Changes in rainfall erosivity from combined effects of multiple factors in China’s Loess Plateau

Zhang, Jingpeng; Ren, Yuling; Jiao, Peng; Xiao, Peiqing; Li, Zhi

doi:10.1016/j.catena.2022.106373

Cited by 15 publications

(7 citation statements)

References 58 publications

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“…The soil erosion process and its changes in the Loess Plateau were largely controlled by the combined effects of rainfall intensity and vegetation distribution [54]. It presented an upward trend of average annual rainfall in the Loess Plateau (Figure 5), and increased heavy rainfall enhanced soil erosion risks [55]. For example, more than 50% of soil erosion was mainly caused by a few heavy rainfall events on the Loess Plateau [16,56].…”

Section: Discussionmentioning

confidence: 99%

Soil Erosion Processes and Geographical Differentiation in Shaanxi during 1980–2015

Lin

Zhao

et al. 2022

Sustainability

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It is important to couple the analysis of the effects of topography, climate, vegetation, and human activities on soil erosion to understand the dynamic characteristics of soil erosion under environmental change. This study investigated the impacts of geographic unit difference on soil erosion in the Shaanxi Province in China through the model simulation (RUSLE) and quantitative analysis. The following results were achieved. First, the amplitudes of environmental change varied at different rates in the three regions over the past 35 years (1980–2015). The frequency of severe rainstorms, the main trigger for soil erosion, intensified most in the Loess Plateau compared to the Qinling Mountains and Guanzhong Plain and led to an increase in the number of geological disasters in that region. In addition, after the 1990s, vegetation improved most in the Loess Plateau, and socioeconomic activities related to urbanization, another important driver of soil erosion, increased the most in Guanzhong Plain. Second, improved vegetation cover was the most significant contributor to the reduction in soil erosion during the past 35 years, although intensified rainstorms and human activities enhanced the risks of soil erosion. Third, the comparative study revealed that different environmental parameters dominated soil erosion processes in the three regions.

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Section: Discussionmentioning

confidence: 99%

Soil Erosion Processes and Geographical Differentiation in Shaanxi during 1980–2015

Lin

Zhao

et al. 2022

Sustainability

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“…The rainfall erosivity factor ( R ) reflects the potential for rainfall‐induced soil erosion. Among the various calculation methods (Loureiro & Coutinho, 2001; Renard et al, 1997; Renard & Freimund, 1994), the method of Zhang and Fu (2003) was adopted to calculate the R‐factor:

M=\alpha \sum \limits_{j=1}^k{\left({D}_j\right)}^{\beta },

\alpha =21.586{\beta}^{7.1891},

\beta =0.8363+18.177{P}_{d12}^{-1}+24.455{P}_{y12}^{-1},

where M is the rainfall erosivity value in the half‐month (MJ mm ha −1 h −1 ), D j is the rainfall on day j with daily rainfall ≥12 mm, and the threshold of 12 mm was determined through the analysis of more than 300 rainfall processes in a small watershed and three small plots of the Loess Plateau (Xie et al, 2000), which has been widely recognized and used in the Loess Plateau (Li, Guan, et al, 2022; Lu et al, 2022; Sun et al, 2014; Zhang et al, 2022), k is the number of days with a daily rainfall ≥12 mm; P d12 is the average daily rainfall (rainfall ≥12 mm); P y12 is the average annual rainfall (rainfall ≥12 mm) and α and β are the pending parameters for the model. The final spatial distribution of R‐factor was obtained using kriging interpolation from the rainfall erosivity of the 13 stations (Figure 1a).…”

Section: Methodsmentioning

confidence: 99%

“…where M is the rainfall erosivity value in the half-month (MJ mm ha À1 h À1 ), D j is the rainfall on day j with daily rainfall ≥12 mm, and the threshold of 12 mm was determined through the analysis of more than 300 rainfall processes in a small watershed and three small plots of the Loess Plateau (Xie et al, 2000), which has been widely recognized and used in the Loess Plateau (Li, Guan, et al, 2022;Lu et al, 2022;Sun et al, 2014;Zhang et al, 2022), k is the number of days with a daily rainfall ≥12 mm; P d12 is the average daily rainfall (rainfall ≥12 mm); P y12 is the average annual rainfall (rainfall ≥12 mm) and α and β are the pending parameters for the model. The final spatial distribution of R-factor was obtained using kriging interpolation from the rainfall erosivity of the 13 stations (Figure 1a).…”

Section: Datementioning

confidence: 99%

An integrated watershed modelling framework to explore the covariation between sediment connectivity and soil erosion

Guo,

Wu,

Liu

et al. 2023

European J Soil Science

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Quantitative identification of the covariation between sediment connectivity and soil erosion can contribute to provide the key information for watershed sediment management. However, this covariation and its spatiotemporal response mechanisms are still unclear, especially whether this covariation can be used as a basis for identifying critical source areas of sediment in large‐scale ecological restored watersheds. In this study, an integrated methodology framework by the RUSLE, index of connectivity (IC), and sediment delivery ratio (SDR) was proposed to visually assess the spatiotemporal characteristics of erosion and sediment yield processes in the Yanhe Watershed with large‐scale ecological restoration from 1985 to 2020, and to identify the covariation between sediment connectivity and erosion in subbasins. The soil erosion estimated by RUSLE has decreased by over 80% since 1985 owing to increased vegetation cover and the effective implementation of soil conservation measures, but the upper reaches still have high erosion intensity due to differences in specific controlling factors such as topographic conditions and land cover, requiring focused soil conservation practice. The IC results showed that as the vegetation restoration and soil conservation measures in the Yanhe Watershed varied from year to year, their spatial and temporal patterns had a strong influence on the distribution of sediment connectivity, some local areas in the middle reaches showed local minima of IC in 1995, 1998 and 2010 mainly due to the implementation of long‐term ecological restoration project. The developed IC‐Erosion maps indicated that areas with high‐connectivity but low‐erosion accounted for over 60% of the total watershed area from 1985 to 1999, demonstrating a reverse correlation between sediment connectivity and erosion. Meanwhile, over 40% of the erosion occurred in a few areas (approximately 20%) with high‐connectivity and high‐erosion from 2000 to 2004, which can be characterized as the critical areas of erosion. The methods and results of this study provide ideas for separately defining both erosion and connectivity, and quantifying bi‐variable erosion‐connectivity classification that can be easily viewed on a scatterplot.This article is protected by copyright. All rights reserved.

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“…In this region, it is well-known that soil erosion seriously affects local agricultural production and economic development (Yu et al, 2021). During the last decades, many authors confirmed losses of soil structure, reduction in soil fertility and agricultural yield, increases in geological hazard risks and disturbances of ecosystem services (Zhang et al, 2022). However, the environmental variability and large extension of this region mean that general control measures are impossible to be designed.…”

Section: Introductionmentioning

confidence: 99%

Vegetation pattern and topography determine erosion characteristics in a semi‐arid sandstone hillslope‐gully system

Zhu,

Yu,

Liu

et al. 2024

European J Soil Science

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The hillslope‐gully system serves as the primary contributor to both runoff and sediment yield. The WEPP (Water Erosion Prediction Project) model is often applied to investigate erosion characteristics at hillslope scale, demonstrating a high level of accuracy in simulating water erosion. In this study, according to in situ field monitoring (2014–2020) at a Pisha sandstone hillslope on the Loess Plateau, China, a total of 50 rainfall events’ data were used as climatic data to calibrate the soil parameters, and 11 different vegetation patterns and four slope gradients of hillslope‐gully systems were installed as inputs for the management and slope data, respectively. In systems A, B, C and D, the hillslope gradients were defined as 5°, 8°, 10° and 12° and the gully gradients as 15°, 20°, 25° and 30°, respectively. The results showed that the steeper the slope, the more severe the erosion. However, there was a critical value for the effect of slope on runoff. When the slope exceeded 8° and the gully exceeded 20°, the runoff no longer increased further and even decreased. The reduction in runoff in hillslope‐gully systems was in the following order (in mm): system D (3.4 ± 0.14) > system C (3.4 ± 0.14) > system B (3.39 ± 0.14) > system A (3.12 ± 0.13). Increasing vegetation cover could reduce erosion. Differences in runoff between vegetation patterns were not significant (p > 0.05) and ranged from 8% to 26%. However, there were significant differences in the sediment yield reduction benefits of different vegetation patterns (p < 0.05), ranging from 17% to 66%. It was observed that vegetation located in the lower slope produced a more pronounced effect in mitigating sediment when the degree of cover was the same. We conclude that implementing watershed management strategies based on the vegetation and topographic attributes of hillslope‐gully systems within the Loess Plateau, especially on Pisha sandstone hillslopes, serves as the fundamental approach to achieving sustainable watershed management.

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Changes in rainfall erosivity from combined effects of multiple factors in China’s Loess Plateau

Cited by 15 publications

References 58 publications

Soil Erosion Processes and Geographical Differentiation in Shaanxi during 1980–2015

Soil Erosion Processes and Geographical Differentiation in Shaanxi during 1980–2015

An integrated watershed modelling framework to explore the covariation between sediment connectivity and soil erosion

Vegetation pattern and topography determine erosion characteristics in a semi‐arid sandstone hillslope‐gully system

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