Aims The Qingzang Plateau is highly sensitive to global climate change. The unique natural conditions lead to extremely vulnerable vegetation and its ecosystem, making this region ideal for analyzing responses of vegetation to climate change. However, different types of vegetation may have different responses to seasonal variability. This study explores and analyzes vegetation changes on the Qingzang Plateau and the response characteristics of different vegetation types to moisture variations (i.e., dry and wet conditions) during the growing season.
Methods The standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) were used here as indicators of dry humidity and vegetation greenness, respectively. Sen’s slope estimation, BFAST model and correlation analyses were used to quantify the spatiotemporal variability of vegetation greenness and its response to drought-wet variations on the Qingzang Plateau from 2000 to 2018.
Important findings Results show that vegetation greenness on the Qingzang Plateau generally increased over the time period analyzed. Additionally, the rate of spatial variation reveals striking regional differences. The breaks of vegetation greenness occurred in most regions during 2012-2015, after which there was general upward trend after the breaks, the various trend is most apparent in northern Qingzang Plateau. Positive correlations between NDVI and multi-time scale SPEI were observed in most regions during the growing season, and gradually increased in the middle and latter part of the growing season. The responses of each vegetation type to SPEI also showed a distinct periodicity during the year. Meadow and steppe areas were more sensitive to multi-time scale SPEI than forest and shrub areas, and this response differed significantly during different stages of the growing season and for different time scales of SPEI.

Aims Water use efficiency (WUE) is a crucial parameter reflecting the coupling of carbon and water cycles in terrestrial ecosystems. Qingzang Plateau (QP) is the ecological barrier of China and its accommodated ecosystem is extremely sensitive to global change. Revealing the ecosystem WUE pattern and the driving forces is critical for improving our understanding on the process and mechanism of carbon and water cycles in the alpine ecosystem of the QP, which are the basis for vegetation conservation and restoration.

Methods Using the Global Land Surface Satellite (GLASS) data, meteorological data and vegetation type data, the spatio-temporal changes of WUE and their responses to temperature, precipitation, solar radiation, vapor pressure deficit (VPD), CO₂ concentration and leaf area index (LAI) during 1982-2018 over the QP were analyzed in this study. The trend magnitude and the influencing factors on WUE were further compared among vegetation types.

Important findings (1) The WUE decreases gradually from southeast to northwest on the QP, with an overall annual mean value of 1.64 g C·kg^-1. Evident differences in WUE are observed among vegetation types, with the highest value in forest and the lowest value in alpine desert. In addition, the WUE in alpine meadow is higher than that in alpine steppe. (2) The QP is prevailed by an increasing trend in WUE. Significantly increasing trends are observed in all vegetation types except for forest and cultural vegetation. Meanwhile, the variation of WUE is dominated by ecosystem gross primary productivity over 77.84% of the study area. (3) The WUE variation is mainly regulated by LAI and CO₂ concentration on the QP, and these two factors both cause positive effects on WUE. Increasing VPD inhibits WUE in alpine steppe, alpine vegetation, cultural vegetation and alpine desert.

Aims The alpine wetland is one of the most important sites for ecological and water conservation in Qingzang Plateau, and also an effective regulator of the local climate. Research is needed to understand the dynamics and drivers of changes in this alpine wetland landscape.
Methods This study was conducted with combination of methods in remote sensing image analysis, GIS spatial analysis and landscape attributes analysis. Changes in the alpine wetland patterns in Maqu County, which is located in the first meander of the Yellow River, was determined for six periodic samplings from 1995 to 2018.
Important findings The alpine wetland area in Maqu County continuously degraded from 1995 to 2010, and decreased by 18 680.31 hm² over the period. From 2010 to 2018, the wetland area increased. Compared with the level in 1990s, the wetland area has generally declined since the beginning of the 21st century. From 1995 to 2010, the patch number and density of the wetland increased continuously, but the average patch size decreased, with increased degree of landscape fragmentation. In contrast, from 2010 to 2015, the patch number and density of wetland decreased. From 2015 to 2018, the patch number and density of wetland increased, and the average patch size first increased and then decreased, with the landscape fragmentation first decreased and then increased. Both the Shannon diversity index and evenness index showed a downward trend from 1995 to 2010; the landscape structure tended to be simpler and the distribution of landscape types became more clustered. From 2010 to 2018, the Shannon diversity and evenness indices showed an upward trend; the landscape structure tended to be more complex, and the landscape types became more diverse and dispersed. Further analyses revealed that the main factors driving the changes in the alpine wetland landscape patterns in the first meander of the Yellow River are evaporation and precipitation, followed by human activities such as the population and the quantity of large livestock. Climate is the main factor driving the changes in the alpine wetland area in the first meander of the Yellow River. Intensive human economic activities have aggravated the wetland changes to some extent.

Aims Accurate assessment of plant aboveground biomass is important for optimizing grassland resource management and for understanding the balance of carbon, water and energy fluxes in grassland ecosystems. This study constructed the optimal empirical models by near-surface remote sensing normalized difference vegetation index (NDVI) data, and then estimated plant aboveground biomass in an alpine grassland on the Qingzang Plateau.
Methods Using the dataset of both the field-measured aboveground biomass and the NDVI_RS observed by plant canopy spectrometer (RapidSCAN), we constructed the empirical models for estimating aboveground biomass in different phases of the growing season across 2018 and 2019. Using the NDVI_Cam time series observed by phenology camera and the estimated models, we simulated seasonal dynamics of aboveground biomass in 2018.
Important findings (1) The seasonal dynamics of NDVI_Cam, NDVI_RS and aboveground biomass exhibited a similar unimodal pattern; however, the timing of peak NDVI (August) preceded that of peak aboveground biomass (July). (2) The best model for estimating aboveground biomass is the power function in May, July and September, and the quadratic equation in June and August. The estimation accuracy ranged from 0.29 to 0.77. (3) The estimation of aboveground biomass based on the models in different phases of growing season (R² = 0.91) showed a higher accuracy compared to that based on the model at a single time (September)(R² = 0.49). Our results suggest that the near-surface remote sensing is an effective approach for estimating alpine grassland aboveground biomass, and further investigation on the seasonal growth of plants will help accurately evaluate grassland resources.

Aims This study demonstrates the consistencies and discrepancies of correlations between climate factors and normalized difference vegetation index (NDVI) in the Protected Zones for Ecological Functions (EFPZs) in China, which provide useful information for monitoring in subsequent studies of vegetation dynamics.
Methods Based on the MODISNDVI data and the grid data for monthly precipitation and air temperatures from 2000 to 2015, the dynamics of NDVI and correlations with climatic factors were examined across 46 EFPZs at two spatial scales, by individual EFPZs and the pixels, using linear tendency and partial correlation methods. In accordance to the analyses, the EFPZs were categorized into different types of climatic influences.
Important findings The overall NDVI across the EFPZs showed an increasing trend, with the average linear slope of 0.045·a^-1. Pixel scale analysis showed that NDVIincreased significantly in the central regions and the northeast of China. Partial correlation coefficients between NDVI and precipitation in the EFPZs varied between -0.30 to 0.72, and were positive for 32 in the EFPZs. Partial correlation between NDVI and air temperature ranged from -0.36 to 0.92, with positive correlations in 39 in the EFPZs. In 50.6% of the pixels, NDVIwas positively correlated with precipitation, mainly in northeast and northwest China. In 64.6% of the pixels,NDVI was positively correlated with air temperatures, mainly in the northeastern and the northern edge of the Qingzang Plateau. Strong temperature-precipitation driving is the main type of climatic influences on NDVI changes across the EFPZs, accounting for 38.7% of the total, with temperature driving type being secondary, accounting for 27.3%; non-climatic driving type accounts for 17.6%. Our results show the NDVI in the EFPZs are significantly correlated with climatic factors concerning precipitation and air temperatures, and that NDVI dynamics in 82.4% of the areas are driven by climate factors. Studying the changes in NDVI and the responses of NDVI to climate factors is very important for understanding the dynamics of vegetation in the EFPZs under climate warming.

Aims To analyse the feasibility of space-for-time method in predicting phenology shifts of Plantago asiatica and Taraxacum mongolicum on the Qinghai-Xizang Plateau, as well as revealing the phenological changes of the two herbaceous plants under climate warming.
Methods The observed phenological data for Plantago asiatica and Taraxacum mongolicum from 10 sites on the Qinghai-Xizang Plateau during 2002-2011, as well as the meteorological data (i.e., daily mean air temperature) were collected. First, multiple linear regression models were bulit between geographic factors (longitude, latitude and altitude) and phenological events/annual mean temperature at different altitude gradients. Then, the longitude and latitude were kept to be unchanged, and the unary linear regression models between phenological events/annual mean temperature and altitude were built. Finally, the altitude was used as the “bridge” to indicate the relationship between the change of phenological events and the change of annual mean temperature.
Important findings The temperature decreased with the increasing altitude (R² > 0.89, p < 0.05), illustrating that changes of altitude gradients can be used to substitute for changes of annual mean temperature. The change in the simulated phenological events of the two herbaceous plants all showed a strong dependence on the change of altitude (R² > 0.70, p < 0.05), which contributed the most among the geographic factors. Strong dependences were observed between the simulated phenological events and the simulated annual mean temperature (R² > 0.93, p < 0.05), showing that phenological events could be predicted by the annual mean temperature with the space-for- time method. For Plantago asiatica, the first leaf date (FLD) and the first flowering date (FFD) occurred earlier with increasing annual mean temperature as 5.1 and 5.4 days per ℃, respectively, while the common leaf coloring date (LCD) occurred later as 4.8 days per ℃. The FLD and FFD of Taraxacum mongolicum advanced by 6.5 days and 7.8 days per ℃ of increase in the mean annual temperature while the LCD delayed by 6.7 days per ℃.

AimsAlpine desert, as the top part of the vertical vegetation spectrum of the Qinghai-Xizang Plateau, is widely distributed in the high altitude zones in the Qilian Mountains (QLM). Its distribution and growth conditions are different from the surrounding area. It is more sensitive to climate change but rarely being studied. In this study, we focused on the dynamic changes and spatiotemporal differences of the alpine desert belt in the QLM under the warming climates from the 1990s to the 2010s.
MethodsThe distribution changes in the alpine desert belt in the QLM during the past three decades were obtained from the thematic mapper and the operational land imager remote sensing digital images by using the decision tree classification and artificial visual interpretation. Spatiotemporal differences of the alpine desert distribution were studied by the overlay analysis. Meanwhile, the relationships between the changes and climates were explored using correlation analysis.
Important findings The results indicated that the alpine desert shrank gradually and lost its area by approximately 348.3 km²·a^-1 in the QLM with climate warming in the past 30 years. The amplitude of the shrinkage increased from east to west. However, its areas expanded in some sections. Collectively, the low boundary of the alpine desert belt moved upwards to higher altitudes at a velocity of 15 m per decade. The maximum upward-‌shifting amplitude lied in the western QLM, followed by the eastern and middle QLM. The vertical zonal shifting was modulated by topography-induced difference in local hydrothermal conditions. The distribution shifts in the alpine desert belt were mainly concentrated in the gentle slope regions. Because of the differences of hydrothermal background, the position shifts were greater in the sunny aspects than in the shady aspects in the eastern and middle QLM, while opposite in the western QLM. The differences in the hydrothermal conditions and regional topography led to the spatiotemporal change differences of the alpine desert distribution. The correlation between the normalized differential vegetation index and climate factors in the transition zone showed that temperature was the main factor affecting the dynamics and spatial differences of the alpine desert belt in the QLM, and climate warming facilitated the alpine meadow below the alpine desert belt by releasing the low temperature limitation on the vegetation growth.

Aims Precipitation use efficiency (PUE) is a key to understanding the coupling between ecosystem carbon and water cycles. Our objective was to probe the spatial PUE pattern and its response to climate on the Qinghai- Xizang Plateau to better understand mechanisms of vegetation productivity and improve ecosystem process models. Methods GLOPEN-CEVSA model was applied to estimate net primary production (NPP) by using the Fraction of Photosynthetically Active Radiation Absorbed by Vegetation (MOD15A2), and spatially interpolated meteorological data in 2000-2008. The modeled NPP was significantly correlated with the observed above-ground net primary productivity (R ² = 0.49, p < 0.001, n = 97). The PUE was calculated as the ratio of NPP to the annual sum of precipitation. Important findings The spatial pattern of PUE showed large differences among vegetation types. Crops had the highest PUE, and alpine meadow had higher PUE than alpine steppe. These differences were related to the precipitation and temperature distribution on the plateau. The PUE was relatively stable and the lowest value of (0.026 ± 0.190) g C·m ^-2·mm ^-1 (mean ± standard deviation) with the highest coefficient of variance (CV) of 721% was where precipitation was < 90 mm. Where precipitation was 90-300 mm, PUE was relatively stable and also low ((0.029 ± 0.074) g C·m ^-2·mm ^-1) with relatively high CV (252%). Together precipitation and air temperature in this precipitation range explained 43.4% of the spatial variance of PUE, and the effect of precipitation was 1.7 times that of temperature (p < 0.001). The area with precipitation from 300-650 mm, mainly covered by alpine steppe (45%), had relatively high PUE ((0.123 ± 0.191) g C·m ^-2·mm ^-1) with a CV of 155%. The significant correlation of PUE with climate factors explained 97.8% spatial variance of PUE. Temperature had the dominant role, having 1.5 times the effect of precipitation. With increasing precipitation, PUE reached a peak of 0.26 g C·m ^-2·mm ^-1 at 650 mm of precipitation and then showed a decreasing trend. The precipitation of the mountainous Nyingchi region, Xizang, is >845 mm, and the region is mainly covered with evergreen needleleaf forest. It has relatively high PUE ((0.210 ± 0.246) g C·m ^-2·mm ^-1) with a minimum CV of 117%. Temperature and precipitation together explained 93.1% of the spatial variation of PUE for Nyingchi. Precipitation was negatively correlated with PUE and its effect was 3.5 times that of temperature.

Aims Phenology refers to periodic appearances of life-cycle events. It is crucial for predicting plant phenological responses to climate change and for identifying the period of carbon-uptake. Tracking the real-time canopy status accurately, especially in harsh environments, is becoming a large challenge for understanding and modeling vegetation-climate interactions. Our objective focuses on how to obtain relatively accurate real-time canopy status in Qinghai-Xizang Plateau using digital camera images.
Methods A standard, commercially available webcam was mounted at the top of the eddy covariance tower at the Damxung Rangeland Station. Images were collected every half an hour from 9:30 a.m. to 5:00 p.m. local time each day. We extracted red, green, and blue color channel brightness data for a region-of-interest (ROI) from each image (ROI, the subset of image, can better describe the target’s characters). The size of ROI is [100:180] and [10:380]), and it composed the different greenness indices according to the equations. We confirmed the best one that can reflect the size of leaf area index and variations in chlorophyll content by comparing different indices.
Important findings The absolute greenness index (2G_RB) is able to describe the canopy status qualitatively and quantitatively and is powerful in tracking community phenological stages. This indicates that digital cameras can be used in monitoring real-time phenology of alpine grassland community. Linear regression analysis of soil moisture indicates greenness is best explained by surface soil moisture (≤10 cm). By comparing canopy phenological events with conventional meteorological data, we also speculate that precipitation plays a critical role in triggering the spring phenological response in semiarid alpine grassland.

Aim Estimating regional variation in vegetation phenology from time-series remote sensing data is important in global climate change studies. However, there are few studies on vegetation phenology for the Qinghai-Tibet Plateau and most are based on field records of stations.

Methods We utilized the dynamic threshold method to explore vegetation phenological metrics (greenup date, length of season and senescence date) of typical grassland in the Northern Tibetan Plateau. We used time-series TERRA/MODIS EVI data for 2001-2010 reconstructed by the asymmetric Gaussian function fitting method to analyze spatial pattern and differentiation of vegetation phenology and its inter-annual variation and to examine the relationship between phenological variation and climate changes.

Important findings The spatial pattern of date of vegetation greenup was embodied by transition from southeast to northwest and vertical zonation in the mountainous topography of the southeast. The vegetation greenup date in approximately sixty percent of the northern Tibetan Plateau had advanced, especially in high mountains. Inter- annual variation of vegetation senescence date was not obvious, and most of the region had natural inter-annual fluctuations. The variation of growing season length is influenced by greenup and senescence dates, but was chiefly affected by advanced greenup date lengthening the growing season. Among the four different climatic zones in the study area, the mountain and valley Nagqu sub-arctic and sub-humid zone and the southern Qinghai sub-arctic and semi-arid zone had the most apparent advanced greenup date and prolonged growing season. Based on measured data from weather stations, increased temperature appears to be a critical factor contributing to earlier greenup and prolonged growing season; however, the relationship between precipitation fluctuations and phenological variation was unclear.

Aims The “Three-River Headwaters” Region, as the headwaters of important rivers and an area sensitive to global climate change, has become a recent research focus. Our objective is to model and assess the spatial-temporal pattern of net primary production ( NPP) and its control mechanisms.

Methods We applied the GLOPEM-CEVSA model, which has been validated with carbon flux observation in forest, grassland and cropland. The main inputs are spatially interpolated meteorological data and fraction of photosynthetically active radiation absorbed by vegetation canopy, using 1 km resolution of the Advanced Very High Resolution Radiometer of the National Oceanic and Atmospheric Administration in 1988-2004.

Important findings Modeled NPP ranged from 36.13 gC·m^-2·a^-1 for desert to 267.90 gC·m^-2·a^-1 for forest, and the mean was 143.17 gC·m^-2·a^-1. Spatially, NPP decreased from southeast to northwest, as influenced by geography and climate. Variability of NPP was the largest in desert (41.75%), was similar for cropland (25.93%), grassland (22.31%) and wetland (24.72%) and was the smallest in forest (20.79%). During 1988-2004, NPP increased at the rate of 7.8-28.8 gC·m^-2 per 10 years in the western area, but decreased 13.1-42.8 gC·m^-2 per 10 years in the central and eastern areas. At 99 and 95% significance levels, the area with NPP increasing (regression slope b > 0) was 13.43% and 20.34%, respectively, of the whole area, and mainly distributed in the western region, while the area with NPP decreasing (b < 0) was 0.75% and 3.77%, respectively, of the whole area and distributed in the central and western areas and was more concentrated near the main rivers at higher significance levels. Increases of NPP in the western area may have been affected by increasing temperature and precipitation, while central and eastern areas may have been impacted by human activities, especially along the Yangtze, Yellow and other rivers with intensive human habitation and where the warmer and drier climate has led to more serious grassland degradation. The effects of human activities on NPP were not analyzed because data on human activity were unavailable and spatial interpolation of the impact is difficult.

Aims We sought to understand the impacts of climate change on vegetation in Northwest China and the relationship between normalized difference vegetation index (NDVI) and climate elements.
Methods Correlation analyses were done using the GIMMS NDVI data and monthly mean temperature and precipitation data from January 1982 to December 2003. We selected different regions in Northwest China, representing major types of vegetation, such as forest, grassland, oasis, and rain-fed cropland, for detailed study.
Important findings We found strong correlations between NDVI and temperature/precipitation, except for the Gobi and other desert areas. Correlation coefficients of NDVI and temperature are higher than them of NDVI and precipitation in almost all regions, particularly for the Hexi Corridor and most of the Xinjiang area. During the vegetation growth period, temperature has greater effect on the various types of vegetation than precipitation. The forests in the higher latitude of Xinjiang area are most sensitive to temperature. This sensitivity reduces in sequence from forests to oases, grasslands and unirrigated croplands. Grasslands are most sensitive to precipitation. The sensitivity to precipitation decreases from grasslands to forests, unirrigated croplands, and oases. In summer, the NDVI of forest decreased during the last 22 years, especially forest in the eastern portion of the northwest. This was related to decreases of precipitation and increases of temperature in these areas. The NDVI in most grassland increased. The trends were significant for high cold meadow and halophytic meadow. Climate warming is the main reason for grass growth speeding up. For oases, the NDVI increases were the most significant. The trends were the highest in Xinjiang oasis. Climate warming was one of the factors driving increases in NDVI. The impact of human activities, such as oasis expanding, crop structure change and crop varieties on the NDVI variation cannot be ignored. The NDVI interannual change was high and varied among the unirrigated croplands. NDVI had a strong positive correlation with precipitation and a negative correlation with temperature. The temperature increase and the precipitation decrease caused the NDVI decrease in these areas.