Driving Forces of the Changes in Vegetation Phenology in the Qinghai–Tibet Plateau

Vegetation cover dynamics and its constraint effect on ecosystem services on the Qinghai-Tibet Plateau under ecological restoration projects

Ecological restoration projects (ERPs) are implemented worldwide to restore degraded ecosystems and promote ecosystem sustainability. In recent years, a series of ERPs have been implemented to enhance vegetation cover in the unique alpine ecosystems of the Qinghai-Tibet Plateau (QTP). However, the current assessment of the ecological benefits of ERPs is relatively single, and the scale and extent of future ecological restoration project implementation cannot be determined. We quantified trends in normalized vegetation index (NDVI) since the implementation of ERPs. Changes in four major ecosystem services were assessed before and after ERPs implementation, including wind erosion protection, soil retention, water yield, and net primary productivity (NPP). The relationship between NDVI and ecosystem services was further explored using a constraint line approach to identify NDVI as a threshold reference for ERPs implementation. The results showed that: (1) since the implementation of ERPs, 21.80% of the regional NDVI of the QTP has increased significantly. (2) After the implementation of ERPs, the average total ecosystem services index (TES) increased from 0.269 in 2000 to 0.285 in 2020. The average soil retention and water yield increased but the NPP and sandstorm prevention decreased slightly. (3) NDVI had no significant constraint effect on soil retention and NPP, but there was a significant constraint effect on wind erosion prevention and water yield. (4) The constraint line of NDVI on TES was S-shaped. After the implementation of ERPs, the TES gradually reached a threshold value when NDVI was 0.65-0.75. Our findings identify significant contributions of ERPs and thresholds for the constraining effects of vegetation cover on ecosystem services, which can inform sustainable ERPs for governments.

Primary driving factors of ecological environment system change based on directed weighted network illustrating with the Three-River Headwaters Region

The primary driving factors of ecological environment change have received significant attention. However, previous research methods for identifying the main drivers of ecological environment change have primarily relied on correlation analysis and regression analysis. While these methods can reveal co-occurrences, associations, and correlations among elemental characteristics, they often struggle to uncover the deep-seated interactions among elements within complex, unstable, nonlinear, and high-dimensional systems. To address this, we used the Three-River Headwaters Region as a case study and introduced a complex network model from the perspective of the ecological environment system to investigate the main driving factors of ecological environment change. In our analysis, we considered 12 factors related to the atmosphere, hydrology, vegetation, and soil, including evaporation, long-wave radiation, short-wave radiation, specific humidity, soil temperature, precipitation rate, soil water content, air temperature, air pressure, vegetation normalization index, wind speed, and natural surface runoff. Watersheds were selected as the fundamental units for constructing ecological environment datasets. We applied the Ensemble Empirical Mode Decomposition (EEMD) method and Hilbert-Huang Transform (HHT) to analyze causal relationships between time series pairs and constructed two directed weighted network models based on sub-catchments. The results showed that both network models yielded consistent conclusions, with the sparse network exhibiting higher efficiency. Radiation and temperature were identified as the primary driving factors of ecosystem change, and the water cycle was determined to be the ultimate manifestation of ecological system change throughout the Three-River Headwaters Region. Furthermore, based on node out-strength, we generated a vegetation protection priority map.

Drier August and colder September slow down the delaying trend of leaf senescence in herbaceous plants on the Qinghai-Tibetan Plateau

Plant phenological shifts on the Qinghai-Tibetan Plateau (QTP) have gained considerable attention over the last few decades. However, temporal changes in plant autumn phenology and the main driving factors remain uncertain. Most previous studies used satellite-derived phenological transition dates and climatic statistics during the preseason, which have relatively large uncertainties and may mask some important climate change characteristics at the intra-annual scale, thus affecting exploration of the underlying phenological change causes. This study collected 1685 phenological records at 27 ground stations on the QTP during 1983-2017. Temporal change trends and break points in leaf senescence date (LSD) of 23 herbaceous species were assessed using least squares regression, a meta-analysis procedure, and the Pettitt test. The main drivers and causes were investigated through correlation analysis and contribution calculation based on LSD observations and monthly climatic data. Results showed that, LSD of QTP herbaceous plants was significantly delayed at a rate of 4.45 days/decade during 1983-2017. Break points were concentrated during 1999-2003, with an overall mean in 2001. After 2001, the delay trend in LSD decreased, falling from 5.26 days/decade to 2.54 days/decade. Air temperature and precipitation were the most important climatic factors that showed closer and more extensive correlations with LSD and greater contributions to the inter-annual variations in LSD. August and September were the most critical period during which climatic factors had higher contributions to the LSD shifts. However, August was drier, with precipitation significantly decreasing and temperature increasing, and September was colder after 2001. Therefore, the declining trend in LSD may be attributed to the drier August and colder September. This study has not only provided reliable field evidence on temporal changes in autumn phenology on the QTP, but has also provided valuable insights into autumn phenological modelling and regional carbon cycling in alpine regions.

Critical role of water conditions in the responses of autumn phenology of marsh wetlands to climate change on the Tibetan Plateau

The Tibetan Plateau, housing 20% of China's wetlands, plays a vital role in the regional carbon cycle. Examining the phenological dynamics of wetland vegetation in response to climate change is crucial for understanding its impact on the ecosystem. Despite this importance, the specific effects of climate change on wetland vegetation phenology in this region remain uncertain. In this study, we investigated the influence of climate change on the end of the growing season (EOS) of marsh wetland vegetation across the Tibetan Plateau, utilizing satellite-derived Normalized Difference Vegetation Index (NDVI) data and observational climate data. We observed that the regionally averaged EOS of marsh vegetation across the Tibetan Plateau was significantly (p < .05) delayed by 4.10 days/decade from 2001 to 2020. Warming preseason temperatures were found to be the primary driver behind the delay in the EOS of marsh vegetation, whereas preseason cumulative precipitation showed no significant impact. Interestingly, the responses of EOS to climate change varied spatially across the plateau, indicating a regulatory role for hydrological conditions in marsh phenology. In the humid and cold central regions, preseason daytime warming significantly delayed the EOS. However, areas with lower soil moisture exhibited a weaker or reversed delay effect, suggesting complex interplays between temperature, soil moisture, and EOS. Notably, in the arid southwestern regions of the plateau, increased preseason rainfall directly delayed the EOS, while higher daytime temperatures advanced it. Our results emphasize the critical role of hydrological conditions, specifically soil moisture, in shaping marsh EOS responses in different regions. Our findings underscore the need to incorporate hydrological factors into terrestrial ecosystem models, particularly in cold and dry regions, for accurate predictions of marsh vegetation phenological responses to climate change. This understanding is vital for informed conservation and management strategies in the face of current and future climate challenges.

A Model between Near-Surface Air Temperature Change and Dynamic Influencing Factors in the Eastern Tibetan Plateau, China

Climate change, characterized by global warming, is profoundly affecting the global environment, politics, economy, and social security. Finding the main causes of climate change and determining their quantitative contributions are key points to making climate decisions on responses to climate change. The Tibetan Plateau (TP) is sensitive to global climate change. Taking the 100 km buffer zones of 45 meteorological stations in the eastern TP as research objects, we conducted an experimental study on temperature change and its influencing factors. Using the least squares multivariate statistical analysis method, a model between the annual and seasonal standardized temperature change and its dynamic influencing factors in the past 20 years was established. The results showed that, in the eastern TP, temperature change was affected by different factors in different periods. Vegetation cover and snow cover were the most correlated factors to temperature change. The influence of carbon dioxide, vegetation cover, and water cover was subject to seasonal changes. Urban cover and bare land cover did not pass the t-test. This research not only provides a theoretical basis for the analysis of temperature change over the TP, but also points out the direction for the analysis of temperature change causes in three polar regions.

Influences of Seasonal Soil Moisture and Temperature on Vegetation Phenology in the Qilian Mountains

Vegetation phenology is a commonly used indicator of ecosystem responses to climate change and plays a vital role in ecosystem carbon and hydrological cycles. Previous studies have mostly focused on the response of vegetation phenology to temperature and precipitation. Soil moisture plays an important role in maintaining vegetation growth. However, our understanding of the influences of soil moisture dynamics on vegetation phenology is sparse. In this study, using a time series of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (MODIS) dataset (2001–2020), the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS) in the Qilian Mountains (QLMs) were extracted. The spatiotemporal patterns of vegetation phenology (SOS, EOS, and LOS) were explored. The partial coefficient correlations between the SOS, EOS, and seasonal climatic factors (temperature, precipitation, and soil moisture) were analyzed. The results showed that the variation trends of vegetation phenology were not significant (p > 0.05) from 2001 to 2020, the SOS was advanced by 0.510 d/year, the EOS was delayed by 0.066 d/year, and the LOS was prolonged by 0.580 d/year. The EOS was significantly advanced and the LOS significantly shortened with increasing altitude. The seasonal temperature, precipitation, and soil moisture had spatiotemporal heterogeneous effects on the vegetation phenology. Overall, compared with temperature and soil moisture, precipitation had a weaker influence on the vegetation phenology in the QLMs. For different elevation zones, the temperature and soil moisture influenced the vegetation phenology in most areas of the QLMs, and spring temperature was the key driving factor influencing SOS; the autumn soil moisture and autumn temperature made the largest contributions to the variations in EOS at lower (<3500 m a.s.l.) and higher elevations (>3500 m a.s.l.), respectively. For different vegetation types, the spring temperature was the main factor influencing the SOS for broadleaf forests, needleleaf forests, shrublands, and meadows because of the relative lower soil moisture stress. The autumn soil moisture was the main factor influencing EOS for deserts because of the strong soil moisture stress. Our results demonstrate that the soil moisture strongly influences vegetation phenology, especially at lower elevations and water-limited areas. This study provides a scientific basis for better understanding the response of vegetation phenology to climate change in arid mountainous areas and suggests that the variation in soil moisture should be considered in future studies on the influence of climate warming and environmental effects on the phenology of water-limited areas.

Impact of Snowpack on the Land Surface Phenology in the Tianshan Mountains, Central Asia

The accumulation and ablation processes of seasonal snow significantly affect the land surface phenology in a mountainous ecosystem. However, the ability of snow to regulate the alpine land surface phenology in the arid regions is not well described in the context of climate change. The impact of snowpack changes on land surface phenology and its driving factors were investigated in the Tianshan Mountains using the land surface phenology metrics derived from satellited products and a snow dataset from downscaled regional climate model simulations covering the period from 1983 to 2015. The results demonstrated that the annual mean start of growing season (SOS) and length of growing season (LOS) experienced a significant (p < 0.05) decrease and increase with a rate of −2.45 days/decade and 2.98 days/decade, respectively. The significantly advanced SOS and increased LOS were mainly seen in the Western Tianshan Mountains and Ili Valley regions with elevations from 2500 to 3500 m a.s.l and below 3000 m a.s.l, respectively. During the early spring, the significant decline in snow cover fraction (SCF) could advance the SOS. In contrast, snowmelt amount and annual maximum snow water equivalent (SWE) have an almost equally substantial positive correlation with annual maximum vegetation greenness. In particular, the SOS of grassland was the most sensitive to variations of snow cover fraction during early spring than that of other vegetation types, and their strong relationship was mainly located at elevations from 1500 to 2500 m a.s.l. Its greenness was significantly controlled by the annual maximum snow water equivalent in all elevation bands. Both decreased SCF and increased temperature in the early spring caused a significant advance of the SOS, consequently prolonging the LOS. Meanwhile, more SWE and snowmelt amount could significantly promote vegetation greenness by regulating the soil moisture. The results can improve the understanding of the snow ecosystem services in the alpine regions under climate change.

Spatiotemporal Characteristics and Heterogeneity of Vegetation Phenology in the Yangtze River Delta

Vegetation phenology and its spatiotemporal driving factors are essential to reflect global climate change, the surface carbon cycle and regional ecology, and further quantitative studies on spatiotemporal heterogeneity and its two-way driving are needed. Based on MODIS phenology, meteorology, land cover and other data from 2001 to 2019, this paper analyzes the phenology change characteristics of the Yangtze River Delta from three dimensions: time, plane space and elevation. Then, the spatiotemporal heterogeneity of phenology and its driving factors are explored with random forest and geographic detector methods. The results show that (1) the advance of start of season (SOS) is insignificant—with 0.17 days per year; the end of season (EOS) shows a significant delay—0.48 days per year. The preseason temperature has a greater contribution to SOS, while preseason precipitation is main factor in determining EOS. (2) Spatial differences of the phenological index do not strictly obey the change rules of latitude at a provincial scale. The SOS of Jiangsu and Anhui is earlier than that of Zhejiang and Shanghai, and EOS shows an obvious double-clustering phenomenon. In addition, a divergent response of EOS with elevation grades is found; the most significant changes are observed at grades below 100 m. (3) Land cover (LC) type is a major factor of the spatial heterogeneity of phenology, and its change may also be one of the insignificant factors driving the interannual change of phenology. Furthermore, nighttime land surface temperature (NLST) has a relatively larger contribution to the spatial heterogeneity in non-core urban areas, but population density (PD) contributes little. These findings could provide a new perspective on phenology and its complex interactions between natural or anthropogenic factors.

Spatial Pattern and Dynamic Change of Vegetation Greenness From 2001 to 2020 in Tibet, China

Due to the cold climate and dramatically undulating altitude, the identification of dynamic vegetation trends and main drivers is essential to maintain the ecological balance in Tibet. The normalized difference vegetation index (NDVI), as the most commonly used greenness index, can effectively evaluate vegetation health and spatial patterns. MODIS-NDVI (Moderate-resolution Imaging Spectroradiometer-NDVI) data for Tibet from 2001 to 2020 were obtained and preprocessed on the Google Earth Engine (GEE) cloud platform. The Theil-Sen median method and Mann-Kendall test method were employed to investigate dynamic NDVI changes, and the Hurst exponent was used to predict future vegetation trends. In addition, the main drivers of NDVI changes were analyzed. The results indicated that (1) the vegetation NDVI in Tibet significantly increased from 2001 to 2020, and the annual average NDVI value fluctuated between 0.31 and 0.34 at an increase rate of 0.0007 year^-1; (2) the vegetation improvement area accounted for the largest share of the study area at 56.6%, followed by stable unchanged and degraded areas, with proportions of 27.5 and 15.9%, respectively. The overall variation coefficient of the NDVI in Tibet was low, with a mean value of 0.13; (3) The mean value of the Hurst exponent was 0.53, and the area of continuously improving regions accounted for 41.2% of the study area, indicating that the vegetation change trend was continuous in most areas; (4) The NDVI in Tibet indicated a high degree of spatial agglomeration. However, there existed obvious differences in the spatial distribution of NDVI aggregation areas, and the aggregation types mainly included the high-high and low-low types; and (5) Precipitation and population growth significantly contributed to vegetation cover improvement in western Tibet. In addition, the use of the GEE to obtain remote sensing data combined with time-series data analysis provides the potential to quickly obtain large-scale vegetation change trends.