Future soil erosion risk — Results of GIS-based model simulations for a catchment in Saxony/Germany

Routschek, Anne; Schmidt, Jürgen; Enke, W.; Deutschlaender, Th.

doi:10.1016/j.geomorph.2013.09.033

Cited by 22 publications

(15 citation statements)

References 19 publications

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“…Precipitation is a primary driving factor of soil erosion; its intensity, amount, duration, timing, and rate directly affect the spatiotemporal distribution of RE and ED. The effect of climate change on soil erosion was observed by Routschek et al [74] to be due to changes in the rainfall. A study also shows that the increased intensity of various climatic parameters, particularly rainfall, has caused an increase in the sediment load [75].…”

Section: Discussionmentioning

confidence: 95%

Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

2018

Atmosphere

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Soil erosion is one of the most critical environmental hazards in the world. Understanding the changes in rainfall erosivity (RE) and erosivity density (ED), as well as their affecting factors, at local and catchment scales in the context of climate warming is an important prerequisite of soil erosion prevention and soil loss risk assessment. The present study identified the variability and trends of RE and ED in terms of both time and space in the Ganjiang River catchment over the period of 1960-2012, and also analyzed and discussed the impact of climate change. The results show that RE and ED in the catchment had great monthly variations and high year-to-year variability. Both presented long-term increasing trends over the entire study period. The highest RE and ED were observed in June and in the eastern and northeast parts of the catchment, which indicated that June was the most susceptible month for soil erosion in this area and the lower reaches of the Ganjiang River was the riskiest area for soil erosion. Finally, the East Asian summer monsoon and climate change were highly correlated with changes in RE and ED.

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Section: Discussionmentioning

confidence: 95%

Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

2018

Atmosphere

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“…Several researchers have attempted to estimate the soil erosion loss in the different regions of the country utilizing remote sensing and GIS technologies (Jain et al 2001;Chowdary et al 2004;Pandey et al 2007;Dabral et al 2008;Ismail and Ravichandran 2008;Dalu et al 2013;Gajbhiye et al 2014). Effect of climate change on soil erosion has been observed by Routschek et al (2014) due to changes in the rainfall, and a study shows that increased intensity of various climatic parameters, particularly rainfall, has caused an increase in the sediment load (Mukundan et al 2013). Higher rate of soil erosion is observed by various researchers in different parts of India using the MMF (Morgan-Morgan-Finney), USLE (Universal Soil Loss Equation), and RUSLE (Revised Universal Soil Loss Equation) models (Prasannakumar et al 2012;Patel and Kathwas 2012;Pandey et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Climate change impact on soil erosion in the Mandakini River Basin, North India

et al. 2016

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Correct estimation of soil loss at catchment level helps the land and water resources planners to identify priority areas for soil conservation measures. Soil erosion is one of the major hazards affected by the climate change, particularly the increasing intensity of rainfall resulted in increasing erosion, apart from other factors like landuse change. Changes in climate have an adverse effect with increasing rainfall. It has caused increasing concern for modeling the future rainfall and projecting future soil erosion. In the present study, future rainfall has been generated with the downscaling of GCM (Global Circulation Model) data of Mandakini river basin, a hilly catchment in the state of Uttarakhand, India, to obtain future impact on soil erosion within the basin. The USLE is an erosion prediction model designed to predict the long-term average annual soil loss from specific field slopes in specified landuse and management systems (i.e., crops, rangeland, and recreational areas) using remote sensing and GIS technologies. Future soil erosion has shown increasing trend due to increasing rainfall which has been generated from the statistical-based downscaling method.

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“…Various authors acknowledged the effects of climate on land degradation outlining the impacts of precipitations and temperature on soil erosion processes. According to Routschek et al (2014), soil erosion is mostly the result of extreme but short rainfall events. Analysis of frequency and intensity of these extreme rainfall events are needed for the calculation of sediment yield, and will be more important in the future due to climate change.…”

Section: Resultsmentioning

confidence: 99%

Soil Erosion in the River Basin of Provala, Montenegro

Spalević¹,

Čurović²,

Barovic³

et al. 2015

AgricultForest

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Use of models in the research of soil erosion processes is at the moment a standard practice and models are necessary tool for better understanding of sediment production and runoff, although their interpretation is limited by large uncertainties, including model parameter uncertainties. In this research we studied the main ecological factors and anthropogenic influences in sediment yield on the Provala basin of Montenegro analyzing the relationships between the physical-geographical, geological, soil, land use, climate characteristics and soil erosion intensity. Real soil losses, G yr , were calculated on 7541 m 3 yr -1 , specific on 597 m 3 km -2 yr -1 . The value of Z coefficient was calculated on 0.482 what indicates that the river basin belongs to the 3 rd destruction category; erosion process is medium. There is a possibility for large flood waves to appear in the studied basin. The results presented in this paper can provide decision support for watershed managers about where the best conservation measures can be implemented effectively and at low cost in the studied area. The methods we used in this study can also be of interest in sediment modeling for other basins in the Region.

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Future soil erosion risk — Results of GIS-based model simulations for a catchment in Saxony/Germany

Cited by 22 publications

References 19 publications

Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

Climate change impact on soil erosion in the Mandakini River Basin, North India

Soil Erosion in the River Basin of Provala, Montenegro

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