New Insight on Soil Loss Estimation in the Northwestern Region of the Zagros Fold and Thrust Belt

Soil loss is one of the most important causes of land degradation. It is an inevitable environmental and socio-economic problem that exists in many physiographic regions of the world, which, besides other impacts, has a direct bearing on agricultural productivity. A reliable estimate of soil loss is critical for designing and implementing any mitigation measures. We applied the widely used Revised Universal Soil Loss Equation (RUSLE) in the Khabur River Basin (KhRB) within the NW part of the Zagros Fold and Thrust Belt (ZFTB). The areas such as the NW Zagros range, characterized by rugged topography, steep slope, high rainfall, and sparse vegetation, are most susceptible to soil erosion. We used the Digital Elevation Model (DEM) of the Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), Harmonized World Soil Database (HWSD), and Landsat imagery to estimate annual soil loss using the RUSLE model. In addition, we estimated sediment yield (SY) at sub-basin scale, in the KhRB where a number of dams are planned, and where basic studies on soil erosion are lacking. Estimation of SY will be useful in mitigation of excessive sedimentation affecting dam performance and watershed management in this region. We determined the average annual soil loss and the SY in the KhRB to be 11.16 t.ha−1.y−1 and 57.79 t.ha−1.y−1, respectively. The rainfall and runoff erosivity (R factor), slope length (L factor), and slope steepness (S factor), are the three main factors controlling soil loss in the region. This is the first study to determine soil loss at the sub-basin scale along with identifying suitable locations for check dams to trap the sediment before it enters downstream reservoirs. The study provides valuable input data for design of the dams to prevent excessive siltation. This study also aims at offering a new approach in relating potential soil erosion to the actual erosion and hypsometric integrals.

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

Journal of Water and Climate Change ◽

10.2166/wcc.2019.181 ◽

2019 ◽

Vol 11 (2) ◽

pp. 529-539 ◽

Cited By ~ 1

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.

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Spatial Targeting of Soil Loss Using RUSLE in GIS: the case of Asokore Mampong Municipality, Ghana

Journal of Applied Geospatial Information ◽

10.30871/jagi.v3i1.1029 ◽

2019 ◽

Vol 3 (1) ◽

pp. 166-172 ◽

Cited By ~ 1

Author(s):

Gift Dumedah ◽

Evans Kyeremanteng ◽

Ema Dari

Keyword(s):

Soil Erosion ◽

Land Cover ◽

Soil Loss ◽

Vegetation Index ◽

Management Practices ◽

Graphical Model ◽

Thermal Emission ◽

Mitigation Measures ◽

Slope Length ◽

Public Data

Soil erosion is a serious environmental problem that is associated with societal impacts including flooding, poor water quality, and loss of plant nutrient leading to low agricultural productivity. Soil erosion wears away the top soil and is controlled by the interaction between several factors including rainfall, steepness of slope, length of slope, vegetation cover, and land management practices. This study developed Geographic Information System (GIS) graphical model based on the Revised Universal Soil Loss Equation (RUSLE), to calculate soil loss in the Asokore Mampong Municipality of the Ashanti region, Ghana. The estimated soil loss was examined the spatial patterns of soil loss and intensity per areas, as an important method for proper planning of management measures. The graphical model was developed using the popular open source GIS software, QGIS, ensuring the availability of the model, automation for any specific area, and its execution to the general public. Data sources used include Digital Elevation Model (DEM) derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), soil properties data obtained from the Global Soil Grids, land cover data from the Global Land Cover by National Mapping Organization (GLCNMO), NDVI (normalized difference vegetation index) data from MODIS (MOD13Q1, 16 Day), and rainfall data from GPCC version 7 (Global Precipitation Climatology Centre). Our results show high levels of soil loss (in tons per hectare per year) in the Municipality, with the capability to spatially target mitigation measures leading to cost effective environmental management.

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

10.22541/au.160946369.92099648/v1 ◽

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.

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Sediment Yield and Soil Loss Estimation Using GIS Based Soil Erosion Model: A Case Study in the MAN Catchment, Madhya Pradesh, India

Environmental Sciences Proceedings ◽

10.3390/ecas2021-10348 ◽

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.

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RUSLE Model based Assessment of Soil Erosion in Parbati River Basin, Central India using Google Earth Engine and GIS

10.5194/egusphere-egu21-1524 ◽

2021 ◽

Author(s):

Rohit Kumar ◽

Benidhar Deshmukh ◽

Kiran Sathunuri

Keyword(s):

Land Use ◽

Soil Erosion ◽

Spatial Pattern ◽

River Basin ◽

Soil Loss ◽

Google Earth ◽

Rusle Model ◽

High Soil ◽

Basin Scale ◽

Google Earth Engine

Land degradation is a global concern posing significant threat to sustainable development. One of its major aspects is soil erosion, which is recognised as one of the critical geomorphic processes controlling sediment budget and landscape evolution. Natural rate of soil erosion is exacerbated due to anthropogenic activities that may lead to soil infertility. Therefore, assessment of soil erosion at basin scale is needed to understand its spatial pattern so as to effectively plan for soil conservation. This study focuses on Parbati river basin, a major north flowing cratonic river and a tributary of river Chambal to identify erosion prone areas using RUSLE model. Soil erodibility (K), Rainfall erosivity (R), and Topographic (LS) factors were derived from National Bureau of Soil Survey and Land Use Planning, Nagpur (NBSS-LUP) soil maps, India Meteorological Department (IMD) datasets, and SRTM30m DEM, respectively in GIS environment. The crop management (C) and support practice (P) factors were calculated by assigning appropriate values to Land use /land cover (LULC) classes derived by random forest based supervised classification of Sentinel-2 level-1C satellite remote sensing data in Google Earth Engine platform. High and very high soil erosion were observed in NE and NW parts of the basin, respectively, which may be attributed to the presence of barren land, fallow areas and rugged topography. The result reveals that annual rate of soil loss for the Parbati river basin is ~319 tons/ha/yr (with the mean of 1.2 tons/ha/yr). Lowest rate of soil loss (i.e. ~36 tons/ha/yr with mean of 0.22 tons/ha/yr) has been observed in the open forest class whereas highest rate of soil loss (i.e. ~316 tons/ha/yr with mean of 32.08 tons/ha/yr) have been observed in gullied area class. The study indicates that gullied areas are contributing most to the high soil erosion rate in the basin. Further, the rate of soil loss in the gullied areas is much higher than the permissible value of 4.5&#8211;11 tons/ha/yr recognized for India. The study helps in understanding spatial pattern of soil loss in the study area and is therefore useful in identifying and prioritising erosion prone areas so as to plan for their conservation.

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Quantification of Soil Losses along the Coastal Protected Areas in Kenya

Land ◽

10.3390/land9050137 ◽

2020 ◽

Vol 9 (5) ◽

pp. 137 ◽

Cited By ~ 3

Author(s):

Yves Hategekimana ◽

Mona Allam ◽

Qingyan Meng ◽

Yueping Nie ◽

Elhag Mohamed

Keyword(s):

Soil Erosion ◽

Protected Areas ◽

Coastal Area ◽

Soil Loss ◽

Meteorological Data ◽

Coastal Areas ◽

Slope Length ◽

Input Parameters ◽

Slope Factor ◽

Factor C

Monitoring of improper soil erosion empowered by water is constantly adding more risk to the natural resource mitigation scenarios, especially in developing countries. The demographical pattern and the rate of growth, in addition to the impairments of the rainfall pattern, are consequently disposed to adverse environmental disturbances. The current research goal is to evaluate soil erosion triggered by water in the coastal area of Kenya on the district level, and also in protected areas. The Revised Universal Soil Loss Equation (RUSLE) model was exercised to estimate the soil loss in the designated study area. RUSLE input parameters were functionally realized in terms of rainfall and runoff erosivity factor (R), soil erodibility factor (K), slope length and gradient factor (LS), land cover management factor (C) and slope factor (P). The realization of RUSLE input parameters was carried out using different dataset sources, including meteorological data, soil/geology maps, the Digital Elevation Model (DEM) and processing of satellite imagery. Out of 26 districts in coastal area, eight districts were projected to have mean annual soil loss rates of >10 t·ha−1·y−1: Kololenli (19.709 t·ha−1·y−1), Kubo (14.36 t·ha−1·y−1), Matuga (19.32 t·ha−1·y−1), Changamwe (26.7 t·ha−1·y−1), Kisauni (16.23 t·ha−1·y−1), Likoni (27.9 t·ha−1·y−1), Mwatate (15.9 t·ha−1·y−1) and Wundanyi (26.51 t·ha−1·y−1). Out of 34 protected areas at the coastal areas, only four were projected to have high soil loss estimation rates >10 t·ha−1·y−1: Taita Hills (11.12 t·ha−1·y−1), Gonja (18.52 t·ha−1·y−1), Mailuganji (13.75.74 t·ha−1·y−1), and Shimba Hills (15.06 t·ha−1·y−1). In order to mitigate soil erosion in Kenya’s coastal areas, it is crucial to regulate the anthropogenic disturbances embedded mainly in deforestation of the timberlands, in addition to the natural deforestation process caused by the wildfires.

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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)

European Scientific Journal ESJ ◽

10.19044/esj.2018.v14n3p524 ◽

2018 ◽

Vol 14 (3) ◽

pp. 524 ◽

Cited By ~ 2

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).

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GIS Based Universal Soil Erosion Estimation in District Chakwal Punjab, Pakistan

International Journal of Economic and Environmental Geology ◽

10.46660/ijeeg.vol11.iss2.2020.443 ◽

2020 ◽

Vol 11 (2) ◽

pp. 30-36

Author(s):

Saima Siddiqui ◽

Mirza Wajid Ali Safi ◽

Aqil Tariq ◽

Naveed Ur Rehman ◽

Syed Waseem Haider

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Management Practices ◽

Rainwater Harvesting ◽

Landsat 8 ◽

River Channels ◽

Slope Length ◽

Raster Calculator ◽

Aster Gdem ◽

Steep Slopes

Soil erosion is a serious environmental problem faced by district Chakwal. Unpredictable short term and high intensity rainfall, improper cultivation and deforestation have accelerated the soil erosion in the district. The agricultural productivity of the study area can be enhanced by understanding, estimating and controlling the root causes of soil erosion. This study was undertaken to estimate and spatially represent the rate of average annual soil erosion in Chakwal using GIS/RS techniques. The soil erosion was estimated using Universal Soil Loss Equation (USLE) model. To find out parameters of USLE, ASTER GDEM of 30 m resolution was used to estimate slope length and elevation of the study area. Landsat 8 satellite imagery of year 2019, was used to prepare land use map using supervised classification. Soil map with texture and geomorphology was used to identify soils of study area and rainfall data of last 7 years was also studied. Finally, the soil loss has been computed using raster calculator of ArcGIS 10.2 software. The average annual soil loss was predicted up to 268,619 tons/acre/year, of which maximum soil erosion was occurring near the steep slopes and river channels. It is necessary to adapt sustainable land management practices to reduce the risk of further soil erosion, by adopting rainwater harvesting and choosing right crops for suitable soil types.

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SOIL EROSION ASSESMENT IN SOUTHERN EVIA ISLAND USING USLE AND GIS

Bulletin of the Geological Society of Greece ◽

10.12681/bgsg.11331 ◽

2017 ◽

Vol 43 (3) ◽

pp. 1572 ◽

Cited By ~ 2

Author(s):

G.D. Bathrellos ◽

H.D. Skilodimou ◽

K.G. Chousianitis

Keyword(s):

Soil Erosion ◽

Land Use Planning ◽

Soil Loss ◽

Numerical Estimate ◽

Point Of View ◽

Average Annual Rate ◽

Slope Length ◽

Erosion Risk ◽

Precipitation Morphology ◽

Major Factors

In the present study the evaluation of soil erosion in Southern Evia Island was carried out. Data related with precipitation, morphology, land cover and lithology were collected. A spatial database was created and the further processing of the collected data was prepared using GIS. The Universal Soil Loss Equation (USLE) was used to predict the spatial distribution of the average annual rate of erosion. Five major factors were used to calculate the soil loss. These are rainfall erositivity (R), soil erodibility (K), slope length and steepness (LS), cropping management (C) and conservation supporting practice (P). Each factor is the numerical estimate of a specific condition that affects the severity of soil erosion. The obtained soil loss values were used to create the erosion risk map. The applied methodology provides a cost effected and rapid estimation of areas that are vulnerable to soil erosion and need immediate attention from soil conservation point of view. Moreover these results can be used to assist land use planning.

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

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-2-w2-207-2015 ◽

2015 ◽

Vol II-2/W2 ◽

pp. 207-214 ◽

Cited By ~ 8

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-1/ y-1. 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.

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