Soil erosion assessment using RUSLE model and GIS in Huluka watershed, Central Ethiopia

Atoma, Habtamu; Suryabhagavan, K. V.; Balakrishnan, M.

doi:10.1007/s40899-020-00365-z

Cited by 75 publications

(39 citation statements)

References 23 publications

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“…K factor reflects the susceptibility of soil aggregates to detachment by raindrops and its transportation by runoff 75 – 77 . The K values were calculated using soil data derived from the DT model simulation 78 based on Eq.…”

Section: Methodsmentioning

confidence: 99%

CMIP5 climate projections and RUSLE-based soil erosion assessment in the central part of Iran

Hateffard

Mohammed

Alsafadi

et al. 2021

Sci Rep

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Soil erosion (SE) and climate change are closely related to environmental challenges that influence human wellbeing. However, the potential impacts of both processes in semi-arid areas are difficult to be predicted because of atmospheric variations and non-sustainable land use management. Thus, models can be employed to estimate the potential effects of different climatic scenarios on environmental and human interactions. In this research, we present a novel study where changes in soil erosion by water in the central part of Iran under current and future climate scenarios are analyzed using the Climate Model Intercomparison Project-5 (CMIP5) under three Representative Concentration Pathway-RCP 2.6, 4.5 and 8.5 scenarios. Results showed that the estimated annual rate of SE in the study area in 2005, 2010, 2015 and 2019 averaged approximately 12.8 t ha−1 y−1. The rangeland areas registered the highest soil erosion values, especially in RCP2.6 and RCP8.5 for 2070 with overall values of 4.25 t ha−1 y−1 and 4.1 t ha−1 y−1, respectively. They were followed by agriculture fields with 1.31 t ha−1 y−1 and 1.33 t ha−1 y−1. The lowest results were located in the residential areas with 0.61 t ha−1 y−1 and 0.63 t ha−1 y−1 in RCP2.6 and RCP8.5 for 2070, respectively. In contrast, RCP4.5 showed that the total soil erosion could experience a decrease in rangelands by − 0.24 t ha−1 y−1 (2050), and − 0.18 t ha−1 y−1 (2070) or a slight increase in the other land uses. We conclude that this study provides new insights for policymakers and stakeholders to develop appropriate strategies to achieve sustainable land resources planning in semi-arid areas that could be affected by future and unforeseen climate change scenarios.

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Section: Methodsmentioning

confidence: 99%

CMIP5 climate projections and RUSLE-based soil erosion assessment in the central part of Iran

Hateffard

Mohammed

Alsafadi

et al. 2021

Sci Rep

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“…Thus, the estimation of SE and sediment yield is important for new dam site selections and existing dams for life and storage capacity calculations. Conventional methods to assess SE risk is expensive and time-consuming [16,29]. The estimation of SE is improved with the development of the Revised Universal Soil Loss Equation (RUSLE) model [30][31][32].…”

Section: Of 21mentioning

confidence: 99%

Torrential Flood Water Management: Rainwater Harvesting through Relation Based Dam Suitability Analysis and Quantification of Erosion Potential

Munir

Ahmad

Rehan³

2021

IJGI

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In this study, a relation-based dam suitability analysis (RDSA) technique is developed to identify the most suitable sites for dams. The methodology focused on a group of the most important parameters/indicators (stream order, terrain roughness index, slope, multiresolution valley bottom flatness index, closed depression, valley depth, and downslope gradient difference) and their relation to the dam wall and reservoir suitability. Quantitative assessment results in an elevation-area-capacity (EAC) curve substantiating the capacity determination of selected sites. The methodology also incorporates the estimation of soil erosion (SE) using the Revised Universal Soil Loss Equation (RUSLE) model and sediment yield at the selected dam sites. The RDSA technique identifies two suitable dam sites (A and B) with a maximum collective capacity of approximately 1202 million m3. The RDSA technique was validated with the existing dam, Gomal-Zam, in the north of Sanghar catchment, where RDSA classified the Gomal-Zam Dam in a very high suitability class. The SE estimates show an average of 75 t-ha−1y−1 of soil loss occurs in the study area. The result shows approximately 298,073 and 318,000 tons of annual average sediment yield (SY) will feed the dam A and B respectively. The SE-based sediment yield substantiates the approximate life of Dam-A and Dam-B to be 87 and 90 years, respectively. The approach is dynamic and can be applied for any other location globally for dam site selection and SE estimation.

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“…RUSLE model is a very powerful method for estimation of soil erosion in specific situations such as dam construction, mining, typhoon, assessing land management measures, LULC change, and mountain areas (Atoma et al, 2020;Chuenchum et al, 2020;Kassawmar et al, 2018;Kim, 2006;Lazzari et al, 2015;Rammahi and Khassaf, 2018;Somprasong and Chaiwiwatworakul, 2015). RUSLE model integrates remote sensing techniques together with GIS for estimation of the quantity of lost sediment in the four scenarios as shown in Equation (1).…”

Section: Rusle Modelmentioning

confidence: 99%

“…Moreover, assessing the suitable scenario is an essential procedure to select the best measure to reduce Cd contamination and protect the Mae Tao watershed. Therefore, RUSLE model was applied to estimate soil loss (Atoma et al, 2020;Batista et al, 2017;Chuenchum et al, 2020;Farhan et al, 2013;Ganasri and Ramesh, 2016;Kassawmar et al, 2018;Kim, 2006;Lazzari et al, 2015;Lee and Lee, 2006;Millward and Mersey, 1999;Ostovari et al, 2017;Rammahi and Khassaf, 2018;Somprasong and Chaiwiwatworakul, 2015;Xu et al, 2012) and was integrated with IDW technique in GIS to estimate the spatial concentration of Cd (Mesnard, 2013;Somprasong and Chaiwiwatworakul, 2015). Soil loss with RUSLE model and Cd concentration with IDW were used as the parameters to estimate Cd contamination in soil loss in the last step (Somprasong and Chaiwiwatworakul, 2015).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Cadmium Contamination in Different Restoration Scenarios by RUSLE Model

Charoenpanyanet

Huttagosol

2020

Environ. Nat. Resour. J.

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The Mae Tao watershed of Thailand faced cadmium (Cd) contamination problems from zinc mining for a long time until the mining area was closed to decrease the level of Cd concentration. This study reproduced the possible scenarios of Cd contamination due to soil loss. Four scenarios of forest restoration were implemented in this study, all of which were calculated with the Revised Universal Soil Loss Equation (RUSLE) integrated with satellite imagery and Geographic Information Systems (GIS). Landsat 8-OLI was acquired and land use/land cover (LULC) was classified in each scenario. Soil loss maps were created. An inverse distance weighting (IDW) technique was used to estimate the concentration of Cd based on the field data consisting of 101 points of measured Cd concentration. Results from RUSLE model and IDW technique were combined to calculate Cd contamination due to soil loss for all four scenarios. Results showed that the restoration of Scenario 3, forest restoration in old and new mining areas in cooperation with reservoir construction, helped decrease Cd contamination the most. The lowest level of Cd contamination from soil loss was found in this scenario by about 156 ha (total of Cd contamination by 165,924.32 ton/year).

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Soil erosion assessment using RUSLE model and GIS in Huluka watershed, Central Ethiopia

Cited by 75 publications

References 23 publications

CMIP5 climate projections and RUSLE-based soil erosion assessment in the central part of Iran

CMIP5 climate projections and RUSLE-based soil erosion assessment in the central part of Iran

Torrential Flood Water Management: Rainwater Harvesting through Relation Based Dam Suitability Analysis and Quantification of Erosion Potential

Estimation of Cadmium Contamination in Different Restoration Scenarios by RUSLE Model

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