Enhancing RUSLE to include runoff‐driven phenomena

RUSLE2 (Revised Universal Soil Loss Equation) is the most recent in the family of Universal Soil Loss Equation (USLE)/RUSLE/RUSLE2 models proven to provide robust estimates of average annual sheet and rill erosion from a wide range of land use, soil, and climatic conditions. RUSLE2's capabilities have been expanded over earlier versions using methods of estimating time‐varying runoff and process‐based sediment transport routines so that it can estimate sediment transport/deposition/delivery on complex hillslopes. In this report we propose and evaluate a method of predicting a series of representative runoff events whose sizes, durations, and timings are estimated from information already in the RUSLE2 database. The methods were derived from analysis of 30‐year simulations using a widely accepted climate generator and runoff model and were validated against additional independent simulations not used in developing the index events, as well as against long‐term measured monthly rainfall/runoff sets. Comparison of measured and RUSLE2‐predicted monthly runoff suggested that the procedures outlined may underestimate plot‐scale runoff during periods of the year with greater than average rainfall intensity, and a modification to improve predictions was developed. In order to illustrate the potential of coupling RUSLE2 with a process‐based channel erosion model, the resulting set of representative storms was used as an input to the channel routines used in Chemicals, Runoff, and Erosion from Agricultural Management Systems (CREAMS) to calculate ephemeral gully erosion. The method was applied to a hypothetical 5‐ha field cropped to cotton in Marshall County, MS, bisected by a potential ephemeral gully having channel slopes ranging from 0·5 to 5% and with hillslopes on both sides of the channel with 5% steepness and 22·1 m length. Results showed the representative storm sequence produced reasonable results in CREAMS indicating that ephemeral gully erosion may be of the same order of magnitude as sheet and rill erosion. Copyright © 2010 John Wiley & Sons, Ltd.     

Managing Soil Erosion Potential by Integrating Digital Elevation Models with the Southern China＇s Revised Universal Soil Loss Equation——A Case Study for the West Lake Scenic Spots Area of Hangzhou, China

In China, many scenic and tourism areas are suffering from the urbanization that results from physical development of tourism projects, leading to the removal of the vegetative cover, the creation of areas impermeable to water, in-stream modifications, and other problems. In this paper, the risk of soil erosion and its ecological risks in the West Lake Scenic Spots （WLSS） area were quantitatively evaluated by integrating the revised universal soil loss equation （RUSLE） with a digital elevation model （DEM） and geographical information system （GIS） software. The standard RUSLE factors were modified to account for local climatic and topographic characteristics reflected in the DEM maps, and for the soil types and vegetation cover types. An interface was created between the Areinfo software and RUSLE so that the level of soil erosion and its ecological risk in the WLSS area could be mapped immediately once the model factors were defined for the area. The results from an analysis using the Areinfo-RUSLE interface showed that the risk value in 93 % of the expanding western part of the WLSS area was moderate or more severe and the soil erosion risk in this area was thus large compared with that in the rest of the area. This paper mainly aimed to increase the awareness of the soil erosion risk in urbanizing areas and suggest that the local governments should consider the probable ecological risk resulting from soil erosion when enlarging and developing tourism areas.     

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 during 2000–2019. 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.     

Uncertainty analysis of soil erosion modelling using different resolution of open-source DEMs

The Digital Elevation Model (DEM) is one of the important parameters of soil erosion assessment and notable uncertainties are found in using different resolutions of the DEM. Revised Universal Soil Loss Equation model has been applied to analyze the effect of open-source DEMs with different resolution and accuracy on the uncertainties of soil erosion modelling in a part of the Narmada river basin in Madhya Pradesh in central India. Selected open-source DEMs are GTOPO30 (1 km), SRTM (30 and 90 m), CARTOSAT (30 m) and ASTER (30 m), used for estimating erosion rate. Results with better accuracy are achieved with the high-resolution DEMs (30 m) with higher vertical accuracy than the coarse resolution DEMs with lower accuracy. This study has presented potential uncertainties introduced by the open-source DEMs in soil erosion modelling for better understanding of appropriate selection and acceptable errors for researchers.     

基于地理探测器的喀斯特不同地貌形态类型区土壤侵蚀定量归因

土壤侵蚀形成机制与影响因素识别是当前研究的核心与前沿议题,然而从多因素综合作用的角度进行定量归因仍需加强。以喀斯特典型峰丛洼地流域为研究区,基于GIS手段和RUSLE模型模拟土壤侵蚀,综合土地利用、坡度、降雨、高程、岩性、植被覆盖度等影响因子,应用地理探测器方法针对喀斯特不同地貌形态类型区进行土壤侵蚀的定量归因研究。结果表明,各影响因子对土壤侵蚀的解释力及因子间耦合作用程度在不同地貌形态类型区差异显著,其中土地利用和坡度是决定土壤侵蚀空间异质的主导因子,但在山地丘陵区,随着地形起伏度的升高,坡度的控制作用下降,即地理探测器q值表现为中海拔丘陵>小起伏中山>中起伏中山;生态探测器显示土地利用对土壤侵蚀的影响相比于其他因子有显著差异;双因子交互作用有助于增强对土壤侵蚀的解释力,土地利用与坡度的协同作用对土壤侵蚀的解释力达到70%以上;对于土壤侵蚀空间分布的差异性检验,风险探测器显示在小起伏中山、中起伏中山等地貌形态类型中,具有显著差异的影响因子分层组合数占比至少55%。因而,喀斯特地区土壤侵蚀的治理应综合考虑不同地貌形态类型区土壤侵蚀影响机制的空间异质性。     

Assessment of soil erosion in a monsoon-dominated mountain river basin in India using RUSLE-SDR and AHP

The long-term average annual soil loss (A) and sediment yield (SY) in a tropical monsoon-dominated river basin in the southern Western Ghats, India (Muthirapuzha River Basin, MRB; area: 271.75 km²), were predicted by coupling the Revised Universal Soil Loss Equation (RUSLE) and sediment delivery ratio (SDR) models. Moreover, the study also delineated soil erosion risk zones based on the soil erosion potential index (SEPI) using the analytic hierarchy process (AHP) technique. Mean A of the basin is 14.36 t ha^?1 year^?1, while mean SY is only 3.65 t ha^?1 year^?1. Although the land use/land cover types with human interference show relatively lower A compared to natural vegetation, their higher SDR values reflect the significance of anthropogenic activities in accelerated soil erosion. The soil erosion risk in the MRB is strongly controlled by slope, land use/land cover and relative relief, compared to geomorphology, drainage density, stream frequency and lineament frequency.     

1992-2013年巢湖流域土壤侵蚀动态变化

基于GIS和RS技术,利用修正的通用土壤流失方程（RUSLE）模型,结合遥感影像、DEM数据、土壤类型数据及相关统计确定了模型中参数因子,计算出巢湖流域1992-2013年土壤侵蚀模数,分析了土壤侵蚀强度的时空动态变化特征。结果表明：巢湖流域土壤侵蚀区域主要呈东北至西南方向分布。微度、轻度、中度、强度、极强和剧烈侵蚀占土壤侵蚀总面积百分比分别是93.46%、6.25%、0.68%、0.19%、0.01%、0.01%。1992-2006年土壤侵蚀模数由510.70 t/（km²·a）减少到129.79 t/（km²·a）,降幅为74.59%,同时植被覆盖率由37.0%增至47.80%,土壤侵蚀的面积比例变化明显,轻度、中度、强度、极强和剧烈侵蚀由8.93%、2.33%、1.32%、0.09%、0.05%分别减少为4.74%、1.39%、0.28%、0.02%、0.01%,微度侵蚀由87.88%增加到94.16%。但2013年土壤微度侵蚀又减少为93.46%,土壤微度侵蚀有向高一级转换趋势。2006-2013年土壤侵蚀模数也由129.79 t/（km²·a）增加到149.44 t/（km²·a）,增幅为15.14%。     

桂江流域土壤侵蚀估算及其时空特征分析

桂江流域的水土流失现状研究对珠江三角洲的水生态安全有重要的现实意义。采用修正的通用土壤流失方程（RUSLE）估算了桂江流域的土壤侵蚀模数与年侵蚀总量,分析流域内土壤侵蚀的时空分布特征,探讨了影响该区域土壤侵蚀强度的自然与人文因素。结果表明,桂江流域51.8%的地表都在发生不同程度的土壤侵蚀。从全流域平均土壤侵蚀强度来看,属于中度侵蚀。从土壤侵蚀面积来看,约85%的地表处于微度、轻度与中度侵蚀。4-6月的全流域平均土壤侵蚀强度最大,侵蚀总量也是最大的。流域的土壤侵蚀主要发生在高程在30~600m的低山丘陵-高地地貌区内的林地与耕地中。流域内岩溶区的土壤侵蚀强度随着石漠化程度从无到中度逐渐增加,轻、中度石漠化区的土壤侵蚀强度达到强度侵蚀等级。      

RETRACTED ARTICLE: Assessment of soil erosion using RUSLE and GIS: a case study of the Maotiao River watershed,Guizhou Province,China

Due to the existence of fragile karst geo-ecological environments, such as environments with extremely poor soil cover, low soil-forming velocity, and fragmentized terrain and physiognomy, as well as inappropriate and intensive land use, soil erosion is a serious problem in Guizhou Province, which is located in the centre of the karst areas of southwestern China; evaluation of soil loss and spatial distribution for conservation planning is urgently needed. This study integrated the revised universal soil loss equation (RUSLE) with a GIS to assess soil loss and identify risk erosion areas in the Maotiao River watershed of Guizhou. Current land use/cover and management practices were evaluated to determine their effects on average annual soil loss and future soil conservation practices were discussed. Data used to generate the RUSLE factors included a Landsat Thematic Mapper image (land cover), digitized topographic and soil maps, and precipitation data. The results of the study compare well with the other studies and local data, and provide useful information for decision makers and planners to take appropriate land management measures in the area. It thus indicates the RUSLE–GIS model is a useful tool for evaluating and mapping soil erosion quantitatively and spatially at a larger watershed scale in Guizhou.     

Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework,Chania, Northwestern Crete,Greece

Soil erosion is a growing problem in southern Greece and particularly in the island of Crete, the biggest Greek island with great agricultural activity. Soil erosion not only decreases agricultural productivity, but also reduces the water availability. In the current study, an effort to predict potential annual soil loss has been conducted. For the prediction, the Revised Universal Soil Loss Equation (RUSLE) has been adopted in a Geographical Information System framework. The RUSLE factors were calculated (in the form of raster layers) for the nine major watersheds which cover the northern part of the Chania Prefecture. The R-factor was calculated from monthly and annual precipitation data. The K-factor was estimated using soil maps available from the Soil Geographical Data Base of Europe at a scale of 1:1,000,000. The LS-factor was calculated from a 30-m digital elevation model. The C-factor was calculated using Remote Sensing techniques. The P-factor in absence of data was set to 1. The results show that an extended part of the area is undergoing severe erosion. The mean annual soil loss is predicted up to ∼200 (t/ha year⁻¹) for some watersheds showing extended erosion and demanding the attention of local administrators.