scholarly journals Application of the Adapted Approach for Crop Management Factor to Assess Soil Erosion Risk in an Agricultural Area of Rwanda

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1056
Author(s):  
Albert Poponi Maniraho ◽  
Richard Mind’je ◽  
Wenjiang Liu ◽  
Vincent Nzabarinda ◽  
Patient Mindje Kayumba ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Rate ◽  
High Probability ◽  
Crop Management ◽  
Agricultural Area ◽  
Erosion Risk ◽  
Western Province ◽  
Soil Erosion Risk ◽  
Soil Erosion Rate

Land use and land cover (LULC) management influences the severity of soil erosion risk. However, crop management (C) is one factor of the Revised Universal Soil Loss Equation (RUSLE) model that should be taken into account in its determination, as it influences soil loss rate estimations. Thus, the present study applied an adapted C-factor estimation approach (CvkA) modified from the former approach (Cvk) to assess the impact of LULC dynamics on soil erosion risk in an agricultural area of Rwanda taking the western province as a case study. The results disclosed that the formerly used Cvk was not suitable, as it tended to overestimate C-factor values compared with the values obtained from t CvkA. An approximated mean soil loss of 15.1 t ha−1 yr−1, 47.4 t ha−1 yr−1, 16.3 t ha−1 yr−1, 66.8 t ha−1 yr−1 and 15.3 t ha−1 yr−1 in 2000, 2005, 2010, 2015 and 2018, respectively, was found. The results also indicated that there was a small increase in mean annual soil loss from 15.1 t ha−1 yr−1 in 2000 to 15.3 t ha−1 yr−1 in 2018 (1.3%). Moreover, the soil erosion risk categories indicated that about 57.5%, 21.8%, 64.9%, 15.5% and 73.8% had a sustainable soil erosion rate tolerance (≤10 t ha−1 yr−1), while about 42.5%, 78.2%, 35.1%, 84.5% and 16.8% had an unsustainable mean soil erosion rate (>10 t ha−1 yr−1) in 2000, 2005, 2010, 2015 and 2018, respectively. A major portion of the area fell under the high and very high probability zones, whereas only a small portion fell under the very low, low, moderate and extremely high probability zones. Therefore, the CvkA approach presents the most suitable alternative to estimate soil loss in the western province of Rwanda with reasonable soil loss prediction results. The study area needs urgent intervention for soil conservation planning, taking into account the implementation of effective conservation practices such as terracing for soil erosion control.

scholarly journals Estimation of Soil Erosion in Nepal Using a RUSLE Modeling and Geospatial Tool

Geosciences ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 147 ◽  
Author(s):  
Pooja Koirala ◽  
Sudeep Thakuri ◽  
Subesh Joshi ◽  
Raju Chauhan
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Rate ◽  
Agricultural Land ◽  
Entire Area ◽  
Barren Land ◽  
Erosion Risk ◽  
Soil Erosion Rate ◽  
Severe Erosion ◽  
The Mean

Soil erosion is a major issue, causing the loss of topsoil and fertility in agricultural land in mountainous terrain. Estimation of soil erosion in Nepal is essential because of its agriculture-dependent economy (contributing 36% to national GDP) and for preparing erosion control plans. The present study, for the first time, attempts to estimate the soil loss of Nepal through the application of the Revised Universal Soil Loss Equation (RUSLE) model. In addition, it analyzes the effect of Land Use and Land Cover (LULC) and slope ( β ) exposition on soil erosion. Nation-wide mean annual soil loss of Nepal is estimated at 25 t ha−1 yr−1 with a total of 369 million tonnes (mT) of potential soil loss. Soil erosion based on the physiographic region of the country shows that the Middle Mountains, High Mountains, High Himal, Chure, and Terai have mean erosion rates of 38.0, 32.0, 28.0, 7.0, and 0.1 t ha−1 yr−1. The soil erosion rate by basins showed that the annual erosions of the Karnali, Gandaki, Koshi, and Mahakali River basins are 135, 96, 79, and 15 mT, respectively. The mean soil erosion rate was significantly high (34 t ha−1 yr−1) for steep slopes (β > 26.8%) and the low (3 t ha−1 yr−1) for gentle slopes (β < 5%). Based on LULC, the mean erosion rate for barren land was the highest (40 t ha−1 yr−1), followed by agricultural land (29 t ha−1 yr−1), shrubland (25 t ha−1 yr−1), grassland (23 t ha−1 yr−1), and forests (22 t ha−1 yr−1). The entire area had been categorized into 6 erosion classes based on the erosion severity, and 11% of the area was found to be under a very severe erosion risk (> 80 t ha−1 yr−1) that urgently required reducing the risk of erosion.

scholarly journals Estimation of soil loss using remote sensing and GIS-based universal soil loss equation in northern catchment of Lake Tana Sub-basin, Upper Blue Nile Basin, Northwest Ethiopia

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Veera Narayana Balabathina ◽  
R. P. Raju ◽  
Wuletaw Mulualem ◽  
Gedefaw Tadele
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Rate ◽  
Soil Erodibility ◽  
Lake Tana ◽  
Erosion Rates ◽  
Soil Erosion Rate ◽  
Soil Loss Equation

Abstract Background Soil erosion is one of the major environmental challenges and has a significant impact on potential land productivity and food security in many highland regions of Ethiopia. Quantifying and identifying the spatial patterns of soil erosion is important for management. The present study aims to estimate soil erosion by water in the Northern catchment of Lake Tana basin in the NW highlands of Ethiopia. The estimations are based on available data through the application of the Universal Soil Loss Equation integrated with Geographic Information System and remote sensing technologies. The study further explored the effects of land use and land cover, topography, soil erodibility, and drainage density on soil erosion rate in the catchment. Results The total estimated soil loss in the catchment was 1,705,370 tons per year and the mean erosion rate was 37.89 t ha−1 year−1, with a standard deviation of 59.2 t ha−1 year−1. The average annual soil erosion rare for the sub-catchments Derma, Megech, Gumara, Garno, and Gabi Kura were estimated at 46.8, 40.9, 30.9, 30.0, and 29.7 t ha−1 year−1, respectively. Based on estimated erosion rates in the catchment, the grid cells were divided into five different erosion severity classes: very low, low, moderate, high and extreme. The soil erosion severity map showed about 58.9% of the area was in very low erosion potential (0–1 t ha−1 year−1) that contributes only 1.1% of the total soil loss, while 12.4% of the areas (36,617 ha) were in high and extreme erosion potential with erosion rates of 10 t ha−1 year−1 or more that contributed about 82.1% of the total soil loss in the catchment which should be a high priority. Areas with high to extreme erosion severity classes were mostly found in Megech, Gumero and Garno sub-catchments. Results of Multiple linear regression analysis showed a relationship between soil erosion rate (A) and USLE factors that soil erosion rate was most sensitive to the topographic factor (LS) followed by the support practice (P), soil erodibility (K), crop management (C) and rainfall erosivity factor (R). Barenland showed the most severe erosion, followed by croplands and plantation forests in the catchment. Conclusions Use of the erosion severity classes coupled with various individual factors can help to understand the primary processes affecting erosion and spatial patterns in the catchment. This could be used for the site-specific implementation of effective soil conservation practices and land use plans targeted in erosion-prone locations to control soil erosion.

scholarly journals Sediment Yield and Soil Loss Estimation Using GIS Based Soil Erosion Model: A Case Study in the MAN Catchment, Madhya Pradesh, India

2021 ◽  
Vol 8 (1) ◽  
pp. 26
Author(s):  
Manti Patil ◽  
Radheshyam Patel ◽  
Arnab Saha
Keyword(s):  
Spatial Distribution ◽  
Soil Erosion ◽  
Soil Loss ◽  
Crop Production ◽  
Erosion Rate ◽  
Agricultural Land ◽  
Reservoir Sedimentation ◽  
Reservoir Water ◽  
Slope Length ◽  
Soil Erosion Rate

Soil erosion is one of the most critical environmental hazards of recent times. It broadly affects to agricultural land and reservoir sedimentation and its consequences are very harmful. In agricultural land, soil erosion affects the fertility of soil and its composition, crop production, soil quality and land quality, yield and crop quality, infiltration rate and water holding capacity, organic matter and plant nutrient and groundwater regimes. In reservoir sedimentation process the consequences of soil erosion process are reduction of the reservoir capacity, life of reservoir, water supply, power generation etc. Based on these two aspects, an attempt has been made to the present study utilizing Revised Universal Soil Loss Equation (RUSLE) has been used in integration with remote sensing and GIS techniques to assess the spatial pattern of annual rate of soil erosion, average annual soil erosion rate and erosion prone areas in the MAN catchment. The RUSLE considers several factors such as rainfall, soil erodibility, slope length and steepness, land use and land cover and erosion control practice for soil erosion prediction. In the present study, it is found that average annual soil erosion rate for the MAN catchment is 13.01-tons/ha/year, which is higher than that of adopted and recommended values for the project. It has been found that 53% area of the MAN catchment has negligible soil erosion rate (less than 2-tons/ha/year). Its spatial distribution found on flat land of upper MAN catchment. It has been detected that 26% area of MAN catchment has moderate to extremely severe soil erosion rate (greater than 10-tons/ha/year). Its spatial distribution has been found on undulated topography of the middle MAN catchment. It is proposed to treat this area by catchment area treatment activity.

scholarly journals Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

2021 ◽  
Vol 13 (21) ◽  
pp. 4360
Author(s):  
Andrew K. Marondedze ◽  
Brigitta Schütt
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Rainfall Erosivity ◽  
Climate Scenarios ◽  
Residential Areas ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Loss Rates

Monitoring urban area expansion through multispectral remotely sensed data and other geomatics techniques is fundamental for sustainable urban planning. Forecasting of future land use land cover (LULC) change for the years 2034 and 2050 was performed using the Cellular Automata Markov model for the current fast-growing Epworth district of the Harare Metropolitan Province, Zimbabwe. The stochastic CA–Markov modelling procedure validation yielded kappa statistics above 80%, ascertaining good agreement. The spatial distribution of the LULC classes CBD/Industrial area, water and irrigated croplands as projected for 2034 and 2050 show slight notable changes. For projected scenarios in 2034 and 2050, low–medium-density residential areas are predicted to increase from 11.1 km2 to 12.3 km2 between 2018 and 2050. Similarly, high-density residential areas are predicted to increase from 18.6 km2 to 22.4 km2 between 2018 and 2050. Assessment of the effects of future climate change on potential soil erosion risk for Epworth district were undertaken by applying the representative concentration pathways (RCP4.5 and RCP8.5) climate scenarios, and model ensemble averages from multiple general circulation models (GCMs) were used to derive the rainfall erosivity factor for the RUSLE model. Average soil loss rates for both climate scenarios, RCP4.5 and RCP8.5, were predicted to be high in 2034 due to the large spatial area extent of croplands and disturbed green spaces exposed to soil erosion processes, therefore increasing potential soil erosion risk, with RCP4.5 having more impact than RCP8.5 due to a higher applied rainfall erosivity. For 2050, the predicted wide area average soil loss rates declined for both climate scenarios RCP4.5 and RCP8.5, following the predicted decline in rainfall erosivity and vulnerable areas that are erodible. Overall, high potential soil erosion risk was predicted along the flanks of the drainage network for both RCP4.5 and RCP8.5 climate scenarios in 2050.

Soil erosion risk in Korean watersheds, assessed using the revised universal soil loss equation

2011 ◽  
Vol 399 (3-4) ◽  
pp. 263-273 ◽  
Author(s):  
Soyoung Park ◽  
Cheyoung Oh ◽  
Seongwoo Jeon ◽  
Huicheul Jung ◽  
Chuluong Choi
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Soil Loss Equation

scholarly journals Assessment of Soil Erosion by the RUSLE Model Using Remote Sensing and GIS: A Case Study of Jilin Province of China

2020 ◽  
Author(s):  
Ping ZHOU ◽  
Yajin GE ◽  
Yue JIANG ◽  
Yanan XIE ◽  
Zhiwen SI ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Soil Conservation ◽  
Soil Loss ◽  
Food Production ◽  
Erosion Rate ◽  
Slope Angle ◽  
Jilin Province ◽  
Rusle Model ◽  
Soil Erosion Rate

The accurate assessment and monitoring of soil erosion is of great significance for guiding food production and ensuring ecological security, and it is a current research hotspot. In this paper, remote sensing and geographic information systems (GISs) are combined with the Revised Universal Soil Loss Equation (RUSLE model) to carry out research on soil erosion monitoring and make a quantitative evaluation. According to five factors, including rainfall erosivity, soil erodibility, topography, vegetation cover, crop management and water and soil conservation measures, the distribution of the soil erosion rate in Jilin Province in 2019 was mapped, and the soil erosion rate was divided into 5 levels according to the degree of erosion, including very slight, slight, moderate, severe and extremely severe erosion. Based on the segmented S-slope factor model and the unique topographical features of the study area, the relationships among the soil erosion rate, erosion risk level, erosion area, erosion amount and slope angle (θ) were systematically analysed, and a slope angle of 15° was identified as the threshold for soil erosion on sloped farmland in Jilin Province. The total soil erosion in Jilin Province was 402.14×106 t in 2019, the average soil erosion rate was 21.6 t·ha-1·a-1, and the average soil loss thickness was 1.6 mm·a-1; these values were far greater than the soil erosion rate risk threshold of 10 t ·Ha-1·a-1. Thus, the province has a strong level of soil erosion. We conclude that soil degradation is accelerating, and food production and the ecological environment will face severe challenges. It is suggested that soil erosion control should be carried out according to different types and slopes of land, with an emphasis on the management of forestland and farmland because forestland and farmland are currently the first types of land to be managed in Jilin Province. This paper aims to explore a timely, fast, efficient and convenient soil erosion monitoring and evaluation method and provide effective monitoring tools for agricultural water and soil conservation, ecological safety management and stable food production in Jilin Province and similar black soil areas.

scholarly journals Terrain Characterization and Soil Erosion Risk Assessment for Watershed Prioritization Using Remote Sensing and GIS (A case study of Nawagaon Maskara Rao Watershed, Saharanpur, India)

2009 ◽  
Vol 23 (1) ◽  
pp. 86
Author(s):  
Beny Harjadi
Keyword(s):  
Remote Sensing ◽  
Risk Assessment ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Conservation ◽  
Soil Loss ◽  
Remote Sensing And Gis ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Conservation Measures

Soil erosion is crucial problem in India where more than 70% of land in degraded. This study is to establish conservation priorities of the sub watersheds across the entire terrain, and suggest suitable conservation measures. Soil conservation practices are not only from erosion data both qualitative SES (Soil Erosion Status) model and quantitative MMF (Morgan, Morgan and Finney) model erosion, but we have to consider LCC (Land Capability Classification) and LULC (Land Use Land Cover). Study demonstrated the use of RS (Remote Sensing) and GIS (Geographic Information System) in soil erosion risk assessment by deriving soil and vegetation parameters in the erosion models. Sub-watersheds were prioritized based on average soil loss and the area falls under various erosion risk classes for conservation planning. The annual rate of soil loss based on MMF model was classified into five soil erosion risk classes for soil conservation measures. From 11 sub watersheds, for the first priority of the watershed is catchment with the small area and the steep slope. Recommendation for steep areas (classes VI, VII, and VIII) land use allocation should be made to maintain forest functions.

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