基于USLE模型的长株潭城市群生态绿心区水土流失研究

[目的]研究2018年长株潭(长沙市—株州市—湘潭市)城市群生态绿心区最佳植被覆盖和水土流失状况,为区域综合治理提供理论依据。[方法]运用GIS和RS技术,以长株潭区域降雨、土壤、地形、土地利用等数据为基础,采用归一化植被指数(NDVI)模型和USLE国际通用土壤流失方程为优选模型。[结果]长株潭绿心区总面积为52 287 hm~2,整体植被状况较好,高覆盖度(75%～100%)面积最大,占绿心区总面积一半以上,为26 598.40 hm~2;中低覆盖度(30%～40%)面积最小,占区域总面积的8.61%,为4 501.91 hm~2。长株潭城市群生态绿心区总侵蚀(不含微度)面积为3 654.24 hm~2,占总面积的6.99%。湘潭市侵蚀比重最高,为8.51%,长沙市次之,为6.67%,株洲市侵蚀总比例最小,为5.68%。工程建设用地在禁止开发区、限制开发区和控制建设区的侵蚀面积分别为963.92,310.74,735.11 hm~2。[结论]受人为因素、城市建设、产业分布等影响,长株潭城市群生态绿心区覆盖度空间呈现西部低,中东部高的格局,工程建设是造成绿心区土壤侵蚀的主要原因。     

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和USLE的龙墩水库小流域土壤侵蚀评估研究

本研究以南京市高淳区龙墩水库流域为研究对象,利用通用土壤流失方程(USLE)模型与地理信息系统(GIS)技术结合的方法对流域土壤侵蚀进行模拟预测。结果表明:整个流域年均土壤侵蚀模数为4 343.46 t/km~2,属中度侵蚀。整个流域微度和轻度侵蚀所占面积比例相对较大,两者所占面积比例之和超过了63%,极强度和剧烈侵蚀虽然所占面积较小,但却产生了超过了70%的侵蚀量。不同土地利用类型中土壤侵蚀强度差异较大,年均侵蚀模数旱田草地水田林地,侵蚀量旱田水田草地林地。通过GIS将整个流域划分为13个子流域,子流域4、5、10由于区域内大部分是旱田,土壤侵蚀模数较大,为流域内土壤侵蚀的关键源区,子流域10侵蚀模数和侵蚀量都比较大,应该重点关注;而子流域1、9和12由于侵蚀总量较大,也应该保持一定的关注。所有子流域土壤侵蚀量都主要来自高强度侵蚀等级,其中以剧烈侵蚀为主。因此,控制土壤侵蚀应该优先考虑高强度侵蚀等级区域。     

基于RS和GIS的岷江流域退耕还林还草的初步研究

目前我国西部地区大力开展改善生态环境的退耕还林还草工程。岷江属于长江上游重要的支流之一 ,其水保工作将对整个长江流域的生态环境建设直接产生影响。退耕还林还草的科学问题在于水土保持 ,利用 RS和GIS数据以及通用水土流失模型对岷江上游进行侵蚀强度分级和填图、坡耕地的提取 ,最后统计分析坡耕地和侵蚀强度间的关系 ,确定区域内退耕还林还草的标准 ,并且对退耕还林还草后生态环境的变化和水土流失量进行了初步预测方法。     

基于GIS和USLE的密云县土壤侵蚀评价及空间特征研究

将地理信息系统与通用土壤流失方程（USLE）相结合进行密云县土壤侵蚀量的预测。在全面调查，收集研究区气候、地形、土壤等基础资料的基础上，通过Landsat—TM影像提取土地利用信息、植被覆盖度信息，并建立土壤侵蚀空间数据库。运用通用土壤侵蚀模型USLE定量计算出土壤侵蚀量，并对研究区土壤侵蚀强度进行了分级，旨在探求密云县土壤侵蚀空间分布的特点及成因，为该区域水土保持和北京市的安全供水提供科学依据。研究表明，地貌类型和土壤类型是密云县土壤侵蚀中影响较大的因子。     

Effectiveness of stone bunds in controlling soil erosion on cropland in the Tigray Highlands, northern Ethiopia

Abstract Use of stone bunds to enhance soil and water conservation was first introduced to Tigray, northern Ethiopia in 1981. This study was designed to examine the factors that control the effectiveness of bunds installed on cropland. Qualitative and quantitative assessments of soil loss and sediment accumulation were conducted on 202 plots at 12 representative sites in Dogu'a Tembien district. Mean annual soil loss from the foot of the bunds due to tillage erosion was estimated at 39kgm⁻¹yr⁻¹ or 20tha⁻¹yr⁻¹, a rate which decreased with increasing age of bund. The assessed mean annual soil loss rate by sheet and rill erosion in the absence of stone bunds is 57tha⁻¹yr⁻¹. The mean measured annual rate of sediment accumulation behind the stone bunds is 119kgm⁻¹yr^−1/sp or 59tha⁻¹yr⁻¹. The measurements show that the introduction of stone bunds to the region has led to a 68% reduction in annual soil loss due to water erosion. This reduction is due to the accumulation of sediment behind the stone bunds, which occurs faster in the early years after construction and decreases as the depression behind the bunds becomes filled with sediment. New stone bunds are particularly effective in trapping sediment in transport, but regular maintenance and increase in height of the bunds is necessary to maintain their effectiveness. The average USLE P factor for stone bunds in the study area is estimated to be 0.32.     

Interrill and rill erodibility in the northern Andean Highlands

There is a lack of quantitative information describing the physical processes causing soil erosion in the Andean Highlands, especially those related to interrill and rill erodibility factors. To assess how susceptible are soils to erosion in this region, field measurements of interrill (Ki) and rill (Kr) erodibility factors were evaluated. These values were compared against two equations used by the Water Erosion Prediction Project (WEPP), and also compared against the Universal Soil Loss Equation (USLE) erodibility factor. Ki observed in situ ranged from 1.9 to 56 × 10⁵ kg s m^− 4 whereas Kr ranged from 0.3 to 14 × 10^− 3 s m^− 1. Sand, clay, silt, very fine sand and organic matter fractions were determined in order to apply WEPP and USLE procedures. Most of the evaluated soils had low erodibility values. However, the estimated USLE K values were in the low range of erodibility values. Stepwise multiple regression analyses were applied to ascertain the influence of the independent soil parameters on the Ki and Kr values. After this, we yield two empirical equations to estimate Ki and Kr under this Andean Highlands conditions. Ki was estimated using as predictors silt and very fine sand, while Kr used as predictors clay, very fine sand and organic matter content. Relationship among Ki, Kr and K are described for the Highland Andean soils.     

USLE在我国的应用和发展

美国的通用土壤流失方程USLE是当今世界上应用最为广泛的水蚀模型。 2 0世纪 70年代中期以来 ,我国在引进、消化、应用USLE方面取得了突破性的进展 ,统一了方程中R、K、S、L、C、P的算法 ,将六大因子的计算完全基于GIS和RS ,提高了监测预报的精度和速度 ,已成功地在国内一些地区得到应用。介绍了USLE的发展过程和在我国的应用与发展     

基于地理信息系统的土壤侵蚀研究

在非点源污染研究过程中 ,土壤流失量的计算是一个重要环节。结合地理信息系统和通用土壤流失方程 ,选择地形、植被、降雨量等因子 ,对松花湖流域常山地区土壤流失量进行了估算 ,并讨论了地形、植被等因子在土壤侵蚀中的影响 ,提出了相应的控制措施     

USLE与WEPP土壤可蚀性因子的关联性分析

通用土壤流失方程USLE与水蚀预测模型WEPP是当前常用的两种土壤侵蚀预测模型,而WEPP物理模型代表着土壤侵蚀预测预报的发展方向。对USLE中的K因子与WEPP中的Ki、Kr因子进行相关分析表明,USLE中的K因子与WEPP中的Ki、Kr因子之间几乎毫无相关性。即无法通过对USLE中可蚀性因子的分析来确定WEPP模型中可蚀性因子的大小。