scholarly journals Prioritization of Watershed Using Remote Sensing and Geographic Information System

2021 ◽  
Vol 13 (16) ◽  
pp. 9456
Author(s):  
Devendra Kumar ◽  
Arvind Dhaloiya ◽  
Ajeet Singh Nain ◽  
Mahendra Paal Sharma ◽  
Amandeep Singh
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Soil Erosion ◽  
Natural Resources ◽  
Best Management Practices ◽  
Soil Loss ◽  
Management Practices ◽  
Agricultural Land ◽  
Geographical Information ◽  
Slope Length

Soil erosion is becoming a major concern at the watershed scale for the environment, natural resources, and sustainable resource management. Therefore, the estimation of soil loss through this phenomenon and the identification of critical soil erosion-prone areas are considered to be key tasks in the soil conservation programme for the design and implementation of best management practices for specific regions or areas. In the present study, revised universal soil loss equation (RUSLE) modelling is combined with remote sensing (RS) and geographical information system (GIS) techniques and used to predict soil erosion and the prioritization of watersheds in Nainital district Uttarakhand, India. For the estimation of soil loss, different factors, namely, rainfall-runoff erosivity (R) factor, soil erodability (K) factor, slope length steepness (LS) factor, cover management (C) factor, and the erosion control practices (P) factor were computed. The data on various other aspects such as land use/land cover (LU/LC), the digital elevation model (DEM), slope, contours, drainage network, soil texture, organic matter, and rainfall were integrated to prepare a database for the RUSLE equation by employing ENVI & QGIS software. The results showed that a major portion (70.26%) of Nainital district is covered with forest, followed by area under fallow and agricultural land. Annual average soil loss ranged between 20 to 80 t ha−1 yr−1 in the study area. Out of 50 watersheds in the study area, 7 watersheds were given top priority for conserving natural resources, while 11 watersheds, mostly in the east-central part of Nainital, were kept under the next priority category. Only 4 watersheds of the total were given lowest priority. Moreover, it was concluded that major portions of Nainital district were in a severely prone category of soil erosion, and therefore required immediate action plans to check soil erosion and evade the possibility of landslides.

scholarly journals Comparison of RUSLE and MMF Soil Loss Models and Evaluation of Catchment Scale Best Management Practices for a Mountainous Watershed in India

2020 ◽  
Vol 13 (1) ◽  
pp. 232
Author(s):  
Susanta Das ◽  
Proloy Deb ◽  
Pradip Kumar Bora ◽  
Prafull Katre
Keyword(s):  
Soil Erosion ◽  
Best Management Practices ◽  
Soil Loss ◽  
Management Practices ◽  
Agricultural Land ◽  
Soil Layer ◽  
Catchment Scale ◽  
Suitable Alternative ◽  
Best Management ◽  
Management Plans

Soil erosion from arable lands removes the top fertile soil layer (comprised of humus/organic matter) and therefore requires fertilizer application which affects the overall sustainability. Hence, determination of soil erosion from arable lands is crucial to planning conservation measures. A modeling approach is a suitable alternative to estimate soil loss in ungauged catchments. Soil erosion primarily depends on soil texture, structure, infiltration, topography, land uses, and other erosive forces like water and wind. By analyzing these parameters, coupled with geospatial tools, models can estimate storm wise and annual average soil losses. In this study, a hilly watershed called Nongpoh was considered with the objective of prioritizing critical erosion hazard areas within the micro-catchment based on average annual soil loss and land use and land cover and making appropriate management plans for the prioritized areas. Two soil erosion models namely Revised Universal Soil Loss Equation (RUSLE) and Modified Morgan–Morgan–Finney (MMF) models were used to estimate soil loss with the input parameters extracted from satellite information and automatic weather stations. The RUSLE and MMF models showed similar results in estimating soil loss, except the MMF model estimated 7.74% less soil loss than the RUSLE model from the watershed. The results also indicated that the study area is under severe erosion class, whereas agricultural land, open forest area, and scrubland were prioritized most erosion prone areas within the watershed. Based on prioritization, best management plans were developed at catchment scale for reducing soil loss. These findings and the methodology employed can be widely used in mountainous to hilly watersheds around the world for identifying best management practices (BMP).

Assessment of Sediment Loss Reduction by Vegetated Ridge Using SWAT

2020 ◽  
Author(s):  
Jiyeong Hong ◽  
Dongjun Lee ◽  
Suhong Kim ◽  
Jonggun Kim ◽  
Kyoung Jae Lim
Keyword(s):  
Point Source ◽  
Best Management Practices ◽  
Soil Loss ◽  
Management Practices ◽  
Agricultural Land ◽  
Assessment Tool ◽  
Loss Reduction ◽  
Agricultural Field ◽  
Slope Length ◽  
Sediment Loss

<p>Pollutants from the agricultural field are the main cause of water pollution in reach. Especially, in heavy rainfall seasons, non-point source pollutants from agricultural land contaminate stream with nitrogen, phosphorus, and other pollutants. To reduce the components of the contaminations that outflow from the agricultural areas, Best Management Practices (BMPs) have been installed. Integrated factors including weather, geographic characteristics and kind of crops should be considered for choosing proper BMPs in each field. In the fields which have long slope-length, the vegetated ridge is one of the best methods and wildly used method to reduce soil loss. In this study, the Soil & Water Assessment Tool (SWAT) was used to assess the effects of the vegetated ridge on streamflow and sediment within non-point source pollutant management areas. The LS factor in the modified Universal Soil Loss Equation (MUSLE) in SWAT was modified in order to simulate sediment loss reduction by the vegetated ridge in the target fields. This study aims to assess sediment loss reduction by implication of the vegetated ridge using SWAT and to propose the importance of vegetated ridge for reducing non-point source pollutants in agricultural fields. For further research, the development of a vegitated ridge application tool for SWAT will be conducted.</p>

scholarly journals Prioritization of Sub-Watersheds to Sediment Yield and Evaluation of Best Management Practices in Highland Ethiopia, Finchaa Catchment

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 650
Author(s):  
Wakjira Takala Dibaba ◽  
Tamene Adugna Demissie ◽  
Konrad Miegel
Keyword(s):  
Soil Erosion ◽  
Best Management Practices ◽  
Sediment Yield ◽  
Soil Loss ◽  
Crop Production ◽  
Management Practices ◽  
Assessment Tool ◽  
Swat Model ◽  
Sediment Yields ◽  
Soil And Water

Excessive soil loss and sediment yield in the highlands of Ethiopia are the primary factors that accelerate the decline of land productivity, water resources, operation and function of existing water infrastructure, as well as soil and water management practices. This study was conducted at Finchaa catchment in the Upper Blue Nile basin of Ethiopia to estimate the rate of soil erosion and sediment loss and prioritize the most sensitive sub-watersheds using the Soil and Water Assessment Tool (SWAT) model. The SWAT model was calibrated and validated using the observed streamflow and sediment data. The average annual sediment yield (SY) in Finchaa catchment for the period 1990–2015 was 36.47 ton ha−1 yr−1 with the annual yield varying from negligible to about 107.2 ton ha−1 yr−1. Five sub-basins which account for about 24.83% of the area were predicted to suffer severely from soil erosion risks, with SY in excess of 50 ton ha−1 yr−1. Only 15.05% of the area within the tolerable rate of loss (below 11 ton ha−1yr−1) was considered as the least prioritized areas for maintenance of crop production. Despite the reasonable reduction of sediment yields by the management scenarios, the reduction by contour farming, slope terracing, zero free grazing and reforestation were still above the tolerable soil loss. Vegetative contour strips and soil bund were significant in reducing SY below the tolerable soil loss, which is equivalent to 63.9% and 64.8% reduction, respectively. In general, effective and sustainable soil erosion management requires not only prioritizations of the erosion hotspots but also prioritizations of the most effective management practices. We believe that the results provided new and updated insights that enable a proactive approach to preserve the soil and reduce land degradation risks that could allow resource regeneration.

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 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 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 Modelisation Du Risque D’érosion Hydrique Par L’équation Universelle Des Pertes En Terre Dans Le Rif Occidental: Cas Du Bassin Versant De Moulay Bouchta (Maroc)

2018 ◽  
Vol 14 (3) ◽  
pp. 524 ◽  
Author(s):  
Anis Zouagui ◽  
Mohamed Sabir ◽  
Mustapha Naimi ◽  
Mohamed Chikhaoui ◽  
Moncef Benmansour
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Rate ◽  
Agricultural Land ◽  
Land Development ◽  
Principal Component ◽  
Slope Length ◽  
Increased Risk ◽  
Effect Of Support ◽  
Soil Losses

Soil erosion causes many environmental and socio-economic problems: loss of biodiversity, decrease in the productivity of agricultural land, siltation of dams and increased risk of flooding. It is therefore essential to establish a detailed evaluation of this process before any spatial planning. To evaluate the effects of soil erosion spatially and quantitatively in order to face this phenomenon, and propose the best conservation and land development strategies, the Universal Soil Loss Equation (USLE) coupled with a geographic information system (GIS) is applied. This model is a multiplication of the five erosion factors: the erosivity of the rain, the erodibility of the soil, the inclination and the slope length, the vegetation cover and the anti-erosion practices. The study area is the Moulay Bouchta watershed (7 889 ha), which is located in the western part of the Rif Mountains, is characterized by a complex and contrasting landscape. The resulting soil loss map shows an average erosion rate of 39.5 (t/ha/yr), 87% of the basin has an erosion rate above the tolerance threshold for soil loss (7 (t/ha/yr)). Soil losses per subbasin range from 16.2 to 81.4 (t/ha/yr). The amount of eroded soil is estimated at 311,591 (t/yr), corresponding to a specific degradation of 12.1 (t/ha/yr). In the absence of any erosion control, 25% of the soil losses would reach the new dam located a little upstream of the basin outlet, reducing its water mobilization capacity to 59,625 (m3/yr). The application of Principal Component Analysis (PCA) to soil erosion factors shows a significant influence of topographic factor (LS) on soil erosion process, followed by the effect of support practices (P), then by soil erodibility (K).

scholarly journals Remote Sensing and Geographic Information System in Water Erosion Assessment

2020 ◽  
Author(s):  
Nirmal Kumar ◽  
S. K. Singh ◽  
G. P. Obi Reddy ◽  
V. N. Mishra ◽  
R. K. Bajpai
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Spatial Data ◽  
Soil Loss ◽  
Water Erosion ◽  
Geographic Information ◽  
Geographical Information ◽  
Comprehensive Overview

The aim of this review paper is to provide a comprehensive overview of geographical information system and remote sensing–based water erosion assessment. With multispectral and multi-temporal low cost data at various resolutions, remote sensing plays an important role for mapping the distribution and severity of water erosion and for modeling the risk and/or potential of soil loss. The ability of geographic information system to integrate spatial data of different types and sources makes its role unavoidable in water erosion assessment. The role of satellite data in identification of eroded lands and in providing inputs for erosion modeling has been discussed. The role of GIS in mapping eroded lands based on experts’ opinion, in generating spatial data inputs from sources other than remote sensing and in integrating the inputs to model the potential soil loss has been discussed.

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