A stochastic estimation of sediment production in an urban catchment using the USLE model

Lisbôa, Érico Gaspar; Blanco, Cláudio José Cavalcante; Maia, Rodrigo; Bello, Leonardo Augusto Lobato

doi:10.1080/02626667.2017.1395031

Cited by 7 publications

(5 citation statements)

References 19 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: 90%

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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: 90%

“…In urban residential areas, these measures are not implemented, so the P-factor was assumed as 1 (Renard et al 1997;Lisbôa et al 2017;Taoufik et al 2020;Michalek et al 2021).…”

Section: Rusle Adaptation For Urban Environmentsmentioning

confidence: 99%

Modeling Sediment Production In Urban Environments: Case Of Russian Cities

Shevchenko,

Seleznev,

Malinovsky

et al. 2024

GES

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“…In some cases, the C-factor and P-factor (support practice factor) have been treated together as the CP factor [4,[98][99][100][101][102][103][104][105][106][107]. For a managed artificial forest, the C-factor is closely related to the forest's age [108].…”

Section: C-factor Estimation Based On Other Methods (M6)mentioning

confidence: 99%

Global Analysis of the Cover-Management Factor for Soil Erosion Modeling

2023

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Land use and management practices (LUMPs) play a critical role in regulating soil loss. The cover-management factor (C-factor) in Universal Soil Loss Equation (USLE)-type models is an important parameter for quantifying the effects of LUMPs on soil erosion. However, accurately determining the C-factor, particularly for large-scale assessments using USLE-type models, remains challenging. This study aims to address this gap by analyzing and comparing the methods used for C-factor quantification in 946 published articles, providing insights into their strengths and weaknesses. Through our analysis, we identified six main categories of methods for C-factor quantification in USLE-type modeling. Many studies have relied on empirical C-factor values for different land-use types or calculated C-factor values based on vegetation indices (VIs) in large study areas (>100 km2). However, we found that no single method could robustly estimate C-factor values for large-scale studies. For small-scale investigations, conducting experiments or consulting the existing literature proved to be more feasible. In the context of large-scale studies, employing methods based on VIs for C-factor quantification can enhance our understanding of the relationship between vegetation changes and soil erosion potential, particularly when considering spatial and spatiotemporal variations. For the global scale, we recommend the combined use of different equations. We suggest further efforts to develop C-factor datasets at large scales by synthesizing field-level experiment data and combining high-resolution satellite imagery. These efforts will facilitate the development of effective soil conservation practices, ensuring sustainable land use and environmental protection.

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“…Here, RCP represents for Representative Concentration Pathway defined by IPCC, and RCP 8.5 scenario imply that emissions continue to rise throughout the 21 st century. The first step is to define the catchment areas, which are the standard units used for installing the technology [20]. The initial analysis of the individual catchments becomes the basis for the subsequent technology installations, establishing the spatial arrangement of the various disaster prevention technologies utilized.…”

Section: Study Area and Datamentioning

confidence: 99%

GIS Based Assessment and Design for Areas Vulnerable to Soil Disasters: Case Study of Namhyeun-dong, South Korea

Jeon

Kang

2020

Sustainability

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Due to climate change, heavy rainfall events that trigger landslips are becoming common. This study investigates patterns in the soil’s response to major rainfall events in mountainous areas and proposes a new approach for resilient disaster prevention technology and recovery based on the effect of soil runoff. Namhyeon-dong within Seoul was selected for the case study because of its vulnerable location between two mountains. A master plan was developed to cope with the predicted soil runoff based on the annual rainfall, local land use and a series of 10-year forecasts covering the period from 2021 to 2100. A total of 22 catchments in the study area were analyzed with Arc Hydro, an ArcGIS plug-in and appropriate technologies proposed to deal with the soil runoff likely to be experienced in each catchment in an extreme disaster. The resulting model was deemed adequate to deal with disasters during the period predicted to represent the highest risk, 2051–2060. The study’s findings will help to forecast disasters from flood that could impact residential areas in mountainous regions, to predict the magnitude of potential soil disasters in individual regions and develop design guidelines for disaster prevention technology based on the predicted amount of soil runoff.

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A stochastic estimation of sediment production in an urban catchment using the USLE model

Cited by 7 publications

References 19 publications

Modeling Sediment Production In Urban Environments: Case Of Russian Cities

Modeling Sediment Production In Urban Environments: Case Of Russian Cities

Global Analysis of the Cover-Management Factor for Soil Erosion Modeling

GIS Based Assessment and Design for Areas Vulnerable to Soil Disasters: Case Study of Namhyeun-dong, South Korea

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