,calculation processes,and parameter calibration under different climatic and topographic backgrounds.Thus,we presented an improved USLE-based model using highly distributed parameters.The R-,C-,and P-factors were modified by the climate zone,country,and topography.This distributed model was applied to estimate the intensity and variations in global soil erosion from 1992 to 2015.We validated the accuracy of this model by comparing simulations with measurements from 11,439 plot years of erosion data.The results showed that i)the average global erosion rate was 5.78 t ha^(-1)year^(-1),with an increase rate of 4.26×10^(-3)t ha^(-1)year^(-1);ii)areas with significantly increasing erosion accounted for 16%of the land with water erosion,whereas those with significantly decreasing erosion accounted for 7%;and iii)areas with severe erosion included the western Ghats,Abyssinian Plateau,Brazilian Plateau,south and east of the Himalayas,and western coast of South America.Intensified erosion occurred mainly on the Amazon Plain and the northern coast of the Mediterranean.This study provides an improved water erosion prediction model and accurate information for researchers and policymakers to identify the drivers underlying changes in water erosion in different regions.
