陕西省耕地土壤可蚀性因子

[目的]土壤可蚀性因子是计算土壤侵蚀的一个重要因子,对陕西省耕地土壤可蚀性因子展开研究,可为陕西地区的耕地土壤侵蚀计算及评价提供科学依据。[方法]以陕西省9个地区的耕地土壤实测数据为基础,利用通用土壤流失方程USLE(universal soil loss equation)、修订土壤流失方程RUSLE2(revised universal soil loss equation version 2)、侵蚀生产力影响模型EPIC(erosion productivity impact calculator)中可蚀性因子K值的计算公式以及几何平均粒径公式和几何平均粒径—有机质Dg-OM公式,计算不同耕地土壤质地条件下的土壤可蚀性因子。[结果]RUSLE2的极细砂粒转换公式在陕西黄土丘陵沟壑区平均低约14.53%,在陕南地区平均高约32.91%,使用修正公式后平均误差分别为7.81%和13.14%;对比分析K值的估算值与实测值,子洲县实测K值为0.002 69〔(t·hm2·h)/(hm2·MJ·mm)〕,Dg-OM模拟计算均值为0.0297〔(t·hm2·h)/(hm2·MJ·mm)〕;水蚀预报模型WEPP(water erosion prediction project)中的细沟间可蚀性(Ki)和细沟可蚀性(Kr),与USLE的K值相关系数分别为0.738 6和0.607 4。[结论]极细砂粒转换修正公式的计算误差小于RUSLE2模型;Dg-OM模型适合陕西黄土丘陵沟壑区及长武县、杨凌区和安康市典型耕地土壤;WEPP中Ki和Kr,当土壤砂粒含量小于30%,USLE的K值与WEPP的Ki和Kr值有强相关性。     

土壤可蚀性在WEPP模型中的应用

影响土壤侵蚀过程的关键因素是侵蚀力和土壤可蚀性。土壤可蚀性是定量计算土壤流失的重要指标和土壤侵蚀预报模型中的必要参数。WEPP是由美国农业部开发的新一代水蚀预测模型 ,该模型以土壤可蚀性为必要参数 ,并将其分解为细沟间可蚀性 (Ki)、细沟可蚀性 (Kr)和临界剪切力 (τc)来预报流域径流形成与土壤侵蚀过程     

四川自然土壤和旱耕地土壤可蚀性特征研究

应用美国通用土壤流失方程 (USLE)和土壤侵蚀预报模型 (WEPP)中的土壤可蚀性K值 ,对四川各类自然土壤和旱耕地土壤可蚀性特征进行了研究。结果表明 :土壤可蚀性K值与土壤理化性质直接相关 ,自然土壤和旱耕地土壤可蚀性K值在 0 2 68～ 0 3 44之间 ,紫色土的分布面积和K值较大 ,是易遭受侵蚀的土壤。应采取增施有机肥、实行坡改梯等措施 ,加强对耕地、高可蚀性土壤侵蚀的综合防治     

美国水力侵蚀预测模型WEPP介绍

<正>美国水力侵蚀预测模型WEPP(the Water ErosionPrediction Project),是近年来开发研制出的一种新的土壤侵蚀预测技术.WEPP克服了美国通用土壤流失方程USLE及其修正版RUSLE在预测中的缺陷,其应用前景十分广阔,大有取代USLE和RUSLE之势.本文对WEPP作一简单介绍;1 USLE、RUSLE建模原理和应用现状土壤侵蚀预测在水土保持措施规划设计中有着广泛的应用;在实际生产中,由于不可能将所有气候条件下生态系统中每一种农业耕作和管理措施对土壤侵蚀的影响均进行实地监测,所以一般根据预测模型对影响侵蚀的因子进行排序,以此为依据来决定应采取的水土保持措施,制订相应的水土保持计划;     

工程堆积体标准小区界定与可蚀性因子改进

将USLE模型应用于工程堆积体侵蚀预报时结果偏差较大,这是因为其标准小区与土壤可蚀性因子不适合直接应用于工程堆积体侵蚀预报。针对这一问题,结合对我国6大水蚀类型区工程堆积体参数的调查、统计、分析,界定工程堆积体标准小区的坡度、坡长;结合国内外已有研究成果,分析论证堆积体可蚀性因子的改进办法,并引用相关数据验证其可行性。结果表明,6大区域工程堆积体坡度、坡长及坡面物质组成均与USLE中规定的标准小区相差较大;为了尽量消除误差,建议工程堆积体标准小区坡度采取众数35°,坡长采取平均值5m;堆积体可蚀性因子更名为土石质因子Tr,并将单位体积土石混合体中石砾总表面积Cs,作为石砾因素指标纳入堆积体土石质因子Tr中,与堆积体物质中土壤可蚀性因子K共同构建工程堆积体土石质因子函数。验证结果表明,堆积体可蚀性因子改进办法是可行的。工程堆积体坡度、坡长的界定及可蚀性因子的改进,对提高基于USLE模型的堆积体侵蚀预报精度有重要意义。     

福建省主要土壤可蚀性特征初探

本以USLE中的土壤可蚀性因子K为指标，利用土壤普查资料对全省主要土壤类型的K值进行了计算分析，结果说明我省主要土壤表层K值为0＝17－0．28之间，属中等易蚀范围，坡耕地K值较高，平均K值为0．25，且因松耕，土壤抗侵蚀力仅为自然坡地的1／11，提出要注意坡耕地的保护，土壤B层K值明显高于A层K值，说明随土壤侵蚀的加剧，土壤抗侵蚀力减弱。     

土壤侵蚀预报模型应用分析

土壤侵蚀预报是提出针对性水土保持措施的前提,土壤侵蚀模型则是土壤侵蚀预报的重要工具与手段。在天镇县的大梁沟、大洼山、石梯梁3条流域,按平缓区、陡坡区2种地形,采用USLE、RUSLE、WEPP三种土壤侵蚀预报模型进行了土壤侵蚀预报。预报结果与实测数值进行对比分析表明:平缓区宜采用WEPP模型,而在陡坡区宜采用USLE模型;RUSLE模型的预报结果误差最大,在该地区不太适用。     

东北典型黑土区剖面粒径分布特征及其可蚀性研究

为了更好的了解黑土区土壤剖面粒径分布以及可蚀性因子特征,本研究以东北典型黑土区鹤北流域为研究区,利用沉降法对不同土地利用方式下土层表面至母质的土壤样品进行粒径分布规律研究,并基于粒径及有机碳分布特征,计算了土样的可蚀性K值,最后对土壤可蚀因子K与WEPP模型中土壤的细沟间侵蚀因子(Inter- rill Erodibility)K_i、细沟侵蚀因子(Rill Erodibility)K_r和临界剪切力因子(Critical Shear)Tc进行相关分析。结果表明:(1)不同剖面下土壤粘粒含量逐层变化不大。而粉粒含量呈现出随土层深度增加而含量减少,砂粒呈现出随土层深度增加而含量增大;(2)除人工林外,其余6个剖面土壤可蚀性因子K值均表现出随土层深度增加而含量增大的趋势;(3)对农地剖面土样分析发现,可蚀性因子K值与细沟侵蚀因子K_r呈极显著正相关,与临界剪切力因子Tc呈极显著负相关,而与细沟间侵蚀因子K_i的正相关性略有降低。     

中国亚热带土壤可蚀性K值预测的不确定性研究

土壤可蚀性K值是土壤侵蚀模型(如USLE和RUSLE)的必要参数,直接套用经验模型估算土壤可蚀性K值会给土壤侵蚀预报带来不可估计的误差。本文以我国亚热带7种典型土壤可蚀性K值的观测值为依据,选用平均绝对误差(MAE)、平均相对误差(MRE)、均方根误差(RMSE)和精度因子(Af)四种数学统计项为指标,评价了诺谟图模型、修正诺谟图模型、EPIC模型、几何平均粒径模型和Torri模型等5种土壤可蚀性K值预测模型的不确定性。结果表明,5种模型的不确定性从小到大的顺序为:Torri模型<修正诺谟图模型和诺谟图模型相似文献   

GIS支持下的海南岛土壤侵蚀空间分布特征

在地理信息系统的支持下,采用通用土壤流失方程（USLE）及其修改式估算了海南岛现实土壤蚀量和潜在土壤侵蚀量,分析了土壤仇蚀因子R,K,LS在海南岛的表现,得到了海南岛现实土壤侵蚀量和潜在土壤蚀量的空间分布特征,研究结果表明,中部山区的现实土壤侵蚀量和潜在土壤量均较其它地区大。     

土壤可蚀性研究现状及展望

<正> 土壤可蚀性(soil erodibility)是指土壤是否易受侵蚀破坏的性能,也就是土壤对侵蚀介质剥蚀和搬运的敏感性。与侵蚀营力一样,土壤可蚀性是影响土壤侵蚀量大小的又一个重要因子。在水土保持学科中,我国习惯上把“水”和“土”并列使用,而国际上则用“soil loss”、“soil erosion”、“soil conservation”或“soil and water conservation”等术语。显然,土壤是水土保持学科的重点。另一方面,土壤侵蚀营力等是土壤流失过程中的外部因素,而土壤性质才是内在因素,因此,水土保持学科中核心的也是重要的问题,应该是土壤保护。土壤可蚀性研究在水土保持研究工作中具有重要意义,国际上把土壤可蚀性研究一直作为水土保持学科研究的重要内容之一。     

水蚀模型USLE与WEPP在紫色土水蚀预测中的应用对比研究

该文分别确定了USLE与WEPP模型的参数指标，通过实测遂宁组紫色土的单次降雨产沙量，对降雨产沙实测值与模型预测值进行比较分析，结果表明：在20°休闲小区模拟预测WEPP模型预测效果多数情况下优于USLE模型；通过多因子贡献分析发现，降雨量因子对产沙量的影响最大，其对休闲区和布设措施小区产沙量的贡献率分别达到了48.0%和64.1%。根据统计学中累积贡献率大于80%确定公共因子的原则，确定降雨量与降雨历时为遂宁组紫色土地区侵蚀产沙量的公共因子。     

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.     

WEPP模型中细沟可蚀性参数估计方法误差的理论分析

细沟土壤侵蚀在坡面土壤侵蚀占有重要地位。土壤可蚀性参数是WEPP模型中计算预报/计算细沟土壤侵蚀中极其重要的参数。WEPP模型现在采用的可蚀性参数是用长的细沟/径流小区试验以细沟侵蚀产沙估计得到的最大可能剥蚀率为基础获得的。该文分析了细沟侵蚀产沙随沟长的变化关系，分析了可蚀性参数估计误差的来源。从理论上推导出了计算现有WEPP可蚀性参数估计误差的计算方法。理论分析表明，对于限定性细沟，可蚀性参数的估计误差主要来源于细沟最大可能剥蚀率的估计值。最大可能剥蚀率的理想估计值是水流载沙量与细沟长度的函数关系在细沟     

Erodibility of agricultural soils on the Loess Plateau of China

Soil erodibility is thought of as the ease with which soil is detached by splash during rainfall or by surface flow. Soil erodibility is an important factor in determining the rate of soil loss. In the universal soil loss equation (USLE) and the revised universal soil loss equation (RUSLE), soil erodibility is represented by an erodibility factor (K). The K factor was defined as the mean rate of soil loss per unit rainfall erosivity index from unit runoff plots. Although high rate of soil loss from the Loess Plateau in China is well known and widely documented, it is remarkable that there is little systematic attempt to develop and validate an erodibility index for soils on the Loess Plateu for erosion prediction. Field experimental data from four sites on the Loess Plateau were analyzed to determine the K factor for USLE/RUSLE and to compare with another erodibility index based on soil loss and runoff commonly used for the region. The data set consists of event erosivity index, runoff, and soil loss for 17 runoff plots with slope ranging from 8.7 to 60.1%. Results indicate that the K factor for USLE/RULSE is more appropriate for agricultural soils on the Loess Plateau than the erodibility index developed locally. Values of the K factor for loessial soils range from 0.0096 to 0.0269 t h/(MJ mm). The spatial distribution of the K value in the study area follows a simple pattern showing high values in areas with low clay content. For the four sites investigated, the K factor was significantly related to the clay content, (K=0.031−0.0013 Cl, r²=0.75), where Cl is the clay content in percent. The measured values of the K factor are systematically lower than the nomograph-based estimates by a factor of 3.3–8.4. This implies that use of the nomograph method to estimate soil erodibility would considerably over-predict the rate of soil loss, and local relationship between soil property and the K factor is required for soil erosion prediction for the region.     

Modelling runoff and soil erosion in logged forests: Scope and application of some existing models

Predictive erosion models are useful tools for evaluating the impact of land-use practices on soil and water properties, and as often used by environmental protection authorities, for setting guidelines and standards for regulation purposes. This study examines the application of three erosion models of varying complexity and design for predicting runoff and soil erosion from logged forest compartments in south eastern Australia. These are: the Universal Soil Loss Equation (USLE), the Water Erosion Prediction Project (WEPP), and TOPOG, a physically based hydrologic modelling package. Data on rates of soil loss and redistribution collected during a series of large-scale rainfall simulator experiments were used as model input parameters and validation. The models were evaluated in terms of general ease of use, input data requirements and accuracy of process understanding and prediction. Results suggest that in this application the USLE overestimated soil loss, and have the limitation that it does not predict sediment yield or sediment redistribution for specific storm events. When used at the hillslope scale, WEPP and TOPOG have predicted runoff and soil loss reasonably well, particularly on disturbed surfaces such as skid trails. On less disturbed surfaces such as the general harvesting area, both models performed less accurately, generally under-predicting soil loss and sediment yield, notably on sites with low observed values. The complexity and data requirements of WEPP and TOPOG limit their usability as a general-purpose, erosion hazard predicting tool. In terms of process understanding, none of the existing models accurately depict the nature and extent of sediment redistribution quantified in the rainfall simulator experiments. In order to advance the application and accuracy of modelling tools in forestry environments, this redistribution process should be considered integral to the refinement and redevelopment of future models.