Estimation of water erosion in abandoned quarries sites using the combination of RUSLE model and geostatistical method

Aouichaty, Nabil; Bouslıhım, Yassine; Hilali, Said; Zouhri, Abdeljalil; Koulali, Yahya

doi:10.1016/j.sciaf.2022.e01153

Cited by 8 publications

(2 citation statements)

References 34 publications

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“…The values of the factors range from 0 to 1, depending on the type of land cover. The lower the value, the more resistant to erosion (Lisbôa et al 2017;Aouichaty et al 2022;Chen et al 2023). The C-factor values are in accordance with the paper (Seleznev et al 2022).…”

Section: Rusle Adaptation For Urban Environmentssupporting

confidence: 89%

Modeling Sediment Production In Urban Environments: Case Of Russian Cities

Shevchenko,

Seleznev,

Malinovsky

et al. 2024

GES

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The aim of this study is to provide a tool to assess sediment production in an urban area. The urban environment is affected by a variety of anthropogenic and natural factors that, in particular, lead to the sediment production. The storage of sediments in the urban landscape negatively affects the quality of the urban environment. The model was developed on the basis of landscape studies conducted in residential areas of six Russian cities. The model takes into account (1) the influence of precipitation, spring snowmelt, and vehicles, (2) the influence of erosion factors for two seasons: warm (t>5°C) and cold (t<5°C), and (3) the presence of disturbed surfaces. The application of the developed model to Ekaterinburg city conditions returned sediment production equal to 1.2 kg/m2/y. A comparison of seasonal values shows that sediment production in cold season is 2.5 times higher than in the warm season. In the absence of the disturbed surfaces, sediment production decreases to 0.44 kg/m2/y. Modeling showed a correlation between sediment production in Russian cities and duration of the cold season. The efficiency of various urban area maintenance practices and cleaning measures were evaluated in terms of sediment production and storage. The developed model presented in this paper is based on research in Russian cities, but can be applied to assess the formation of sediment and measures to reduce the value of its accumulation in the urban environment in different regions of the world.

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Section: Rusle Adaptation For Urban Environmentssupporting

confidence: 89%

Modeling Sediment Production In Urban Environments: Case Of Russian Cities

Shevchenko,

Seleznev,

Malinovsky

et al. 2024

GES

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“…Additionally, many similar studies have been carried out in Morocco, Ethiopia, Romania, Sri Lanka, Tunisia, Switzerland, Iran, Nigeria, and more to quantify the soil erosion and their spatial distribution at watershed level (Aouichaty et al, 2022;Eniyew et al, 2021;Kebede et al, 2021;Patriche, 2023;Guduru & Jilo, 2023;Bircher et al, 2019;Olika et al, 2023;Fang et al, 2019;Cheikha et al, 2023;Getu et al, 2022;Ostovari et al, 2017;Joshi et al, 2023;Balabathina et al, 2020). These studies revealed that a massive amount of productive soil is being lost annually, creating risk to food security, livelihoods, and environmental sustainability.…”

Section: Introductionmentioning

confidence: 99%

Prioritization of Sub-Watershed Based on Soil Loss Estimation Using RUSLE Model

Deka,

Das,

Hazarika

et al. 2024

International Journal of Applied Geospatial Research

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Soil erosion is one of the most crucial land degradation problems and is considered the most critical environmental hazard worldwide. The present study uses remote sensing data integrated with the geographical information system (GIS) technique and the revised universal soil loss equation (RUSLE) model for assessing the annual average soil loss of the Digaru watershed of India for 1999 and 2020. The estimated mean gross yearly soil loss from the entire watershed was 102716 t yr-1 in 1999 and 178931.6 t yr-1 in 2020. The overall average soil loss rate increased significantly between 1999 and 2020, rising from 4.73 t—ha-1yr-1 to 8.43 t—ha-1yr-1. The sub-watersheds are prioritized as high (≥ 40 t ha−1yr−1), moderate (20–40 t ha−1yr−1), and low (<20 t ha−1yr−1) based on the spatial distribution of soil erosion. Seven sub-watersheds have been grouped under low priority, followed by seven under moderate priority and one under high priority. This study demands instant attention for soil and water conservation efforts in highly eroded watershed areas.

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