RUSLE applied in a GIS framework: Calculating the LS factor and deriving homogeneous patches for estimating soil loss

The RUSLE (Revised Universal Soil Loss Equation) is integrated within a GIS framework to calculate soil loss spatially. For this module, algorithms and procedures were developed to derive the slope length factor (L) and steepness factor (S) from a DEM, then integrated with the R, K, C, and P factors to develop homogeneous patches (sub‐units) within each field or river basin. Soil loss is determined for each patch within a study unit, and then combined to determine the fields' or river basin's average annual and total soil loss. Two case studies are presented. The first case study, in central Massachusetts, compares estimated soil loss values obtained for individual fields using the Idrisi RUSLE module to USDA‐NRCS RUSLE field data. While soil loss results were similar, the RUSLE module allows fields to be partitioned into more similar units than practical in the field. This permits detailed spatial analysis of soil‐loss patterns. The second case study compares soil‐loss estimates for a catchment in southwestern Flanders, Belgium. This model–model comparison contrasts the results from the RUSLE module to the WATEM model—a grid cell based model based on the USLE/RUSLE but conceptualized in a multi‐flow context. Results between the predicted soil losses utilizing the two different approaches are significantly correlated. However, estimated soil losses are consistently higher for the WATEM model. This likely reflects the differences between how the two models compute L as well as the contribution of ephemeral gullies and flow convergence which are incorporated in WATEM but not in RUSLE.     

Estimating the rainfall erosivity for management planning in the Eastern Desert,Egypt

Soil erosion is one of the major causes of land degradation in arid areas. Soil erosion models, e.g. the revised universal soil loss equation (RUSLE), use arithmetical expressions to explore relationships among various processes occurring in the terrain. The established model includes soil parameters, slope, climate and human activities to estimate the water erosion rate and sediment yield. In this study, an approach was adopted to integrate RUSLE model and geographic information system to detect erosion vulnerability and determine the soil erosion risk in the study area. The study area is situated in the Eastern Desert, Egypt. Ground truth data were examined to represent two regions: Luxor-Suhag and Suhag–ElMinya. These regions are exampled by four dry valleys named Sannor, Tarfa, Asyut and Qena, which are planned for agricultural development. The results indicate high risk of water erosion and sediment load discharge into the cultivated land in Luxor–Suhag region. The other region of Suhag–ElMinya is moderately affected by water and sediment load discharge. A higher soil erosion rate was found in Qena wadi followed by Asyut, then Tarfa and Sannur, respectively.     

通用土壤流失方程最新研究改进分析

通用土壤流失方程(USLE)是水动力土壤侵蚀研究领域应用广泛的经验模型。其结构简单,所需输入数据量少,计算结果可满足一定精度下,土壤侵蚀预测的要求。模型的改进使其应用范围扩大,适用性增强,计算精度提高。通用土壤流失方程的发展趋势:其一,传统方法,不改变模型的结构,通过引进合理的新参数或优化参数的取值来提高模型的预测能力;其二,非传统方法,通过模糊逻辑或人工神经网络方法来改变模型的结构,使土壤侵蚀影响因子的确定更加灵活合理,土壤流失量的计算结果更加精确可信。     

梯田建设和淤地坝淤积对土壤侵蚀影响的定量分析

为了研究梯田建设和淤地坝淤积对流域土壤侵蚀的影响,本文首先建立了包含梯田、坡耕地、陡坡草地以及坝地在内的黄土高原丘陵沟壑区流域简化模型,并定义了流域的先锋期、过渡期以及顶级期三个状态,使用修正通用土壤流失方程(RUSLE) 分析了不同时期的流域土壤侵蚀模数。结果显示:先锋期与顶级期是流域水土保持治理的极限状态,先锋期峁边线上部土壤侵蚀模数为299.56 t×hm^-2×a^-1,下部土壤侵蚀模数为136.64 t×hm^-2×a^-1,平均侵蚀模数为229.74 t×hm^-2×a^-1;顶级期峁边线上部土壤侵蚀模数为39.10 t×hm^-2×a^-1,下部土壤侵蚀模数为1.10 t×hm^-2×a^-1,平均侵蚀模数为22.81 t×hm^-2×a^-1;在过渡期,随着梯田面积比例的增加,峁边线上部土壤侵蚀模数呈指数减少,而随着淤积高度的增加,峁边线下部土壤侵蚀模数呈线性减少,文章最后对这一结论进行了实证分析。     

基于RUSLE的广东南岭土壤侵蚀敏感性研究

基于修正的通用水土流失方程RUSLE和GIS技术,分析了影响土壤侵蚀敏感性的降雨侵蚀力因子、土壤可蚀性因子、坡度坡长因子以及植被覆盖与管理因子,并生成单要素敏感性评价图,在此基础上,评价研究区土壤侵蚀敏感性,探讨不同土壤侵蚀敏感性的分布规律及其主导因子的空间分异特征。结果表明：降雨侵蚀力因子的变化范围为8 181.52~14 621.56（MJ·mm）/（hm2·h·a）,土壤可蚀性因子为0.146~0.238（t·hm2·h）/（hm2·MJ·mm）,坡度坡长因子为0~612.615,植被覆盖与管理因子为0.101~1.183,土壤侵蚀的最大值和平均值分别为7 016.44和137.69 t/（km2·a）,土壤侵蚀敏感性以低度敏感和较低敏感为主,不同影响因子在敏感性分区的变化范围不同,其中地形因子和植被覆盖与管理因子对土壤侵蚀最为敏感。     

Assessment of Soil Erosion Susceptibility Using Empirical Modeling

Soil erosion is one of the most serious land degradation problems all over the world,causing irreversible land quality reduction.In this paper,we modify the Revised Universal Soil Loss Equation(RUSLE) model by replacing the factors of slope length and gradient with Sediment Transport Index(STI).The Digital Elevation Model,terrain parameters,Normalized Difference Vegetation Index(NDVI),and rainfall data are used as inputs to the model.Along with the application of remote sensing techniques and ground survey measurements,erosion susceptibility maps are produced.The revised models are then used to obtain the optimal estimate of soil erosion susceptibility at Alianello of southern Italy,which is prone to soil erosion.The soil loss estimated from the modified RUSLE model shows a large spatial variance,ranging from 10 to as much as 7000 ton ha 1 yr 1.The high erosion susceptible area constitutes about 46.8% of the total erosion area,and when classified by land cover type,33% is "mixed bare with shrubs and grass",followed by 5.29% of "mixture of shrubs and trees",with "shrubs" having the lowest percentage of 0.06%.In terms of slope types,very steep slope accounts for a total of 40.90% and belongs to high susceptibility,whereas flat slope accounts for only 0.12%,indicating that flat topography has little effect on the erosion hazard.As far as the geomorphologic types are concerned,the type of "moderate steep-steep slopes with moderate to severe erosion" is most favorable to high soil erosion,which comprises about 9.34%.Finally,we validate the soil erosion map from the adapted RUSLE model against the visual interpretation map,and find a similarity degree of 71.9%,reflecting the efficiency of the adapted RUSLE model in mapping the soil erosion in this study area.     

Uncertainty of soil erosion modelling using open source high resolution and aggregated DEMs

Digital Elevation Model (DEM) is one of the important parameters for soil erosion assessment. Notable uncertainties are observed in this study while using three high resolution open source DEMs. The Revised Universal Soil Loss Equation (RUSLE) model has been applied to analysis the assessment of soil erosion uncertainty using open source DEMs (SRTM, ASTER and CARTOSAT) and their increasing grid space (pixel size) from the actual. The study area is a part of the Narmada river basin in Madhya Pradesh state, which is located in the central part of India and the area covered 20,558 km². The actual resolution of DEMs is 30 m and their increasing grid spaces are taken as 90, 150, 210, 270 and 330 m for this study. Vertical accuracy of DEMs has been assessed using actual heights of the sample points that have been taken considering planimetric survey based map (toposheet). Elevations of DEMs are converted to the same vertical datum from WGS 84 to MSL (Mean Sea Level), before the accuracy assessment and modelling. Results indicate that the accuracy of the SRTM DEM with the RMSE of 13.31, 14.51, and 18.19 m in 30, 150 and 330 m resolution respectively, is better than the ASTER and the CARTOSAT DEMs. When the grid space of the DEMs increases, the accuracy of the elevation and calculated soil erosion decreases. This study presents a potential uncertainty introduced by open source high resolution DEMs in the accuracy of the soil erosion assessment models. The research provides an analysis of errors in selecting DEMs using the original and increased grid space for soil erosion modelling.     

Validation of official erosion modelling based on high‐resolution radar rain data by aerial photo erosion classification

The universal soil loss equation (USLE) is the most frequently applied erosion prediction model and it is also implemented as an official decision‐making instrument for agricultural regulations. The USLE itself has been already validated using different approaches. Additional errors, however, arise from input data and interpolation procedures that become necessary for field‐specific predictions on a national scale for administrative purposes. In this study, predicted event soil loss using the official prediction system in Bavaria (Germany) was validated by comparison with aerial photo erosion classifications of 8100 fields. Values for the USLE factors were mainly taken from the official Bavarian high‐resolution (5 × 5 m²) erosion cadastre. As series of erosion events were examined, the cover and management factor was replaced by the soil loss ratio. The event erosivity factor was calculated from high‐resolution (1 × 1 km², 5 min), rain gauge‐adjusted radar rain data (RADOLAN). Aerial photo erosion interpretation worked sufficiently well and average erosion predictions and visual classifications correlated closely. This was also true for data broken down to individual factors and different crops. There was no reason to assume a general invalidity of the USLE and the official parametrization procedures. Event predictions mainly suffered from errors in the assumed crop stage period and tillage practices, which do not reflect interannual and farm‐specific variation. In addition, the resolution of radar data (1 km²) did not seem to be sufficient to predict short‐term erosion on individual fields given the strong spatial gradients within individual rains. The quality of the input data clearly determined prediction quality. Differences between USLE predictions and observations are most likely caused by parametrization weaknesses but not by a failure of the model itself. Copyright © 2017 John Wiley & Sons, Ltd.     

西汉康陵封土侵蚀和高度估算研究

利用RUSLE土壤侵蚀模型,定量估算了2 000 a来西汉康陵的封土土壤侵蚀量。结果表明,2 000 a来帝陵土壤侵蚀约123 563.63 t,约占帝陵封土的9.65%,康陵封土总质量约1 403 753.23 t。按照西汉帝陵覆斗形封土的外观,2 000 a以来封土土壤侵蚀高度在1.22～1.59 m之间。研究结果为秦汉帝陵封土壤侵蚀研究提供了一种新的思路和方法。