Soil erosion modeled with USLE, GIS, and remote sensing: a case study of Ikkour watershed in Middle Atlas (Morocco)

Jazouli, Aafaf El; Barakat, Ahmed; Ghafiri, Abdessamad; Moutaki, Saida El; Ettaqy, Abderrahim; Khellouk, Rida

doi:10.1186/s40562-017-0091-6

Cited by 121 publications

(69 citation statements)

References 34 publications

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“…The results of the combination of effective factors in erosion showed that soil erosion is most associated with the C factor, followed by LS and R factors, which is in accordance with the results of El Jazouli et al [73]. This means that land uses with low and poor density of vegetation cover have the maximum amount of C factor and the greater risk of erosion.…”

Section: Discussionsupporting

confidence: 88%

Modeling the Impact of Climate Change and Land Use Change Scenarios on Soil Erosion at the Minab Dam Watershed

et al. 2019

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Climate and land use change can influence susceptibility to erosion and consequently land degradation. The aim of this study was to investigate in the baseline and a future period, the land use and climate change effects on soil erosion at an important dam watershed occupying a strategic position on the narrow Strait of Hormuz. The future climate change at the study area was inferred using statistical downscaling and validated by the Canadian earth system model (CanESM2). The future land use change was also simulated using the Markov chain and artificial neural network, and the Revised Universal Soil Loss Equation was adopted to estimate soil loss under climate and land use change scenarios. Results show that rainfall erosivity (R factor) will increase under all Representative Concentration Pathway (RCP) scenarios. The highest amount of R was 40.6 MJ mm ha−1 h−1y−1 in 2030 under RPC 2.6. Future land use/land cover showed rangelands turning into agricultural lands, vegetation cover degradation and an increased soil cover among others. The change of C and R factors represented most of the increase of soil erosion and sediment production in the study area during the future period. The highest erosion during the future period was predicted to reach 14.5 t ha−1 y−1, which will generate 5.52 t ha−1 y−1 sediment. The difference between estimated and observed sediment was 1.42 t ha−1 year−1 at the baseline period. Among the soil erosion factors, soil cover (C factor) is the one that watershed managers could influence most in order to reduce soil loss and alleviate the negative effects of climate change.

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Section: Discussionsupporting

confidence: 88%

Modeling the Impact of Climate Change and Land Use Change Scenarios on Soil Erosion at the Minab Dam Watershed

et al. 2019

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“…Ashiagbor et al [36] likewise modelled the spatial distribution of soil erosion in the Densu River Basin of Ghana using RUSLE and GIS tools, and used the model to explore the connection between the catchment's soil erosion and the contributory factors. El Jazouli et al [19] evaluated soil erosion susceptibility in the Middle Atlas Mountain-Morocco using the USLE and the spectral index approach and realized an agreement between the two. Kayet et al [37] used the RUSLE and Soil Conservation Service (SCS) curve number (CN) to estimate soil loss in the Kiruburu and Meghahatuburuu mining sites.…”

Section: Introductionmentioning

confidence: 94%

“…Thus, effective catchment management is needed to ensure the sustainability of natural resources both for the current and future generation [17,18]. This will require timely information on the rate and amount of soil loss and delineation of degraded areas [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…As such empirical and physically-based models have been developed for soil loss estimations and predictions [23][24][25]. The Revised Universal Soil Loss Equation (RUSLE) [26] which is the updated form of Universal Soil Loss Equation (USLE) [5] is one of the widely utilized empirical models for the estimation of soil loss [19,27,28]. RUSLE was firstly developed in the USA to forecast long-term average erosion under different management systems [1,26].…”

Section: Introductionmentioning

confidence: 99%

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Spatial distribution of soil erosion and sediment yield in the Pra River Basin

et al. 2020

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This study demonstrates the application of Revised Universal Soil Loss Equation (RUSLE) and Sediment Distributed Delivery (SEDD) models integrated with Geographic Information System (GIS) to estimate gross soil loss and the sediment delivery of the Pra River Basin in Ghana. Digital Elevation Model, land use map, rainfall data and soil map were input to the model to display the spatial distribution of soil erosion and sediment in the basin. The model estimated an annual soil erosion of 1.28 × 10 6 t/year and an average sediment yield of 2.70 t/ha/year in the basin. Results showed that about 21.3% of the basin is susceptible to severe and very severe erosion. The model results showed that soil erosion rate varied with land use types. It also showed that the Lower Ofin sub-basin has the highest erosion rate. The study demonstrates that the RUSLE and SEDD model integrated with GIS provides relatively easy, cost-effective and fast approach in the estimation of spatially distributed soil erosion and sediment yield of river basins. The results will help in the planning and management of natural resources to ensure sustainable development of the Pra River Basin.

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“…A erosão do solo é um processo que ocorre naturalmente e é um fenômeno geológico normal, associado ao ciclo hidrológico. Ademais, pode ser considerado também, como um processo gradual que ocorre quando o impacto da água separa e remove partículas do solo causando a deterioração do solo (FARIA et al, 2003;SILVA et al, 2013;CORRÊA et al, 2015;EL JAZOULI et al, 2017).…”

Section: Introductionunclassified

Índice De Vulnerabilidade Do Solo Na Bacia Hidrográfica Do Rio Das Pedras – Mg

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Marrama

et al. 2019

R. gest. sust. ambient.

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O presente estudo visou a possibilidade de validação de um Índice de Vulnerabilidade do Solo através de informações de “tipo”, “uso” e “declividade” do solo na Bacia Hidrográfica do Rio das Pedras, em Uberlândia - MG, com o auxílio de técnicas de Geoprocessamento a partir da utilização do software ArcGis versão 10.3. Os resultados obtidos foram ponderados seguindo a metodologia proposta por Crepani (1996), resultando um mapa final discriminado em cinco classes distintas, que variam nos seguintes níveis de vulnerabilidade: Estável (1,0); Moderadamente Estável (1,0 – 1,5); Medianamente Estável (1,5 - 2,0); Moderadamente Vulnerável (2,0 - 2,5); Vulnerável (2,5 - 3,0). Destacou-se o predomínio da classe Medianamente Estável ao longo da bacia (55,3%) e a ocorrência de áreas mais vulneráveis na região do baixo curso da mesma, a partir da associação entre solos mais jovens, declividades mais intensas e usos agrossilvipastoris. Pôde-se concluir que a utilização de técnicas de geoprocessamento se mostrou fundamental para executar a análise de vulnerabilidade no âmbito de bacias hidrográficas. Ademais, a bacia em estudo pode ser considerada estável, sendo que nas áreas em que predominam as classes mais vulneráveis, faz-se necessário o direcionamento prioritário de ações de recuperação e conservação por parte dos agentes.

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Soil erosion modeled with USLE, GIS, and remote sensing: a case study of Ikkour watershed in Middle Atlas (Morocco)

Cited by 121 publications

References 34 publications

Modeling the Impact of Climate Change and Land Use Change Scenarios on Soil Erosion at the Minab Dam Watershed

Modeling the Impact of Climate Change and Land Use Change Scenarios on Soil Erosion at the Minab Dam Watershed

Spatial distribution of soil erosion and sediment yield in the Pra River Basin

Índice De Vulnerabilidade Do Solo Na Bacia Hidrográfica Do Rio Das Pedras – Mg

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