Impacts of climate change on vegetation phenology and net primary productivity in arid Central Asia

Wu, Lizhou; Ma, Xiaofei; Dou, Xin; Zhu, Jianting; Zhao, Chengyi

doi:10.1016/j.scitotenv.2021.149055

Cited by 106 publications

(71 citation statements)

References 92 publications

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“…Remote sensing data, characterized by time continuity and large spatial scales, is an e cient approach to monitor the growth status and cover of vegetation (Goward 1989), and has been widely used in vegetation response to climate change studies (Li et al 2015b;Wu et al 2021). In this study, we used the Global Inventory Monitoring and Modeling Studies (GIMMS) NDVI dataset from 1982 to 2014 at a spatial resolution of 8 km × 8 km and a bimonthly time resolution.…”

Section: Ndvi Datamentioning

confidence: 99%

“…However, previous studies on climate change have mainly focused on the analysis of statistical distributions of climate variables, such as mean, maximum, and minimum values, while neglecting their association with ecosystems ( Zhou et al (2015) found that the warming trend in the GLRCA initially enhanced the greenness of vegetation before 1991, but then the continued warming trend inhibited further increase in greenness. In addition, several studies have also demonstrated that the increase in temperature prolonged the growing season, and in turn increased ecosystem productivity in some areas of the GLRCA (Bohovic et al 2016;Wu et al 2021). However, it remained uncertain how key bio-temperature thresholds changed over the GLRCA, and different bio-temperature may induce various impacts on terrestrial ecosystems under global warming.…”

Section: Introductionmentioning

confidence: 99%

“…In general, DT 20 occurred mainly in summer, and high temperatures could further increase ecosystem drought.Recently, the negative effects of high temperatures on ecosystems have been widely reported in many regions, especially in arid and semi-arid areas(Baumbach et al 2017;Li et al 2021b). Under high temperature stress, vegetation photosynthesis is weakened or even stalled, while respiration is enhanced, Vegetation phenology, including SGS, EGS, and LGS, is a sensitive signal indicating the response of vegetation dynamics to climate change(Richardson et al 2012;Wu et al 2021). Plenty of remote sensing data and ground observation data demonstrated that spring phenology has advanced and fall phenology has delayed owing to global warming(Dong et al 2012;Sun et al 2020;Wu et al 2021).…”

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confidence: 99%

“…2.2.2. Correlation analysisTo examine the response of vegetation dynamics to temperature changes, we analyzed correlations between annual NDVI and various bio-temperature indicators using Pearson's correlation coe cient based on pixels(Wu et al 2021). …”

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confidence: 99%

“…It implied that the response of vegetation dynamics to temperature drivers was highly variable due to different vegetation characteristics and environmental conditions(Li et al 2021b;Luo et al 2020). A slight increase in temperature would exert a positive impact on regional vegetation growth by reducing frost days and extending the growing season(Wu et al 2021;Zhao et al 2021). At the same time, a signi cant positive correlation between annual NDVI and temperature indicators was observed at high elevations in the southeast because cold temperature is a serious constraint to vegetation growth in the region(Jiang et al 2017).…”

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confidence: 99%

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Spatial and Temporal Variability of Key Bio-Temperature Indicators and their Effects on Vegetation Dynamics in the Great Lakes Region of Central Asia

Gao

Zhao

Liu

2022

Preprint

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Dryland ecosystems are fragile to climate change due to harsh environmental condition. Climate change affects vegetation growth primarily by altering some key bio-temperature thresholds. Key bio-temperatures are closely related to vegetation growth, and slight changes could produce substantial effects on ecosystem structure and function. Therefore, this study selected the number of days with daily mean temperature above 0 °C (DT0), 5 °C (DT5), 10 °C (DT10), 20 °C (DT20), the start of growing season (SGS), the end of growing season (EGS), and the length of growing season (LGS) as bio-temperature indicators to analyze the response of vegetation dynamics to climate change in the Great Lakes Region of Central Asia (GLRCA) for the period 1982–2014. On the regional scale, DT0, DT5, DT10, and DT20 exhibited an overall increasing trend. Spatially, most of the study area showed that the negative correlation between DT0, DT5, DT10, DT20 with annual NDVI increased with increasing bio-temperature thresholds. Especially, more than 88.3% of the study area showed a negative correlation between annual NDVI and DT20, as increased DT20 exacerbated ecosystem drought. Moreover, SGS exhibited insignificantly advanced trend, and EGS experienced a significantly delayed trend. The overall extending trend in LGS was mainly attributed to the delayed EGS. Besides, our study revealed that about 54.7% of the study area showed a negative correlation between annual NDVI and LGS, especially in the north, indicating a negative effect of climate warming on vegetation growth in the drylands. The results of this study will help assess the stability of vegetation to climate variability, and predict the response of vegetation to future climate change in the GLRCA.

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Section: Ndvi Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Spatial and Temporal Variability of Key Bio-Temperature Indicators and their Effects on Vegetation Dynamics in the Great Lakes Region of Central Asia

Gao

Zhao

Liu

2022

Preprint

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Spatial and temporal dynamics of desertification and its driving mechanism in Hexi region

Zhang

Guan

et al. 2022

Land Degrad Dev

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Investigating the spatio‐temporal patterns of desertification and its driving mechanisms is crucial for efforts to improve the ecology and environment and combat desertification. Based on meteorological and satellite remote sensing data, this paper analyzed the dynamic characteristics of desertification in the Hexi region of arid and semiarid land in Northwest China from 2000 to 2018 using a dimidiate pixel model; revealed the relationship between desertification evolution characteristics and climatic variations and anthropogenic events based on the change characteristics of net primary productivity (NPP); and then clarified the response of desertification to climatic variations and anthropogenic events using a geodetector model. Outcomes show that: (1) overall desertification in Hexi region is at a serious level, but during the study period, severe desertification decreased, mainly concentrated in the east and southwest, and non‐desertification increased, mainly near the Qilian Mountains and Shule River basin; (2) there were both spatial and temporal dynamics during the study period, with severe desertification decreasing by 11% and moderate, light, and non‐desertification increasing by 3%, 5%, and 3% respectively; (3) during 2000–2005, human activities, climate change, and their joint contribution to desertification reversal were similar, i.e., 23%, 40%, and 36%, and the former two contributed equally to desertification expansion, 38% and 41%, respectively; climate change predominated in desertification reversal in 2005–2010 and 2015–2018, while human activities predominated in desertification expansion; human activities predominated in desertification reversal and climate change predominated in desertification expansion in 2010–2015. This research can provide a theoretical basis and technical support for ecological and environmental management and sustainable socio‐economic development in arid and semiarid regions.

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Evaluation of spatiotemporal variation and impact factors for vegetation net primary productivity in a typical open‐pit mining ecosystem in northwestern China

Wang,

Cui,

Yang

et al. 2024

Land Degrad Dev

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The vegetation net primary productivity (NPP) is a key indicator for evaluating vegetation carbon sequestration. Exploring its spatiotemporal changes and impact factors is essential for coal mining and ecological restoration in open‐pit mining areas. This study utilized the Carnegie‐Ames‐Stanford‐Approach (CASA) model to calculate monthly vegetation NPP in the Xiwan mine area, a typical open‐pit mine in northwestern China. The trend, stability, and persistence analysis were conducted, along with the development of a grading method to examine the vegetation NPP spatiotemporal variation across different land cover types. Statistical grading and correlation analysis were used to explore the relationships between the topographical factors, meteorological factors, human activities, and vegetation NPP. The following results were obtained: (1) The vegetation NPP in the study area exhibited a high stability and anti‐persistent decrease in trend. NPP reached a peak of 143.49 g C/(m2 year) in 2017, but declined to a low of 118.38 g C/(m2 year) in 2021. (2) The vegetation NPP decreases with increasing elevation and slope, and a relatively strong correlation with temperature and precipitation was also observed. (3) The impact intensity of human activities on vegetation NPP exhibited a rising and fluctuating volatile trend. In 2021, the inhibition of vegetation NPP by human activities reached its peak at 166.42 g C/(m2 year), with an impact effect share of 36.9%. This research provides a comprehensive framework for vegetation NPP analysis in open‐pit mining, offering valuable insights for ecological conservation in mining ecosystems.

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Impacts of climate change on vegetation phenology and net primary productivity in arid Central Asia

Cited by 106 publications

References 92 publications

Spatial and Temporal Variability of Key Bio-Temperature Indicators and their Effects on Vegetation Dynamics in the Great Lakes Region of Central Asia

Spatial and Temporal Variability of Key Bio-Temperature Indicators and their Effects on Vegetation Dynamics in the Great Lakes Region of Central Asia

Spatial and temporal dynamics of desertification and its driving mechanism in Hexi region

Evaluation of spatiotemporal variation and impact factors for vegetation net primary productivity in a typical open‐pit mining ecosystem in northwestern China

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