scholarly journals Soil Loss and Erosion Potential Estimation of Jhimruk Watershed, Nepal

2021 ◽  
Author(s):  
Parbati Pandey ◽  
Anup Gurung
Keyword(s):  
Local Governments ◽  
Soil Loss ◽  
Erosion Rate ◽  
Remote Sensing Data ◽  
Mean Annual Precipitation ◽  
Rainfall Erosivity ◽  
Conservation Priority ◽  
Barren Land ◽  
Soil Map ◽  
Elevation Model

Abstract The Revised Universal Soil Loss Equation (RUSLE) model was used in a Geographic Information System (GIS) to estimate the soil loss of the Jhimruk Watershed, Lumbini Province, Nepal. This research also aimed to calculate the erosional soil loss status of the local governments lying inside the watershed. For this, remote sensing data obtained from various sources were used to generate the factor maps to calculate the soil loss through RUSLE. A 13 year mean annual precipitation data from the 8 meteorological stations in and around the watershed was used to calculate the rainfall erosivity (R) factor. For the soil erodibility factor (K), the soil map of the watershed was clipped from the digital soil map of the world provided by FAO. Aster Digital Elevation Model of 30m resolution was used for the generation of LS factor map. For the computation of C-factor, the landcover map of the watershed produced in Arc GIS 10.2.1 through supervised classification of the Sentinal imagery of 10m resolution was used. The values were assigned based on the literatures in the case of C and P factors.The mean annual soil loss of the watershed was found to be 13.4 tons per hectare per year (t/ha/yr.). However, the soil loss was calculated to be as high as 182 t/ha/yr. 68.82% of the total area of the watershed lie under very low erosion class and thus have low conservation priority whereas 7.73 % of the total area lie under extremely high erosion class and thus have a conservation priority class of 1.The mean erosion rate from the barren land was found to be highest (23.179 t/ha/yr.) followed by agricultural (21.40 t/ha/yr.) and forest area had the lowest erosion rate i.e. 7.90 t/ha/yr.

scholarly journals Soil loss estimation using geographic information system in enfraz watershed for soil conservation planning in highlands of Ethiopia

2016 ◽  
Vol 5 (2) ◽  
pp. 21-30 ◽  
Author(s):  
Gizachew Tiruneh ◽  
Mersha Ayalew
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Soil Loss ◽  
Satellite Images ◽  
Geographic Information ◽  
Rainfall Erosivity ◽  
Gis And Remote Sensing ◽  
Digital Elevation ◽  
Elevation Model ◽  
Soil Loss Rate

Accelerated soil erosion is a worldwide problem because of its economic and environmental impacts. Enfraz watershed is one of the most erosion-prone watersheds in the highlands of Ethiopia, which received little attention. This study was, therefore, carried out to spatially predict the soil loss rate of the watershed with a Geographic Information System (GIS) and Remote Sensing (RS). Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions was used to estimate potential soil losses by utilizing information on rainfall erosivity (R) using interpolation of rainfall data, soil erodibility (K) using soil map, vegetation cover (C) using satellite images, topography (LS) using Digital Elevation Model (DEM) and conservation practices (P ) using satellite images. Based on the analysis, about 92.31% (5914.34 ha) of the watershed was categorized none to slight class which under soil loss tolerance (SLT) values ranging from 5 to 11 tons ha-1 year-1. The remaining 7.68% (492.21 ha) of land was classified under moderate to high class about several times the maximum tolerable soil loss. The total and an average amount of soil loss estimated by RUSLE from the watershed was 30,836.41 ton year-1 and 4.81 tons ha-1year-1, respectively.Int. J. Agril. Res. Innov. & Tech. 5 (2): 21-30, December, 2015

scholarly journals RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection: A Case of Fincha Catchment, Ethiopia

2021 ◽  
Vol 14 ◽  
pp. 117862212110462
Author(s):  
Meseret Wagari ◽  
Habtamu Tamiru
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Physical Factors ◽  
Rainfall Erosivity ◽  
Rusle Model ◽  
Raster Calculator ◽  
Gis Environment ◽  
Elevation Model ◽  
The Individual ◽  
P Factor

In this study, Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) platforms were successfully applied to quantify the annual soil loss for the protection of soil erosion in Fincha catchment, Ethiopia. The key physical factors such as rainfall erosivity ( R-factor), soil erodibility ( K-factor), topographic condition (LS-factor), cover management ( C-factor), and support practice ( P-factor) were prepared in GIS environment from rainfall, soil, Digital Elevation Model (DEM), Land use/Land cover (LULC) respectively. The RUSLE equation was used in raster calculator of ArcGIS spatial tool analyst. The individual map of the derived factors was multiplied in the raster calculator and an average annual soil loss ranges from 0.0 to 76.5 t ha−1 yr−1 was estimated. The estimated annual soil loss was categorized based on the qualitative and quantitative classifications as Very Low (0–15 t ha−1 yr−1), Low (15–45 t ha−1 yr−1), Moderate (45–75 t ha−1 yr−1), and High (>75 t ha−1 yr−1). It was found from the generated soil erosion severity map that about 45% of the catchment area was vulnerable to the erosion with an annual soil loss of (>75 t ha−1 yr−1), and this demonstrates that the erosion reduction actions are immediately required to ensure the sustainable soil resources in the study area. The soil erosion severity map generated based on RUSLE model and GIS platforms have a paramount role to alert all stakeholders in controlling the effects of the erosion. The results of the RUSLE model can also be further considered along with the catchment for practical soil loss protection practices.

scholarly journals Spatial Estimation of Soil Erosion Using RUSLE Modeling: the Case of kaffa Zone, South western Ethiopia

2021 ◽  
Author(s):  
Mesfin Anteneh ◽  
Dereje Biru
Keyword(s):  
Soil Loss ◽  
Satellite Images ◽  
Sustainable Land Use ◽  
Rainfall Erosivity ◽  
Model Estimate ◽  
Digital Elevation ◽  
Gis And Rs ◽  
Sentinel 2A ◽  
Elevation Model ◽  
Land Use And Management

Abstract This research was administered to spatially predict the soil loss rate of kaffa zone using model estimate and GIS. Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions was accustomed estimate potential soil losses by utilizing information on rainfall erosivity (R) using interpolation of rainfall data, soil erodibility (K) using DSMW soil map, vegetation cover (C) using Sentinel-2A satellite images, topography (LS) using Digital Elevation Model (DEM) and conservation practices (P ) using DEM and satellite images. supported the analysis, the mean and total annual soil loss potential of the study area was 30 tons ha-1 year-1 and 36264.5tons ha-1 year-1, respectively. The result also showed that about 2.89, 8.02, 15.31 and 73.78% of the study area were classified a slight, moderate, high and very high with values ranging 0 to 15 ,15 to50,50 to 200, and > 200 tons ha-1 year-1, respectively. The study demonstrates that the RUSLE using GIS and RS provides great advantage to spatially analyze multi-layer of knowledge. The expected amount of soil loss and its spatial distribution could facilitate sustainable land use and management.

Assessment of Soil Loss using Revised Universal Soil Loss Equation (RUSLE): A Remote Sensing and GIS Approach

2018 ◽  
Vol 2 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Ajaykumar Kadam ◽  
B. N. Umrikar ◽  
R. N. Sankhua
Keyword(s):  
Remote Sensing ◽  
Soil Loss ◽  
Remote Sensing Data ◽  
Tropical Rainfall Measuring Mission ◽  
Remote Sensing And Gis ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Gis Environment ◽  
Soil Loss Equation

A comprehensive methodology that combines Revised Universal Soil Loss Equation (RUSLE), Remote Sensing data and Geographic Information System (GIS) techniques was used to determine the soil loss vulnerability of an agriculture mountainous watershed in Maharashtra, India. The spatial variation in rate of annual soil loss was obtained by integrating raster derived parameter in GIS environment. The thematic layers such as TRMM [Tropical Rainfall Measuring Mission] derived rainfall erosivity (R), soil erodibility (K), GDEM based slope length and steepness (LS), land cover management (C) and factors of conservation practices (P) were calculated to identify their effects on average annual soil loss. The highest potential of estimated soil loss was 688.397 t/ha/yr. The mean annual soil loss is 1.26 t/ha/yr and highest soil loss occurs on the main watercourse, since high slope length and steepness. The spatial soil loss maps prepared with RUSLE method using remote sensing and GIS can be helpful as a lead idea in arising plans for land use development and administration in the ecologically sensitive hilly areas.

scholarly journals Assessment of Soil Erosion at Multiple Spatial Scales Following Land Use Changes in 1980-2017 in the Black Soil Region, (NE) China

2020 ◽  
Author(s):  
Haiyan Fang ◽  
Zemeng Fan
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Rate ◽  
Spatial Scales ◽  
Land Use Changes ◽  
Arable Land ◽  
Rainfall Erosivity ◽  
Erosion Rates ◽  
Soil Erosion Rate

Impact of land use and land cover (LULC) change on soil erosion is still imperfectly understood, especially in northeastern China (NEC). Based on the Revised Universal Loss Equation (RUSLE), the variability of soil erosion at different spatial scales following land use changes in1980, 1990, 2000, 2010, and 2017 was analyzed. The regionally spatial patterns of soil loss coincided with the topography, rainfall erosivity, soil erodibility, and use patterns, and around 45% soil loss came from arable land. Regionally, soil erosion rates increased from 1980 to 2010 and decreased from 2010 to 2017, ranging from 3.91 to 4.45 t ha-1 yr-1 with an average of 4.22 t ha-1 yr-1 in 1980-2017. The rates of soil erosion less than 1.41 t ha-1 yr-1 decreased from 1980 to 2010, and increased from 2010 to 2017, and opposite changing patterns occurred in higher erosion classes (i.e., above 5 t ha-1 yr-1). At a provincial scale, Liaoning Province experienced the highest soil erosion rate of 9.43 t ha-1 yr-1, followed by Jilin Province, the east Inner Mongolia, and Heilongjing Province. Arable land continuously increased at the expense of forest in the high-elevation and steep-slope areas from 1980 to 2010, and decreased from 2010 to 2017, resulting in increased areas with erosion rates higher than 7.05 t ha-1 yr-1. At a county scale, around 75% of the countries had soil erosion rate higher than its tolerance level. The county numbers with higher erosion rate increased in 1980-2010 and decreased in 2010- 2017, resulting from the sprawl and withdrawal of arable land. The results indicate that appropriate policies can control soil loss through limiting arable land sprawl in areas of unfavorable regions in the NEC.

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 Assessing soil loss using GIS based RUSLE methodology. Case of the Bou Namoussa watershed – North-East of Algeria

2018 ◽  
Vol 36 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Chemss Eddine Bouhadeb ◽  
Mohamed Redha Menani ◽  
Hamza Bouguerra ◽  
Oussama Derdous
Keyword(s):  
Vegetation Cover ◽  
Soil Loss ◽  
Erosion Rate ◽  
Geographical Information ◽  
Rainfall Erosivity ◽  
Natural Environments ◽  
North East ◽  
The North ◽  
Soil Erosion Rate ◽  
Very High

AbstractThis study aims to estimating annual soil erosion rate and its spatial distribution in the Bou Namoussa water-shed located in the North-East of Algeria by applying the revised universal soil loss equation (RUSLE) within a Geographical Information System environment (GIS). The application of the RUSLE model in different natural environments and on every scale takes into account five key factors namely: the rainfall erosivity, the soil erodibility, the steepness and length of slopes, the vegetation cover and the conservation support practices. Each of these factors was generated in GIS as a raster layer, their combination, resulted in the development of a soil loss map indicating an average erosion rate of 7.8 t·ha−1·y−1. The obtained soil loss map was classified into four erosion severity classes; low, moderate, high and very high severity representing respectively 40, 30.48, 22.59 and 6.89% of the total surface. The areas, showing moderate, high and very high erosion rates which represent more than half of the basin area were found generally located in regions having high erodibility soils, steep slopes and low vegetation cover. These areas should be considered as priorities in future erosion control programs in order to decrease the siltation rate in the Cheffia reservoir.

scholarly journals Estimativa do Potencial Natural de Erosão dos Solos da Bacia Hidrográfica do Alto Gurguéia, Piauí-Brasil, com uso de Sistema de Informação Geográfica / Estimation of the Natural Soil Erosion Potential of the Upper Gurguéia Basin, Piauí-Brazil (...)

2017 ◽  
Vol 27 (1) ◽  
pp. 84
Author(s):  
Reurysson Chagas de Sousa Morais ◽  
Marta Celina Linhares Sales
Keyword(s):  
Environmental Planning ◽  
Remote Sensing Data ◽  
Natural Soil ◽  
Physical Parameters ◽  
Rainfall Erosivity ◽  
Topographic Factor ◽  
Digital Elevation ◽  
Triggering Factor ◽  
Elevation Model ◽  
Basin Area

<p>O objetivo deste trabalho é apresentar uma estimativa do Potencial Natural de Erosão (PNE) dos solos da bacia hidrográfica do alto Gurguéia (BHAG), Piauí, calculado por meio do produto dos parâmetros físicos da Equação Universal de Perda de Solo: erosividade das chuvas (fator R), fator topográfico (fator LS) e erodibilidade dos solos (fator K) apoiado com uso de Sistema de Informação Geográfica e dados de Sensoriamento Remoto. O fator R foi calculado utilizando dados de precipitação estimados por satélite, enquanto o fator LS foi obtido a partir do modelo digital de elevação Alos World 3D processado utilizando a ferramenta LS-TOOL. Já os valores do fator K foram calculados com base na composição granulométrica do horizonte A dos solos da bacia. Os resultados indicam que a BHAG apresenta baixo potencial natural de erosão, tendo em vista que 80% da área da bacia apresenta PNE ≤ 400 t.ha<sup>-1</sup>.ano<sup>-1</sup>, no entanto, nas áreas declivosas associadas às escarpas das bordas dos planaltos, chapadas e superfícies residuais, esses valores ultrapassam 1.600 t.ha<sup>-1</sup>.ano<sup>-1</sup> (6,4% da área total) demostrando forte influência do fator topográfico na estimativa do PNE em detrimento aos demais parâmetros, comprovado por meio da análise de correlação entre PNE e os fatores R, K e LS. A identificação da topográfica como fator principal de desencadeamento de processos erosivos frente à condição de excessiva erosividade das chuvas na BHAG (R<sub>média</sub> = 6.297 Mj.mm.ha<sup>-1</sup>.h<sup>-1</sup>.ano<sup>-1</sup>) evidencia a alto risco de erosão a que estão submetidas as áreas da bacia que apresentam declividade acentuada, condição que demanda a adoção de planejamento ambiental com vista a disciplinar o uso e ocupação compatibilizado as categorias de uso às condições ambientais da bacia.</p><p><strong>Palavras–chave:</strong> degradação dos solos, geoprocessamento, rio Gurguéia.</p><p><strong>Abstract</strong></p><p>The objective of this work is to present an estimate of the Natural Potential Erosion (NPE) of the soils of the upper Gurguéia basin, Piauí, calculated using the product of the physical parameters of the Universal Soil Loss Equation: rainfall erosivity factor R), topographic factor (LS factor) and soil erodibility (factor K) supported using Geographic Information System and Remote Sensing data. The R factor was calculated using estimated satellite precipitation data, while the LS factor was obtained from the Alos World 3D digital elevation model processed using the LS-TOOL tool. The K factor values were calculated based on the granulometric composition of the A horizon of the soils of the basin. The results indicate that the upper Gurguéia basin presents a low natural erosion potential, considering that 80% of the basin area presents NPE ≤ 400 t.ha<sup>-1</sup>.y<sup>-1</sup>, however, in the sloping areas associated with the escarpments of the plateau borders , plateaus and residual surfaces, these values exceed 1,600 t.ha<sup>-1</sup>.y<sup>-1</sup> (6.4% of the total area), demonstrating a strong influence of the topographic factor in the estimation of NPE in detriment to the other parameters, as evidenced by the analysis of correlation between NPE and R, K and LS factors. The identification of topography as the main triggering factor for erosive processes in the upper Gurguéia basin (R<sub>average</sub> = 6,297 Mj.mm.ha<sup>-1</sup>.h<sup>-1</sup>.y<sup>-1</sup>) shows a high risk of erosion are submitted to areas of the basin that show marked declivity, a condition that requires the adoption of environmental planning in order to discipline the use and occupation compatible the categories of use to the environmental conditions of the basin.</p><p><strong>Keywords</strong>: soil degradation, geoprocessing, Gurguéia river.</p>

scholarly journals Predicting soil erosion using Rusle and NDVI time series from TM Landsat 5

2014 ◽  
Vol 49 (3) ◽  
pp. 215-224 ◽  
Author(s):  
Daniel Fonseca de Carvalho ◽  
Valdemir Lucio Durigon ◽  
Mauro Antonio Homem Antunes ◽  
Wilk Sampaio de Almeida ◽  
Paulo Tarso Sanches de Oliveira
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Central Area ◽  
Remote Sensing Data ◽  
High Water ◽  
Rainfall Erosivity ◽  
Erosion Risk ◽  
R Factor ◽  
Factor C ◽  
Soil Losses

The objective of this work was to evaluate the seasonal variation of soil cover and rainfall erosivity, and their influences on the revised universal soil loss equation (Rusle), in order to estimate watershed soil losses in a temporal scale. Twenty-two TM Landsat 5 images from 1986 to 2009 were used to estimate soil use and management factor (C factor). A corresponding rainfall erosivity factor (R factor) was considered for each image, and the other factors were obtained using the standard Rusle method. Estimated soil losses were grouped into classes and ranged from 0.13 Mg ha-1 on May 24, 2009 (dry season) to 62.0 Mg ha-1 on March 11, 2007 (rainy season). In these dates, maximum losses in the watershed were 2.2 and 781.5 Mg ha-1 , respectively. Mean annual soil loss in the watershed was 109.5 Mg ha-1 , but the central area, with a loss of nearly 300.0 Mg ha-1 , was characterized as a site of high water-erosion risk. The use of C factor obtained from remote sensing data, associated to corresponding R factor, was fundamental to evaluate the soil erosion estimated by the Rusle in different seasons, unlike of other studies which keep these factors constant throughout time.

scholarly journals Soil Loss Estimation for Conservation Planning in the Welmel Watershed of the Genale Dawa Basin, Ethiopia

Agronomy ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 777 ◽  
Author(s):  
Yared Mesfin Tessema ◽  
Justyna Jasińska ◽  
Lemma Tiki Yadeta ◽  
Marcin Świtoniak ◽  
Radosław Puchałka ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Land Management ◽  
Water Conservation ◽  
Soil Loss ◽  
Management Practices ◽  
Rainfall Erosivity ◽  
Sustainable Livelihood ◽  
Digital Elevation ◽  
Elevation Model ◽  
Agricultural Yields

As a form of environmental degradation, soil degradation directly or indirectly affects many lives through decreased agricultural yields, increased flooding and habitat loss. Soil loss has been increasing in most parts of the world and is most pronounced in tropical developing countries where there is poor or zero soil and water conservation (SWC) planning and management activities. Identifying areas prone to soil erosion has also been inadequate, having not been informed by dedicated scientific studies. This is true of the poorly understood watershed of Welmel in the Oromia region of Ethiopia, where most livelihoods heavily rely upon agriculture. To plan effective SWC management techniques, a solid knowledge of spatial variations across different climate, land use and soil erosion is essential. This study has aimed at identifying potential areas needing SWC practices through conducting a spatial modeling of soil erosion within the Welmel watershed’s Genale Dawa basin using a geographic information system (GIS), remote sensing (RS), multiple factors as land uses and climate. The Welmel catchment is located in southeastern Ethiopia and extends between 5°0′0″ N–7°45′00″ N and 39°0′0″ E–41°15′0″ E. The revised universal soil loss equation (RUSLE), which was previously adapted to Ethiopian conditions, was used to estimate potential soil loss. It used information on interpolated rainfall erosivity (R), soil erodibility (K), vegetation cover (C) and topography (LS) from a digital elevation model (DEM) and that of conservation practices (P) from satellite images. The study demonstrates that the RUSLE using GIS and RS considering different climates and land management practices provides a great advantage in that it allows one to spatially analyze multilayer data in order to identify soil erosion-prone areas and thereby develop the most appropriate watershed management strategy. The mean soil loss was determined to be 31 tons ha−1 year−1 and it varied between 0 and 169 tons ha−1 year−1. About 79% of the watershed lies within the tolerable level of 11 tons ha−1 year−1. However, the remaining 21% has a high soil truncation trait, mainly due to its steeper slope and use as cultivated land. Our study identifies cultivated and deforested areas of the watershed as the potential SWC practice demanding areas. Thus, the application of RUSEL using GIS across different land management practices and climate zones is a potential tool for identifying SWC demanding sites. This remains helpful in efforts towards sustainable land management practices for the sustainable livelihood of the local human population.

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