scholarly journals Spatial Estimation of Soil Erosion Using RUSLE Modeling: A case study of Dolakha District, Nepal

2020 ◽  
Author(s):  
Pawan Thapa
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Agricultural Land ◽  
Management Planning ◽  
Erosion Risk ◽  
Spatial Estimation ◽  
Soil Erosion Rate ◽  
Severe Erosion ◽  
Planning Processes

Abstract Background: In the mountainous areas, soil erosion causes the topsoil loss which decreases fertility in the agricultural land. Spatial estimation of soil erosion essential for an agriculture-dependent country like Nepal for developing erosion control plans. This study was conducted for estimation of soil erosion impacts on Dolakha using the Revised Universal Soil Loss Equation (RUSLE) model, also analyzes the effect of Land Use and Land Cover (LULC) on soil erosion.Results: The soil erosion rate had been categorized into 6 erosion classes based on the erosion severity, and 5.01% of the areas was found to be under extreme severe erosion risk (> 80 Mg ha-1yr-1) that should be addressed by decision-maker for reducing its rate and consequences. Followed by 10 % areas were classified under high to severe with the rate of soil loss range from 10 to 80 Mg ha-1yr-1. While 15% and 70% of areas remained in moderate and low-risk zone respectively. The results suggest this district northeastern part suffers from a high soil erosion risk due to steep slope.Conclusions: The result produces a spatial distribution of soil erosion over Dolakha, which can be applied for conservation and management planning processes, at the policy level, by land-use planners and decision-makers.

scholarly journals Spatial Estimation of Soil Erosion Using RUSLE Modeling: A case study of Dolakha District

2020 ◽  
Author(s):  
Pawan Thapa
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Agricultural Land ◽  
Management Planning ◽  
Erosion Risk ◽  
Spatial Estimation ◽  
Soil Erosion Rate ◽  
Severe Erosion ◽  
Planning Processes

Abstract Background: Soil erosion is one of the major causes of the topsoil loss, that decreases fertility in the agricultural land of a mountainous region. Spatial estimation of soil erosion is necessary for an agriculture-dependent country like Nepal for preparing erosion control plans. The purpose of this study was to estimate soil erosion of Dolakha using the Revised Universal Soil Loss Equation (RUSLE) model, which analyzes the effect of Land Use and Land Cover (LULC) and slope exposition on soil erosion.Results: The soil erosion rate had been categorized into 6 erosion classes based on the erosion severity, and 5.01% of the areas was found to be under extreme severe erosion risk (> 80 Mg ha-1yr-1) that need to address by decision-maker for reducing the risk of erosion. Followed by 10 % areas were classified under high to severe with rate of soil loss range from 10 to 80 Mg ha-1yr-1. While 15% and 70% areas were still remain in moderate and low risk zone respectively. The study demonstrated the northeastern part of district suffer high soil erosion risk due to steep slope and rugged landforms.Conclusions: The result produce spatial distribution of soil erosion over Dolakha, which can be applied for conservation and management planning processes, at the policy level, by land use planners and decision-makers.

scholarly journals Spatial Estimation of Soil Erosion Using RUSLE Modeling: A case study of Dolakha District, Nepal

2020 ◽  
Author(s):  
Pawan Thapa
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Agricultural Land ◽  
Decision Makers ◽  
Management Planning ◽  
Erosion Risk ◽  
Spatial Estimation ◽  
The North ◽  
Soil Erosion Rate ◽  
Severe Erosion

Abstract Background: Soil erosion causes topsoil loss, which decreases fertility in agricultural land. Spatial estimation of soil erosion essential for an agriculture-dependent country like Nepal for developing its control plans. This study evaluated impacts on Dolakha using the Revised Universal Soil Loss Equation (RUSLE) model; analyses the effect of Land Use and Land Cover (LULC) on soil erosion. Results: The soil erosion rate categorized into six classes based on the erosion severity, and 5.01% of the areas found under extreme severe erosion risk (> 80 Mg ha-1yr-1) addressed by decision-makers for reducing its rate and consequences. Followed by 10 % classified between high and severe range from 10 to 80 Mg ha-1yr-1. While 15% and 70% of areas remained in a moderate and low-risk zone, respectively. Result suggests the area of the north-eastern part suffers from a high soil erosion risk due to steep slope. Conclusions: The result produces a spatial distribution of soil erosion over Dolakha, which applied for conservation and management planning processes, at the policy level, by land-use planners and decision-makers.

scholarly journals Spatial Estimation of Soil Erosion Using RUSLE Modeling: A case study of Dolakha District, Nepal

2020 ◽  
Author(s):  
Pawan Thapa
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Agricultural Land ◽  
Decision Makers ◽  
Management Planning ◽  
Erosion Risk ◽  
Spatial Estimation ◽  
The North ◽  
Soil Erosion Rate ◽  
Severe Erosion

Abstract Background: Soil erosion causes topsoil loss, which decreases fertility in agricultural land. Spatial estimation of soil erosion essential for an agriculture-dependent country like Nepal for developing its control plans. This study evaluated impacts on Dolakha using the Revised Universal Soil Loss Equation (RUSLE) model; analyses the effect of Land Use and Land Cover (LULC) on soil erosion.Results: The soil erosion rate categorized into six classes based on the erosion severity, and 5.01% of the areas found under extreme severe erosion risk (> 80 Mg ha-1yr-1) addressed by decision-makers for reducing its rate and consequences. Followed by 10 % classified between high and severe range from 10 to 80 Mg ha-1yr-1. While 15% and 70% of areas remained in a moderate and low-risk zone, respectively. Result suggests the area of the north-eastern part suffers from a high soil erosion risk due to steep slope.Conclusions: The result produces a spatial distribution of soil erosion over Dolakha, which applied for conservation and management planning processes, at the policy level, by land-use planners and decision-makers.

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.

Assessing the Impacts of Land Use Policy on Soil Erosion Risk

2001 ◽  
pp. 146-156
Author(s):  
A. Van Rompaey ◽  
G. Govers
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Soil Degradation ◽  
Agricultural Land ◽  
Soil Depth ◽  
Degradation Process ◽  
Drainage Ditches ◽  
Nitrogen And Phosphorus ◽  
Erosion Risk

Soil erosion is regarded as a major and widespread soil degradation process. The consequences of soil erosion occur both on- and off-site. On-site consequences are particularly important on agricultural land where the redistribution of soil within a field, the loss of soil from a field, the breakdown of soil structure and the decline in organic matter and nutrients result in a reduction of the cultivable soil depth and a decline in soil fertility (Morgan, 1996). Off-site problems result from sedimentation downstream which reduces the capacity of rivers and drainage ditches, enhances the risk of flooding, blocks irrigation canals and shortens the design life of reservoirs (Verstraeten and Poesen, 1999). Sediment is also a pollutant in its own right, and through the chemicals absorbed it can increase the levels of nitrogen and phosphorus in water bodies and result in eutrophication (Steegen et al., subm.). The rate of soil loss is normally expressed in units of mass or volume per unit area per unit time. Young (1969) quotes annual rates of the order of 0.0045 Mg ha-1 for areas of moderate relief and 0.45 Mg ha-1 for steep relief. For comparison, rates from agricultural land are in the range of 5 to 500 Mg ha-1 (Morgan, 1996; Van Rompaey et al., 2000).

scholarly journals Analysis of Ownership Data from Consolidated Land Threatened by Water Erosion in the Vlára Basin, Slovakia

2020 ◽  
Vol 13 (1) ◽  
pp. 51
Author(s):  
Alexandra Pagáč Mokrá ◽  
Jakub Pagáč ◽  
Zlatica Muchová ◽  
František Petrovič
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Agricultural Land ◽  
Water Erosion ◽  
Land Consolidation ◽  
Land Fragmentation ◽  
Erosion Risk ◽  
Definition Of ◽  
The Relationship ◽  
Soil Loss Equation

Water erosion is a phenomenon that significantly damages agricultural land. The current land fragmentation in Slovakia and the complete ambiguity of who owns it leads to a lack of responsibility to care for the land in its current condition, which could affect its sustainability in the future. The reason so much soil has eroded is obvious when looking at current land management, with large fields, a lack of windbreaks between them, and no barriers to prevent soil runoff. Land consolidation might be the solution. This paper seeks to evaluate redistributed land and, based on modeling by the Universal Soil Loss Equation (USLE) method, to assess the degree of soil erosion risk. Ownership data provided information on how many owners and what amount of area to consider, while taking into account new conditions regarding water erosion. The results indicate that 2488 plots of 1607 owners which represent 12% of the model area are still endangered by water erosion, even after the completion of the land consolidation project. The results also presented a way of evaluating the territory and aims to trigger a discussion regarding an unambiguous definition of responsibility in the relationship between owner and user.

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 Estimating Human Impacts on Soil Erosion Considering Different Hillslope Inclinations and Land Uses in the Coastal Region of Syria

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2786 ◽  
Author(s):  
Safwan Mohammed ◽  
Hazem G. Abdo ◽  
Szilard Szabo ◽  
Quoc Bao Pham ◽  
Imre J. Holb ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Cover ◽  
Soil Loss ◽  
Agricultural Land ◽  
Human Impacts ◽  
Coastal Region ◽  
Water Erosion ◽  
Land Uses ◽  
Erosion Processes

Soils in the coastal region of Syria (CRoS) are one of the most fragile components of natural ecosystems. However, they are adversely affected by water erosion processes after extreme land cover modifications such as wildfires or intensive agricultural activities. The main goal of this research was to clarify the dynamic interaction between erosion processes and different ecosystem components (inclination, land cover/land use, and rainy storms) along with the vulnerable territory of the CRoS. Experiments were carried out in five different locations using a total of 15 erosion plots. Soil loss and runoff were quantified in each experimental plot, considering different inclinations and land uses (agricultural land (AG), burnt forest (BF), forest/control plot (F)). Observed runoff and soil loss varied greatly according to both inclination and land cover after 750 mm of rainfall (26 events). In the cultivated areas, the average soil water erosion ranged between 0.14 ± 0.07 and 0.74 ± 0.33 kg/m2; in the BF plots, mean soil erosion ranged between 0.03 ± 0.01 and 0.24 ± 0.10 kg/m2. The lowest amount of erosion was recorded in the F plots where the erosion ranged between 0.1 ± 0.001 and 0.07 ± 0.03 kg/m2. Interestingly, the General Linear Model revealed that all factors (i.e., inclination, rainfall and land use) had a significant (p < 0.001) effect on the soil loss. We concluded that human activities greatly influenced soil erosion rates, being higher in the AG lands, followed by BF and F. Therefore, the current study could be very useful to policymakers and planners for proposing immediate conservation or restoration plans in a less studied area which has been shown to be vulnerable to soil erosion processes.

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.

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