Effects of rainfall and rocky desertification on soil erosion in karst area of Southwest China

Monitoring and evaluating the evolution of rocky desertification timely and studying the characteristics of soil erosion under different rainfall patterns are of great scientific significance for regional soil and water conservation,rocky desertification control and ecological environment construction.Four periods of remote sensing image data from 2005 to 2020 were selected to study the evolution characteristics of rocky desertification and its impact on soil erosion in the controlled boundary area of Shibantang hydrological station of Yeji River Watershed,Guizhou Province,China.According to the 408 erosive rainfall events,the soil erosion under different rainfall patterns in the watershed was analyzed.The results showed that:erosive rainfall events in the study area were mainly pattern A,accounting for 57.4%of the total rainfall events;the second was pattern B,accounting for 28.9%of the total rainfall events;the rainfall pattern of C occurred occasionally.Among them,pattern A was the main rainfall pattern leading to soil and water loss and had the largest contribution rate to soil erosion in the watershed.From 2005 to 2020,the area of rocky desertification showed a decreasing trend,accounting for 72.2%from 87.9%.Spatially,rocky desertification has mainly concentrated in the middle south of the watershed since 2010,while the rocky desertification mainly concentrated in the middle and north before 2010.The effects of different grades of rocky desertification on soil erosion were different,and the soil erosion modulus in areas with the medium,severe and extremely severe rocky desertification was generally small.The soil erosion modulus estimated by the RUSLE(Revised Universal Soil Loss Equation)model was still much higher than that calculated by the data measured by the hydrological monitoring station.Therefore,the application of the RUSLE model in karst area needs to be further modified.These results can provide reference for rocky desertification control,soil erosion control and fragile ecosystem restoration in karst area.

Rainfall erosivity and sediment dynamics in the Himalaya catchment during the Melamchi flood in Nepal

Rainfall erosivity is an indicator of rainfall potential to cause soil erosion.The Melamchi extreme flood occurred on June-15 and recurred on July-31,2021 in Nepal.During these flooding events,a large volume of sediments were eroded,transported and deposited due to the high rainfall erosivity of the basin.In this study,the temporal and the spatial distribution of rainfall erosivity within the Melamchi River Basin was estimated and further linked to sediment discharge and concentration at various sites along the river segments.The daily rainfall data for the event year 2021 of the entire basin were used.Validation was performed by post-flooding grain size sampling.The result showed that rainfall and rainfall erosivity exhibit pronounced intensity within the Melamchi River basin,particularly at Sermathang and Tarkeghang,both located in the middle section of the basin.The average annual rainfall in the Melamchi region was 3140.39 mm with an average annual erosivity of 18302.06(MJ mm)/(ha h yr).The average daily erosivity of the basin was 358.67(MJ mm)/(ha h)during the first event and 1241(MJ mm)/(ha h)for the second event.In the upper section of sampling,the sediment size ranged from 0.1 mm to>8 mm and was poorly graded.However,the lower region had smaller sediment ranging from 0.075 mm to>4.75 mm and also well graded.The smaller size(<1 mm)sediment passing was much higher in the Chanaute(78%)and Melamchi(66.5%)river segments but the larger size(>100 mm)sediments were passed relatively higher from the Kiwil(8.20%)and Ambathan(8.39%)river segments.During premonsoon and monsoon seasons,the highest sediment concentration was found to be 563.8 g/L and 344.3 g/L in Bhimtar and the lowest was 238.5 g/L and 132.1 g/L at the Ambathan,respectively.The sediment concentration during the pre-monsoon was found to be higher than the sediment concentration during the monsoon season in the Melamchi River.The more erosive regions in the basin were associated with the presence of highly fractured rock,weathered rocks and a thrust(weak)zone.The higher rainfall erosivity at upstream and the higher sediment concentration at downstream during flooding events have coincided well in the basin.Thus,the estimation of rainfall erosivity at the catchment scale and its influences on sediment concentration in the river are crucial for erosion control measures during flooding times in the Himalaya.

Assessing the effects of vegetation and precipitation on soil erosion in the Three-River Headwaters Region of the Qinghai-Tibet Plateau,China

Soil erosion in the Three-River Headwaters Region(TRHR)of the Qinghai-Tibet Plateau in China has a significant impact on local economic development and ecological environment.Vegetation and precipitation are considered to be the main factors for the variation in soil erosion.However,it is a big challenge to analyze the impacts of precipitation and vegetation respectively as well as their combined effects on soil erosion from the pixel scale.To assess the influences of vegetation and precipitation on the variation of soil erosion from 2005 to 2015,we employed the Revised Universal Soil Loss Equation(RUSLE)model to evaluate soil erosion in the TRHR,and then developed a method using the Logarithmic Mean Divisia Index model(LMDI)which can exponentially decompose the influencing factors,to calculate the contribution values of the vegetation cover factor(C factor)and the rainfall erosivity factor(R factor)to the variation of soil erosion from the pixel scale.In general,soil erosion in the TRHR was alleviated from 2005 to 2015,of which about 54.95%of the area where soil erosion decreased was caused by the combined effects of the C factor and the R factor,and 41.31%was caused by the change in the R factor.There were relatively few areas with increased soil erosion modulus,of which 64.10%of the area where soil erosion increased was caused by the change in the C factor,and 23.88%was caused by the combined effects of the C factor and the R factor.Therefore,the combined effects of the C factor and the R factor were regarded as the main driving force for the decrease of soil erosion,while the C factor was the dominant factor for the increase of soil erosion.The area with decreased soil erosion caused by the C factor(12.10×10^3 km^2)was larger than the area with increased soil erosion caused by the C factor(8.30×10^3 km^2),which indicated that vegetation had a positive effect on soil erosion.This study generally put forward a new method for quantitative assessment of the impacts of the influencing factors on soil erosion,and also provided a scientific basis for the regional control of soil erosion.

Temporal variability of global potential water erosion based on an improved USLE model

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.

Forest soil conservation based on eco-service provision unit method and its value in Anji County,Huzhou,Zhejiang,China

We propose an eco-service provision unit method for estimating the benefit and spatial differences of forests in controlling soil erosion.A total of 197 eco-service provision units were grouped on 1424.43 km2 of forest according to differences in vegetation,slope,soil,and rainfall.The amount of soil conservation and its economic value were estimated.The forests in Anji County prevent4.08 9 105 tons of soil from eroding annually,thereby avoiding 1.36 9 104 tons of nutrient loss（on-site cost） and preventing 149 tons of nutritive elements from entering water systems（off-site cost）.From an economic perspective,the soil nutrient conservation in the forests of Anji County generated an annual benefit of 43.37 million RMB（Chinese Currency,6.20 RMB = US$1）.On average,each hectare of ecological forest contributed up to 436 RMB annually because of soil conservation.Ecological complexes with higher rainfall intensity,such as broadleaf forest and red soil on slope gradients [25°,contributed the highest soil conservation benefits.This study identified and quantified the dominant contributors and magnitudes of soil conservation provided by forests.This information can benefit decision making regarding differentiated ecological compensation policies.

Identifying the driving factors of sediment delivery ratio on individual flood events in a long-term monitoring headwater basin

The Sediment Delivery Ratio(SDR) has multi-fold environmental implications both in evaluating the soil and water losses and the effectiveness of conservation measures in watersheds. Various factors, including hydrological regime and watershed properties, may influence the SDR at interannual timescales. However, the effect of certain important dynamic factors, such as rainfall peak distribution, runoff erosion power and sediment bulk density, on the sediment delivery ratio of single flood events(SDRe) has received little attention. The Qiaogou headwater basin is in the hilly-gully region of the Chinese Loess Plateau, and it encompasses a 0.45 km^2 catchment. Three large-scale field runoff plots at different geomorphological positions were chosen to obtain the observation data, and the 20-year period between 1986 and 2005 is presented. The results showed that the SDRe of the Qiaogou headwaters varied from 0.49 to 2.77. Among the numerous influential factors, rainfall and runoff were the driving factors causing slope erosion and sediment transport. The rainfall erosivity had a significant positive relationship with the sediment transport modulus(R^2=0.85, P<0.01) but had no significant relationship with SDRe. The rainfall peak coefficient was significantly positively correlated with the SDRe(R^2=0.64, P<0.05), indicating the influence of rainfall energy distribution on the SDRe. The runoff erosion power index was not only significantly related to the sediment transport modulus(R^2=0.84, P<0.01) but also significantly related to the SDRe(R^2=0.57, P<0.01). In addition, the relative bulk density was significantly related to the SDRe, indicating that hyper-concentrated flow characteristics contributed to more transported sediment in the catchment. Thus, the rainfall peak coefficient, runoff erosion power and sediment relative bulk density could be used as dynamic indexes to predict the SDRe in the hilly areas of the Chinese Loess Plateau.

Estimation of USLE crop and management factor values for crop rotation systems in China

Soil erosion on cropland is a major source of environmental problems in China ranging from the losses of a non-renewable resource and of nutrients at the source to contamination of downstream areas. Regional soil loss assessments using the Universal Soil Loss Equation (USLE) would supply a scientiifc basis for soil conservation planning. However, a lack of in-formation on the cover and management (C) factor for cropland, one of the most important factors in the USLE, has limited accurate regional assessments in China due to the large number of crops grown and their complicated rotation systems. In this study, single crop soil loss ratios (SLRs) were col ected and quantiifed for 10 primary crops from past studies or re-ports. The mean annual C values for 88 crop rotation systems in 12 cropping system regions were estimated based on the combined effects of single crop SLRs and the percentage of annual rainfal erosivity (R) during the corresponding periods for each system. The C values in different cropping system regions were compared and discussed. The results indicated that the SLRs of the 10 primary crops ranged from 0.15 to 0.74. The mean annual C value for al 88 crop rotation systems was 0.34, with a standard deviation of 0.12. The mean C values in the single, double and triple cropping zones were 0.37, 0.36 and 0.28, respectively, and the C value in the triple zone was signiifcantly different from those in single and double zones. The C values of dryland crop systems exhibited signiifcant differences in the single and triple cropping system regions but the differences in the double regions were not signiifcant. This study is the ifrst report of the C values of crop rotation systems in China at the national scale. It wil provide necessary and practical parameters for accurately assessing regional soil losses from cropland to guide soil conservation plans and to optimize crop rotation systems.

Estimating rainfall erosivity by incorporating seasonal variations in parameters into the Richardson model

Rainfall erosivity is an important climatic factor for predicting soil loss. Through the application of high-resolution pluviograph data at 5 stations in Huangshan City, Anhui Prov- ince, China, we analyzed the performance of a modified Richardson model that incorporated the seasonal variations in parameters α andβ. The results showed that （1） moderate to high seasonality was presented in the distribution of erosive rainfall, and the seasonality of rainfall erosivity was even stronger; （2） seasonal variations were demonstrated in both parameters α and β of the Richardson model; and （3） incorporating and coordinating the seasonality of parameters αandβgreatly improved the predictions at the monthly scale. This newly modi- fied model is therefore highly recommended when monthly rainfall erosivity is required, such as, in planning soil and water conservation practices and calculating the cover-management factor in the Universal Soil Loss Equation （USLE） and Revised Universal Soil Loss Equation （RUSLE）.

Rainfall erosivity estimation over the Tibetan plateau based on high spatial-temporal resolution rainfall records

The Tibetan Plateau(TP)in China has been experiencing severe water erosion because of climate warming.The rapid development of weather station network provides an opportunity to improve our understanding of rainfall erosivity in the TP.In this study,1-min precipitation data obtained from 1226 weather stations during 2018–2019 were used to estimate rainfall erosivity,and subsequently the spatial-temporal patterns of rainfall erosivity in the TP were identified.The mean annual erosive rainfall was 295 mm,which accounted for 53%of the annual rainfall.An average of 14 erosive events occurred yearly per weather station,with the erosive events in the wet season being more likely to extend beyond midnight.In these cases,the precipitation amounts of the erosive events were found to be higher than those of the daily precipitations,which may result in implicit bias as the daily precipitation data were used for estimating the rainfall erosivity.The mean annual rainfall erosivity in the TP was 528 MJ mm·ha^(-1)·h^(-1),with a broader range of 0–3402 MJ mm·ha^(-1)·h^(-1),indicating a significant spatial variability.Regions with the highest mean annual rainfall erosivity were located in the forest zones,followed by steppe and desert zones.Finally,the precipitation phase records obtained from 140 weather stations showed that snowfall events slightly impacted the accuracy of rainfall erosivity calculation,but attention should be paid to the erosion process of snowmelt in the inner part of the TP.These results can be used as the reference data for soil erosion prediction in normal precipitation years.

Effect of time resolution of rainfall measurements on the erosivity factor in the USLE in China

Rainfall erosivity,one of the factors in the Universal Soil Loss Equation.quantifies the effect of rainfall and runoffon soil erosion.High-resolution data are required to compute rainfall erosivity,but are not widely available in many parts of the world.As the temporal resolution of rainfall measurement decreases,computed rainfall erosivity decreases.The objective of the paper is to derive a series of conversion factors as a function of the time interval to compute rainfall erosivity so that the R factor computed using data at different time intervals could be converted to that computed using 1-min data.Rainfall data at 1-min intervals from 62 stations over China were collected to first compute the~ue'R factor values.Underestimation of the R factor was systematically evaluated using data aggregated at 5,6.10,15,20,30,and 60-min to develop conversion factors for the R factor and the 1-in-10-year storm EI30 values.Compared with true values,the relative error in R factor using data at fixed intervals of≤10min was<10%for at least 44 out of 62 stations.Errors increased rapidly when the time interval of the rainfall data exceeded 15 min.Relative errors were>10%using 15-min data for 66.1%of stations and>20%using 30-min data for 61.3%of stations.The conversion factors for the R factor,ranging from 1.051 to 1.871 for 5 to 60-min data,are higher than those for the 1-in-10-years storm EI30,ranging from 1.034 to 1.489 for the 62stations.

Change in rainfall erosivity in the past and future due to climate change in the central part of India

Temporal change in rainfall erosivity varies due to the rainfall characteristic(amount,intensity,frequency,duration),which affects the conservation of soil and water.This study illustrates the variation of rainfall erosivity due to changing rainfall in the past and the future.The projected rainfall is generated by SDSM(Statistical DownScaling Model)after calibration and validation using two GCMs(general circulation model)data of HadCM3(A2 and B2 scenario)and CGCM3(A1B and A2 scenario).The selected study area is mainly a cultivable area with an agricultural based economy.This economy depends on rainfall and is located in a part of the Narmada river basin in central India.Nine rainfall locations are selected that are distributed throughout the study area and surrounding.The results indicate gradually increasing projected rainfall while the past rainfall has shown a declined pattern by Mann–Kendall test with statistical 95%confidence level.Rainfall erosivity has increased due to the projected increase in the future rainfall(2080 s)in comparison to the past.Rainfall erosivity varies from
32.91%to 24.12%in the 2020s,
18.82 to 75.48%in 2050 s and 20.95–202.40%in 2080s.The outputs of this paper can be helpful for the decision makers to manage the soil water conservation in this study area.

Natural disaster in the mountainous region of Rio de Janeiro state,Brazil:Assessment of the daily rainfall erosivity as an early warning index

Rainfall erosivity is defined as the potential of rain to cause erosion.It has great potential for application in studies related to natural disasters,in addition to water erosion.The objectives of this study were:ⅰ)to model the Rday using a seasonal model for the Mountainous Region of the State of Rio de Janeiro(MRRJ);ⅱ)to adjust thresholds of the Rday index based on catastrophic events which occurred in the last two decades;andⅲ)to map the maximum daily rainfall erosivity(Rmaxday)to assess the region's suscepti-bility to rainfall hazards according to the established Rday limits.The fitted Rday model presented a satisfactory result,thereby enabling its application as a Rday estimate in MRRJ.Events that resulted in Rday>1500 MJ ha-1.mm.h-1.day-1 were those with the highest number of fatalities.The spatial distribution of Rmaxday showed that the entire MRRJ has presented values that can cause major rainfall.The Rday index proved to be a promising indicator of rainfall disasters,which is more effective than those normally used that are only based on quantity(mm)and/or intensity(mm.h-1)of the rain.

Long-term trends of precipitation and erosivity over Northeast China during 1961-2020

Northeast China(NEC)is one of the vital commercial grain bases in China and it has suffered from soil erosion due to prolonged cultivation and lack of protection.To determine long-term trends of precipi-tation and rainfall erosivity over NEC during the latest decades,daily precipitation for the entire year during 1961-2020 and hourly precipitation for the warm season(May to September)during 1971-2020 were collected for 192 and 126 stations,respectively.Three seasons,including the cold season(October to April),early warm season(May to June),and late warm season(July to September)were divided according to the combination of precipitation and vegetation.Results demonstrate:(1)Daily precipita-tion reveals total precipitation and rainfall erosivity in the cold season and early warm season increase significantly at relative rates of 3.1%-6.1%compared with the average during 1961-2020,and those in the late warm season decrease insignificantly.(2)Hourly precipitation reveals storms occurring in the early and late warm seasons have undergone significant increasing changes,which shift towards longer storm duration,larger amount,peak intensity,kinetic energy,and rainfall erosivity during 1971-2020.Moreover,the frequency of extreme storms increased.(3)Rainfall erosivities estimated from daily pre-cipitation during 1971-2020 increase insignificantly for the early and late warm season,whereas those from hourly precipitation increase significantly(6.1%and 5.5%,respectively),which indicates daily precipitation may not be able to capture the trend fully under the warming background,and precipi-tation at higher resolutions than the daily scale is necessary to detect trends of rainfall erosivity more accurately.