scholarly journals Estimation of annual soil erosion dynamics (2005 - 2015) in Pakistan using Revised Universal Soil Loss Equation (RUSLE)

2021 ◽  
Author(s):  
Hammad Gilani ◽  
Adeel Ahmad ◽  
Isma Younes ◽  
Sawaid Abbas
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Management Practices ◽  
Climatic Factors ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Conservation Practice ◽  
Administrative Units ◽  
Soil Loss Equation

Abrupt changes in climatic factors, exploitation of natural resources, and land degradation contribute to soil erosion. This study provides the first comprehensive analysis of annual soil erosion dynamics in Pakistan for 2005 and 2015 using publically available climatic, topographic, soil type, and land cover geospatial datasets at 1 km spatial resolution. A well-accepted and widely applied Revised Universal Soil Loss Equation (RUSLE) was implemented for the annual soil erosion estimations and mapping by incorporating six factors; rainfall erosivity (R), soil erodibility (K), slope-length (L), slope-steepness (S), cover management (C) and conservation practice (P). We used a cross tabular or change matrix method to assess the annual soil erosion (ton/ha/year) changes (2005-2015) in terms of areas and spatial distriburtions in four soil erosion classes; i.e. Low (<1), Medium (1–5], High (5-20], and Very high (>20). Major findings of this paper indicated that, at the national scale, an estimated annual soil erosion of 1.79 ± 11.52 ton/ha/year (mean ± standard deviation) was observed in 2005, which increased to 2.47 ±18.14 ton/ha/year in 2015. Among seven administrative units of Pakistan, in Azad Jammu & Kashmir, the average soil erosion doubled from 14.44 ± 35.70 ton/ha/year in 2005 to 28.03 ± 68.24 ton/ha/year in 2015. Spatially explicit and temporal annual analysis of soil erosion provided in this study is essential for various purposes, including the soil conservation and management practices, environmental impact assessment studies, among others.

scholarly journals Application of Revised Universal Soil Loss Equation (Rusle) Model to Assess Soil Erosion in “Kalu Ganga” River Basin in Sri Lanka

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
D. L. D. Panditharathne ◽  
N. S. Abeysingha ◽  
K. G. S. Nirmanee ◽  
Ananda Mallawatantri
Keyword(s):  
Land Use ◽  
Sri Lanka ◽  
Soil Erosion ◽  
River Basin ◽  
Soil Loss ◽  
Management Practices ◽  
Agricultural Land ◽  
Universal Soil Loss Equation ◽  
Rusle Model ◽  
Soil Loss Equation

Soil erosion is one of the main forms of land degradation. Erosion contributes to loss of agricultural land productivity and ecological and esthetic values of natural environment, and it impairs the production of safe drinking water and hydroenergy production. Thus, assessment of soil erosion and identifying the lands more prone to erosion are vital for erosion management process. Revised Universal Soil Loss Equation (Rusle) model supported by a GIS system was used to assess the spatial variability of erosion occurring at Kalu Ganga river basin in Sri Lanka. Digital Elevation Model (30 × 30 m), twenty years’ rainfall data measured at 11 rain gauge stations across the basin, land use and soil maps, and published literature were used as inputs to the model. The average annual soil loss in Kalu Ganga river basin varied from 0 to 134 t ha−1 year−1 and mean annual soil loss was estimated at 0.63 t ha−1 year−1. Based on erosion estimates, the basin landscape was divided into four different erosion severity classes: very low, low, moderate, and high. About 1.68% of the areas (4714 ha) in the river basin were identified with moderate to high erosion severity (>5 t ha−1 year−1) class which urgently need measures to control soil erosion. Lands with moderate to high soil erosion classes were mostly found in Bulathsinghala, Kuruwita, and Rathnapura divisional secretarial divisions. Use of the erosion severity information coupled with basin wide individual RUSLE parameters can help to design the appropriate land use management practices and improved management based on the observations to minimize soil erosion in the basin.

scholarly journals Estimation of Soil Loss from Watershed for Identifying High Risk Erosion Zones Using GIS

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Chandra Sekhar Matli ◽  
Nimmy John
Keyword(s):  
High Risk ◽  
Soil Erosion ◽  
Watershed Management ◽  
Soil Conservation ◽  
Soil Loss ◽  
Management Practices ◽  
Universal Soil Loss Equation ◽  
Risk Zones ◽  
Economic Constraints ◽  
Soil Loss Equation

Soil loss from watersheds significantly influences the fertility soils and natural environment and hence it is a serious concern across the globe. Soil conservation is the top priority in watershed management though it is impractical to completely control soil erosion from all parts of watershed and hence achieve soil conservation. As controlling soil erosion in watersheds at micro level is difficult, broad measures which are economical and feasible are recommended for soil conservation. In order to plan suitable conservation techniques, it is essential to prioritize watersheds based on vulnerability to soil erosion. For identifying suitable soil conservation methods, it is necessary to consider critical erosion zones, threats to lives and property, socio-economic constraints and local challenges. Assessment of soil erosion is very important for arriving at the prioritization of watersheds for soil conservation. This paper reports the findings of the study carried out on Janagoan Mandal in Warangal District with hell of GIS techniques. Estimation of soil loss from the watershed is estimated using Universal Soil Loss Equation (USLE). Using the data available with various agencies, average annual erosion is estimated by developing GIS maps for six major watershed parameters. The watershed has been divided into sub watersheds and prioritization study is carried out considering factors that influence soil erosion. Using GIS tool and Universal Soil Loss Equation (USLE), the soil loss from the watershed is estimated and high risk zones are demarked. Soil loss from 80% of the watershed area is in the range of 0 - 200 tons/ha/year, while the high risk zones of erosion are about 12% of the area. Watershed management practices are recommended to reduce the soil loss from the high risk zones.

scholarly journals ESTIMATION OF ANNUAL AVERAGE SOIL LOSS, BASED ON RUSLE MODEL IN KALLAR WATERSHED, BHAVANI BASIN, TAMIL NADU, INDIA

2015 ◽  
Vol II-2/W2 ◽  
pp. 207-214 ◽  
Author(s):  
S. Abdul Rahaman ◽  
S. Aruchamy ◽  
R. Jegankumar ◽  
S. Abdul Ajeez
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Tamil Nadu ◽  
Rainfall Erosivity ◽  
Storm Events ◽  
Rill Erosion ◽  
Slope Length ◽  
Conservation Practice ◽  
Erosion Risk ◽  
Wide Range

Soil erosion is a widespread environmental challenge faced in Kallar watershed nowadays. Erosion is defined as the movement of soil by water and wind, and it occurs in Kallar watershed under a wide range of land uses. Erosion by water can be dramatic during storm events, resulting in wash-outs and gullies. It can also be insidious, occurring as sheet and rill erosion during heavy rains. Most of the soil lost by water erosion is by the processes of sheet and rill erosion. Land degradation and subsequent soil erosion and sedimentation play a significant role in impairing water resources within sub watersheds, watersheds and basins. Using conventional methods to assess soil erosion risk is expensive and time consuming. A comprehensive methodology that integrates Remote sensing and Geographic Information Systems (GIS), coupled with the use of an empirical model (Revised Universal Soil Loss Equation- RUSLE) to assess risk, can identify and assess soil erosion potential and estimate the value of soil loss. GIS data layers including, rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C) and conservation practice (P) factors were computed to determine their effects on average annual soil loss in the study area. The final map of annual soil erosion shows a maximum soil loss of 398.58 t/ h<sup>-1</sup>/ y<sup>-1</sup>. Based on the result soil erosion was classified in to soil erosion severity map with five classes, very low, low, moderate, high and critical respectively. Further RUSLE factors has been broken into two categories, soil erosion susceptibility (A=RKLS), and soil erosion hazard (A=RKLSCP) have been computed. It is understood that functions of C and P are factors that can be controlled and thus can greatly reduce soil loss through management and conservational measures.

scholarly journals The overshadow of the human evolvement process in the dynamics of soil drift of an agricultural watershed in Nilgiri Hills, India

2021 ◽  
Author(s):  
Sangeetha Ramakrishnan ◽  
Ambujam Neelakanda Pillai Kanniperumal
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Agricultural Watershed ◽  
Rainfall Erosivity ◽  
Landsat Images ◽  
Slope Length ◽  
Data Format ◽  
Conservation Practice ◽  
Soil Erosion Rate ◽  
Agricultural Planning

The Nilgiri Biosphere, being one of the critical catchments, a small agricultural watershed of Udhagamandalam has been analysed to show the need to improve the agriculture by reducing the soil erosion. For this study, the land use and land cover classification was undertaken using Landsat images to highlight the changes that have occurred between 1981 and 2019. The Revised Universal Soil Loss Equation (RUSLE) method and the Geographic Information System (GIS) was used in this study to determine the soil erosion vulnerability of Sillahalla watershed in the Nilgiri Hills in Tamilnadu. This study will help to promote the economic development of the watershed with proper agricultural planning and erosion management. This study focuses on the estimation of the average annual soil loss and to classify the spatial distribution of the soil loss as a map with the RUSLE method and GIS. To estimate the average annual soil loss of the study area, GIS layers of the RUSLE factors like rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C) and conservation practice (P) were computed in a raster data format. The total soil loss and average annual soil loss of the study area for 1981–1990,1991–2000, 2001–2010, 2011–2019 were found to be 0.2, 0.254, 0.3, 0.35 million t/year and 31.33, 37.78, 46.7, 51.89 t/ha/year, respectively. The soil erosion rate is classified into different classes as per the FAO guidelines and this severity classification map was prepared to identify the vulnerable areas.

Identification of Soil Erosion Prone Areas of Madhya Pradesh using USLE/ RUSLE

2021 ◽  
Vol 58 (02) ◽  
pp. 177-191
Author(s):  
Ashwini Suryawanshi ◽  
Anupam Kumar Nema ◽  
Rahul Kumar Jaiswal ◽  
Sukant Jain ◽  
Saswat Kumar Kar
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Madhya Pradesh ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Rusle Model ◽  
Percentage Area ◽  
Support Practice Factor ◽  
Factor C ◽  
Soil Loss Equation

Soil erosion is caused due to the dynamic action of erosive agents, mainly water, and is a major threat to the environment. Primary aim of the present study was to study the soil loss dynamics, and identify the environmental hotspots in Madhya Pradesh to aid decision-makers to plan and prioritize appropriate conservation measures. Universal Soil Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE) models were applied for erosion rate estimation by generating thematic maps of R (Rainfall erosivity factor), K (Soil erodibility factor), LS (Topographic factor), C (Cover and management factor), and P (Support practice factor) factors by using several input parameters in QGIS software. Subsequently, the different classes of soil erosion and percentage area under these classes were identified. The average annual soil erosion for the entire state as obtained from the USLE and RUSLE model were 5.80 t.ha-1.yr-1 and 6.64 t.ha-1.yr-1, respectively. The areas under severe risk were 1.09 % and 1.80 %, and very severe risk areas were 1.57 % and 1.83 % as estimated by USLE and RUSLE model, respectively. As compared to RUSLE model, USLE model underestimated rate of soil erosion for most river basins of the state as well as for the entire state

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 by RUSLE Model using Remote Sensing and GIS of Indla-Ghatkhed, District-Amravati, Maharashtra

2021 ◽  
pp. 247-253
Author(s):  
N. W. Ingole ◽  
S. S. Vinchurkar
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Crop Cover ◽  
Conservation Practice ◽  
Slope Length Factor ◽  
Pixel Value ◽  
Factor C ◽  
Soil Erosion Assessment

The catchment boundary of Indla Ghatkhed watershed covers an area about 14..62 sq km. The erosion is a natural geomorphic process occurring continually over the earth’s surface and it largely depends on topography, vegetation, soil and climatic variables and, therefore, exhibits pronounced spatial variability due to catchments heterogeneity and climatic variation. This problem can be circumvented by discrediting the catchments into approximately homogeneous sub-areas using Geographic Information System (GIS). Soil erosion assessment modeling was carried out based on the Revised Universal Soil Loss Equation (RUSLE). A set of factors are involved in RUSLE equation are A = Average annual soil loss (mt/ha/year), R = Rainfall erosivity factor (mt/ha/year), k = Soil erodibility factor, LS = Slope length factor, C = Crop cover management factor, P = Supporting conservation practice factor. These factors extracted from different surface features by analysis and brought in to raster format. The output depicts the amount of sediment rate from a particular grid in spatial domain and the pixel value of the outlet grid indicates the sediment yield at the outlet of the watershed.

scholarly journals ASSESSMENT AND MAPPING OF RAINFALL EROSIVITY INDEX (R) FOR MAJHA REGION, PUNJAB IS A STATE IN NORTHERN, INDIA

2021 ◽  
Vol 9 (3) ◽  
pp. 15-23
Author(s):  
Prashant Kumar
Keyword(s):  
Cover Crop ◽  
Soil Loss ◽  
Management Practices ◽  
Rainfall Intensity ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Northern India ◽  
Close Observation ◽  
Erosivity Index ◽  
Soil Loss Equation

Purpose: This study gives a critical assessment of the rainfall erosivity factor (R) for selected sites in the Majha region, representing different locations use of mean monthly rainfall data.  Methodology: By applying empirical methods, the rainfall intensity for all the locations were obtained and was further determined at three different intervals of 30-minutes, 45-minutes and 60-minutes, respectively. The rainfall erosivity factor (R) was calculated by the revised universal soil loss equation (RUSLE). Main Findings: Using RUSLE, the rainfall erosivity factor (R) for each of the locations was measured as follows; EI = 3878.49 (MJmmha-1hr-1), EI = 4013.71 (MJmmha-1hr-1), EI = 4302.24 (MJmmha-1hr-1) for Majha region of Amritsar, Tarntaran and Pathankot respectively. A close observation of the data obtained revealed that as rainfall intensity increased with the duration, the rainfall erosivity index reduced or decreased. Implications of study: Nevertheless, it is expected that if proper cover crop and management practices are applied despite the region, the study area falls within, rainfall erosivity can be cushioned, thus reducing further erosion tendencies and enhancing food production chances from productive lands within the area. The novelty of study: The rainfall erosivity factor (R) was calculated by the revised universal soil loss equation (RUSLE).

scholarly journals Mapping erosion prone areas in the Bouhamdane watershed (Algeria) using the Revised Universal Soil Loss Equation through GIS

2017 ◽  
Vol 32 (1) ◽  
pp. 13-23 ◽  
Author(s):  
Hamza Bouguerra ◽  
Abderrazak Bouanani ◽  
Kamel Khanchoul ◽  
Oussama Derdous ◽  
Salah Eddine Tachi
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Control ◽  
Water Erosion ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Vegetative Cover ◽  
Erosion Risk ◽  
Very High ◽  
Soil Loss Equation

Abstract Soil erosion by water is a major problem that the Northern part of Algeria witnesses nowadays; it reduces: the productivity of agricultural areas due to the loss of lands, and leads to the loss of storage capacity in reservoirs, the deterioration of water quality etc. The aim of this study is to evaluate the soil losses due to water erosion, and to identify the sectors which are potentially sensitive to water erosion in the Bouhamdane watershed, that is located in the northeastern part of Algeria. To this end, the Revised Universal Soil Loss Equation (RUSLE) was used. The application of this equation takes into account five parameters, namely the rainfall erosivity, topography, soil erodibility, vegetative cover and erosion control practices. The product of these parameters under GIS using the RUSLE mathematical equation has enabled evaluating an annual average erosion rate for the Bouhamdane watershed of 11.18 t·ha-1·y-1. Based on the estimates of soil loss in each grid cell, a soil erosion risk map with five risk classes was elaborated. The spatial distribution of risk classes was 16% very low, 41% low, 28% moderate, 12% high and 3% very high. Most areas showing high and very high erosion risk occurred in the lower Bouhamdane watershed around Hammam Debagh dam. These areas require adequate erosion control practices to be implemented on a priority basis in order to conserve soil resources and reduce siltation in the reservoir.

scholarly journals Assessment of Soil Erosion Hazard in Prambanan District Using RUSLE (Revised Universal Soil Loss Equation)

2021 ◽  
Vol 884 (1) ◽  
pp. 012010
Author(s):  
S. A Mulya ◽  
N. Khotimah
Keyword(s):  
Soil Loss ◽  
Hazard Index ◽  
Universal Soil Loss Equation ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Erosion Hazard ◽  
Erosion Processes ◽  
Factor C ◽  
Soil Loss Equation

Abstract Prambanan District which located in Daerah Istimewa Yogyakarta Province has the potential for land degradation due to erosion processes. With the characteristics of annual rainfall more than 2000 mm / year, topography with a slope of more than 20% in upland areas, as well as the conversion of upland to dryland agriculture are factors that can trigger the erosion process more quickly. If the rate of erosion speed exceeds the ability of the soil to regenerate the soil body, its productivity will be disrupted and accelerate the formation of critical soil. Therefore, it is necessary to know the estimated rate of erosion, tolerable distribution of erosion, and the potential danger of erosion that occurs. The purpose of this study was to (1) predict the rate of erosion, (2) calculate the permissible erosion value, (3) identify the rate & index of erosion hazard. Data were collected using field surveys and soil sampling using stratified random sampling techniques with land units as the unit of analysis. The value of erosion was predicted using the Revised Universal Soil Loss Equation (RUSLE) method. The RUSLE method is described by the following equation, A=R*K*L*S*C*P, where; A as estimated averages annual loss of soil, R is the rainfall erosivity factor, K is the soil erodibility factor, LS is the slope length factor, C is the cover management factor, & P is the conservation practice factor. The results showed that the erosion value ranged from 0.39 - 268.55 tons/ha/year. Permissible erosion ranges from 8.4 – 15 tons/ha/year for Latosol and 27.4 ton/ha/year for Regosol. The Rate of Erosion Hazard is dominated by moderate erosion, covering an area of 1330.7 ha or 31.8% of the total area. The Erosion Hazard Index is dominated by the low class (<1.0) which is covered over 2703.1 ha or 64.61% of the total area.

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