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Altay Prefecture, a typical arid region in northwestern China, has experienced the climate transition from warming-drying to warming-wetting since 1980s and has attracted widespread attention. Nonetheless, it is still unclear how climate change has influenced the distribution of vegetation in this region. In this paper, a reaction–diffusion model of the climate–vegetation system is proposed to study the impact of climate change (precipitation, temperature and carbon dioxide concentration) on vegetation patterns in Altay Prefecture. Our results indicate that the tendency of vegetation growth in Altay Prefecture improved gradually from 1985 to 2010. Under the current climate conditions, the increase of precipitation results in the change of vegetation pattern structures, and eventually vegetation coverage tends to be uniform. Moreover, we found that there exists an optimal temperature where the spot vegetation pattern structure remains stable. Furthermore, the increase in carbon dioxide concentration induces vegetation pattern transition. Based on four climate change scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6), we used the power law range (PLR) to predict the optimal scenario for the sustainable development of the vegetation ecosystem in Altay Prefecture. [ABSTRACT FROM AUTHOR]

Drought is a natural hazard caused by long-term precipitation deficiency and high evaporation resulted from high temperature, which is among the most common disasters around the world. In recent decades, with the intensification of climate change and human activities, drought occurrence is becoming more frequent in China, causing negative impacts on agricultural production, ecological environment and social economy. Therefore, it is significant to explore the impact of drought on vegetation for agricultural production and healthy development of ecological systems. In this paper, the response and sensitivity of vegetation to drought in different climatic zones in China were analyzed, and the effects of different climatic factors, including water balance (precipitation-potential evapotranspiration), annual precipitation, mean temperature and sunshine hours, on the relationship between different vegetation types including forestland, grassland and cultivated land and drought were explored, so as to reveal the response mechanism of ecological vegetation to drought from a macro perspective. Using the self-calibrating Palmer drought severity index (scPDSI) and normalized difference vegetation index (NDVI) data for the period of 1982-2015, the response rule and sensitivity of different types of vegetation to drought were analyzed by applying time-delay correlation method, and the influence of various climate factors on the relationship between vegetation and drought was analyzed by applying linear regression method. The results of the research showed that in northwest China, Inner Mongolia, north China plain and southern Qinghai-Tibet plateau, the correlation of shorter lag time scPDSI (1-3 months) and NDVI was close (Rmax>0.4), which indicated that vegetation growth was more sensitive to drought in these regions and that vegetation biomass were influenced mainly by the spatiotemporal characteristics of the water availability. In the humid areas of south China, including the middle and lower reaches of the Yangtze River and the Pearl River basin, the precipitation is abundant so that the correlation between NDVI and scPDSI was negative, and the response of vegetation was not sensitive to drought. The growth of vegetation is mainly controlled by energy factors such as temperature and sunshine hours. The relationship between vegetation and drought was significantly affected by the average annual water balance, precipitation, sunshine hours and temperature, among which the annual water balance was the main water controlling factor (R²=0.45). It can be concluded that the change of water availability was the key factor behind the change of vegetation vigor. Sunshine hours were the main energy controlling factor. When average annual temperature was less than 5, the effect of ℃ temperature on the correlation between scPDSI and NDVI was also significant. The sensitive areas of drought impact on vegetation were mainly in the northern arid regions, while the southern humid areas had strong resilience and resilience to drought. The sensitivity of different types of vegetation to drought varied greatly, and the order of sensitivity is grassland >forestland>cultivated land. The results can provide a scientific basis for the evaluation of the impact of drought on ecological vegetation in different regions of China, and thus help to guide agricultural and environmental production, protect vulnerable ecosystems and provide implications for sustainable socioeconomic development. [ABSTRACT FROM AUTHOR]

Ecosystems in arid regions with sandy substrates are vulnerable to various environmental challenges. Understanding the vegetation evolution of such regions over a long timescale and the possible drivers of change is important for optimizing ecological restoration. In this paper, we present multi-proxy data to understand Holocene vegetation dynamics and their drivers in the Horqin Sandy Land in northern China. Phytoliths are used to reconstruct vegetation; grain size and magnetic susceptibility are used to reconstruct the climate; and sedimentary charcoal contents and the numbers of regional archaeological sites are used to reconstruct the fire activity and human activity intensity, respectively. Results indicate that the Horqin Sandy Land was covered mainly by grassland vegetation during the Holocene, and that the trajectory of vegetation evolution was as follows: sparse and species-poor Leymus chinensis grassland from 9300 to 6150 cal yr BP, relatively sparse and species-rich mesophytic herb grassland from 6150 to 2300 cal yr BP, and dense and species-rich L. chinensis grassland from 2300 to 250 cal yr BP. Climate change was the main driver of vegetation evolution, but fire and human activities also influenced the vegetation to varying degrees. Fire made the greatest contribution to the vegetation change during the 6150–2300 cal yr BP interval, and human activities contributed throughout the 8200–2300 cal yr BP interval. The evolution of plant species richness and the number of C 3 plants show a significant ∼1000-yr periodicity during 9300–4500 cal yr BP, which we speculate was caused by the effect of solar activity on East Asian monsoon intensity, which likely controlled the vegetation change on a millennial timescale. • The Holocene vegetation in Horqin Sandy Land is qualitatively reconstructed. • Climate dominated the vegetation evolution, while fire and human activity were additional influences. • The vegetation change shows a ∼ 1000-yr periodicity during 9300–4500 cal yr BP. • The vegetation change on a millennium cycle was likely driven by the solar activity. [ABSTRACT FROM AUTHOR]

Warming of the climate system is unequivocal, and the change of climate variables will eventually have a great impact on vegetation cover and agricultural practices, especially in the arid area Xinjiang in China, whose agriculture and ecosystems are heavily vulnerable to climate change. In this paper, normalized difference vegetation index (NDVI) was used to study the vegetation growth and its response to climate change in Xinjiang. Firstly, two NDVI datasets (Global Inventory Modeling and Mapping Studies (GIMMS) and Moderate Resolution Imaging Spectroradiometer (MODIS)) were merged through a pixel-wise regression analysis to obtain a long time series of NDVI data, and then, relationships between yearly NDVI and yearly climate variables, and monthly NDVI and monthly climate variables were extensively investigated for grassland and cropland in northern and southern Xinjiang, respectively. Results show the following: (1) there was an increasing trend in NDVI for both grassland and cropland in both northern and southern Xinjiang over the past decades and trends were significant except that for grassland in northern Xinjiang; (2) precipitation and evaporation were more important than temperature for grassland in northern Xinjiang, while precipitation and temperature were more important than evaporation for grassland in southern Xinjiang and cropland in both northern and southern Xinjiang; (3) NDVI was highly correlated with accumulated monthly precipitation instead of monthly precipitation, and there was a lagged effect of precipitation, temperature, and evaporation on NDVI change. However, lagged effects were only significant in specific months. The results could be helpful to agricultural practices; e.g., based on lagged effect of precipitation, irrigation in July is very important for crop growth. [ABSTRACT FROM AUTHOR]

[Display omitted] • The proportion of vegetation improvement area in QLMs reached 65.06% in the past 21 years, while 52% of the area will be continuously improved in the future. • The distribution of the NDVI in the QLMs maintained a high spatial agglomeration. • Annual sunshine duration is the main meteorological factor affecting vegetation change in Qilian Mountains. • The interaction between different driving factors can improve the explanatory power of vegetation changes compared with a single driving factor. Vegetation is an important indicator reflecting ecological environment stability, and monitoring vegetation change and understanding its potential driving force have important guiding significance for the adjustment and implementation of ecological restoration measures. As an ecological security barrier in northwest China, the Qilian Mountains (QLMs) play an important role in promoting the green development of social and economic development. Currently, the comprehensive driving effects of natural and anthropogenic factors on vegetation change in the QLMs are not clear. Based on the normalized difference vegetation index (NDVI), the spatiotemporal characteristics and trend changes in vegetation in the QLMs were systematically analyzed from 2000 to 2020 in this paper. The effects of natural and anthropogenic driving factors on vegetation change were explored using a geographic detector (GeoDetector). The results showed that the vegetation has improved continuously in the past 21 years, but the overall vegetation coverage was still low. The vegetation distribution was highly clustered, with a decreasing trend from east to west. Annual sunshine duration (q statistic = 0.3347) and distance to the rivers (q statistic = 0.2649) had the greatest explanatory power for vegetation change, while slope, aspect, and landform type had the least explanatory power. The interaction between elevation and sunshine duration, temperature and precipitation, temperature and sunshine duration, elevation and precipitation had the most explanatory power for vegetation change. Finally, we determined the ranges or categories of driving factors that were most suitable for vegetation growth by using a risk detector. The results of this study can help us further understand the potential driving mechanism of vegetation coverage variation in the QLMs and provide theoretical guidance for relevant managers to formulate ecological restoration measures and land management policies in the next step. It is of great significance to maintain the stability of the fragile ecological environment and prevent land degradation in arid and semiarid areas of Northwest China. [ABSTRACT FROM AUTHOR]

• Spatiotemporal variation in vegetation on the Loess Plateau was investigated. • Precipitation was the greatest driving factor. • There was a bivariate or nonlinear enhancement between all factors. • Key factors' adaptation range or type suitable for vegetation cover was identified. Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. Studying vegetation cover dynamics variation and its drivers is critical to understanding the interactions between vegetation and ecosystems. The Loess Plateau (LP) is located in a semi-arid and semi-humid region with severe soil erosion and fragile ecology. This paper used the annual maximum Normalized Difference Vegetation Index (NDVI ymax) and growing season mean NDVI (NDVI gsmean) as the vegetation cover indicator. The vegetation cover variation of LP from 2000 to 2020 was analyzed using Sen's slope and Mann-Kendall test. Then the influence of natural and anthropogenic factors on the driving mechanisms of spatial vegetation differentiation was explored by the Geodetector model. The results revealed that the growth trends of NDVI ymax and NDVI gsmean were 0.075/10 years and 0.038/10 years, respectively, and the areas with improved vegetation cover accounted for 92.67 % and 88.58 % of the total area. The vegetation cover of the southeastern and northern parts of the LP decreased significantly; however, the central, southwestern, and northeastern parts exhibited a remarkable improvement. Precipitation, vegetation type, soil type, temperature, and land use type were the key driving forces, ranked differently on NDVI ymax and NDVI gsmean. However, precipitation was the most critical factor both on NDVI ymax and NDVI gsmean. The interaction detection showed non-linear and mutual enhancement, with no independent factor. The findings of our study can assist in identifying the vegetation cover status of the LP, as well as the driving forces, which can provide theoretical support for the formulation of environmental conservation policies. [ABSTRACT FROM AUTHOR]

• Both GPP and GPP max in Chinese grasslands showed increases at different space–time scales. • Trend analysis and breakpoint tests were used for the first time to monitor GPP change simultaneously in Chinese grasslands. • GPP max × CUP is a robust indicator to explain annual-scale variation in GPP. At the ecosystem level, Gross Primary Productivity (GPP) was defined as the organic compounds formed by plants that absorb atmospheric CO 2 through photosynthesis and sequester carbon in plant bodies. How to accurately assess the spatiotemporal evolution of ecosystem carbon indicators has become a critical issue to be addressed. Chinese grasslands are located in a typical arid and semi-arid climate zone and are sensitive to global changes, which will inevitably have serious impacts on the function and structure of Chinese grasslands. Based on this, our paper takes Chinese grassland ecosystems as the research area, and used multi-source GPP dataset from terrestrial ecosystem model simulations and remote sensing satellite observations to quantitatively analyze the spatiotemporal evolution patterns of GPP in Chinese grasslands over the past 40 years. We combined trend analysis and breakpoint test, and then to analyze the mechanism components of GPP space–time variability. The main results are as follows: (1) The model and remote sensing estimated GPP results showed that more than 80% of Chinese grasslands show a significant increasing trend over the past 40 years, with growth rates ranging from 0.68 to 3.13 g C/m−2 year−1. (2) GPP max also shows that more than 80% of Chinese grasslands are growing rapidly, with an overall growth rate of more than 0.1 g C/m−2 year−1 in each region. The overall long-term trends and interannual variability of multi-source GPP and GPP max are generally consistent, yet vegetation dynamics in local areas are still uncertain. (3) The breakpoint test showed that 'monotonically increasing' was the largest breakpoint type of GPP in Chinese grasslands (33.09%), and the direction of change of GPP in Chinese grasslands before and after the breakpoint was also increasing. (4) GPP max × CUP explained 91% of the temporal variability of annual-scale GPP in Chinese grasslands from a mechanistic view, and we found that the peak photosynthetic growth and the length of the phenological period synergistically controlled the interannual variability of GPP in Chinese grasslands. Under the scenario of rapid global change, our study accurately assessed the long-term dynamics of GPP and its mechanism-driven of grassland ecosystems in China, which is helpful for estimating and predicting the carbon budget of grassland ecosystems in China, and has important guiding significance for policy formulation to mitigate climate change. [ABSTRACT FROM AUTHOR]

As an important part of the global ecosystem, wetlands and their dynamics greatly influence regional eco-environment systems. To understand the distributions, change processes and temporal-spatial characteristics of the wetlands of the inland river basin in an arid region (Heihe River Basin, HRB), this paper employed multi-source remote sensing data to facilitate multi-temporal monitoring of the HRB wetland using a wetland information extraction method. First, we performed monitoring of these wetlands for the years 2000, 2007, 2011 and 2014; then, we analyzed the variation characteristics of the spatial-temporal dynamics of the wetlands in the HRB over the last 15 years via the landscape dynamic change model and the transformation matrix. In addition, we studied the possible driving mechanisms of these changes. The research results showed that the total area of the HRB wetlands had decreased by 2959.13 hectares in the last 15 years (Since 2000), and the annual average loss was 1.09%. The dynamics characterizing the HRB wetlands generally presented a trend of slow increase after an initial decrease, which can be classified into three stages. From 2000 to 2007, the total wetland area rapidly decreased; from 2007 to 2011, the area slowly decreased; and from 2011 to 2014, the area gradually increased. The dynamic changing processes characterizing the wetland resources were ascribed to a combination of natural processes and human activities. The main driving mechanisms of wetland dynamic changes include climatic conditions, upper reach water inflows, population, water resources, cultivated area, and policy. The findings of this study can served as reference and support for the conservation and management of wetland resources in the HRB. [ABSTRACT FROM AUTHOR]