Modification of the RUSLE slope length and steepness factor (LS-factor) based on rainfall experiments at steep alpine grasslands

Schmidt, Simon; Tresch, Simon; Meusburger, Katrin

doi:10.1016/j.mex.2019.01.004

Cited by 78 publications

(38 citation statements)

References 16 publications

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“…Each of the RUSLE-factors (excluding the P factor) was calculated separately and adapted to the specific environmental conditions of Swiss grasslands. The generation of the RUSLE factor maps (rainfall erosivity, Schmidt et al, 2016;soil erodibility, Schmidt, Ballabio, Alewell, Panagos, & Meusburger, 2018c; cover and management, Schmidt, Alewell, & Meusburger, 2018b; slope length and steepness, Schmidt, Tresch, & Meusburger, 2019) is explained in detail in the individual sections and in Table 1.…”

Section: Datasetsmentioning

confidence: 99%

“…Finally, the L and S factors were adapted to the complex alpine topography (Table 2). Slope length were originally constrained to a maximal flow threshold of 100 m to account for the whole agricultural area in Switzerland (Schmidt et al, 2019). However, flow measurements in the Swiss alpine grasslands revealed short flow length less than 2 m due to high surface roughness and infiltration capacity.…”

Section: Mappingmentioning

confidence: 99%

“…2) provides monthly soil erosion risk maps for Swiss grasslands (Figure 2). Note that while the K-factor (Schmidt et al, 2018c), R-factor (Schmidt et al, 2016), and LS-factor (Schmidt et al, 2019) are available for the whole of Switzerland, the C-factor (Schmidt et al, 2018b) is limited to the grassland areas of Switzerland (Schmidt et al, 2018a) and thus presets the extent of the erosion modelling.…”

Section: Mappingmentioning

confidence: 99%

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Monthly RUSLE soil erosion risk of Swiss grasslands

2019

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This study presents the first mapping of soil erosion risk modelling based on the Revised Universal Soil Loss Equation (RUSLE) at a sub-annual (monthly) temporal resolution and national scale (100 m spatial resolution). The monthly maps show highest water erosion rates on Swiss grasslands in August (1.25 t ha-1 month-1). In summer, the mean monthly soil loss by water erosion is 48 times higher than the mean soil loss in winter. Considering the annual average fraction of green vegetation cover of 54%, the predicted soil erosion rate for the Swiss national grassland area would add up to a total eroded soil mass of 5.26 Mt yr-1. The RUSLE application with an intact 100% vegetation cover would largely reduce the soil loss to an average annual rate of 0.14 t ha-1 year-1. These findings clearly highlight the importance to consider and maintain the current status of the vegetation cover for soil erosion prediction and soil conservation, respectively.

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Section: Datasetsmentioning

confidence: 99%

Section: Mappingmentioning

confidence: 99%

Section: Mappingmentioning

confidence: 99%

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Monthly RUSLE soil erosion risk of Swiss grasslands

2019

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“…In a given area, the magnitude of soil erosion rate is varied, both temporally and spatially, due to the existing Fig. 1 Boundary of the study area local condition mainly biophysical and land management variables (Wischmeier and Smith 1978;Renard et al 1997 andMorgan 2005). The central idea is-the collection of spatial data is crucial (Lulseged et al 2017).…”

Section: Data Collection and Processingmentioning

confidence: 99%

Spatial modeling of erosion hotspots using GIS-RUSLE interface in Omo-Gibe river basin, Southern Ethiopia: implication for soil and water conservation planning

Girma

Gebre

2020

Environ Syst Res

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Soil degradation due to soil erosion is one of the major environmental threats in developing countries. In resource limited conditions, computing the spatial distribution of soil erosion risk has become an essential and practical mechanism to implement soil conservation measures. This study aimed to assess the spatial distribution of soil loss in Omo-Gibe river basin using the integration of computer-based RUSLE and ArcGIS 10.7.1 to identify areas that require erosion prevention priority. Once raster layer of the input parameters was created, overlay analysis was carried to assess the spatial distribution of soil loss. The estimated annual soil loss varies from 0-279 t ha −1 yr −1 with a mean annual soil loss of 69 t ha −1 yr −1. The empirical analysis also confirmed that the basin losses a total of about 89.6 Mt of soil annually. Out of the total area; 7% was in very sever class, 4.8% was found in the sever and 8.7% was categorized in very high range. The remaining area were ranging from low to high erosion risk class. The influence of the combined LS factor for soil loss is significant. It was observed that small area of the Omo-Gibe basin contributed for the significant amount of soil loss. The finding of this study is in a good agreement with previous studies. Compared to the country permissible soil loss rate, 26% of the entire basin significantly exceeds the country threshold value (TSL = 18 t ha −1 yr −1). As a result, precedence and immediate attention should be given to those erosion prone areas. The study output could deliver watershed management experts and policy makers for better management implementation and resource allocation based on the local context.

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“…This study used the DEM-ASTER at 30-meter resolution downloaded from US Geological survey. The LS is the ratio of observed soil loss related to the soil loss of standardized plot (22.13) as indicated in (Schmidt et al 2019). The LS value is considered to have values between 0.02-48 for Ethiopian condition (Hurni 1985) and the study area is ranging from 0-21.32.…”

Section: Slope Length and Steepness (Ls) Factormentioning

confidence: 99%

Impact of Land Use and Land Cover Change on Soil Erosion Using Rusle Model And Gis: A Case of Temeji Watershed, Western Ethiopia

Moisa¹,

Negash²,

Merga³

et al. 2020

Preprint

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BackgroundThe impact of Land Use/Land Cover (LULC) conversion on soil resources is getting global attention. Soil erosion is one the critical environmental problems worldwide with high severity in developing countries due to land degradation. This study integrates the Revised Universal Soil Loss Equation (RUSLE) model with a Geographic Information Systems (GIS) to estimate the impacts of LU/LC conversion on the mean annual soil loss in Temeji watershed. In this study, LU/LC change of Temeji watershed were assessed from 2000 to 2020 by using 2000 Landsat ETM+ and 2020 Landsat OLI/TIRS images and classified using supervised maximum likelihood classification algorithms. ResultsResults indicates that majority of the LU/LC in the study area is vulnerable to soil erosion. Our findings show that cultivated land had the highest average soil loss rate in Temeji watershed. High soil loss is observed when grass and forest land were converted into cultivated land with mean soil loss of 88.8t/ha/yr and 86.9t/ha/yr in 2020. Results revealed that about 6608.5ha (42.8%) and 8391.8ha (54.4%) were categorized under severe classes in 2000 and 2020, respectively.ConclusionsThe results can definitely support policy makers and environmental managers in implementation of soil and water conservation practices and erosion risk prevention and mitigation strategies in Temeji watershed.

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Modification of the RUSLE slope length and steepness factor (LS-factor) based on rainfall experiments at steep alpine grasslands

Abstract: Graphical abstract

Cited by 78 publications

References 16 publications

Monthly RUSLE soil erosion risk of Swiss grasslands

Monthly RUSLE soil erosion risk of Swiss grasslands

Spatial modeling of erosion hotspots using GIS-RUSLE interface in Omo-Gibe river basin, Southern Ethiopia: implication for soil and water conservation planning

Impact of Land Use and Land Cover Change on Soil Erosion Using Rusle Model And Gis: A Case of Temeji Watershed, Western Ethiopia

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