Regional modeling of soil sensitivity to water erosion in JIJEL region (Algeria) using MCA and GIS

Nehai, Soumeya Atika; Guettouche, Mohamed Saïd; Saadoud, Djouher

doi:10.1007/s12518-020-00316-5

Cited by 5 publications

(2 citation statements)

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“…In the long run, it has a negative impact on the ecology of aquatic areas as well as the shape of existing rivers. Soil erosion in hilly region is primarily influenced by steep slope, rainfall intensity, soil characteristics, land use and land cover are all factors that encourage significant surface runoff with huge sediments (Nehai et al, 2020). According to studies, climate change directly influences the rate of soil erosion by modifying the pattern of precipitation and temperature, as well as the area's land use, rate of infiltration, biomass yield, surface runoff, and moisture content of soil (Li et al, 2016;Nearing et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Soil Erosion Estimation Using Geospatial Technology: A Study of Jyadul Khola Basin, Gorkha, Nepal

2022

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Estimation of soil erosion from drainage basins is essential while assessing the severity and its impact on agriculture, forests, barren land, waterbodies, and built- up areas. Jyadul Khola basin significantly affects the ecological processes that feed into the Budhigandaki River in the south-eastern side of Gorkha District. This paper has attempted to estimate the mean erosion rate based on the erosion severity classes. Remotely sensed Ziyuan-3 satellite image processed in Earth Resources Data Analysis System (ERDAS) Imagine, Geographical Information System (GIS), and Revised Universal Soil Loss Equation (RUSLE) model were used in this study. The land use land cover (LULC) classification results were validated by using confusion matrix by computing overall accuracy and kappa coefficient which is 95% and 0.94 respectively. The basin had been classified into 6 categories based on erosion severity. The results indicated 92.7% of land (0-5 t ha–1 yr–1) is low severe followed by 2.39% (10-20 t ha–1 yr–1) moderate, 2.07% (5-10 t ha–1 yr–1) high, 2.04% (20-40 t ha–1 yr–1) very high, 0.67% (40-80 t ha–1 yr–1) severe and 0.10% of land (>80 t ha–1 yr–1) which is very severe for soil erosion. The total annual mean soil loss was found to be 13526.60 t yr–1 and soil erosion classes ranges from 0 to 305.34 t ha–1 yr–1 for the entire study area. Kuwapani, Lakuri Bhanjyang, Khadkagaun, Garapani and Kaulebhagar area are the most susceptible to soil erosion. It is observed that barren land, steep slopes, and high intensity of rainfall are major factors for soil erosion hazard. This outcome can serve as a foundation for decision-makers to conserve high risk areas and plan effective measure to lessen impending disasters.

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Section: Introductionmentioning

confidence: 99%

Soil Erosion Estimation Using Geospatial Technology: A Study of Jyadul Khola Basin, Gorkha, Nepal

2022

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“…In hilly areas, soil erosion is the most serious threat which mostly depends on slope steepness, rainfall intensity, soil texture and its property and land cover, which stimulate high runoff with high sediment load capacity (Nehai et al 2020). Water erosion is one of the most serious causes of land degradation and about 11 million km 2 land area was affected globally (Sato et al 1994).…”

Section: Introductionmentioning

confidence: 99%

Soil Erosion Estimation Using RUSLE Modeling and Geospatial Tool: Case Study of Kathmandu District, Nepal

Dahal

2020

Forestry: J. Inst. Fores. Nep.

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Revised Universal Soil Loss Equation (RUSLE) model is applied in this study to evaluate the risk of erosion in Kathmandu district. The calculation of erosion requires certain data from various sources available in different formats and scales. Geographic Information System (GIS) was used which allowed considerable time savings in the processing of spatial data, screening the effects of each factor affecting soil erosion. Among various erosion factors, topography, rainfall, soil properties, and soil conservation practices were used for the study. Average soil loss was calculated by multiplying these factors. Final results of soil erosion rates were separated into six classes based on erosion severity, in which 2.18% of land (> 80Mg ha-1yr-1), followed by 2.85% of land (40-80 Mg ha-1yr-1), 5.56% of land (20-40 Mg ha-1yr-1), 8.73% of land (10-20 Mg ha-1yr-1), 10.53% of land (5-10 Mg ha-1yr-1) and 70.14% of land (0-5 Mg ha-1yr-1), falls under very severe, severe, very high, moderate and low severity zone respectively. Area having high slope length (LS) factor has high erosion rate. In Dakshinkali, Nagarjun and Budanilkantha area, there is high erosion rate. From the result, spatial distribution of soil erosion across Kathmandu district, can be applied for management and controlling the erosion.

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Mapping potential soil erosion using RUSLE, Remote Sensing, and GIS: a case study in the watershed of Oued El Ardjem, Northwest Algeria

et al. 2021

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Regional modeling of soil sensitivity to water erosion in JIJEL region (Algeria) using MCA and GIS

Cited by 5 publications

References 35 publications

Soil Erosion Estimation Using Geospatial Technology: A Study of Jyadul Khola Basin, Gorkha, Nepal

Soil Erosion Estimation Using Geospatial Technology: A Study of Jyadul Khola Basin, Gorkha, Nepal

Soil Erosion Estimation Using RUSLE Modeling and Geospatial Tool: Case Study of Kathmandu District, Nepal

Mapping potential soil erosion using RUSLE, Remote Sensing, and GIS: a case study in the watershed of Oued El Ardjem, Northwest Algeria

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