Large scale soil erosion modeling for a mountainous watershed

Ping, Zhou; Nieminen, Juhana; Tokola, Timo; Luukkanen, Olavi; Oliver, T.

doi:10.2495/geo060071

Cited by 5 publications

(5 citation statements)

References 30 publications

(22 reference statements)

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“…Jiang et al (2004) studied the influence of vegetation type on hydrological processes at the landscape scale and found that the vegetation cover is positively correlated with precipitation. Soil erosion has been modeled and the ecoenvironmental vulnerability of mountainous watershed mapped (Ma et al 2003; Li et al 2006; Zhou et al 2006). He et al (2006) studied landscape changes from 1974 to 1995 in the UMR basin, and Lu et al (2006) studied the differential responses of shrubs and herbs to soil variables.…”

Section: Introductionmentioning

confidence: 99%

Vegetation Dynamics and Forest Landscape Restoration in the Upper Min River Watershed, Sichuan, China

Ping

Luukkanen

Tokola

et al. 2007

Restoration Ecology

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The Upper Min River (UMR) watershed, with an area of 23,000 km 2 in the Upper Yangtze basin in Sichuan province, is ecologically and environmentally fragile. This situation is due to natural catastrophes and increasing anthropogenic disturbance. Forest cover has decreased dramatically, especially along the main Min River. Restoration of the vegetation in a watershed needs to consider the natural vegetation distribution and dynamics. In the UMR watershed, 625 sample plots were randomly placed to study the soil types, current vegetation distribution, vegetation dynamics, afforestation, and potential tree species for restoration. We investigated the relationship between vegetation types and soil orders, predicted the percentages of potential tree species for forest landscape restoration using logistic regression, identified priority areas for rapid restoration, and pinpointed areas for forest restoration where low precipitation is a constraint. The results showed that the vegetation types were well correlated with soil orders, and the latter could be used to deduce the potential vegetation for areas of degraded secondary forest. Priority areas for rapid restoration were demonstrated, and the difficult areas with precipitation as a limiting factor for vegetation restoration were specified. Suitable tree species were suggested for restoration on different soil orders at different elevations.

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Section: Introductionmentioning

confidence: 99%

Vegetation Dynamics and Forest Landscape Restoration in the Upper Min River Watershed, Sichuan, China

Ping

Luukkanen

Tokola

et al. 2007

Restoration Ecology

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“…It is evident that Equation ( 2) is not feasible for large-scale investigations, because of the data availability [6]. Hence, the C-factor value has been calculated from VC through the CC subfactor for large-scale soil erosion modeling in some studies [27][28][29] as Equation ( 3):…”

Section: C-factor Values Based On Field Experimentsmentioning

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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“…R-factor is the long term annual average of the product of rainfall kinetic energy (KE) in MJ•ha −1 and the maximum rainfall intensity in 30 minutes (I 30 ) in mm•h −1 [19]. The rainfall factor can vary from year to year, so an average over a number of years is usually used [20].…”

Section: R-factor Determinationmentioning

confidence: 99%