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Due to global warming and other climate changes, it is increasingly important to study the response of regional environmental changes and dynamic changes in vegetation to climate change. Based on meteorological data from the last 60 years, this paper calculates the humidity index of western China under a wide range of long time series in different regions and explores the cross-correlation effect between series by offering a comparison with NDVI data, to analyze the cross-correlation between wet and dry changes and changes in vegetation in western China on a spatial scale. The results show that the spatial distribution of the interdecadal humidity index is different between different regions in western China. For example, the semi-arid and the semi-humid zones of the Weihe River region exhibit significant changes, while the Xinjiang and Qinghai–Tibet regions show a trend of constant wetness, on the whole, and the Sichuan and Yunnan–Guizhou regions are relatively humid and the distribution of wetness and dryness is relatively stable. The distribution of high and low values of the humidity index is very obvious and consistent with that of the distribution of desert bare land and precipitation in western China. In common with the distribution in the humidity index, the maximum correlation number between the NDVI and the humidity index in the whole western region is also significantly different in spatial distribution. There is a positive correlation between the NDVI and the humidity index in 99% of the study area. However, the delay in response time of the NDVI to changes in the humidity index in each region is inconsistent. For example, changes in the NDVI lag changes in the humidity index in the Menggan region by generally either 2 months or 5 months, while in the Sichuan region the delay in response time is generally 3 months. The variation and trend in dry and wet areas are closely related to the geographical location, climate zone, and topographic terrain, which may be the reason for the differences in the distribution of vegetation types and the response time to dry and wet changes. There is significant interaction between the humidity index and the vegetation type or precipitation distribution in western China. The positive correlation between the NDVI and the humidity index means that the positive effect is more sensitive, and the response of grassland is the most sensitive in the ecosystem. [ABSTRACT FROM AUTHOR]

The increased growth of vegetation has the potential to slow global climate warming. Therefore, analyzing and predicting the response assessment of Chinese vegetation to climate change is of great significance to studies of global warming. In this paper, we examine the spatiotemporal dynamics of vegetation leaf area index (LAI) values in China from 1981 to 2017 and their correlations with meteorological (hydrothermal) factors based on trend analysis and correlation analysis. We further construct an LAI prediction model based on hydrothermal conditions. The climate data obtained under different scenarios in the CMIP5 and CMIP6 climate models were used to predict the dynamic change trend of vegetation LAI from 2021 to 2100. The results show that most areas of China (72.82%) showed an improving trend in vegetation LAI from 1981 to 2017, during which the annual average LAI value increased at a rate of 0.0029 year−1. Vegetation LAI in China was significantly correlated with climatic factors (temperature, precipitation, and evapotranspiration), and the LAI prediction model constructed based on hydrothermal conditions had a high accuracy (Pearson's Cor value is 0.9729). From 2021 to 2100, approximately 2/3 of China's vegetation LAI area showed an improvement trend, and the impact of climate change on vegetation LAI predictions under the high emission scenario was greater than that under the low emission scenario. This research can provide a basis for studies on the climatic drivers of vegetation change and the global vegetation dynamic model. [ABSTRACT FROM AUTHOR]

Given that already‐observed temperature increase within cities far exceeds the projected global temperature rise by the end of the century, urban environments often offer a unique opportunity for studying ecosystem response to future warming. However, the validity of thermal gradients in space serving as a substitute for those in time is rarely tested. Here, we investigated vegetation phenology dynamics in China's 343 cities and empirically test whether phenological responses to spatial temperature rise in urban settings can substitute for those to temporal temperature rise in their natural counterparts based on satellite‐derived vegetation phenology and land surface temperature from 2003 to 2018. We found prevalent advancing spring phenology with "high confidence" and delaying autumn phenology with "medium confidence" under the context of widespread urban warming. Furthermore, we showed that space cannot substitute for time in predicting phenological shifts under climate warming at the national scale and for most cities. The thresholds of ~11°C mean annual temperature and ~600 mm annual precipitation differentiated the magnitude of phenological sensitivity to temperature across space and through time. Below those thresholds, there existed stronger advanced spring phenology and delayed autumn phenology across the spatial urbanization gradients than through time, and vice versa. Despite the complex and diverse relationships between phenological sensitivities across space and through time, we found that the directions of the temperature changes across spatial gradients were converged (i.e., mostly increased), but divergent through temporal gradients (i.e., increased or decreased without a predominant direction). Similarly, vegetation phenology changes more uniformly over space than through time. These results suggested that the urban environments provide a real‐world condition to understand vegetation phenology response under future warming. [ABSTRACT FROM AUTHOR]

Previous research has documented a tight positive relationship between vegetation activity and growing season air temperature in China's temperate zone (TC). However, this relationship may change over time following alternations in other environmental factors. Using the linear regression analysis and the moving windows based on partial correlation analysis method, the temporal variations of responses of vegetation NDVI to rising air temperature during 1982–2015 in the TC were examined. The results showed that the interannual partial correlation between NDVI and air temperature (RNDVI−T, include RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin, represents the partial correlation between NDVI and Tmean, Tmax, and Tmin, respectively) for the growing season (GS) in a 17−year moving window showed a significant decreasing trend during the last 34 years, mainly due to decreasing RNDVI−T in summer and autumn. The area with a significant decrease of RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin for the GS approximately accounted for 52.36%, 45.63%, and 49.98% of the TC, respectively. For the seasonal patterns of RNDVI−T, the regions with a significant downward trend in all seasons were higher than those with a significant upward trend. We also found a more significant and accelerating decrease of RNDVI−T for warm years compared to cold years, implying a decoupling or even a reverse correlation between NDVI and air temperature with continuous climate warming over the TC. Overall, our study provided evidence that the impact of Tmean, Tmax, and Tmin on vegetation activities exhibited a weakening trend and cautioned using results from interannual time scales to constrain the decadal response of vegetation growth to future global warming. [ABSTRACT FROM AUTHOR]

Understanding of the vegetation dynamics is essential for addressing the potential threats of terrestrial ecosystem. In recent years, the vegetation coverage of the Yangtze River Basin (YRB) has increased significantly, yet the spatio-temporal variations and potential driving meteorological factors of carbon use efficiency (CUE) under the context of global warming are still not clear. In this study, MODIS-based public-domain data during 2000–2015 was used to analyze these aspects in the YRB, a large river basin with powerful ecological functions in China. Spatio-temporal variations of CUE in different sub-basins and land cover types were investigated and the correlations with potential driving meteorological factors were examined. Results revealed that CUE in the YRB had strong spatiotemporal variability and varied remarkably in different land cover types. For the whole YRB, the average CUE of vegetated land was 0.519, while the long-term change trend of CUE was obscure. Along the rising altitude, CUE generally showed an increasing trend until the altitude of 3900 m and then followed by a decreasing trend. CUE of grasslands was generally higher than that of croplands, and then forest lands. The inter-annual variation of CUE in the YRB is likely to be driven by precipitation as a strong positive partial correlation between the inter-annual variability of CUE and precipitation was observed in most of sub-basins and land cover types in the YRB. The influence of temperature and relative humidity is also outstanding in certain regions and land cover types. Our findings are useful from the view point of carbon cycle and reasonable land cover management under the context of global warming. [ABSTRACT FROM AUTHOR]

Studies of the vegetation response to abrupt climatic events may provide valuable insights into assessing the impacts of such events under future global warming scenarios. Here we present a detailed record of regional vegetation change based on high‐resolution pollen records for the intervals 9000–7000 and 5000–3000 cal a bp from the Hulun Lake region of north‐eastern China. Our aims were to determine the regional expression of the 8.2‐ and 4.2‐ka events in north‐eastern China, especially their effects on vegetation composition. The results show that the response of the vegetation of the Hulun Lake region to the climatic cooling of the 8.2‐ka event was relatively minor; however, the 4.2‐ka event, which resulted in climatic aridification, resulted in pronounced vegetation degradation. We suggest that the specific characteristics of the 8.2‐ and 4.2‐ka events, together with their duration and the regional climatic background on an orbital time scale, were the main factors responsible for the contrasting vegetation responses. The strengthening and northward shift of the Intertropical Convergence Zone (ITCZ) tended to offset the cooling effect of the 8.2‐ka event on regional vegetation in the mid–high latitudes of East Asia; in contrast, the decrease in summer insolation in the Northern Hemisphere and the southward shift of the ITCZ may have intensified the drought effect of the 4.2‐ka event in East Asia and the associated vegetation degradation. Under future global warming scenarios, both extreme cooling events and extreme drought events may cause a deterioration of the ecological environment; however, the resulting regional vegetation responses may be substantially different. [ABSTRACT FROM AUTHOR]

This study attempts to model alpine tundra vegetation dynamics in a tundra region in the Qinghai Province of China in response to global warming. We used Rasterbased cellular automata and a Geographic Information System to study the spatial and temporal vegetation dynamics. The cellular automata model is implemented with IDRISI's Multi-Criteria Evaluation functionality to simulate the spatial patterns of vegetation change assuming certain scenarios of global mean temperature increase over time. The Vegetation Dynamic Simulation Model calculates a probability surface for each vegetation type, and then combines all vegetation types into a composite map, determined by the maximum likelihood that each vegetation type should distribute to each raster unit. With scenarios of global temperature increase of 1 to 3°C, the vegetation types such as Dry Kobresia Meadow and Dry Potentilla Shrub that are adapted to warm and dry conditions tend to become more dominant in the study area. [ABSTRACT FROM AUTHOR]

Research on vegetation variation is an important aspect of global warming studies. The quantification of the relationship between vegetation change and climate change has become a central topic and challenge in current global change studies. The source region of the Yellow River (SRYR) is an appropriate area to study global change because of its unique natural conditions and vulnerable terrestrial ecosystem. Therefore, we chose the SRYR for a case study to determine the driving forces behind vegetation variation under global warming. Using the Normalized Difference Vegetation Index (NDVI) and climate data, we investigated the NDVI variation in the growing season in the region from 1998 to 2016 and its response to climate change based on trend analysis, the Mann–Kendall trend test and partial correlation analysis. Finally, an NDVI–climate mathematical model was built to predict the NDVI trends from 2020 to 2038. The results indicated the following: (1) over the past 19 years, the NDVI showed an increasing trend, with a growth rate of 0.00204/a. There was an upward trend in NDVI over 71.40% of the region. (2) Both the precipitation and temperature in the growing season showed upward trends over the last 19 years. NDVI was positively correlated with precipitation and temperature. The areas with significant relationships with precipitation covered 31.01% of the region, while those with significant relationships with temperature covered 56.40%. The sensitivity of the NDVI to temperature was higher than that to precipitation. Over half (56.58%) of the areas were found to exhibit negative impacts of human activities on the NDVI. (3) According to the simulation, the NDVI will increase slightly over the next 19 years, with a linear tendency of 0.00096/a. From the perspective of spatiotemporal changes, we combined the past and future variations in vegetation, which could adequately reflect the long-term vegetation trends. The results provide a theoretical basis and reference for the sustainable development of the natural environment and a response to vegetation change under the background of climate change in the study area. [ABSTRACT FROM AUTHOR]