Soil erosion rates assessed by RUSLE and PESERA for a Chinese Loess Plateau catchment under land‐cover changes

Li, Pengfei; Zang, Yuzhe; Ma, Doudou; Yao, Wanqiang; Holden, Joseph; Irvine, Brian; Zhao, Guangju

doi:10.1002/esp.4767

Cited by 32 publications

(16 citation statements)

References 84 publications

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“…Biological-control ( B ) factor refers to the ratio of the soil erosion amounts of land with vegetation cover or field management, and that of continuously fallowed land under certain conditions [ 40 , 41 ]. In this study, we extracted NDVI values and calculated the vegetation coverage by using Equation (11) according to Li et al (2020) [ 42 ] based on remote sensing images captured from June to September during 2000 to 2015; B factor was obtained according to the relationship between B factor and the land use types, and vegetation coverage ( Table 1 ) [ 43 ]. The vegetation coverage was calculated as follows:

where f is the vegetation coverage, and NDVI min and NDVI max are the minimum and maximum NDVI values.…”

Section: Methodsmentioning

confidence: 99%

Effects of Vegetation Restoration on Soil Erosion on the Loess Plateau: A Case Study in the Ansai Watershed

Wei

Zhao

Wang

2021

IJERPH

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Large-scale vegetation restoration greatly changed the soil erosion environment in the Loess Plateau since the implementation of the “Grain for Green Project” (GGP) in 1999. Evaluating the effects of vegetation restoration on soil erosion is significant to local soil and water conservation and vegetation construction. Taking the Ansai Watershed as the case area, this study calculated the soil erosion modulus from 2000 to 2015 under the initial and current scenarios of vegetation restoration, using the Chinese Soil Loess Equation (CSLE), based on rainfall and soil data, remote sensing images and socio-economic data. The effect of vegetation restoration on soil erosion was evaluated by comparing the average annual soil erosion modulus under two scenarios among 16 years. The results showed: (1) vegetation restoration significantly changed the local land use, characterized by the conversion of farmland to grassland, arboreal land, and shrub land. From 2000 to 2015, the area of arboreal land, shrub land, and grassland increased from 19.46 km2, 19.43 km2, and 719.49 km2 to 99.26 km2, 75.97 km2, and 1084.24 km2; while the farmland area decreased from 547.90 km2 to 34.35 km2; (2) the average annual soil erosion modulus from 2000 to 2015 under the initial and current scenarios of vegetation restoration was 114.44 t/(hm²·a) and 78.42 t/(hm²·a), respectively, with an average annual reduction of 4.81 × 106 t of soil erosion amount thanks to the vegetation restoration; (3) the dominant soil erosion intensity changed from “severe and light erosion” to “moderate and light erosion”, vegetation restoration greatly improved the soil erosion environment in the study area; (4) areas with increased erosion and decreased erosion were alternately distributed, accounting for 48% and 52% of the total land area, and mainly distributed in the northwest and southeast of the watershed, respectively. Irrational land use changes in local areas (such as the conversion of farmland and grassland into construction land, etc.) and the ineffective implementation of vegetation restoration are the main reasons leading to the existence of areas with increased erosion.

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where f is the vegetation coverage, and NDVI min and NDVI max are the minimum and maximum NDVI values.…”

Section: Methodsmentioning

confidence: 99%

Effects of Vegetation Restoration on Soil Erosion on the Loess Plateau: A Case Study in the Ansai Watershed

Wei

Zhao

Wang

2021

IJERPH

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“…Firstly, we established a summer corn-winter rape rotation system, field crop yields in each period, and the conversion of the added amount of corn stalks at each removal rate. Then we import rotation patterns to the management sub-module of the RUSLE 2 (version 2.6.1.9) model to estimating the C and P factors during the rotation period [33].…”

Section: Ruslementioning

confidence: 99%

The Crop Residue Removal Threshold Ensures Sustainable Agriculture in the Purple Soil Region of Sichuan, China

Zhang

Ren³

et al. 2021

Sustainability

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Sichuan, a hilly area in southwestern China, is recommended as a bioethanol production base because of its abundant crop residue resources. However, removing the crop straw for bioethanol may negatively affect soil fertility and productivity due to the local purple soil vulnerability. To explore the impact of crop residue removal on soil fertility and productivity and meet the needs of sustainable agriculture, we conducted a crop residue removal experiment by measuring the soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) contents, and crop yield in the purple soil region in southwest China. Soil erosion was also simulated by Revised Universal Soil Loss Equation version 2 (RUSLE 2). The results showed that soil erosion increased with the increase of the straw removal rate. Compared with 0% removal treatment, the SOC content reduced at other removal rate treatments, especially for long-term residue removal. The effect of residue removal on soil TN and TP was not consistent within one year. After two years, residue removal greater than 25% caused a decrease in TN by 1.6–3.7%, and straw removal greater than 50% caused a TP decrease by 8.5–9.3%. More than 25% of the residue removed reduced maize and canola yields, and TN and TP content. However, all crop residue removal treatments resulted in SOC content reduction and soil erosion deterioration. In conclusion, crop residue removal was not recommended due to agricultural sustainability in Sichuan, China.

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“…Supported by GIS and remote-sensing technologies, the RUSLE model has been used as a spatial-distribution model in predicting soil loss around the world. Many researchers on the Loess Plateau have developed local specific methods for calculating the factors of the RUSLE model (Fu et al, 2011;Li et al, 2020;Tang, Xu, Bennett, & Li, 2015). The R factor is defined as the potential ability of the rainfall to cause soil erosion, which is determined by the amount, intensity, terminal velocity and drop size of rainfall (Thomas, Joseph, & Thrivikramji, 2018).…”

Section: Soil Erosion Prediction Using the Rusle Modelmentioning

confidence: 99%

“…It exists because not everything is known, as is the case with soil erosion risk assessment (Xu, Xu, & Meng, 2012). However, earlier researches on soil erosion risk mainly focused on past and static soil erosion, paying less attention to future and dynamic soil erosion (Fernández & Núñez, 2011;Li et al, 2020). Future climate change can be simulated using a climate model, such as a general circulation model (GCM), and the outputs of the climate model are input into the hydrological model for soil erosion simulation.…”

Section: Introductionmentioning

confidence: 99%

Dynamic changes in soil erosion risk and its driving mechanism: A case study in the Loess Plateau of China

Xin-shi

Song

et al. 2020

European J Soil Science

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The Loess Plateau has long been considered as a very fragile area that suffers from serious water erosion. With the onset of global climate fluctuations and regional implementation of soil and water conservation measures, it is of great significance to effectively reveal the variations in soil erosion risk and its driving mechanism on the Loess Plateau. Taking the Yanwachuan watershed as a case study and using rainfall, soil, digital elevation model (DEM) and land‐use data, this study applied the Revised Universal Soil loss Equation (RUSLE) model with geographic information system (GIS) technology to analyse the temporal and spatial variations of soil erosion risk and to evaluate the effects of land‐use change and climate change on soil erosion from 1981 to 2016. Future soil erosion risk was also assessed under different land‐use and climate‐change scenarios. Results showed that annual soil loss in the Yanwachuan watershed presented a significant decreasing trend with a rate of 47.928 t km−2 a−1 and that average annual soil loss was 3,543.7 t km−2 from 1981 to 2016. However, some areas, even with good vegetation cover, still had a rather high soil erosion risk (exceeding the severe erosion rate of 5,000 t km−2 in 2000–2016) if located in hilly and gully slope regions. Land‐use change and climate variation contributed 37.2% and 62.8%, respectively, to reducing soil erosion, showing that climate variation played a leading role in current soil‐erosion reduction. In addition, the average soil losses in the 2020s, 2030s and 2040s under different scenarios are predicted to increase by 8.6–42.6% compared with the average soil loss (2,993.8 t km−2) in 2001–2016. Future climate change may be the main driving factor in enhancing the risk of soil erosion. Hence, there is an urgent need to strengthen soil and water conservation research and management in future years.Highlights This study assessed current and future water erosion risk on the Loess Plateau using the RUSLE model. Areas with steep slopes were still experiencing serious soil erosion risk in 2000–2016. Climate change was the main driving factor for soil erosion variation. Future erosion risk is expected to increase under different climate and land‐use scenarios.

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Soil erosion rates assessed by RUSLE and PESERA for a Chinese Loess Plateau catchment under land‐cover changes

Cited by 32 publications

References 84 publications

Effects of Vegetation Restoration on Soil Erosion on the Loess Plateau: A Case Study in the Ansai Watershed

Effects of Vegetation Restoration on Soil Erosion on the Loess Plateau: A Case Study in the Ansai Watershed

The Crop Residue Removal Threshold Ensures Sustainable Agriculture in the Purple Soil Region of Sichuan, China

Dynamic changes in soil erosion risk and its driving mechanism: A case study in the Loess Plateau of China

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