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.

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 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 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 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.

GIS estimation of annual average soil loss rate from Hangar River watershed using RUSLE

2019 ◽  
Vol 11 (2) ◽  
pp. 529-539 ◽  
Author(s):  
Mahmud Mustefa ◽  
Fekadu Fufa ◽  
Wakjira Takala
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Loss Rate ◽  
Study Finding ◽  
Mitigation Measures ◽  
Rainfall Erosivity ◽  
Vegetative Cover ◽  
Annual Average ◽  
Slope Length ◽  
Soil Loss Rate

Abstract Currently, soil erosion is the major environmental problem in the Blue Nile, Hangar watershed in particular. This study aimed to estimate the spatially distributed mean annual soil erosion and map the most vulnerable areas in Hangar watershed using the revised universal soil loss equation. In this model, rainfall erosivity (R-factor), soil erodibility (K-factor), slope steepness and slope length (LS-factor), vegetative cover (C-factor), and conservation practice (P-factor) were considered as the influencing factors. Maps of these factors were generated and integrated in ArcGIS and then the annual average soil erosion rate was determined. The result of the analysis showed that the amount of soil loss from the study area ranges from 1 to 500 tha−1 yr−1 with an average annual soil loss rate of 32 tha−1 yr−1. Considering contour ploughing with terracing as a fully developed watershed management, the resulting soil loss rate was reduced from 32 to 19.2 tha−1 yr−1. Hence, applying contour ploughing with terracing effectively reduces the vulnerability of the watershed by 40%. Based on the spatial vulnerability of the watershed, most critical soil erosion areas were situated in the steepest part of the watershed. The result of the study finding is helpful for stakeholders to take appropriate mitigation measures.

scholarly journals Potential Soil Loss Estimation and Erosion-Prone Area Prioritization Using RUSLE, GIS, and Remote Sensing in Chereti Watershed, Northeastern Ethiopia

2021 ◽  
Vol 14 ◽  
pp. 117862212098581
Author(s):  
Ajanaw Negese ◽  
Endalkachew Fekadu ◽  
Haile Getnet
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Conservation Planning ◽  
Water Conservation ◽  
Soil Loss ◽  
Rainfall Erosivity ◽  
Gis And Remote Sensing ◽  
Soil Erosion Rate ◽  
Conservation Measures ◽  
Prone Area

Soil erosion by water is the major form of land degradation in Chereti watershed, Northeastern Ethiopia. This problem is exacerbated by high rainfall after a long period of dry seasons, undulating topography, intensive cultivation, and lack of proper soil and water conservation measures. Hence, this study aimed to estimate the 23 years (1995-2018) average soil erosion rate of the watershed and to identify and prioritize erosion-vulnerable subwatersheds for conservation planning. The integration of the revised universal soil loss equation (RUSLE), geographic information system, and remote sensing was applied to estimate the long-term soil loss of the watershed. The RUSLE factors such as rainfall erosivity ( R), soil erodibility ( K), topography ( LS), cover and management ( C), and support and conservation practices ( P) factors were computed and overlayed to estimate the soil loss. The result showed that the annual soil loss rate of the watershed ranged up to 187.47 t ha−1 year−1 in steep slope areas with a mean annual soil loss of 38.7 t ha−1 year−1, and the entire watershed lost a total of about 487 057.7 tons of soil annually. About 57.9% of the annual watershed soil loss was generated from 5 subwatersheds which need prior intervention for the planning and implementation of soil conservation measures. The integrated use of RUSLE with GIS and remote sensing was found to be indispensable, less costly, and effective for the estimation of soil erosion, and prioritization of vulnerable subwatersheds for conservation planning.

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 Changes in soil erosion and sediment transport based on the RUSLE model in Zhifanggou watershed, China

2018 ◽  
Vol 377 ◽  
pp. 9-18
Author(s):  
Lei Wang ◽  
Ju Qian ◽  
Wen-Yan Qi ◽  
Sheng-Shuang Li ◽  
Jian-Long Chen
Keyword(s):  
Sediment Transport ◽  
Soil Erosion ◽  
Soil Loss ◽  
Geographical Information Systems ◽  
Gansu Province ◽  
Geographical Information ◽  
Soil Composition ◽  
Landsat Images ◽  
Slope Length ◽  
Terrain Parameters

Abstract. In this paper, changes of sediment yield and sediment transport were assessed using the Revised Universal Soil Loss Equation (RUSLE) and Geographical Information Systems (GIS). This model was based on the integrated use of precipitation data, Landsat images in 2000, 2005 and 2010, terrain parameters (slope gradient and slope length) and soil composition in Zhifanggou watershed, Gansu Province, Northwestern China. The obtained results were basically consistent with the measured values. The results showed that the mean modulus of soil erosion is 1224, 1118 and 875 t km−2 yr−1 and annual soil loss is 23 130, 21 130 and 16 536 in 2000, 2005 and 2010 respectively. The measured mean erosion modulus were 1581 and 1377 t km−2 yr−1, and the measured annual soil loss were 29 872 and 26 022 t in 2000 and 2005. From 2000 to 2010, the amount of soil erosion was reduced yearly. Very low erosion and low erosion dominated the soil loss status in the three periods, and moderate erosion followed. The zones classified as very low erosion were increasing, whereas the zones with low or moderate erosion were decreasing. In 2010, no zones were classified as high or very high soil erosion.

scholarly journals Regional soil erosion assessment based on a sample survey and geostatistics

2018 ◽  
Vol 22 (3) ◽  
pp. 1695-1712 ◽  
Author(s):  
Shuiqing Yin ◽  
Zhengyuan Zhu ◽  
Li Wang ◽  
Baoyuan Liu ◽  
Yun Xie ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Sample Survey ◽  
Shaanxi Province ◽  
Rainfall Erosivity ◽  
Soil Erodibility ◽  
Slope Length ◽  
Slope Steepness ◽  
Soil Erosion Assessment

Abstract. Soil erosion is one of the most significant environmental problems in China. From 2010 to 2012, the fourth national census for soil erosion sampled 32 364 PSUs (Primary Sampling Units, small watersheds) with the areas of 0.2–3 km2. Land use and soil erosion controlling factors including rainfall erosivity, soil erodibility, slope length, slope steepness, biological practice, engineering practice, and tillage practice for the PSUs were surveyed, and the soil loss rate for each land use in the PSUs was estimated using an empirical model, the Chinese Soil Loss Equation (CSLE). Though the information collected from the sample units can be aggregated to estimate soil erosion conditions on a large scale; the problem of estimating soil erosion condition on a regional scale has not been addressed well. The aim of this study is to introduce a new model-based regional soil erosion assessment method combining a sample survey and geostatistics. We compared seven spatial interpolation models based on the bivariate penalized spline over triangulation (BPST) method to generate a regional soil erosion assessment from the PSUs. Shaanxi Province (3116 PSUs) in China was selected for the comparison and assessment as it is one of the areas with the most serious erosion problem. Ten-fold cross-validation based on the PSU data showed the model assisted by the land use, rainfall erosivity factor (R), soil erodibility factor (K), slope steepness factor (S), and slope length factor (L) derived from a 1 : 10 000 topography map is the best one, with the model efficiency coefficient (ME) being 0.75 and the MSE being 55.8 % of that for the model assisted by the land use alone. Among four erosion factors as the covariates, the S factor contributed the most information, followed by K and L factors, and R factor made almost no contribution to the spatial estimation of soil loss. The LS factor derived from 30 or 90 m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data worsened the estimation when used as the covariates for the interpolation of soil loss. Due to the unavailability of a 1 : 10 000 topography map for the entire area in this study, the model assisted by the land use, R, and K factors, with a resolution of 250 m, was used to generate the regional assessment of the soil erosion for Shaanxi Province. It demonstrated that 54.3 % of total land in Shaanxi Province had annual soil loss equal to or greater than 5 t ha−1 yr−1. High (20–40 t ha−1 yr−1), severe (40–80 t ha−1 yr−1), and extreme (> 80 t ha−1 yr−1) erosion occupied 14.0 % of the total land. The dry land and irrigated land, forest, shrubland, and grassland in Shaanxi Province had mean soil loss rates of 21.77, 3.51, 10.00, and 7.27 t ha−1 yr−1, respectively. Annual soil loss was about 207.3 Mt in Shaanxi Province, with 68.9 % of soil loss originating from the farmlands and grasslands in Yan'an and Yulin districts in the northern Loess Plateau region and Ankang and Hanzhong districts in the southern Qingba mountainous region. This methodology provides a more accurate regional soil erosion assessment and can help policymakers to take effective measures to mediate soil erosion risks.

scholarly journals Soil loss estimation and severity mapping using RUSLE model and GIS: a case study in Megech Watershed, Ethiopia

2022 ◽  
Author(s):  
Legese Abebaw Getu ◽  
Attila Nagy ◽  
Hailu Kendie Addis
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Rate ◽  
Risk Category ◽  
Model Parameters ◽  
Main Concern ◽  
Slope Length ◽  
Rusle Model ◽  
Soil Erosion Rate ◽  
Very High

Abstract AbstractBackground: Soil erosion is the most serious problem that affects economic development, food security, and ecosystem services which is the main concern in Ethiopia. This study focused on quantifying soil erosion rate and severity mapping of the Megech watershed for effective planning and decision-making processes to implement protection measures. The RUSLE model integrated with ArcGIS software was used to conduct the present study. The six RUSLE model parameters: erosivity, erodibility, slope length and steepness, cover management, and erosion control practices were used as input parameters to predict the average annual soil loss and identify erosion hotspots in the watershed. Results: The RUSLE estimated 1,399,210 tons yr-1 total soil loss from the watershed with a mean annual soil loss of 32.84 tons ha-1yr-1. The soil erosion rate was varied from 0.08 to greater than 500 tons ha-1yr-1. A severity map with seven severity classes was created for 27 sub-watersheds: low (below 10), moderate (10-20), high (20-30), very high (30-35), severe (35-40), very severe (40-45) and extremely severe (above 45) in which the values are in tons ha-1yr-1. The area coverage was 6.5%, 11.1%, 8.7%, 22%, 30.9%, 13.4%, and 7.4% for low, moderate, high, very high, severe, very severe, and extremely severe erosion classes respectively. Conclusion: About 82 % of the watershed was found in more than the high-risk category which reflects the need for immediate land management action. This paper could be important for decision-makers to prioritize critical erosion hotspot areas for comprehensive and sustainable management of the watershed.

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