Evaluation of soil erosion vulnerability in Hubei Province of China using RUSLE model and combination weighting method

Soil erosion has been recognized as a critical environmental issue worldwide.While previous studies have primarily focused on watershed-scale soil erosion vulnerability from a natural factor perspective,there is a notable gap in understanding the intricate interplay between natural and socio-economic factors,especially in the context of spatial heterogeneity and nonlinear impacts of human-land interactions.To address this,our study evaluates the soil erosion vulnerability at a provincial scale,taking Hubei Province as a case study to explore the combined effects of natural and socio-economic factors.We developed an evaluation index system based on 15 indicators of soil erosion vulnerability:exposure,sensitivity,and adaptability.In addition,the combination weighting method was applied to determine index weights,and the spatial interaction was analyzed using spatial autocorrelation,geographical temporally weighted regression and geographical detector.The results showed an overall decreasing soil erosion intensity in Hubei Province during 2000 and 2020.The soil erosion vulnerability increased before 2000 and then.The areas with high soil erosion vulnerability were mainly confined in the central and southern regions of Hubei Province(Xiantao,Tianmen,Qianjiang and Ezhou)with obvious spatial aggregation that intensified over time.Natural factors(habitat quality index)had negative impacts on soil erosion vulnerability,whereas socio-economic factors(population density)showed substantial spatial variability in their influences.There was a positive correlation between soil erosion vulnerability and erosion intensity,with the correlation coefficients ranging from-0.41 and 0.93.The increase of slope was found to enhance the positive correlation between soil erosion vulnerability and intensity.

Soil erosion susceptibility mapping of Hangu Region,Kohat Plateau of Pakistan using GIS and RS-based models

Soil erosion is a crucial geo-environmental hazard worldwide that affects water quality and agriculture,decreases reservoir storage capacity due to sedimentation,and increases the danger of flooding and landslides.Thus,this study uses geospatial modeling to produce soil erosion susceptibility maps(SESM)for the Hangu region,Khyber Pakhtunkhwa(KPK),Pakistan.The Hangu region,located in the Kohat Plateau of KPK,Pakistan,is particularly susceptible to soil erosion due to its unique geomorphological and climatic characteristics.Moreover,the Hangu region is characterized by a combination of steep slopes,variable rainfall patterns,diverse land use,and distinct soil types,all of which contribute to the complexity and severity of soil erosion processes.These factors necessitate a detailed and region-specific study to develop effective soil conservation strategies.In this research,we detected and mapped 1013 soil erosion points and prepared 12 predisposing factors(elevation,aspect,slope,Normalized Differentiate Vegetation Index(NDVI),drainage network,curvature,Land Use Land Cover(LULC),rainfall,lithology,contour,soil texture,and road network)of soil erosion using GIS platform.Additionally,GIS-based statistical models like the weight of evidence(WOE)and frequency ratio(FR)were applied to produce the SESM for the study area.The SESM was reclassified into four classes,i.e.,low,medium,high,and very high zone.The results of WOE for SESM show that 16.39%,33.02%,29.27%,and 21.30%of areas are covered by low,medium,high,and very high zones,respectively.In contrast,the FR results revealed that 16.50%,24.33%,35.55%,and 23.59%of the areas are occupied by low,medium,high,and very high classes.Furthermore,the reliability of applied models was evaluated using the Area Under Curve(AUC)technique.The validation results utilizing the area under curve showed that the success rate curve(SRC)and predicted rate curve(PRC)for WOE are 82%and 86%,respectively,while SRC and PRC for FR are 85%and 96%,respectively.The validation results revealed that the FR model performance is better and more reliable than the WOE.

Estimation and mapping of water erosion and soil loss:Application of Gavrilovic erosion potential model(EPM)using GIS and remote sensing in the Assif el mal Watershed,Western high Atlas

Water erosion is a serious problem that leads to soil degradation,loss,and the destruction of structures.Assessing the risk of erosion and determining the affected areas has become crucial in order to understand the main factors influencing its evolution and to minimize its impacts.This study focuses on evaluating the risk of erosion in the Assif el mal watershed,which is located in the High Atlas Mountains.The Erosion Potential Model(EPM)is used to estimate soil losses depending on various parameters such as lithology,hydrology,topography,and morphometry.Geographic information systems and remote sensing techniques are employed to map areas with high erosive potential and their relationship with the distribution of factors involved.Different digital elevation models are also used in this study to highlight the impact of data quality on the accuracy of the results.The findings reveal that approximately 59%of the total area in the Assif el mal basin has low to very low potential for soil losses,while 22%is moderately affected and 19.9%is at high to very high risk.It is therefore crucial to implement soil conservation measures to mitigate and prevent erosion risks.

Combining RUSLE model and the vegetation health index to unravel the relationship between soil erosion and droughts in southeastern Tunisia

Droughts and soil erosion are among the most prominent climatic driven hazards in drylands,leading to detrimental environmental impacts,such as degraded lands,deteriorated ecosystem services and biodiversity,and increased greenhouse gas emissions.In response to the current lack of studies combining drought conditions and soil erosion processes,in this study,we developed a comprehensive Geographic Information System(GIS)-based approach to assess soil erosion and droughts,thereby revealing the relationship between soil erosion and droughts under an arid climate.The vegetation condition index(VCI)and temperature condition index(TCI)derived respectively from the enhanced vegetation index(EVI)MOD13A2 and land surface temperature(LST)MOD11A2 products were combined to generate the vegetation health index(VHI).The VHI has been conceived as an efficient tool to monitor droughts in the Negueb watershed,southeastern Tunisia.The revised universal soil loss equation(RUSLE)model was applied to quantitatively estimate soil erosion.The relationship between soil erosion and droughts was investigated through Pearson correlation.Results exhibited that the Negueb watershed experienced recurrent mild to extreme drought during 2000–2016.The average soil erosion rate was determined to be 1.8 t/(hm2•a).The mountainous western part of the watershed was the most vulnerable not only to soil erosion but also to droughts.The slope length and steepness factor was shown to be the most significant controlling parameter driving soil erosion.The relationship between droughts and soil erosion had a positive correlation(r=0.3);however,the correlation was highly varied spatially across the watershed.Drought was linked to soil erosion in the Negueb watershed.The current study provides insight for natural disaster risk assessment,land managers,and stake-holders to apply appropriate management measures to promote sustainable development goals in fragile environments.

Assessment of soil erosion by RUSLE model using remote sensing and GIS-A case study of Nethravathi Basin

Soil erosion is a serious problem arising from agricultural intensification, land degradation and other anthropogenic activities. Assessment of soil erosion is useful in planning and conservation works in a watershed or basin. Modelling can provide a quantitative and consistent approach to estimate soil erosion and sediment yield under a wide range of conditions. In the present study, the soil loss model, Revised Universal Soil Loss Equation （RUSLE） integrated with GIS has been used to estimate soil loss in the Nethravathi Basin located in the southwestern part of India. The Nethravathi Basin is a tropical coastal humid area having a drainage area of 3128 km2 up to the gauging station. The parameters of RUSLE model were estimated using remote sensing data and the erosion probability zones were determined using GIS. The estimated rainfall erosivity, soil erodibility, topographic and crop management factors range from 2948.16 to 4711.4 MJ/mm.ha-1hr-1/year, 0.10 to 0.44 t ha-1 -MJ-1.mm 1, 0 to 92,774 and 0 to 0.63 respectively. The results indicate that the estimated total annual potential soil loss of about 473,339 t/yr is comparable with the measured sediment of 441,870 t/yr during the water year 2002 2003. The predicted soil erosion rate due to increase in agricultural area is about 14,673.5 t/yr. The probability zone map has been derived by the weighted overlay index method indicate that the major portion of the study area comes under low probability zone and only a small portion comes under high and very high probability zone. The results can certainly aid in implementation of soil management and conservation practices to reduce the soil erosion in the Nethravathi Basin.

Analysing the relationship between drought and soil erosion using vegetation health index and RUSLE models in Godavari middle sub-basin,India

Drought is a natural phenomenon posing severe implications for soil,groundwater and agricultural yield.It has been recognized as one of the most pervasive global change drivers to affect the soil.Soil being a weakly renewable resource takes a long time to form,but it takes no time to degrade.However,the response of soil to drought conditions as soil loss is not manifested in the existing literature.Thus,this study makes a concerted effort to analyze the relationship between drought conditions and soil erosion in the middle sub-basin of the Godavari River in India.MODIS remote sensing data was utilized for driving drought indices during 2000-2019.Firstly,we constricted Temperature condition index(TCI)and Vegetation Condition Index(VCI)from Land Surface Temperature(LST)and Enhanced Vegetation Index(EVI)derived from MODIS data.TCI and VCI were then integrated to determine the Vegetation Health Index(VHI).Revised Universal Soil Loss Equation(RUSLE)was utilized for estimating soil loss.The relationship between drought condition and vegetation was ascertained using the Pearson correlation.Most of the northern and southern watersheds experienced severe drought condition in the sub-basin during2000-2019.The mean frequency of the drought occurrence was 7.95 months.The average soil erosion in the sub-basin was estimated to be 9.88 t ha^(-1)year^(-1).A positive relationship was observed between drought indices and soil erosion values(r value being 0.35).However,wide variations were observed in the distribution of spatial correlation.Among various factors,the slope length and steepness were found to be the main drivers of soil erosion in the sub-basin.Thus,the study calls for policy measures to lessen the impact of drought and soil erosion.

Study on the Topographic Effect on Soil Erosion Using RUSLE Model for Small Size Watershed

Soil erosion and subsequent sedimentation have caused serious environmental and soil degradation problems in Okinawa Prefecture,Japan.This research aims at evaluating an availability of the Revised Universal Soil loss Equation（RUSLE） for predicting the range of soil loss values for the Nago watershed in Okinawa.It shows that climatic conditions substantially influence the rainfall amount as a function of the I30 of the rainfall event.The rate of soil loss is higher with increasing in altitude due to greater slope steepness.By rainfall data analysis,it is concluded that the large difference in soil loss between 2000 and 2001 was due to concentrated heavy rainfall in the rainy season or the typhoon season.

基于RUSLE模型的京津冀地区土壤侵蚀时空变化分析

【目的】土壤侵蚀是一种日益严重的生态问题,已经成为全球性的环境挑战,对人类的生存和可持续发展产生了极大的威胁。因此基于多源遥感数据,【方法】通过RUSLE模型和地理探测器模型研究了1990—2020年京津冀地区土壤侵蚀在时空尺度上的动态变化,并对其驱动因素进行了分析。【结果】结果显示:(1) 1990—2020年京津冀地区的土壤侵蚀总体呈现东南低西北高的分布特征,且以微度和轻度侵蚀为主,其百分比达到73.79%。(2)京津冀大部分地区土壤侵蚀等级主要由高向微强度侵蚀转移,且转移比例都在50%以上,整体有所好转,但存在局部加剧。(3)各个影响因子对土壤侵蚀的解释力依次为:坡度>高程>土地覆盖类型>植被覆盖度>降雨量>人口密度>GDP。【结论】相关结论可为水土流失治理以及防治提供一定的科学参考,同时也可为其他地区的土壤侵蚀治理提供参考。

Study on Soil Erosion Model Under Different Slopes in Southwest Karst Mountain Area

The aim was to further research soil erosion characteristics and accurately predict soil erosion amount in karst areas. Based on field surveys and research achievements available, yellow soils, which are widely distributed, were chosen as test soil samples and slope, rain intensity, vegetation coverage and bare-rock ratio were taken as soil erosion factors. Artificial rain simulation instruments （needle-type） were made use of to simulate correlation of rain intensity, vegetation coverage, and bare-rock ratio with soil erosion quantity. Furthermore, multiple-factor linear regression analysis, stepwise regression analysis and multiple-factor non-linear regression analy- sis were made to establish a multiple-factor formula of soil erosion modulus with dif- ferent slopes and select regression models with high correlation coefficients. The re- sults show that a non-linear regression model reached extremely significant level or significant level （0.692〈FF〈0.988） and linear regression model achieved significant lev- el （0.523〈FF〈0.634）. The effects of erosion modulus changed from decreasing to in- creasing and the erosion factors from high to low were rain intensity, vegetation cov- erage and bare-rock ratio when slope gradient was at 6~, 16~, 26~ and 36~. The mod- el is of high accuracy for predicting gentle slope and abtupt slope, which reveals correlation of erosion modulus with erosion factors in karst areas.

基于RUSLE模型的青藏高原土壤保持功能定量评价

土壤保持功能是青藏高原生态系统的主要调节功能之一,准确评估青藏高原土壤保持功能的时空变化规律,是确保该地区顺利开展水土保持和生态环境治理工作的前提。本研究通过收集气象、土壤、土地利用、DEM和NDVI等数据,利用RUSLE模型对1982—2020年青藏高原土壤保持功能的时空特征进行动态评估。结果表明:1982—2020年青藏高原的土壤保持量呈波动增加趋势,土壤保持能力由南向北逐渐减弱,高值区主要集中在青藏高原东南部的川西和藏东的高山深谷;在过去的近40 a中,青藏高原土壤侵蚀强度发生明显转换,其主要特征是由高一级的中度侵蚀强度向低一级的轻度或微度侵蚀强度转换,说明青藏高原近40 a内土壤保持状况不断改善;不同地形条件下青藏高原土壤保持能力也有明显差异,主要表现为起伏度小的高海拔地区土壤保持能力普遍较弱;就不同的土地利用类型而言,林地区域的土壤保持能力最强,而未利用地土壤保持能力最弱。近40 a来,青藏高原土壤保持能力不断增强,但仍存在部分区域的土壤保持能力较弱。未来在重视和保护土壤保持能力较强的林地区域的同时,应加强起伏度小的高海拔地区水土治理工作,制定分级分区的土壤侵蚀防治措施,进一步增强青藏高原地区的土壤保持功能。

基于RUSLE模型和地理探测器的鄂西南土壤侵蚀脆弱性评价

[目的]水土流失是鄂西南突出的生态问题,合理评估该区域的土壤侵蚀脆弱性,并探究其驱动机制是采取有效水土保持措施的前提。[方法]从人地耦合系统视角分析自然和社会经济因素对鄂西南土壤侵蚀的影响,从暴露度、敏感性和适应能力3个方面构建了鄂西南土壤侵蚀脆弱性评价指标体系,并采用综合赋权法确定指标权重。[结果](1)2010—2020年鄂西南土壤侵蚀脆弱性总体上呈现先降低后升高的趋势,强烈及以上强度的土壤侵蚀脆弱性呈现零星的碎片化分布格局,且主要集中在鄂西南的中南部和东部宜昌市辖区的西部地区;(2)研究区敏感性最高的区域集中在海拔800~1500 m,敏感性在坡度25°~35°最大,并呈现出向两侧递减的趋势,当坡度>15°时,较高及以上敏感性面积急剧增加;(3)较高及以上土壤侵蚀脆弱性高于土壤侵蚀强度,较低及以下土壤侵蚀脆弱性低于土壤侵蚀强度,土壤侵蚀脆弱性与土壤侵蚀强度存在协同变化趋势;(4)土壤侵蚀脆弱性的分布格局是多因素协同作用造成的,地理探测器的分析表明坡度、植被覆盖度、教育质量和城镇化率对土壤侵蚀脆弱性的解释力较强。[结论]未来需要高度重视对坡度>15°地区植被覆盖的保护,并通过提高当地的教育质量,加强水土保持的宣传力度,提升当地居民的水土保持意识。

RUSLE模型对黄土高原退耕植被恢复坡面土壤侵蚀的模拟效果分析

[目的]为了探究RUSLE模型对黄土高原退耕植被恢复坡面土壤侵蚀模拟的效果。[方法]基于陕北安塞区坊塌小流域内10个径流小区2016-2022年的降雨产流产沙监测资料,通过RUSLE模型中各因子在黄土高原的不同常用算法之间的变换组合,模拟144种因子组合下各退耕植被恢复坡面的土壤侵蚀量,采用纳什效率系数(NSE)和均方根误差(RMSE)评价模型模拟结果的有效性。[结果]利用RUSLE模型144种因子组合模拟的退耕植被恢复坡面土壤侵蚀量,NSE范围为-38.47~0.19,RMSE范围为1.92~12.65 t/(hm 2·a),模拟效果较差,所选各因子算法难以适应退耕植被恢复坡面上的土壤流失量的评估,还需要对RUSLE模型各因子进一步改进。运用RUSLE模型对黄土高原退耕植被恢复坡面土壤侵蚀模拟时,建议尽可能采用时间分辨率高的数据减小对R因子的计算误差,综合考虑土壤有机质含量、土壤粒径与团粒结构组成和容重等土壤理化性质对K因子的影响,以及植被覆盖度、植被高度、枯落物、生物结皮等对C因子的影响,充分考虑10°以上的坡度,细化其LS因子的算法。[结论]RUSLE模型无法很好地适用于黄土高原退耕植被恢复坡面土壤侵蚀的模拟。研究结果为土壤侵蚀模型的研发及RUSLE模型在黄土高原植被恢复坡面的应用提供一定参考依据。

基于RUSLE模型的雅鲁藏布江流域土壤侵蚀评价

[目的]研究雅鲁藏布江流域土壤侵蚀时空变化特征,并分析气候和植被覆盖变化对土壤侵蚀的影响,以期为高寒区土壤侵蚀防治、生态系统保护和水土资源开发利用提供理论支撑。[方法]以雅鲁藏布江流域为研究区,采用RUSLE模型定量评估了1980—2017年流域土壤侵蚀的时空变化特征。[结果]1980—2017年,雅江流域土壤侵蚀强度整体呈现先减小后增加的趋势,1980—1999年年均土壤侵蚀模数波动下降,2000—2017年年均土壤侵蚀模数则呈现不显著上升趋势;流域中上游地区土壤侵蚀变化较为显著,下游地区侵蚀强度先增加后减小。年均土壤侵蚀模数与降雨侵蚀力呈显著正相关关系,Pearson相关系数为0.92,而与NDVI关系不显著。不同土地利用类型中,土壤侵蚀最强烈的是未利用地,其次是稀疏草地,由于其面积占比最高,对流域总侵蚀量的贡献比超过54%。[结论]降雨是影响雅江流域土壤侵蚀强度变化的主要因素,未来土壤侵蚀防治的重点区域应为流域东部下游降雨量较大的地区,重点防范极端降雨造成的水土流失。

Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala,India,using Revised Universal Soil Loss Equation(RUSLE) and geo-information technology

A comprehensive methodology that integrates Revised Universal Soil Loss Equation （RUSLE） model and Geographic Information System （GIS） techniques was adopted to determine the soil erosion vulner- ability of a forested mountainous sub-watershed in Kerala, India. The spatial pattern of annual soil erosion rate was obtained by integrating geo-environmental variables in a raster based GIS method. GIS data layers including, rainfall erosivity （R）, soil erodability （K）, slope length and steepness （LS）, cover management （C） and conservation practice （P） factors were computed to determine their effects on average annual soil loss in the area. The resultant map of annual soil erosion shows a maximum soil loss of 17.73 t h-1 y i with a close relation to grass land areas, degraded forests and deciduous forests on the steep side-slopes （with high LS ）. The spatial erosion maps generated with RUSLE method and GIS can serve as effective inputs in deriving strategies for land planning and management in the environmentally sensitive mountainous areas.

Modelling and mapping soil erosion potential in China

Soil erosion is an important environmental threat in China.However,quantitative estimates of soil erosion in China have rarely been reported in the literature.In this study,soil loss potential in China was estimated by integrating satellite images,field samples,and ground observations based on the Revised Universal Soil Loss Equation(RUSLE).The rainfall erosivity factor was estimated from merged rainfall data using Collocated CoKriging(ColCOK)and downscaled by geographically weighted regression(GWR).The Random Forest(RF)regression approach was used as a tool for understanding and predicting the relationship between the soil erodibility factor and a set of environment factors.Our results show that the average erosion rate in China is 1.44 t ha^(–1) yr^(–1).More than 60%of the territory in China is influenced by soil erosion limitedly,with an average potential erosion rate less than 0.1 t ha^(–1) yr^(–1).Other unused land and other forested woodlands showed the highest erosion risk.Our estimates are comparable to those of runoff plot studies.Our results provide a useful tool for soil loss assessments and ecological environment protections.

Assessment for soil loss by using a scheme of alterative sub-models based on the RUSLE in a Karst Basin of Southwest China

Accurate assessment of soil loss caused by rainfall is essential for natural and agricultural resources management. Soil erosion directly affects the environment and human sustainability. In this work, the empirical and contemporary model of revised universal soil loss equation （RUSLE） was applied for simulating the soil erosion rate in a karst catchment using remote sensing data and geographical information systems. A scheme of alterative sub-models was adopted to calculate the rainfall erosivity （R）, soil erodibility （K）, slope length and steepness （LS）, cover management （C） and conservation practice （P） factors in the geographic information system （GIS） environment. A map showing the potential of soil erosion rate was produced by the RUSLE and it indicated the severe soil erosion in the study area. Six classes of erosion rate are distinguished from the map： 1） minimal, 2） low, 3） medium, 4） high, 5） very high, and 6） extremely high. The RUSLE gave a mean annual erosion rate of 30.24 Mg ha-1 yr-1 from the 1980s to 2000s. The mean annual erosion rate obtained using RUSLE is consistent with the result of previous research based on in situ measurement from 1980 to 2009. The high performance of the RUSLE model indicates the reliability of the sub-models and possibility of applying the RUSLE on quantitative estimation. The result of the RUSLE model is sensitive to the slope steepness, slope length, vegetation factors and digital elevation model （DEM） resolution. The study suggests that attention should be given to the topographic factors and DEM resolution when applying the RUSLE on quantitative estimation of soil loss.

Assessment of soil erosion in a tropical mountain river basin of the southern Western Ghats,India using RUSLE and GIS

Revised Universal Soil Loss Equation(RUSLE) model coupled with transport limited sediment delivery(TLSD) function was used to predict the longtime average annual soil loss, and to identify the critical erosion-/deposition-prone areas in a tropical mountain river basin, viz., Muthirapuzha River Basin(MRB; area=271.75 km^2), in the southern Western Ghats, India. Mean gross soil erosion in MRB is 14.36 t ha^(-1) yr^(-1), whereas mean net soil erosion(i.e., gross erosion-deposition) is only 3.60 t ha^(-1) yr^(-1)(i.e., roughly 25% of the gross erosion). Majority of the basin area(~86%) experiences only slight erosion(<5 t ha^(-1) yr^(-1)), and nearly 3% of the area functions as depositional environment for the eroded sediments(e.g., the terraces of stream reaches, the gentle plains as well as the foot slopes of the plateau scarps and the terrain with concordant summits). Although mean gross soil erosion rates in the natural vegetation belts are relatively higher, compared to agriculture, settlement/built-up areas and tea plantation, the sediment transport efficiency in agricultural areas and tea plantation is significantly high,reflecting the role of human activities on accelerated soil erosion. In MRB, on a mean basis, 0.42 t of soil organic carbon(SOC) content is being eroded per hectare annually, and SOC loss from the 4th order subbasins shows considerable differences, mainly due to the spatial variability in the gross soil erosion rates among the sub-basins. The quantitative results, on soil erosion and deposition, modelled using RUSLE and TLSD, are expected to be beneficial while formulating comprehensive land management strategies for reducing the extent of soil degradation in tropical mountain river basins.

Spatial Estimation of Soil Erosion Risk Using RUSLE Approach, RS, and GIS Techniques: A Case Study of Kufranja Watershed, Northern Jordan

Wadi Kufranja catchment (126.3 km2), northern Jordan, was selected to estimate annual soil loss using the Revised Universal Soil Loss Equation (RUSLE), remote sensing (RS), and geographic information system (GIS). RUSLE factors (R, K, LS, C and P) were computed and presented by raster layers in a GIS environment, then multiplied together to predict soil erosion rates, and to generate soil erosion risk categories and soil erosion severity maps. The estimated potential average annual soil loss is 10 ton·ha-1·year-1 for the catchment, and the potential erosion rates from recognized erosion classes ranged from 0.0 to 1850 ton·ha-1·year-1. About 42.1% (5317.23 ha) of the catchment area was predicted to have moderate risk of erosion, with soil loss between 5 - 25 ton·ha-1·year-1. Risk of erosion is severe to extreme over 31.2% (3940.56 ha) of the catchment, where calculated soil loss is 25 - 50 and >50 ton·ha-1·year-1. Apart from the gentle slopes of the alluvial fan (Krayma town and surroundings), the lower and the middle reaches of the watershed suffer from severe to extreme erosion risk. High terrain, slope steepness, removal of vegetation, and poor conservation practices are the most prominent causes of soil erosion.

基于修正通用土壤流失方程(RUSLE)的蒙山区域水土流失敏感性评价

水土流失敏感性评价作为区域生态质量评价的重要内容,对判定水土流失程度、检验水土保持成效起着重要作用。本文基于修正的通用土壤流失方程(RUSLE),对降雨侵蚀力(R)、土壤可蚀性(K)、地形起伏度(LS)、植被覆盖(C)及水土保持措施(P)5个评价因子进行分级赋值并根据通用土壤流失方程进行计算。研究表明:1)蒙山区域的水土流失敏感性整体呈中部和南部高、东部和北部低的条带状空间分布特征;2)蒙山区域水土流失潜在危险程度的值较低,整体的水土流失敏感性中度偏轻。本文根据量化结果进行分区,得出蒙山区域水土流失防治重点,对推动蒙山区域可持续发展及促进区域生态修复均具有重要意义。

Quantitative Model for Estimating Soil Erosion Rates Using 137 Cs *1

A quantitative model was developed to relate the amount of 137 Cs loss from the soil profile to the rate of soil erosion. According to mass balance model, the depth distribution pattern of 137 Cs in the soil profile, the radioactive decay of 137 Cs, sampling year and the difference of 137 Cs fallout amount among years were taken into consideration. By introducing typical depth distribution functions of 137 Cs into the model, detailed equations for the model were got for different soils. The model shows that the rate of soil erosion is mainly controlled by the depth distribution pattern of 137 Cs, the year of sampling, and the percentage reduction in total 137 Cs. The relationship between the rate of soil loss and 137 Cs depletion is neither linear nor logarithmic. The depth distribution pattern of 137 Cs is a major factor for estimating the rate of soil loss. Soil erosion rate is directly related with the fraction of 137 Cs content near the soil surface. The influences of the radioactive decay of 137 Cs, sampling year and 137 Cs input fraction are not large compared with others.