Vulnerability of grassland ecosystems to climate change in the Qilian Mountains, northwest China

Du, Qinqin; Sun, Yanhua; Guan, Qingyu; Pan, Ninghui; Wang, Qingzheng; Ma, Yuxin; Li, Huichun; Liang, Lushuang

doi:10.1016/j.jhydrol.2022.128305

Cited by 38 publications

(21 citation statements)

References 77 publications

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“…Additionally, the alpine meadow and alpine grassland at high‐altitude exhibited higher sensitivity and vulnerability, making them more susceptible to the impacts of extreme climate change (Q. Du et al., 2022; L. Li et al., 2019a). The increase in spring precipitation and decrease in summer temperature had led to degradation of grassland above 4,000 m ASL (Figure 8).…”

Section: Discussionmentioning

confidence: 99%

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Effects of Spring Phenology on Grassland Growth in Qilian Mountains Across Multiple Spatiotemporal Scales

Sun,

Guan,

et al. 2023

JGR Biogeosciences

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Investigating the differences in grassland growth and its response to advancing spring phenology and climate change across various spatiotemporal scales is crucial for the sustainable development of alpine mountain regions under a warming and wetting climate. This study analyzed the growth dynamics of grassland in the Qilian Mountains from 2000 to 2019 at seasonal and periodic scales. Using partial wavelet coherency and partial correlation analysis, we investigated the influence of temperature, precipitation, and spring phenology on grassland growth. Additionally, we quantified the dominant factors affecting grassland growth in different ecological zones and elevational gradient. The results indicated that the mean normalized difference vegetation index increased at an average rate of 1.1 and 2.8 × 10−3/year for grassland in spring and summer, respectively. Furthermore, the start of vegetation growing season (SOS) has advanced 6.0 days during 2000–2019. The dominant factor for grassland growth shifted from spring temperature to summer precipitation. The importance of SOS ranked second, significantly influencing 29.3% and 25.1% of grassland during spring and summer, respectively. Grasslands situated above 4,000 m above sea level (ASL) were more sensitive to climate change, with SOS exerting the greatest influence within the elevation range of 2,800–4,000 m ASL. Additionally, the phase difference in the time‐frequency scale of partial wavelet coherency revealed a lag effect of approximately 1 month for summer grassland growth to respond to precipitation. These findings can provide a scientific basis for optimizing global vegetation models and predicting the productivity of alpine mountain grasslands.

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Section: Discussionmentioning

confidence: 99%

“…Additionally, the alpine meadow and alpine grassland at high-altitude exhibited higher sensitivity and vulnerability, making them more susceptible to the impacts of extreme climate change (Q. Du et al, 2022;L. Li et al, 2019a).…”

Section: Spatial Heterogeneity In the Response Of Grassland Growth To...mentioning

confidence: 99%

Effects of Spring Phenology on Grassland Growth in Qilian Mountains Across Multiple Spatiotemporal Scales

Sun,

Guan,

et al. 2023

JGR Biogeosciences

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“…These results align with earlier studies that suggested the degradation of grassland in northwestern China was primarily due to human‐induced disturbance, while restoration was credited to climate variation (Zhou et al, 2014). As the most important pastoral area in northern China, overgrazing, and population explosion have brought about a reduction in grassland coverage in Qinghai, Inner Mongolia, Ningxia, and so forth, aggravated soil erosion and desertification, leading to damage to the grassland ecosystem (Du et al, 2022; Li et al, 2018). The degradation of grassland in Qinghai, Xinjiang, and Inner Mongolia were relatively obvious.…”

Section: Discussionmentioning

confidence: 99%

Untangling the effects of climate variation and human interference on grassland dynamics in North China

Ren,

Liu,

Wen

et al. 2023

Land Degrad Dev

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Climatic and anthropogenic disturbances play pivotal roles in shaping the dynamics of grassland. Quantifying their impacts on grassland variation is essential to ensure sustainable grassland management. In this study, we employed the Thornthwaite Memorial and Carnegie–Ames–Stanford‐approach (CASA) models to investigate the spatiotemporal effects of these two variables on grassland variation in northern China from 2000 to 2016, using the net primary productivity (NPP) as a measure. Our findings reveal that approximately 25.92% of the grassland in northern China experienced degradation, while restored grasslands occupied 45% of the total grassland area. The average grassland actual NPP (ANPP) and human‐induced NPP decreased at rates of −0.60 and −5.62 gC m−2 a−1, respectively. Conversely, potential NPP exhibited an upward trend with an average increase of 2.27 gC m−2 a−1. Furthermore, grassland ANPP showed a projected increase in most parts of northern China. Climate change emerged as the primary driver for grassland restoration in Xinjiang, Qinghai, and Inner Mongolia, leading to an increase of 21582.79 Gg C in grassland NPP. In contrast, human activities were the dominant catalysts for grassland degradation, resulting in a reduction of 51932.3 Gg C in grassland NPP. Human‐induced grassland degradation was ubiquitous in northwest and northeast China. With the exception of slope grassland, climate change primarily influenced the restoration of most grassland types, while human activities were the primary cause of degradation. Our analysis indicated a strong correlation between temperature and grassland degradation, while precipitation played a pivotal role in grassland restoration in northern China. Human interference demonstrated both positive and negative impacts on grassland changes. In conclusion, the increase in precipitation and the implementation of ecological restoration plans have effectively promoted the restoration of grasslands in northern China.

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“…Regarding the high evaporation intensity, grasslands are more vulnerable (Du et al, 2022). It is a challenge for vegetation if available soil moisture changes in the future.…”

Section: Discussionmentioning

confidence: 99%

The ecohydrological response to soil moisture based on the distributed hydrological assimilation model in the mountain region

Li,

Cong,

Yang

2023

Ecohydrology

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In the mountain region, there is high spatial variability of soil moisture, which limited soil moisture sites may not express. Data assimilation methods can combine the advantages of model simulation and data observation. This study constructs the distribution of soil moisture based on the distributed hydrological assimilation model and explores the ecohydrological response to soil moisture in the mountain region. The relationship between the coefficient of variation and mean soil moisture shows a hysteresis pattern. The terrain conditions where high soil moisture occurs in the study area are an altitude of about 3500 m, a gentle slope, and a shady slope. The low value of soil moisture is characterized by banded aggregation distribution. There is an initial threshold for the contribution of soil moisture to watershed runoff, and the threshold value in the study area of this paper is 0.28 cm3/cm3. The regions with dense vegetation exhibit higher spatial variability in soil moisture. Vegetation plays a heterogeneous role in the spatial variability of soil moisture when precipitation events occur. The study contributes to a deeper understanding of ecohydrological processes by providing regional‐scale data and uncovering the relationships between soil moisture and various ecohydrological factors.

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Vulnerability of grassland ecosystems to climate change in the Qilian Mountains, northwest China

Cited by 38 publications

References 77 publications

Effects of Spring Phenology on Grassland Growth in Qilian Mountains Across Multiple Spatiotemporal Scales

Effects of Spring Phenology on Grassland Growth in Qilian Mountains Across Multiple Spatiotemporal Scales

Untangling the effects of climate variation and human interference on grassland dynamics in North China

The ecohydrological response to soil moisture based on the distributed hydrological assimilation model in the mountain region

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