Evaluation of soil loss estimation using RUSLE model and SCS-CN method in hilltop mining areas (original) (raw)
Related papers
Evaluation of soil loss estimation using the RUSLE model and SCS-CN method in hillslope mining areas
International Soil and Water Conservation Research, 2018
Mining operations result in the generation of barren land and spoil heaps which are subject to high erosion rate during the rainy season. The present study uses the Revised Universal Soil Loss Equation (RUSLE) and SCS-CN (Soil Conservation Service-Curve Number) process to estimate in Kiruburu and Meghahatuburu mining sites areas. The geospatial model of annual average soil loss rate was determined by integrating environmental variables parameters in a raster pixels-based GIS framework. GIS layers with, rainfall passivity and runoff erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management(C) and conservation practice (P) factors were calculated to determine their effects on annual soil erosion in the study area. The coefficient of determination (r 2) was 0.834, which indicates a strong correlation of soil loss with runoff and rainfall. Sub-watersheds 5,9,10 and 2 experienced high level of highly runoff. Average annual soil loss was calculated (30*30 m raster grid cell) to determine the critical soil loss areas (Sub-watershed 9 and 5). Total soil erosion area was classified into five class, slight (10,025 ha), moderate (3125 ha), high (973 ha), very high (260 ha) and severe (53 ha). The resulting map shows greatest soil erosion of 440 t h-1 y-1 (severe) through connection to grassland, degraded and open forestry on the erect mining side-escutcheon. The Landsat pan sharpening image and DGPS survey field data were used in the verification of soil erosion results.
Estimation of soil erosion using Revised Universal Soil Loss Equation and GIS
A comprehensive methodology that integrates Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) techniques were adopted to determine the soil erosion vulnerability of a forested mountainous sub-watershed in Kerala, India. The spatial pattern of annual soil erosion rate was obtained by integrating geo-environmental variables in a raster-based GIS method. 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 area. The resultant map of annual soil erosion shows a maximum soil loss of 282.2 t/h/yr with a close relation to grassland areas, degraded forests and deciduous forests on the steep side-slopes. The spatial erosion maps generated with RUSLE method and GIS can serve as effective inputs in deriving strategies for land planning and management in the environmentally sensitive mountainous areas.
Mapping soil erosion in a river basin of madhaya pradesh using remote sensing and gis
For the protection of the land from erosion, it is essential to measure and locate soil loss. Revised Universal Soil Loss Equation, RUSLE, can estimate soil erosion potential on cell-by-cell raster-based GIS data frame. For the present work, Hiran River at Patan, Madhya Pradesh was selected for estimation of soil loss. The study aimed for qualitative assessment of soil erosion prone areas by calculating soil loss using RUSLE. Models, like RUSLE, require less data making soil erosion estimation practicable within larger scales as monthly precipitation data; digital elevation model, soil map, land use and land cover types and slope length and steepness were used to determine the RUSLE values. One of the most important parameters of RUSLE is C factor that represents effects of vegetation and other land covers. Estimating C factor in this study involves the use of Normalized Difference Vegetation Index (NDVI), an indicator which shows vegetation cover, using the regression equation in Spatial Analyst tool of ArcGIS 10.1 software. The Quantitative assessment has effectively been accomplished by calculating rates of soil loss and developing soil loss severity maps of the study areas using soil loss equation model RUSLE. This study has demonstrated GIS as a valuable tool in determining soil erosion and assisting the estimation of soil loss.
ISPRS International Journal of Geo-Information
Land degradation caused by soil erosion is considered among the most severe problems of the 21stcentury. It poses serious threats to soil fertility, food availability, human health, and the world ecosystem. The purpose of the study is to make a quantitative mapping of soil loss in the Chitral district, Pakistan. For the estimation of soil loss in the study area, the Revised Universal Soil Loss Equation (RUSLE) model was used in combination with Remote Sensing (RS) and Geographic Information System (GIS). Topographical features of the study area show that the area is more vulnerable to soil loss, having the highest average annual soil loss of 78 ton/ha/year. Maps generated in the study show that the area has the highest sediment yield of 258 tons/ha/year and higher average annual soil loss of 450 tons/ha/year. The very high severity class represents 8%, 16% under high, 21% under moderate, 12% under low, and 13% under very low soil loss in the Chitral district. The above study is help...
Soil erosion is one of the serious issues threatening the environment. This degrading phenomenon deteriorates the soil fertility and drastically affects the agricultural practices. As a consequence, the productivity of soil is affected unquestionably. In this regard, there is a need to take up conservation and management measures which can be applied to check further soil erosion. Even though, soil erosion is a mass process spread across the watershed, it is not economically viable to implement conservation techniques to the entire watershed. However, a method is a prerequisite to identify the most vulnerable areas and quantify the soil erosion. In this context, Revised Universal Soil Loss Equation (RUSLE) has been adopted to estimate soil erosion in the semi-arid Andipatti Watershed of Tamil Nadu, India. This model takes into consideration the parameters including runoff-rainfall erosivity factor (R), soil erodability Factor (K), topographic factor (LS), cropping management factor (C), and support practice factor (P). All these layers are prepared in a geographical information system (GIS) platform using various data sources and data preparation methods. The results of the study indicate that the annual average soil loss within the watershed is about 6 t/ha/yr (metric ton per hectare per year). Higher soil erosion is observed in the landuse classes of gullied wasteland, open scrub forest and degraded plantation. The soil erosion risk is extremely higher on the steep slopes and adjoining foot hills. Based on the average soil erosion values of different landuse classes and characteristics of land, a proposed landuse map was prepared. The estimated soil erosion and the proposed landuse map could be an effective input for drawing sustainable watershed development measures.
A comparative study of soil erosion models based on GIS and remote sensing
ISH Journal of Hydraulic Engineering, 2020
In most of the developing and underdeveloped countries, the demand of crop is not fulfilled because of less production, and it is further decreasing due to soil erosion. The sheet erosion affects the fertility of soil, and due to other erosion like rill and gully erosion, the land area is reduced. The silent hazard namely soil erosion is reducing the crop production and has become one of the alarming problems of the whole world. Advancement of technology and development of various models based on remote sensing and GIS has played a significant role in the estimation of soil erosion in the catchment using various empirical models. Still it is a challenge to apply those models and investigate soil erosion-prone areas and quantify erosion. In this study, Universal Soil Loss Equation (USLE) and the Unit Stream Power based Soil Erosion/Deposition (USPED) models were used to identify soil erosion in the Upper Lake, Bhopal in India. Different factors affecting soil erosion were studied, and maps of soil loss due to USLE and USPED models were generated. The soil loss using USPED model was found to be 764481.5 tonnes/yr, whereas using USLE model it was found to be 711700 tones/yr.
Journal of Scientific Research and Reports
Over cultivation, deforestation and free grazing are major factors facilitating soil erosion. Nowadays; in lower parts of Muga watershed soil erosion become as a continuous environmental problem. In this study an attempt has been made to modeling soil loss and identify the most erosion sensitive areas by using Revised Universal Soil Loss Equation integrated with GIS and remote sensing techniques for planning appropriate conservation measures in Muga watershed. The annual soil loss amount was estimated by using the Revised Universal Soil Loss Equation (RUSLE). Digital Elevation Model, digital soil map, thirty years rainfall records of six stations, and land cover data (Landsat images) were used to develop RUSLE soil loss variables. The annual soil loss rate from the catchment were estimated by integrating RUSLE parameters using raster calculator tool. The annual soil loss rate varies between 0.02 ton/ha/yr and 41.789 ton/ha/yr. The total annual soil loss in the watershed was 59751.41 tones, of these, 12806.15 tons were lost from 371.19 km 2 , 26562.44 tons from 214.30 km 2 , 15300.94 tons from 50.52 km 2 , 4059.05 tons from 4.61 km 2 , and 1022.83 tons from 0.37 km 2 of land per year. The rate of soil eroion was high in the lower part of the watershed. Slope gradient and length factor was the main factor for soil erosion increment followed by Support Practice (P) factor. As result of soil erosion cross tabulation; steep slopes, Rendzic leptosols and dominantly cultivated areas were detected as very severe erosivity. Therefore, the lower parts of the study needs to undertake effective soil and water conservation practices.
Soil erosion is one of the major environmental degradation, which is caused by natural process like rainfall, high wind forces and also anthropogenic activities with improper utilization of lands with respect to agricultural activities, developmental activities and leading to severe erosion. The present study is conducted to quantify the soil erosion by water force. The revised universal soil loss equation methodology was adopted and these equations were brought into Geographical Information System (GIS) environment. The average annual rainfall with 34years was used to find the rainfall erosive factor, the soil erodibility factor was found using the properties of soil that consists the percentage of clay, loam and silt. Then topographic slope and length were found using ASTER digital elevation model and crop factor were derived from remotely sensed Landsat images and practice factor were set to one due to no conservation practices in study area. The result were found that 267.19 Km2 are having no erosion, 88.80 Km2 are having low erosion with less than 50 ton/ha/year, 28.54 Km2 are having moderate erosion with 50-100 ton/ha/year of soil erosion, 35.03 Km2 are having severe erosion with 100- 150 ton/ha/year and 96.76 Km2 are having extreme erosion with more than 150 ton/ha/year. The suitable conservation method should be adopted.
Soil erosion assessment for watershed management is a world-wide concern for ecologists and land users. In this study soil erosion was predicted using Universal Soil Loss Equation (USLE) for Katteri watershed in Nilgiris, Tamilnadu. Topographically the study area falls under steep slope and undulating terrain comprising 49% of tea plantation, 23% of horticultural crops, and 24% of forest area. The IRS-IC LISS III satellite imagery was classified and used for preparing the landuse/landcover which estimates cover management factor (C) and the landuse practice factor (P). Using digital elevation model (DEM) as input, the slope length factor (LS) was determined by AML programme. Soil erodibility (K) values were measured for all mapped soil types in the study area. The rainfall erosivity factor (R) was directly computed from rainfall intensities. All the above mentioned USLE factors, with associated attribute data, were digitally encoded in a GIS database and converted into 30 m grid cells. Simultaneous overlaid operation on these five layers produced a resultant layer of soil erosion rate in t ha -1 yr -1 . This research confirms that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Katteri watershed.
Soil Erosion Analysis Using GIS and RS in Makawanpur District, Nepal
Journal of Forest and Natural Resource Management
Although soil erosion is a common phenomenon and a serious hazard in many areas of the Makawanpur district, it is still challenging to estimate and assess the amount of soil erosion. This study investigates the distribution of soil erosion in the Makawanpur district using the Revised Universal Soil Loss Equation (RUSLE) and Geographic Information System (GIS). RUSLE model parameters were collected from various sources. Topography, rainfall, soil characteristics, and soil conservation techniques were considered in the study, among many other erosion factors. These variables were multiplied to determine the average soil loss. Based on the severity of the erosion, the final results of soil erosion rates were divided into six classes. Very serious class accounts for 11.31% of the land (>80 t h-1 yr-1), followed by severe which is 9.76% of the land with erosion rate rates ranging from (40-80 t h-1 yr-1), very high is 17.41% of the land with rates ranging from (20-40 t h-1 yr-1), follo...