Vegetation greening partly offsets the water erosion risk in China from 1999 to 2018
Introduction
Soils play a critical role in ecological and ecosystem service provision (Borrelli et al., 2017, Wuepper et al., 2020). The United Nations (UN) set forth 17 Sustainable Development Goals (SDGs) (United Nations, 2015), 13 of which are related directly or indirectly to soil (Keesstra et al., 2016). Healthy soil is vital for achieving these SDGs. Water erosion is a great threat to soil and is recognized as one of the most important forms of soil degradation (Panagos et al., 2015a, Wang et al., 2016b). The latest FAO (Food and Agriculture Organization of the United Nations) report on global soil resources emphasizes that agricultural soil erosion is a global environmental threat (A.R et al., 2015). Soil water erosion is affected by climate change. Climate change affects precipitation patterns changes and vegetation distribution, influencing runoff and erosion. In recent decades, precipitation variability and intensity have increased (IPCC, 2014, Steffen et al., 2016), aggravating the effect of rainfall pressure on soil erosion. Additionally, the increase in global vegetation observed in recent decades has resulted in a series of cascading effects in the soil system (Borrelli et al., 2020, Chen et al., 2019, Piao et al., 2020). Overall, vegetation, soil and water erosion have nonlinear changes in response to climate change (Li and Fang, 2016).
Given the recent global commitments to SDG 15, which highlighted the necessity for a land degradation-neutral world by 2020 (United Nations, 2015), an analysis of water erosion changes under climate change and vegetation change is essential. Numerous studies have been conducted to monitor and assess water erosion at large scales (Borrelli et al., 2013, Liu et al., 2019, Panagos et al., 2015b, Teng et al., 2016). Process-based models are not mature enough to be applied on a broad spatial scale in the current state (Borrelli et al., 2017, Jetten et al., 2003, Panagos and Katsoyiannis, 2019). Empirical models for estimating soil erosion, such as the Revised Universal Loss Equation (RUSLE), can meet most assessment requirements (Liu et al., 2019, Naipal et al., 2015). Although its parameters have some uncertainty, they have been useful in identifying the spatiotemporal pattern and drivers responsible for the soil erosion rate and related risks (Boardman and Poesen, 2007, Hengl et al., 2014, Wuepper et al., 2020). The RUSLE model quantifies the five factors that impact the erosion process, including rainfall erosivity (R), soil erodibility (K), slope length and slope steepness (LS), cover management (C), and support practices (P) (Renard et al., 1997). Among these indicators, R and C are closely related to climate change and vegetation growth, respectively (García-Ruiz et al., 2015, Panagos et al., 2017). Changes in erosive precipitation and vegetation distribution are the major driving forces of water erosion changes at large scales (Zhou et al., 2016). Thus, variations in R and C are important for assessing the changes in water erosion.
However, there is a strong interaction between vegetation and precipitation. The effects of these two factors may cancel each other out in the water erosion process. Vegetation decreases water erosion, while intense precipitation increases it (de Jong et al., 2013, Mankin et al., 2018, Huang et al., 2020, Piao et al., 2020). Vegetation protects the soil against erosion by increasing soil protection, intercepting rainfall and reducing the raindrop kinetic energy of raindrops, thus decreasing rainfall erosivity (Li et al., 2010, Piao et al., 2020). However, an increase in rainfall amount and intensity can increase rainfall erosivity, which may weaken vegetation surface protection (Fenta et al., 2017, Sun et al., 2013). Therefore, the interaction between precipitation and vegetation is of great importance for assessing water erosion. Although some research has been conducted to analyse the interactive effects of vegetation and precipitation on water erosion, most have been focused at local scales (Ciampalini et al., 2020, Zhang et al., 2020, Zhou et al., 2016). Liu et al. (2019) explored the interaction between precipitation and vegetation in water erosion changes at the global scale. However, studies at a global scale can be uncertain and are normally based on low-resolution datasets and simplified calculation methods; additionally, the results are often not validated.
Water erosion is an important threat in China. The total area affected by soil erosion is approximately 37.18% of the territory. Water erosion areas account for 45.17% of the soil erosion area (MWR et al., 2010). To reduce water erosion and prevent land degradation, China has implemented large-scale conservation programs (Bryan et al., 2018), including the Grain-for-Green Program (GFGP), which has been recognized as the world’s most extensive ecological restoration program (Ouyang et al., 2016, Yu et al., 2020). Considering China’s recent greening after the establishment of conservation programs, an assessment of the spatiotemporal change in water erosion risk is of utmost importance.
With climate change-induced rainfall intensification, the land surface is more exposed to raindrop kinetic energy, increasing the risk of water erosion (Guo et al., 2020, Panagos et al., 2017). In contrast, increasing vegetation cover, as has been observed in China, may mitigate this risk (Bryan et al., 2018, Li et al., 2010, Zhang et al., 2015). Given the widely implemented vegetation restoration programs, this paper aims to test the hypothesis of whether greening in China partly offsets the increase in rainfall pressure on water erosion risk. A framework for assessing water erosion risk is developed in this study, based on the model developed at a global level by Liu et al. (2019). The framework combines the PSR (Pressure-State-Response) and the RUSLE (Revised Universal Soil Loss Equation) model. To reduce the model uncertainty and ensure the model effectiveness, this work is based on a high-resolution dataset and evaluates the model performance. This approach represents a pivotal step to ensure the reliability, robustness and consistency of the method (e.g., Alewell et al., 2019). This was not considered in the global work conducted by Liu et al. (2019). Therefore, this work represents an advancement the complements the work carried out by Liu et al. (2019), thus providing an improved understanding of the interaction between vegetation and precipitation on water erosion at a national level. Additionally, an indicator contribution index was constructed to quantitatively compare the impact of each index on the risk changes. The objectives of this work were to 1) explore the spatiotemporal patterns and dynamics of water erosion risk in China during 1999–2018; 2) examine the relative importance of rainfall and vegetation in changing the water erosion risk; and 3) analyse the influence of vegetation greening on rainfall contribution patterns.
Based on the above objectives, we present the risk indicator system of PSR based on the RUSLE. Trend detection was introduced to identify the risk and the indicator changes. Moreover, the impacts of indicators on risk changes were quantified based on the contribution indexes. Finally, the contributions of risk indicators were mapped. This study provides information for protecting soil and water and evaluating the efficacy of soil erosion control projects in China.
Section snippets
Study area
The study area is shown in Fig. 1. China has nine major basins. The continental basin, Yangtze River basin, Songhua and Liaohe River basin, Southwest basin, Yellow River basin, Pearl River basin, Huaihe River basin, Haihe River basin, and Southeast Basin cover 3.34, 1.80, 1.24, 0.85, 0.81, 0.57, 0.32, 0.32 and 0.24 million km2, respectively. The Yellow, Yangtze, and Pearl Rivers contribute more than 90% of China's sediment loads and are among the world’s 25 rivers with the highest sediment
Model validation
The RUSLE model was validated using sediment data from the five major river basins in China. The results showed that the RUSLE and its parameters applied in this work were reliable (R2 = 0.6241, n = 35, p < 0.01; Fig. 3) to estimate the soil erosion conditions in China.
Risk indicator trends
The maps of the estimated mean annual values of the erosion factors are shown in Fig. S2, and the spatial pattern of the mean annual water erosion risk is presented in Fig. 4a. The high-risk regions were mainly distributed in the
The spatial heterogeneity in the contribution factors
The contribution of C was higher than that of R in southern China, while a higher contribution of R was observed in the northern part of the country. The main reason for this difference is the different environmental contexts in the south and north. Generally, water erosion is more sensitive to high-intensity precipitation changes in northern China because vegetation coverage is low in the north (Liu et al., 2012). The interception of erosive rainfall by vegetation is minor in areas with sparse
Conclusions
The present study assessed China's water erosion risk over the last two decades since the implementation of the Grain for Green Program (1999–2018) and identified the risk contribution factors. Overall, a decreasing pattern was observed in water erosion risk. The rainfall pressure exhibited an upward trend, while the vegetation experienced a greening trend. Water erosion risk change was mainly attributable to precipitation change, whereas vegetation greening contributed more to mitigating risk.
CRediT authorship contribution statement
Han Wang: Data curation, Methodology, Visualization, Writing - original draft. Wenwu Zhao: Supervision, Writing - review & editing. Changjia Li: Writing - review & editing. Paulo Pereira: Supervision, Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 41991232), the National Key R&D Program of China (No. 2017YFA0604704), the Fundamental Research Funds for the Central Universities of China, and State Key Laboratory of Earth Surface Processes and Resource Ecology. Paulo Pereira was supported by Croatian Science Foundation through the project “Soil erosion and degradation in Croatia” (UIP-2017-05-7834) (SEDCRO). The authors also acknowledge the help of Yanxin Liu
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