Estimation of soil erosion in some sections of Lower Jinsha River based on RUSLE

Jiang, Liguang; Yao, Zhijun; Liu, Zhaofei; Wu, Shanshan; Wang, Rui; Wang, Lei

doi:10.1007/s11069-014-1569-6

Cited by 57 publications

(28 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The correlation analysis of erosion rate with slope and elevation in the study area revealed that the interrelationship between erosion and slope was very significant (r = 0.72, P \ 0.001). The present results also incorporated the previous study by Jiang et al (2015).…”

Section: Resultssupporting

confidence: 84%

“…On the other hand, dense forests play an important role in soil conservation. Therefore, the afforestation program in this region is essential and should be implemented strictly (Sharma 2010;Bhunia and Shit 2013;Jiang et al 2015). The calculated R factor for the study area is shown in Table 4.…”

Section: Resultsmentioning

confidence: 99%

“…Soil erosion decreases exponentially with increase in vegetation cover (Jiang et al 2015;Shit et al 2013). Plant cover reduces soil erosion by intercepting raindrops, enhancing infiltration, slowing down the movement of runoff (Gyssels et al 2005;Wang et al 2013).…”

Section: Crop Management Factor (C)mentioning

confidence: 99%

“…The geo-spatial technology is cost-effective and timeefficient in this regard. Fewer studies have been carried out using different methods of GIS techniques and remotely sensed data are effective in identifying and mapping land degradation risks and modeling of soil loss (Pandey et al 2007(Pandey et al , 2009Rahman et al 2009;Nagaraju et al 2011;Sinha and Joshi 2012;Nasre et al 2013;Baroudy and Moghanm 2014;Jiang et al 2015). The present study focused on the prognostic modeling capabilities of geospatial technology based soil erosion potential model to delineate the probable soil erosion potential areas in subtropical region.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India

Shit¹,

Nandi

Bhunia³

2015

Model. Earth Syst. Environ.

View full text Add to dashboard Cite

An appraisal of spatial and quantitative information on soil erosion is a major challenge for human sustainability. The present study demonstrates the prognostic modeling capabilities of geo-spatial technology based on soil erosion potential model to assess the effects of implementing land use changes within sub-tropical region in India. The Revised Universal Soil Loss Equation (RUSLE) integrated with geo-spatial technology was used to produce predictive soil erosion map. Rainfall pattern, soil type, topography, crop system and management practices and geo-spatial data were used to predict the soil erosion model. Results shows 74.77 % of the study area is marked as low potential zone for soil erosion, whereas 5.58 % ([10 t h -1 year -1 ) is very high erosion risk zone. The present information may help recognizing areas that are vulnerable to soil loss and the proposed method will be used for generalized planning and assessment purposes for supervision and preserve the soil erosion.

show abstract

Section: Resultssupporting

confidence: 84%

Section: Resultsmentioning

confidence: 99%

Section: Crop Management Factor (C)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India

Shit¹,

Nandi

Bhunia³

2015

Model. Earth Syst. Environ.

View full text Add to dashboard Cite

show abstract

“…Otras investigaciones de Lee (2004); Kheir et al (2006); Vrieling (2006) Vergari (2015); Jiang et al (2015); Aucelli et al (2016) aplican, retoman, y proponen otras metodologías para el estudio y mapeo de la erosión. Yang y Zhu (2003) explican que para el estudio de la erosión se usan actualmente los sensores remotos y los Sistemas de Información Geográfica (GIS) y que se dividen en la interpretación visual, análisis espectrales, determinación de parámetros, así como la interpretación interactiva con criterios de experto y de computadora.…”

Section: Introductionunclassified

Estudio de denudación en piroclastos Tierra Blanca Joven dentro el área metropolitana de San Salvador

Vásquez¹,

Chávez²,

Gracias³

2017

Rev. Geol. Amér. Central

View full text Add to dashboard Cite

Severe problems of denudation are happening in Tierra Blanca Joven pyroclasts within the Metropolitan Area of San Salvador, affecting infrastructure and housing projects. This leads to necessary and constant mitigation works. To understand the behavior and characteristics of these pyroclasts a monitoring program began. This was made through the installation in some sectors of 3/8 inch rods to recognize changes of erosion and sedimentation. With the help of remote sensors, historical changes were identified in the drainages. For this area a map of erodibility with the Universal Equation of Soil Loss was obtained. The results show the importance and influence of climatic factors, tectonic, human actions and changes of the base level of drainage. This can be used to build criteria for research and propose protection zones in the river drainage system. The monitoring probes to be useful and is necessary to improve it.

show abstract

Predicting soil erosion and sediment delivery in large, data‐sparse, mountainous basins

Tan,

Liu,

2024

Earth Surf Processes Landf

View full text Add to dashboard Cite

Intense hydroelectric exploitation requires information on sediment flux to prevent the potential siltation of cascade reservoirs. As the main sediment source for the Yangtze River, the lower Jinsha River Basin (LJRB) lacks data on its sediment budget, which urges a thorough investigation to provide reliable estimates. This paper proposed a modelling framework to study soil erosion, sediment delivery, and sediment yield in this critical area with an emphasis on analyzing the underlying controls. Over the past 50 years, basin average erosion rates decreased from 26.5 to 23.7 t ha−1 a−1, with erosion hotspots (>50 t ha−1 a−1) comprising only 21.1% of the basin area but contributing 69.5% of the gross erosion. Local steepness and sparse landcover conditions were found to be directly responsible for high erosions, rather than precipitation or soils. With a dimensionality reduction strategy to remove redundant factors, the optimal variables for modelling sediment delivery ratio (SDR) were identified as Specific Catchment Area, Maximum Elevation, and Drainage Area. These three variables all had a positive correlation with SDR, and the established model can explain 86% of the SDR variation. Further modelling revealed regionalised SDRs ranging from 0.07 to 0.38 for 39 subbasins, about 30% of which were no less than 0.35. Regionalised specific sediment yield (SSY) ranged from 1.3 to 16.9 t ha−1 a−1 with 38% of the high SSYs (above 8 t ha−1 a−1) concentrating in the reservoir area from Wudongde to Xiluodu that poses siltation risks. This study addressed the scarcity of sediment budget knowledge in the LJRB, and the coupled model of erosion and sediment delivery has been validated with satisfactory predictions. Therefore, the modelling framework proposed may be adopted to diverse river basins for its low parameter requirements and solid modelling performance.

show abstract

Estimation of soil erosion in some sections of Lower Jinsha River based on RUSLE

Cited by 57 publications

References 39 publications

Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India

Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India

Estudio de denudación en piroclastos Tierra Blanca Joven dentro el área metropolitana de San Salvador

Predicting soil erosion and sediment delivery in large, data‐sparse, mountainous basins

Contact Info

Product

Resources

About