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 (12.10×10³ km²) was larger than the area with increased soil erosion caused by the C factor (8.30×10³ km²), 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.     

海南省东方市60年来降雨量及降雨侵蚀力变化趋势

[目的]分析海南省东方市降雨量、侵蚀性降雨量及降雨侵蚀力在不同时间尺度上的趋势变化及其相关性,为该区生态环境建设、水土流失治理及土壤侵蚀机理研究提供科学支持。[方法]根据该地区1956-2015年60a的逐日降雨量数据资料,采用变异系数、趋势系数和气候趋势率分析不同时间尺度的降雨、侵蚀性降雨和降雨侵蚀力的变化趋势。[结果](1)东方市1956—2015年60a来年均降雨量、年均侵蚀性降雨量、年均降雨侵蚀力分别为982.9±36.9mm,816.1±37.6mm和9 441.7±554.2 MJ·mm/(hm~2·h·a),变异系数分别为29.1%,35.7%,45.5%。(2)60a来降雨量、侵蚀性降雨量及降雨侵蚀力年际变化均呈一定的增加趋势,趋势系数分别为0.129,0.156,0.198。季、月尺度上变化差异较大,但总体变化格局相似,均呈单峰型分布。(3)降雨量、侵蚀性降雨量及降雨侵蚀力两两之间有极强的线性相关性,且乘幂方程较线性回归方程能更好的反映两两之间的关系。[结论]60a来降雨量、侵蚀性降雨量、降雨侵蚀力均呈现较明显的年际增加趋势,且两两之间呈幂函数关系。     

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 runoff on 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 ‘true’ 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 62 stations.     

基于GIS的罗玉沟流域降雨侵蚀力时空分布规律研究

降雨侵蚀力是建立通用土壤流失方程USLE及RUSLE的6个最基本因子之一,它反映了降雨引起土壤侵蚀的潜在能力.罗玉沟流域是黄土高原丘陵沟壑区第三副区的典型小流域,此区域水土流失极为严重.采用章文波修正的Richardson日降雨侵蚀力模型,处理该流域15年日降雨资料,估算降雨侵蚀力.通过分析得出:(1)该流域年均降雨侵...     

基于修正简易模型的陕北黄土丘陵沟壑区降雨侵蚀力分布特征

降雨侵蚀力经典模型计算结果准确,但计算过程繁琐、数据量大且难获取;简易模型计算便捷,但结果不够准确.本文分析了8种黄土丘陵沟壑区降雨侵蚀力模型的差异,并对简易模型进行修正.以经典模型为基准值,对与经典模型结果最为接近的简易模型进行修正,基于修正后的简易模型分析黄土丘陵沟壑区降雨侵蚀力的时空分布特征.在此过程中用到的方法主要是数理统计法和模型差异分析方法.经典模型更能准确估算陕北黄土丘陵沟壑区降雨侵蚀力;拟合模型y=0.849x-29.651可以提高章文波降雨侵蚀力简易模型的模拟精度(拟合优度0.734);陕北黄土丘陵沟壑区2006-2012年间降雨侵蚀力总体呈现上升趋势;汾川河流域、清涧河流域上游降雨侵蚀力较高,下游次之;延河流域、大理河流域下游降雨侵蚀力较高,上游次之.降雨侵蚀力简易算法经修正后可以较好的估算黄土丘陵沟壑区的降雨侵蚀力的时空分布特征,陕北黄土丘陵沟壑区2006-2012年间降雨侵蚀力时空分布不均,降雨侵蚀力整体较高.     

降雨侵蚀力新算法在豫西山区应用的初步研究

降雨侵蚀力是定量监测评价一个地区土壤侵蚀状况的重要因子之一,找到适宜简便的计算方法十分重要.卜兆宏提出的降雨侵蚀力新算法具有简便易用的优点.本文利用位于豫西山区鲁山县的3个水文雨量站10年185次的自记降雨过程资料,以经典算法年R值为基准,对新算法在该区的适用性进行了分析评价.结果表明:新算法结果与经典值存在高度的一致性,一致性高达90.2%,模型有效系数为96.4%,相对误差为7.7%,说明新算法能够在该区应用,可以采取此法对该区的降雨侵蚀力进行深入的分析研究;并同时对资料摘读时应该注意的问题进行了讨论.     

国内外降雨侵蚀力简易计算方法的比较

降雨是引起土壤侵蚀的主要动力 ,用 EI30 指标度量。计算 EI30 时 ,需要降雨过程资料 ,而且计算繁琐和费时 ,为此 ,很多学者采用常规降雨资料计算降雨侵蚀力的简易算法。它们往往利用区域资料经验拟合求得 ,如要推广使用 ,需要进行验证。利用我国 10个气象站降雨过程和常规降雨资料 ,通过对国内外 9种比较有代表性的多年平均年降雨侵蚀力简易计算方法的比较 ,建议采用年雨量的指数函数形式作为估算我国多年平均年降雨侵蚀力的简易计算方法。为保证计算结果的区域稳定性 ,应分不同区域进行拟合。     

降雨特性和土壤结构对溅蚀的影响

选用黄土高原地区的安塞黄绵土、绥德黄绵土、杨陵粘黄土、杨陵农地耕层土进行人工降雨溅蚀试验 ,研究了降雨特性和土壤结构对雨滴溅蚀的影响。结果表明 :土壤溅蚀量与降雨强度相关关系的最佳函数为指数函数 ;将降雨动能与雨滴中数直径的乘积 ( Ed50 )定义为降雨溅蚀力 ,降雨溅蚀力与溅蚀量呈线性相关关系。降雨溅蚀力是降雨潜在溅蚀能力的反映 ,对溅蚀降雨侵蚀力因子的研究有一定参考价值 ;溅蚀总量随降雨历时的增加而增加 ,而溅蚀率则随降雨历时的增加而减小 ,其变化过程可用幂函数描述 ;原状土的溅蚀量仅为其扰动土溅蚀量的2 2 %～ 3 0 % ,随降雨强度增大 ,雨滴打击力对土壤结构的破坏作用增强 ,使原状土与扰动土溅蚀量间的差异缩小。     

Estimation of rainfall erosivity using 5- to 60-minute fixed-interval rainfall data from China

The 30-min rainfall erosivity index (EI₃₀) is commonly used in the Universal Soil Loss Equation for predicting soil loss from agricultural hillslopes. EI₃₀ is calculated from the total kinetic energy and the maximum 30-min rainfall intensity of a storm. Normally, EI₃₀ values are calculated from breakpoint rainfall information taken from continuous recording rain gauge charts, however, in many places in China and other parts of the world the detailed chart-recorded rain gauge data relative to storm intensities are not readily available, while hourly rainfall is readily available. The objective of this study was to assess the accuracy of EI₃₀ estimations based on 5-, 10-, 15-, 30-, and 60-min time-resolution rainfall data as compared to EI₃₀ estimations from breakpoint rainfall information. 456 storm events from five soil conservation stations in eastern China were used. The values of EI₃₀ based on the fixed-time-interval data were less than those calculated from breakpoint data. The average conversion factors (ratio of values calculated from the breakpoint data to those from the fixed-interval data) for the five stations decreased from 1.105 to 1.009 for the estimation of E values, from 1.668 to 1.007 for I₃₀ values, and from 1.730 to 1.014 for EI₃₀ values as the time resolution increased from 60 to 5 min. The maximum 30-min rainfall intensity was the major source of error in estimating EI₃₀ for 60-min fixed-interval data, while storm kinetic energy played a proportionately more significant role as the fixed-interval data decreased from 60 to 5 min.     

北京地区降雨侵蚀力简易计算方法研究

降雨侵蚀力反映了降雨对土壤侵蚀影响的潜在能力。降雨侵蚀力经典算法所需的降雨过程资料较难获得,一般利用各种类型雨量资料建立降雨侵蚀力的简易算法,为模型的参数输入服务。利用北京10个水文站25年2 894次降雨过程资料。其中5个站点用于建立日、月、年降雨侵蚀力简易计算公式,另外五个站点用语模型检验。研究结果表明,不同类型雨量资料估算降雨侵蚀力的精度不同,用日或月雨量资料直接估算日或月降雨侵蚀力时,模型的误差较大。用日、月或年雨量估算年降雨侵蚀力时,模型的误差较小,约有一半的样本相对误差绝对值小于20%,三个模型相比,日雨量模型估算的平均相对误差最小。用日、月或年雨量估算多年平均年降雨侵蚀力时,模型的误差最小,所有样本的相对误差绝对值均小于20%,平均相对误差绝对值最小值只有0.8%,最大值也小于7%,三个模型相比,日雨量模型的估算精度最高。因此在具体应用过程中可以根据资料的占有情况来决定相应的降雨侵蚀力估算模型。本研究结果可以为北京地区土壤侵蚀量估算和水土资源评价提供参数服务。