Assessing land degradation in the Upper Manyame sub-catchment area, Zimbabwe using the RUSLE prediction model

The Loess Plateau has long been considered as a very fragile area that suffers from serious water erosion. With the onset of global climate fluctuations and regional implementation of soil and water conservation measures, it is of great significance to effectively reveal the variations in soil erosion risk and its driving mechanism on the Loess Plateau. Taking the Yanwachuan watershed as a case study and using rainfall, soil, digital elevation model (DEM) and land‐use data, this study applied the Revised Universal Soil loss Equation (RUSLE) model with geographic information system (GIS) technology to analyse the temporal and spatial variations of soil erosion risk and to evaluate the effects of land‐use change and climate change on soil erosion from 1981 to 2016. Future soil erosion risk was also assessed under different land‐use and climate‐change scenarios. Results showed that annual soil loss in the Yanwachuan watershed presented a significant decreasing trend with a rate of 47.928 t km−2 a−1 and that average annual soil loss was 3,543.7 t km−2 from 1981 to 2016. However, some areas, even with good vegetation cover, still had a rather high soil erosion risk (exceeding the severe erosion rate of 5,000 t km−2 in 2000–2016) if located in hilly and gully slope regions. Land‐use change and climate variation contributed 37.2% and 62.8%, respectively, to reducing soil erosion, showing that climate variation played a leading role in current soil‐erosion reduction. In addition, the average soil losses in the 2020s, 2030s and 2040s under different scenarios are predicted to increase by 8.6–42.6% compared with the average soil loss (2,993.8 t km−2) in 2001–2016. Future climate change may be the main driving factor in enhancing the risk of soil erosion. Hence, there is an urgent need to strengthen soil and water conservation research and management in future years.Highlights This study assessed current and future water erosion risk on the Loess Plateau using the RUSLE model. Areas with steep slopes were still experiencing serious soil erosion risk in 2000–2016. Climate change was the main driving factor for soil erosion variation. Future erosion risk is expected to increase under different climate and land‐use scenarios.

The present investigation was carried out within the Peddavagu watershed, which is located in India. The necessary datasets, including soil, land use land cover, rainfall, and digital elevation model, were processed and analysed within a Geographic Information System framework. To evaluate soil loss within the watershed, the present investigation employed the revised universal soil loss equation (RUSLE) model. Subsequently, the sediment yield is estimated based on the sediment delivery ratio (SDR). The average annual soil loss was estimated at 17.91 tonnes/hectare/year, which is high soil erosion risk. The RUSLE model's accuracy is 82.1%. Moreover, the findings revealed that sub-watersheds (SW) 9 and SW 3 exhibited the maximum and minimum average annual soil loss. The Peddavagu watershed's SDR was 0.210. Annually, 3.76 tonnes/hectare/year of sediment were transported to the Peddavagu watershed outlet. The findings revealed that SW 9 and SW 5 exhibited the maximum and minimum average annual sediment yield. The model's performance was evaluated by comparing its predictions with gauge data for validation. The observed actual data indicated a yield of 3.66 tonnes/hectare/year, while the model predicted a yield of 3.76 tonnes/hectare/year. This resource offers significant insights for policymakers and decision-makers on sustainable watershed management techniques.

GIS-based assessment of soil erosion and sediment yield using the revised universal soil loss equation (RUSLE) model in the Murredu Watershed, Telangana, India

Watershed-based soil erosion and sediment yield modeling in the Rib watershed of the Upper Blue Nile Basin, Ethiopia

Flat lakes with a large catchment area are especially affected by sediment inputs. The Kolleru Lake catchment (south-eastern India) with a surface area of approximately 6121 km2 belongs to such types of lake basins. The study’s main objective was the assessment of both soil erosion and sediment yield concentration rate of the Kolleru catchment. The study was conducted using the revised universal soil loss equation (RUSLE) model due to its simple and good applicability for soil erosion estimation. Data such as rainfall, soil texture, topography, crop cover management, and support practice factor were integrated into the modeling using RUSLE and ArcGIS. Field data were used both to analyze the soil texture and the slope length factor. The results showed that average annual soil loss was estimated with 13.6 t/ha/year, classifying the Kolleru Lake Basin under a very high erosion rate category. About 38% of the catchment area has encountered slight soil loss. Areas covered with moderate, strong, severe, very severe erosion potential zones are 29%, 17%, 9%, and 5.5%, respectively. This study identified that upland areas with less vegetation cover exported high potential erosion rates. Unlike the soil loss, the sediment delivery ratio values for the catchment were not affected by land use, while it showed a strong relationship with the catchment drainage system. Whereas, the average annual sediment yield was determined with 7.61 t/ha/year, had identified with the same pattern of the soil erosion. Catchment topography, vegetation, drainage system, soil properties, and land use cover played a major role in exporting the highest sedimentation. The outcome of these studies can be used among others to identify critical erosion areas on a pixel basis for the planning of erosion management practices.

ABSTRACTThe 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.

This study was aimed at predicting soil erosion risk in the Buyukcekmece Lake watershed located in the western part of Istanbul, Turkey, by using Revised Universal Soil Loss Equation (RUSLE) model in a GIS framework. The factors used in RUSLE were computed by using different data obtained or produced from meteorological station, soil surveys, topographic maps, and satellite images. The RUSLE factors were represented by raster layers in a GIS environment and then multiplied together to estimate the soil erosion rate in the study area using spatial analyst tool of ArcGIS 9.3. In the study, soil loss rate below 1 t/ha/year was defined as low erosion, while those >10 t/ha/year were defined as severe erosion. The values between low and severe erosion were further classified as slight, moderate, and high erosion areas. The study provided a reliable prediction of soil erosion rates and delineation of erosion-prone areas within the watershed. As the study revealed, soil erosion risk is low in more than half of the study area (54%) with soil loss <1 t/ha/year. Around one-fifth of the study area (19%) has slight erosion risk with values between 1 and 3 t/ha/year. Only 11% of the study area was found to be under high erosion risk with soil loss between 5 and 10 t/ha/year. The severe erosion risk is seen only in 5% of the study area with soil loss more than 10 t/ha/year. As the study revealed, nearly half of the Buyukcekmece Lake watershed requires implementation of effective soil conservation measures to reduce soil erosion risk.

A highly visible form of soil erosion is gully, a significant geomorphological feature, resulting from water erosion and causing land degradation and deterioration. In arid and semi-arid environment, gully erosion is conceived as an important source of sediment supply washing out the top fertile soil and exposing lower soil layers. The present study is conducted on the lateritic terrain of Rupai watershed of eastern plateau fringe of India, where water erosion is a serious concern. In order to prepare a gully erosion vulnerability mapping, the analytical hierarchy process (AHP) model coupled with geospatial technology is adopted taking into account thirteen bio-physical factors. It is revealed that around 49% area of the watershed belongs to high to very high gully erosion vulnerability zone (GEVZ) followed by moderate risk zone of 31.64%. This model is validated performing an accuracy assessment, which is calculated to be 90.91%, and the value of Kappa co-efficient is 0.86. It is imperative to estimate the average annual soil loss alongside of delineating GEVZ; thus, the revised universal soil loss equation (RUSLE) model is used with geospatial technology. It unveils that the average estimated soil loss of the watershed varies from < 15 to 431 t ha-1 y-1. Around 29% of the study area experiences high to very high (57 to > 147 t ha-1 y-1) soil erosion risk, where 68% area endures low level of soil erosion risk (< 15 t ha-1 y-1). The study of gully morphology depicts gully depth ranging from < 1 to 5m (small to medium gully) with V and U shapes. Results obtained from this study may help in planning and management of land use and soil erosion conservation.

Soil erosion modelling at European scale by using high resolution input layers

Modeling soil erosion using RUSLE and GIS at watershed level in the upper beles, Ethiopia

본 연구에서는 낙동강유역을 대상으로 토양 침식 및 유실의 위험성을 분석 및 평가하기 위해 토지이용도를 세부적으로 분석하여 유역별 토양침식 발생의 위험성을 순위화하였다. 또한, 토양침식량을 RUSLE 모형을 이용하여 산정하였고 토지이용도 분석 결과와 함께 토양침식 위험성이 높은 유역을 평가하였다. 최종적으로 해당 유역에 산사태 위험지도와의 비교를 통해 유역내 토양유실 대책 수립을 위한 자료의 활용 방안을 분석하였다. 분석 결과, 전체 낙동강유역내 토양침식 위험성이 높은 것으로 선정된 유역은 내성천유역으로 토지이용도 분석결과와 RUSLE 모형의 결과에서 모두 토양유실 측면에서 위험성이 높은 것으로 나타났다. RUSLE 모형 결과에서 토양침식량이 높은 것으로 나타난 지역과 산사태 위험지역의 분포는 유사한 것으로 나타났으나, 하천 주변의 토지이용에 따른 토양유실의 위험성은 RUSLE를 이용한 산정 결과에서만 확인할 수 있었다. The land use map of the Nakdong River watershed was classified by each land use contents and analyzed to rank the risk of soil loss and erosion. Also, the soil loss and erosion was evaluated in the Nakdong River watershed using Revised Universal Soil Loss Equation (RUSLE) and the subbasin with high risk of soil loss was evaluated with the analysis results of land use contents. Finally, the analyzed results were also compared with the landslide risk map, hence the practical application methods using developed and analyzed results were considered in this study. As a result of land use analysis and RUSLE calculation, it was represented that the Naesung Stream watershed had the high risk for soil loss among the subbasins of the Nakdong River watershed. It was also presented that the high risk area identified by computation of RUSLE was corresponding to the landslide risk area. However, the high risk of soil erosion by land use near the river or wetland was confirmed only through the calculation results of RUSLE.

Soil erosion risk map for Swiss grasslands : a dynamic approach to model the spatio-temporal patterns of soil loss

Soil erosion risk was calculated using the coordination of information on the environment (CORINE) model in this study. The aim of the study is to determine the soil erosion risk of Niğde province, taking into account soil properties, slope and land use. Potential (PSER) and actual soil erosion risks (ASER) were determined using factors which were soil properties, slope, climatic factors, and land cover data. Data were produced using Arc-GIS 10.3 software, and results were obtained on these maps. 34.72% of the soils were classified as moderately actual soil erosion risk, which located in the eastern and southeast part of the study area.51.66% and 13.62% of the soils were classified as low and high actual soil erosion risk, respectively. Areas which have low actual soil erosion risk are located in the middle part, and areas which have high actual soil erosion risk are located in the northwest part of the area. The areas which were categorized as low potential soil erosion risk were increased from 23.52% to 51.66% in the actual soil erosion risk, after combining the land cover map. On the other hand, the total areas classified as high and moderate actual soil erosion risk decreased from 76.48% to 48.34% in the actual soil erosion risk due to land cover types. Soil texture, land cover, and slope are the most important factors that affect erosion risk. This study indicated that the CORINE model integrated with GIS (Geographic Information Systems) and RS (Revised Universal Soil Loss Equation) has a very effective and accurate potential for soil erosion risk assessment.

Soil erosion is a process accelerated by natural and anthropogenic disturbances over time and space, leading to land degradation and causing geomorphological change. It is difficult to investigate the spatial and temporal distribution of soil erosion and sedimentation in data-scare areas, in that case, the use of simplified methods to analyze soil erosion and sediment connectivity variations over time and space can help. Sediment connectivity denotes the transfer of sediment from source to sink areas through channel systems of landscape compartments within a watershed. In this study, we aimed to investigate sediment yield (SY) variation over time and space and understand the link between hillslope soil erosion and sediment connectivity to identify hotspot areas in the Rogativa catchment (&amp;#8764;53 km2) in Southeast Spain. The (specific) sediment yield (S)SY was estimated by combining the Revised Universal Soil Loss Equation (RUSLE) model with the sediment delivery ratio (SDR). The SDR was calculated based on the Index of Connectivity (IC). In the channels, 100% delivery was assumed. In the Rogativa catchment, 58 check dams were constructed in 1976/77. Their trapping efficiency, obtained from field observations of sediment retained behind the checkdams in 2003, was included in the SDR estimation of the checkdams. SY was estimated from accumulated hillslope soil erosion in the local stream network while accounting for sedimentation through the SDR. Soil erosion, IC, SDR, and (S)SY were quantified and compared for the years 1956, 1977, 2001, and 2016, for which different land use maps were available. SY model results for the year 2001 were compared with observed SY (in 2003) behind the check dams. Only for about half of the checkdams, model results were comparable. This is investigated further and could be explained by complex sediment dynamics within the channels and between checkdams (i.e. one check dam retaining part of the sediment, the next downstream checkdam as well, etc) &amp;#8211; these dynamics are not included in the RUSLE-SDR model. The RUSLE-generated soil erosion and sediment connectivity signatures (IC, SDR, and (S) SY) showed higher values in the channels and croplands than in hillslopes and decreased over time due to significant changes in land use and construction of check dams in the catchment. Moreover, the combined proportion of erosion-connectivity patterns showed about 7% of the area adjacent to some of the streams was found both highly erodible and highly connected, which indicates an adverse erosion-prone part. It is possible to apply this method to understand SY amount and distribution and identify hotspot locations in drainage systems with limited field data in data-scarce semi-arid areas like the Rogativa catchment. However, more field observations to validate the models to identify hotspot locations and investigate river network systems rather than focusing only on hillslopes, which could help to know where to intervene in the catchment.Keywords: Soil erosion-RUSLE, Sediment connectivity, Sediment delivery ratio, Sediment yield, hotspot location

Estimating sediment yield in a river is a challenging task in the water resources field. Different methods are available for estimating sediment erosion and yield, but generally they are not spatially distributed in nature. This paper presents the application of the Revised Universal Soil Loss Equation (RUSLE) for estimating soil erosion and integrates it with spatially distributed Sediment Delivery Ratio (SDR) to calculate sediment yield in a Himalayan river. The study area is Kabeli sub-catchment, located upstream of the Koshi River Basin in the eastern part of Nepal. The Kabeli River is where numerous hydropower projects are envisaged, and sediment-related issues are of major concern. With the use of the RUSLE, the mean annual soil erosion is estimated at 35.96 tons/ha/yr. The estimated specific sediment yield (SSY) from the distributed SDR method is 6.74 tons/ha/yr, which is close to the observed SSY of 7.26 tons/ha/yr using the data records of ~8 years. Based on correlation analysis, the topographic factor (LS) is the most sensitive RUSLE parameter with respect to sediment erosion. The sloping areas near the river hillslope are particularly vulnerable to soil erosion. The results indicate that the approach employed in this study may be potentially applied in other catchments with similar physiographic characteristics for the estimation of sediment yield.