Detection and attribution of vegetation greening trend in China over the last 30 years

Response of alpine vegetation function to climate change in the Tibetan Plateau: A perspective from solar-induced chlorophyll fluorescence

Vegetation change in the Tibetan Plateau (TP) is a crucial indicator of climate change in alpine regions. Previous studies have reported an overall greening trend in the vegetation structure across the TP, especially in its northeastern part, in response to a warming climate. However, variations in the vegetation function and the possible drivers remain poorly understood. Considering the optimal temperature for plants in TP is usually higher than the current temperature, our hypothesis is the function and structure of alpine vegetation have changed synchronously over past few decades. To test this hypothesis, we analyzed satellite-observed solar-induced chlorophyll fluorescence (SIF) and leaf area index (LAI) in the Yellow River source (YRS) region in the northeastern TP to quantify the long-term trends in vegetation functional and structural states, respectively. The results suggest that from 1982 to 2018, SIF increased significantly in 77.71 % of the YRS area, resulting in a significant upward trend of 0.52 × 10-3 mW m-2 nm-1 sr-1 yr-1 (p < 0.001) for the regional-mean SIF. This represents a 16.1 % increase in SIF, which is close in magnitude to the increase in LAI over the same period. The synchronous changes between vegetation function and structure suggest that improved greenness corresponds to a similar level of change in carbon uptake across YRS. Additionally, we used a multiple regression approach to quantify the contribution of climatic factors to SIF changes in YRS. Our analyses show that the increases in SIF were primarily driven by rising temperatures. Spatially, temperature dominated SIF changes in most parts of YRS, except for certain dry parts in the northern and western YRS, where precipitation had a greater impact. Our results are crucial for a comprehensive understanding of climate regulations on vegetation structure and function in high-elevation regions.

Human-earth system dynamics in China's land use pattern transformation amidst climate fluctuations and human activities

Amid rapid environmental changes, the interplay between climate change and human activity is reshaping land use, emphasizing the significance of human-earth system dynamics. This study, rooted in human-earth system theory, explores the complex relationships between land use patterns, climate change, and human activities across China from 1996 to 2022. Using a comprehensive analytical framework that combines Geographical Detector (GeoDetector), Random Forest (RF) model, Data Envelopment Analysis (DEA), Spearman's rank correlation, and k-means clustering, we analyzed data from national land surveys, climate records, and nighttime light observations. Our findings indicate a significant, though regionally varied, transformation in land use: arable land decreased by 1.67 %, driven by intense urbanization and policy shifts, particularly in rapidly urbanizing Jiangsu province where arable land diminished by 19.19 %. In contrast, construction land in the northern regions increased by 225.91 million hectares. Climatic influences are apparent, with rising temperatures positively correlating with arable land expansion in the Northeast and Northwest, and urban land in Jiangsu province increasing by 35.51 %. Variations in precipitation patterns were linked to changes in forested areas. This study highlights the dynamic and intricate interactions within the human-earth system, stressing the urgent need for sustainable land management and climate adaptation strategies that improve land use efficiency and resilience. Our research offers a solid foundation for informed policy-making in land management and climate adaptation, advocating a human-earth system science approach to address future environmental and societal challenges.

Towards understanding climate change impacts: monitoring the vegetation dynamics of terrestrial national parks in Indonesia

Monitoring vegetation dynamics in terrestrial national parks (TNPs) is crucial for ensuring sustainable environmental management and mitigating the potential negative impacts of short- and long-term disturbances understanding the effect of climate change within natural and protected areas. This study aims to monitor the vegetation dynamics of TNPs in Indonesia by first categorizing them into the regions of Sumatra, Jawa, Kalimantan, Sulawesi, and Eastern Indonesia and then applying ready-to-use MODIS EVI time-series imageries (MOD13Q1) taken from 2000 to 2022 on the GEE cloud-computing platform. Specifically, this research investigates the greening and browning fraction trends using Sen's slope, considers seasonality by analyzing the maximum and minimum EVI values, and assesses anomalous years by comparing the annual time series and long-term median EVI value. The findings reveal significantly increasing greening trends in most TNPs, except Danau Sentarum, from 2000 to 2022. The seasonality analysis shows that most TNPs exhibit peak and trough greenness at the end of the rainy and dry seasons, respectively, as the vegetation response to precipitation increases and decreases. Anomalies in seasonality that is affected by climate change was detected in all of the regions. To increase TNPs resilience, suggested measures include active reforestation and implementation of Assisted Natural Regeneration, strengthen the enforcement of fundamental managerial task, and forest fire management.

Impact of greening trends on biogenic volatile organic compound emissions in China from 1985 to 2022: Contributions of afforestation projects

The rapid expansion of green areas in China has enhanced carbon sinks, but it also presents challenges regarding increased biogenic volatile organic compound (BVOC) emissions. This study examines the impact of greening trends on BVOC emissions in China from 1985 to 2001 and from 2001 to 2022, focusing on evaluating long-term trends in BVOC emissions within eight afforestation project areas during these two periods. Emission factors for 62 dominant tree species and provincial Plant Functional Types were updated. The BVOC emission inventories were developed for China at a spatial resolution of 27 km × 27 km using the Model of Emissions of Gases and Aerosols from Nature. The national BVOC emissions in 2018 were estimated at 54.24 Tg, with isoprene, monoterpenes, sesquiterpenes, and other BVOC contributing 26.94 Tg, 2.29 Tg, 0.44 Tg, and 24.57 Tg, respectively. Over the past 37 years, BVOC emissions experienced a slow growth rate of 1.7 % (0.79 Tg) during 1985-2001, followed by a significant increase of 12 % (6 Tg) from 2001 to 2022. BVOC emissions in the eight afforestation project areas increased by 2 % and 20 % during the two periods. From 2001 to 2022, at the regional scale, the Shelterbelt program for the middle reaches of the Yellow River area exhibited the largest rate of increase (43 %) in BVOC emissions. The Shelterbelt program for the upper and middle reaches of the Yangtze River made the most largest contribution (45 %) to the national increase in BVOC emissions. Afforestation projects have shifted towards planting more broadleaf trees than needleleaf trees from 2001 to 2022, and there also showed a change from herbaceous plants to broadleaf trees. These trends have led to higher average emission factors for vegetation, resulting in increased BVOC emissions. It underscores the importance of considering BVOC emissions when evaluating afforestation initiatives, emphasizing the need to balancing ecological benefits with potential atmospheric consequences.

Revisiting the hydrological legacy of revegetation on China's Loess Plateau using Eagleson's ecohydrological perspective

Revegetation has resulted in a trend of increasing vegetation greenness on the Chinese Loess Plateau. However, it remains unclear whether the regional vegetation coverage exceeds hydroclimatic limitations in the context of revegetation, and the hydrological effects of greening are controversial. Eagleson's optimality hypothesis can explain some of the hydrological effects on the Loess Plateau. Here, building on previous research, the geospatial vegetation states were estimated for pre- and post-revegetation periods on the Loess Plateau from 1982 to 2015 using Eagleson's ecological optimality theory. Additionally, a drought composite analysis approach was utilized to investigate the hydrological effects related to drought (including sensitivity and partitioning) under various vegetation states. It was found that revegetation increased the proportion of catchments in the equilibrium state and decreased the proportion in the disturbed state, owing to a wetter climate compared with the pre-revegetation period. Root-zone soil drought, driven by precipitation (P) deficit, asymmetrically triggered hydrological effects for both the pre- and post-revegetation periods, with reduced runoff (Q) for both periods and a decrease in evapotranspiration (ET) during the pre-revegetation period but an increase in ET during the post-revegetation period. Moreover, catchments in an equilibrium state exhibited lower sensitivity between ET and P, and more stable partitioning of ET with regards to P, compared with those in a disturbed state. These results underscore the theoretical framework that an equilibrium state is crucial for maintaining ecosystem ET. Our results highlight the necessity of considering the hydrologic regulation of vegetation states when assessing the hydrological effects of vegetation change.

The seasonal variability of future evapotranspiration over China during the 21st century

The evapotranspiration (ET) plays a crucial role in shaping regional climate patterns and serves as a vital indicator of ecosystem function. However, there remains a limited understanding of the seasonal variability of future ET over China and its correlation with environmental drivers. This study evaluated the skills of 27 models from the Six Phase of Coupled Model Intercomparison Project in modeling ET and the Bayesian Model Averaging (BMA) method was employed to merge monthly simulated ET based on the top five best-performing models. The seasonal changes in ET under three climate scenarios from 2030 to 2099 were analyzed based on the BMA-merged ET, which was well validated with observed ET collected from fourteen flux sites across China. Significant increasing ET over China are projected under all seasons during 2030-2099, with 0.05-0.13 mm yr-1, 0.11-0.23 mm yr-1, and 0.20-0.41 mm yr-1 under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively. Relative to the historical period (1980-2014), the relative increase in ET over China is highest in winter and lowest in summer. Seasonal ET increases significantly in all seven climate sub-regions under high forcing scenario. Higher ET increase is generally found in southeastern humid regions, while lowest ET increase occurs in northwest China. At the country level, the primary factor driving ET increase during spring, summer, and autumn seasons is the increasing net radiation and warming. In contrast, ET increase during winter is influenced not only by energy factors but also by vegetation-related factors. Future seasonal ET increase is predominantly driven by increasing energy factors in the southeastern humid region and Tibetan Plateau, while seasonal ET changes in the northwest region prevailingly depend on soil moisture. Results indicate that China will experience a "wet season will get wetter, and dry season will become drier" in the 21st century with high radiation forcing scenario.

Predicting suitable habitat for the endangered tree Ormosia microphylla in China

Climate change has significantly influenced the growth and distribution of plant species, particularly those with a narrow ecological niche. Understanding climate change impacts on the distribution and spatial pattern of endangered species can improve conservation strategies. The MaxEnt model is widely applied to predict species distribution and environmental tolerance based on occurrence data. This study investigated the suitable habitats of the endangered Ormosia microphylla in China and evaluated the importance of bioclimatic factors in shaping its distribution. Occurrence data and environmental variables were gleaned to construct the MaxEnt model, and the resulting suitable habitat maps were evaluated for accuracy. The results showed that the MaxEnt model had an excellent simulation quality (AUC = 0.962). The major environmental factors predicting the current distribution of O. microphylla were the mean diurnal range (bio2) and precipitation of the driest month (bio14). The current core potential distribution areas were concentrated in Guangxi, Fujian, Guizhou, Guangdong, and Hunan provinces in south China, demonstrating significant differences in their distribution areas. Our findings contribute to developing effective conservation and management measures for O. microphylla, addressing the critical need for reliable prediction of unfavorable impacts on the potential suitable habitats of the endangered species.

Characteristics and drivers of vegetation productivity sensitivity to increasing CO2 at Northern Middle and High Latitudes

Understanding and accurately predicting how the sensitivity of terrestrial vegetation productivity to rising atmospheric CO2 concentration (β) is crucial for assessing carbon sink dynamics. However, the temporal characteristics and driving mechanisms of β remain uncertain. Here, observational and CMIP6 modeling evidence suggest a decreasing trend in β at the Northern Middle and High Latitudes during the historical period of 1982-2015 (-0.082 ± 0.005% 100 ppm-1 year-1). This decreasing trend is projected to persist until the end of the 21st century (-0.082 ± 0.005% 100 ppm-1 year-1 under SSP370 and -0.166 ± 0.006% 100 ppm-1 year-1 under SSP585). The declining β indicates a weakening capacity of vegetation to mitigate warming climates, posing challenges for achieving the temperature goals of the Paris Agreement. The rise in vapor pressure deficit (VPD), that triggers stomata closure and weakens photosynthesis, is considered as the dominated factor contributing to the historical and future decline in β, accounting for 62.3%-75.2% of the effect. Nutrient availability and water availability contribute 15.7%-21.4% and 8.5%-16.3%, respectively. These findings underscore the significant role of VPD in shaping terrestrial carbon sink dynamics, an aspect that is currently insufficiently considered in many climate and ecological models.

Assessing the effects of climate and human activity on vegetation change in Northern China

Fractional vegetation cover (FVC) has changed significantly under various disturbances over northern China in recent decades. This research examines the dynamics of FVC and how it is affected by climate and human activity during the period of 1990-2018 in northern China. The effects of climate change (i.e., temperature, precipitation, solar radiation, and soil moisture) and human activity (socioeconomic data and land use) on vegetation coverage change in northern China from 1990 to 2018 were quantified using the Sen + Mann-Kendall test, partial correlation analysis, and structural equation modelling (SEM) methods. The findings of this research indicate the following: (1) From 1990 to 2018, the overall trend in FVC in northern China was increased. The areas with obvious increases were mainly situated on the northern slope of Tianshan Mountains, Xinjiang, the Loess Plateau, the Northeast China Plain, and the Sanjiang Plain, while the areas with distinct degradation were located in the Inner Mongolia Plateau, the Changbai Mountain and the eastern part of north China. (2) In the past 29 years, the FVC in northern China has been mainly affected by precipitation and soil moisture. (3) Based on structural equation modelling, we discovered that certain variables impacted the main factors influencing the amount of FVC in northern China. Human activity has had a larger impact on FVC than climate change. Our findings can accelerate the comprehension of vegetation dynamics and their underlying mechanisms and provide a theoretical basis for regional ecological environmental protection.

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.

Carbon cycle responses to climate change across China's terrestrial ecosystem: Sensitivity and driving process

Investigations into the carbon cycle and how it responds to climate change at the national scale are important for a comprehensive understanding of terrestrial carbon cycle and global change issues. Contributions of carbon fluxes to the terrestrial sink and the effects on climate change are still not fully understood. In this study, we aimed to explore the relationship between ecosystem production (GPP/SIF/NDVI) and net ecosystem carbon exchange (NEE) and to investigate the sensitivity of carbon fluxes to climate change at different spatio-temporal scales. Furthermore, we sought to delve into the carbon cycle processes driven by climate stress in China since the beginning of the 21st century. To achieve these objectives, we employed correlation and sensitivity analysis techniques, utilizing a wide range of data sources including ground-based observations, remote sensing observations, atmospheric inversions, machine learning, and model simulations. Our findings indicate that NEE in most arid regions of China is primarily driven by ecosystem production. Climate variations have a greater influence on ecosystem production than respiration. Warming has negatively impacted ecosystem production in Northeast China, as well as in subtropical and tropical regions. Conversely, increased precipitation has strengthened the terrestrial carbon sink, particularly in the northern cool and dry areas. We also found that ecosystem respiration exhibits heightened sensitivity to warming in southern China. Moreover, our analysis revealed that the control of terrestrial carbon cycle by ecosystem production gradually weakens from cold/arid areas to warm/humid areas. We identified distinct temperature thresholds (ranging from 10.5 to 13.7 °C) and precipitation thresholds (approximately 1400 mm yr-1) for the transition from production-dominated to respiration-dominated processes. Our study provides valuable insights into the complex relationship between climate change and carbon cycle in China.

Temperate grassland vegetation restoration influenced by grazing exclusion and climate change

Grasslands are one of the most important terrestrial biomes, supporting a wide range of ecological functions and services. Grassland degradation due to overgrazing is a severe issue worldwide, especially in developing regions. However, observations from multiple sources have shown that temperate grasslands in China have significantly increased during the past two decades. It remains controversial what factors have driven the vegetation restoration in this region. In this study, we combined remote-sensing images and field survey datasets to quantify the contributions of different factors to vegetation restoration in six temperate grasslands in northern China. Across the six grasslands, the Normalized Difference Vegetation Index (NDVI) increased by 0.003-0.0319 year-1. The average contributions of grazing exclusion and climate change to the NDVI increase were 49.23 % and 50.77 %, respectively. Precipitation change was the primary climate factor driving vegetation restoration, contributing 50.76 % to the NDVI variance. By contrast, climate warming tended to slow vegetation restoration, and atmospheric CO2 concentration change contributed little to the NDVI increase in the temperate grasslands. These results emphasize the significant contributions of both climate change and human management to grassland vegetation restoration.

Landscape evolution in China's key ecological function zones during 1990-2015

Landscape evolution has profound effects on ecosystems. Recently, some studies suggest that China has implemented plans leading in the greening of the world by mainly describing the changes based on satellite data. However, few studies have analyzed the policy effect on ecosystem improvement from the perspective of landscape pattern evolution. Among the numerous ecological policy plans, China's key ecological function zones plan is an important one. In this study, we focus on depicting the long-term and large-scale landscape evolution in China's key ecological function zones, which are accounting for 40.2% of China's land area, and include four-type ecoregions where ecosystems are fragile or important, to comprehensively explore the environmental influences of policy planning. For this purpose, we first described the landscape composition changes and conversion mechanisms in China's key ecological function zones from 1990 to 2015. Then we captured the detailed pattern evolution characteristics by landscape indices. The results show that these ecoregions were mostly evolving in an unfavorable direction in these 25 years, i.e. destruction of habitats and increment of fragmentation. Although greening areas increased based on other recent researches, the landscape pattern became worse, indicating it is necessary for the detailed analysis of landscape ecology and more accurate ecological planning. We also found the deterioration of the ecological environment had been uncharacteristically stopped or even improved in wind prevention and sand fixation ecoregions and biodiversity maintenance ecoregions after the implementation of this plan. Furthermore, we assumed that the policy is more prominent in these prohibiting sabotages and protecting areas with fragile ecological bases, which may be caused by the differentiated transfer payments in different ecoregions. Finally, some planning suggestions, such as stricter land use control, the regional balance of ecological transfer payments and deepening of ecological migration policies, etc., were proposed for promoting better future environmental changes.

The rising human footprint in the Tibetan Plateau threatens the effectiveness of ecological restoration on vegetation growth

Ecological restoration projects in the Tibetan Plateau aimed to reverse ecosystem degradation and safeguard the fragile alpine ecological environment. However, it is still being determined if the vegetation restoration is successful on a large scale or reaches the expected magnitude, restricting our ability to adapt practices to maximise the benefit. With multiple vegetation indices (VIs: NDVI, LAI, and GPP) from satellite observations and random forest machine-learning models, we performed an attribution study on vegetation growth trends caused by climate change and human activities. Then, we further explored the relationship between vegetation growth and ecological projects and human footprint without the influence of climate. The results showed that climatic change was a relatively strong driver of vegetation growth. The positive contributions of ecological restoration occurred only in half of the plateau due to the increased human footprint. Vegetation enhancement resulting from ecological restoration occurred in 13.1%-23.1% of the plateau. Among these values, ecological restoration counteracted the negative climate effects in 4.7%-8.3% of the plateau (about half of the negative climate effect area). In forest and grassland protection areas, the ecological restoration was more successful. The study provides a better understanding of the role of ecological projects in vegetation restoration and potential threats to its effectiveness. This is essential to improve future restoration projects.

Grassland vegetation decline is exacerbated by drought and can be mitigated by soil improvement in Inner Mongolia, China

Grassland activity is highly susceptible to drought while drivers from climate and soil attributes can largely affect drought propagation. However, understanding how these drives regulate the risk of vegetation decline under drought conditions remains limited, potentially impeding the adoption of appropriate adaptation strategies. To address this knowledge gap, we conducted a case study focusing on grassland activity in Inner Mongolia, China. In this study, we applied copula theorem to estimate the conditional probabilities of vegetation decline under drought conditions. Additionally, we utilized a structural equation model and a machine learning approach to identify the relative contributions of external drivers to the risk of vegetation decline. Our findings demonstrated a positive correlation between anomalies in vegetation activity and the status of water balance, and grassland vegetation in drier regions exhibited a more rapid response to water deficit. Increasing water deficit continuously reduced vegetation activity with risks of 77.27 %, 83.83 %, and 88.35 % under moderate, severe, and extreme drought conditions, respectively. Furthermore, the risks of vegetation decline under drought conditions were primarily governed by climate attributes, followed by soil properties and topography. Soil with high soil organic carbon stock content contributed significantly to mitigating the adverse effects of drought on grassland vegetation. In addition, we detected nonlinear patterns among environmental drivers and vegetation decline risks caused by drought. These findings highlight the importance of climate, soil properties, topography, and their intricate interconnections in regulating vegetation decline. This knowledge provides valuable insights into drought risk management for vegetation in advance and offers potential solutions to enhance vegetation resistance in the face of extreme drought events.

Satellite-observed increasing coupling between vegetation productivity and greenness in the semiarid Loess Plateau of China is not captured by process-based models

Global vegetation has experienced notable changes in greenness and productivity since the early 1980s. However, the changes in the relationship between productivity and greenness, i.e., the coupling, and its underlying mechanisms, are poorly understood. The Loess Plateau (LP) is one of China's most significant areas for vegetation greening. Yet, it remains poorly documented what changes in the coupling between productivity and greenness are and how environmental and anthropogenic factors affect this coupling in the LP over the past four decades. We investigated the interannual trend of coupling between Gross Primary Productivity (GPP) and Leaf Area Index (LAI), i.e., the GPP-LAI coupling, and its response to climate factors and afforestation in the LP using long-term remote-sensed LAI, GPP and Solar-induced Chlorophyll Fluorescence (SIF). We found a monotonically increasing trend in the GPP-LAI coupling in the LP from 1982 to 2018 (0.0043 yr-1, p < 0.05), in which the significant trend in the northwest LP was driven by increasing soil water and landcover change, e.g., increased grassland and afforestation. An ensemble of 11 state-of-the-art ecosystem models from the TRENDY project failed to capture the observed monotonically increasing trend of the GPP-LAI coupling in the LP. The consistent projection of a decreasing GPP-LAI coupling in LP during 2019-2100 by 22 Earth System Models (ESMs) under various future scenarios should be treated with caution due to the identified inherent uncertainties in the ecosystem component in ESMs and the notable biases in the simulation of future climate conditions. Our study highlights the need to enhance the key mechanisms that regulate the coupling relationships between photosynthesis and canopy structure in indigenized ecosystem models to accurately estimate the ecosystem change in drylands under global climate change.

Driving model of land use change on the evolution of carbon stock: a case study of Chongqing, China

Terrestrialecosystems are significant carbon sinks and are crucial for understanding the regional and global carbon cycles, energy flow, and climate change. As land use change is a significant process affecting ecosystem carbon stocks and striving for land degradation neutrality (LDN), studying it is essential for comprehending the evolution of regional carbon sink functions and achieving sustainable development goals. The drastically diverse land use patterns in each of the study area's regions resulted in significant differences in carbon stock. This study explores the evolution traits of carbon stocks based on land use data and their driving mechanisms in Chongqing during the past 30 years by using spatial analysis, the InVEST model, and geographic probes. The results demonstrate that from 1990 to 2020, land degradation in Chongqing was made worse by the demand for land for construction land, but the strategy of converting cropland back to forests raised the carbon stock of forest land. The overall result is a decrease in total carbon stocks of 5.1078 Tg or 1.5%. The main pathway for carbon loss pathway in the evolution of carbon stock is the conversion of cropland to construction land, and the primary carbon compensation pathway is the conversion of grassland and cropland to forest land, with a spatial distribution characterized by "higher in the whole area and obvious local differences." The land use intensity index has the most significant influence on the evolution of carbon stock. Moreover, the interaction of pairwise factors played a more important role in affecting the evolution of carbon stocks than did each factor individually. The case study in this paper shows that land use change is a significant driving mechanism for the evolution of carbon stock, and the development of a driving model theory is appropriate for deciphering the trajectory of carbon stock evolution and offering research suggestions for other regions.

Past and projected future patterns of fractional vegetation coverage in China

With the intensifying climate change and the strengthening ecosystem management, quantifying the past and predicting the future influence of these two factors on vegetation change patterns in China need to be analyzed urgently. By constructing a framework model to accurately identify fractional vegetation coverage (FVC) change patterns, we found that FVC in China from 1982 to 2018 mainly showed linear increase (29.5 %) or Gaussian decrease (27.4 %). FVC variation was mainly affected by soil moisture in the Qi-North region and by vapor pressure deficit in other regions. The influence of environmental change on FVC, except for Yang-Qi region in the southwest (-2.0 %), played a positive role, and weakened from the middle (Hu-Yang region: 2.7 %) to the northwest (Qi-North region: 2.4 %) to the east (Hu-East region: 0.8 %). Based on five machine learning algorithms, it was predicted that under four Shared Socioeconomic Pathways (SSPs, including SSP126、SSP245、SSP370、SSP585) from 2019 to 2060, FVC would maintain an upward trend, except for the east, where FVC would rapidly decline after 2039. FVC in the eastern region experienced a transition from past growth to future decline, suggesting that the focus of future ecosystem management should be on this region.

Simulation and prediction of changes in tree species composition in subtropical forests of China using a nonlinear difference equation system model

Changes in tree species composition are one of the key aspects of forest succession. In recent decades, significant changes have occurred in the tree species composition of subtropical forests in China, with a decrease in coniferous trees and an increase in broad-leaved trees. This study focuses on Zhejiang Province, located in the subtropical region of China, and utilizes seven inventories from the National Continuous Forest Inventory (NCFI) System spanning 30 years (1989-2019) for modeling and analysis. We categorized tree species into three groups: pine, fir, and broadleaf. We used the proportion of biomass in a sample plot as a measure of the relative abundance of each tree species group. A novel nonlinear difference equation system (NDES) model was proposed. A NDES model was established based on two consecutive survey datasets. A total of six models were established in this study. The results indicated that during the first two re-examination periods (1989-1994, 1994-1999), there was significant fluctuation in the trend of tree species abundance, with no consistent pattern of change. During the latter four re-examination periods (1999-2004, 2004-2009, 2009-2014, 2014-2019), a consistent trend was observed, whereby the abundance of the pine group and the fir group decreased while the abundance of the broad-leaved group increased. Moreover, over time, this pattern became increasingly stable. Although the abundances of the pine group and the fir group have been steadily declining, neither group is expected to become extinct. The NDES model not only facilitates short-term, medium-term, and even long-term predictions but also employs limit analysis to reveal currently obscure changing trends in tree species composition.

Spatiotemporal change patterns and driving factors of land surface temperature in the Yunnan-Kweichow Plateau from 2000 to 2020

In this study, the spatiotemporal change patterns and driving factors of land surface temperature (LST) on the Yunnan-Kweichow Plateau (YKP) during 2000-2020 are investigated by using the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST dataset provided by National Tibetan Plateau Data Center. The YKP LST spatiotemporal change patterns are revealed at annual, seasonal, monthly, and daily scales. Furthermore, seven driving factors such as air temperature, land cover types, normalized difference vegetation index, precipitation, solar radiation, elevation, and latitude are quantified the impacts on LST spatial heterogeneity at annual scale. The main findings are as follows: (1) Annual mean LST increases by 0.016 K/year. Annual mean daytime LST slightly decreases by 0.009 K/year. Annual mean nighttime LST significantly increases by 0.042 K/year. (2) The trend and seasonal components of the daily, daily mean daytime, and daily mean nighttime LST have five and four breakpoints respectively, indicating that the variation of LST is unstable during 2000-2020 on the YKP. (3) The LST lapse rates at nighttime are generally higher than those at daytime on the YKP at the annual, seasonal, and monthly scales. The LST maximum lapse rate is 0.59 K/100 m in summer nighttime, and the LST minimum lapse rate is 0.18 K/100 m in winter daytime. (4) The controlling effects of seven factors are generally stronger in the nighttime than those in the daytime. The factors of elevation and air temperature dominate the LST spatial distribution on the YKP, with a contribution rate of >70 %. In addition, the interactions among the seven factors are all enhancing the effects on the spatial distribution of annual mean LST, including bivariate enhancement and nonlinear enhancement. This study contributes to the mitigation and adaptation to climate change of LST in the plateau and plays a theoretical reference role in formulating corresponding policies for environmental protection.

Vegetation-induced asymmetric diurnal land surface temperatures changes across global climate zones

Unprecedented global vegetation greening during past decades is well known to affect annual and seasonal land surface temperatures (LST). However, the impact of observed vegetation cover change on diurnal LST across global climatic zones is not well understood. Using global climatic time-series datasets, we investigated the long-term growing season daytime and nighttime LST changes globally and explored associated dominant contributors including vegetation and climate factors including air temperature, precipitation, and solar radiation. Results revealed asymmetric growing season mean daytime and nighttime LST warming (0.16 °C/10a and 0.30 °C/10a, respectively) globally from 2003 to 2020, as a result, the diurnal LST range (DLSTR) declined at 0.14 °C/10a. The sensitivity analysis indicated the LST response to changes in LAI, precipitation, and SSRD mainly concentrated during daytime instead of nighttime, however, which showed comparable sensitivities for air temperature. Combining the sensitivities results and the observed LAI and climate trends, we found rising air temperature contributes to 0.24 ± 0.11 °C/10a global daytime LST warming and 0.16 ± 0.07 °C/10a nighttime LST warming, turns to be the dominant contributor to the LST changes. Increased LAI cooled global daytime LST (-0.068 ± 0.096 °C/10a) while warmed nighttime LST (0.064 ± 0.046 °C/10a); hence LAI dominates declines in DLSTR trends (-0.12 ± 0.08 °C/10a), despite some day-night process variations across climate zones. In Boreal regions, reduced DLSTR was due to nighttime warming from LAI increases. In other climatic zones, daytime cooling, and DLSTR decline, was induced by increased LAI. Biophysically, the pathway from air temperature heats the surface through sensible heat and increased downward longwave radiation during day and night, while the pathway from LAI cools the surface by enhancing energy redistribution into latent heat rather than sensible heat during the daytime. These empirical findings of diverse asymmetric responses could help calibrate and improve biophysical models of diurnal surface temperature feedback in response to vegetation cover changes in different climate zones.

The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial

BACKGROUND

The Qinghai-Tibet Plateau is the "sensitive area" of climate change, and also the "driver" and "amplifier" of global change. The response and feedback of its carbon dynamics to climate change will significantly affect the content of greenhouse gases in the atmosphere. However, due to the unique geographical environment characteristics of the Qinghai-Tibet Plateau, there is still much controversy about its carbon source and sink estimation results. This study designed a new algorithm based on machine learning to improve the accuracy of carbon source and sink estimation by integrating multiple scale carbon input (net primary productivity, NPP) and output (soil heterotrophic respiration, Rh) information from remote sensing and ground observations. Then, we compared spatial patterns of NPP and Rh derived from the fusion of multiple scale data with other widely used products and tried to quantify the differences and uncertainties of carbon sink simulation at a regional scale.

RESULTS

Our results indicate that although global warming has potentially increased the Rh of the Qinghai-Tibet Plateau, it will also increase its NPP, and its current performance is a net carbon sink area (carbon sink amount is 22.3 Tg C/year). Comparative analysis with other data products shows that CASA, GLOPEM, and MODIS products based on remote sensing underestimate the carbon input of the Qinghai-Tibet Plateau (30-70%), which is the main reason for the severe underestimation of the carbon sink level of the Qinghai-Tibet Plateau (even considered as a carbon source).

CONCLUSIONS

The estimation of the carbon sink in the Qinghai-Tibet Plateau is of great significance for ensuring its ecological barrier function. It can deepen the community's understanding of the response to climate change in sensitive areas of the plateau. This study can provide an essential basis for assessing the uncertainty of carbon sources and sinks in the Qinghai-Tibet Plateau, and also provide a scientific reference for helping China achieve "carbon neutrality" by 2060.

Vegetation growth enhancement modulated by urban development status

Cities are natural laboratories for studying the vegetation response to global change due to their own climatic, atmospheric, and biological conditions. However, whether the urban environment promoted vegetation growth is still uncertain. Using the Yangtze River Delta (YRD), an economic powerhouse of modern China, as a case study, this paper investigated the impact of urban environment on vegetation growth at three scales: cities, sub-cities (rural-urban gradient) -pixels. Based on the satellite observations of vegetation growth indicated during 2000-2020, we explored the direct (replacement of original land by impervious surfaces) and indirect impact (e.g., climatic environment) of urbanization on vegetation growth and their trends with urbanization level. We found that significant greening accounted for 43.18 %, and significant browning accounted for 3.60 % of the pixels in the YRD. Urban area was turning green faster than suburban area. Moreover, land use change intensity (D) was a representation of the direct impact ω_d of urbanization. The direct impact of urbanization on vegetation growth was positively correlated with the intensity of land use change. Furthermore, vegetation growth enhancement due to indirect impact ω_i occurred in 31.71 %, 43.90 % and 41.46 % of the YRD cities in 2000, 2010 and 2020. And vegetation enhancement occurred in 94.12 % of highly urbanized cities in 2020, while in medium and low urbanization cities, the averaged indirect impact was near zero or even negative, proving that vegetation growth enhancement was modulated by urban development status. Also, the growth offset (τ) was most pronounced in high urbanization cities (4.92 %), but there was no growth compensation in medium urbanization cities (-4.48 %) and low urbanization cities (-57.47 %). When urbanization intensity reached a threshold value of 50 % in highly urbanized cities, the growth offset (τ) tended to saturate and remained unchanged. Our findings have important implications for understanding the vegetation response to continuing urbanization process and future climate change.

Comprehensive evaluation system for vegetation ecological quality: a case study of Sichuan ecological protection redline areas

Dynamic monitoring and evaluation of vegetation ecological quality (VEQ) is indispensable for ecological environment management and sustainable development. Single-indicator methods that have been widely used may cause biased results due to neglect of the variety of vegetation ecological elements. We developed the vegetation ecological quality index (VEQI) by coupling vegetation structure (vegetation cover) and function (carbon sequestration, water conservation, soil retention, and biodiversity maintenance) indicators. The changing characteristics of VEQ and the relative contribution of driving factors in the ecological protection redline areas in Sichuan Province (EPRA), China, from 2000 to 2021 were explored using VEQI, Sen's slope, Mann-Kendall test, Hurst index, and residual analysis based on the XGBoost (Extreme gradient boosting regressor). The results showed that the VEQ in the EPRA has improved over the 22-year study period, but this trend may be unsustainable in the future. Temperature was the most influential climate factor. And human activities were the dominant factor with a relative contribution of 78.57% to VEQ changes. This study provides ideas for assessing ecological restoration in other regions, and can provide guidance for ecosystem management and conservation.

Should time-lag and time-accumulation effects of climate be considered in attribution of vegetation dynamics? Case study of China's temperate grassland region

Although the time-lag and time-accumulation effects (TLTAEs) of climatic factors on vegetation growth have been investigated extensively, the uncertainties caused by disregarding TLTAEs in the attribution analysis of long-term changes in vegetation remain unclear. This hinders our understanding of the associated changes in ecosystems and the effects of climate change. In this study, using multiple methods, we evaluate the biases of attribution analyses of vegetation dynamics caused by the non-consideration of TLTAEs in the temperate grassland region (TGR) of China from 2000 to 2019. Based on the datasets of the normalized difference vegetation index (NDVI), temperature (TMP), precipitation (PRE), and solar radiation (SR), the temporal reaction patterns of vegetation are analyzed, and the relationships among these variables under two scenarios (considering and disregarding TLTAEs) are compared. The results indicate that most areas of the TGR show a greening trend. A time-lag or time-accumulation effect of the three climatic variables is observed in most areas with significant spatial differences. The lagged times of the vegetation response to PRE are particularly prominent, with an average of 2.12 months in the TGR. When the TLTAE is considered, the areas where changes in the NDVI are affected by climatic factors expanded significantly, whereas the explanatory power of climate change on NDVI change increased by an average of 9.3% in the TGR; these improvements are more prominent in relatively arid areas. This study highlights the importance of including TLTAEs in the attribution of vegetation dynamics and the assessment of climatic effects on ecosystems.

Thresholds for triggering the propagation of meteorological drought to hydrological drought in water-limited regions of China

Propagation thresholds that trigger a transition between meteorological drought and hydrological drought are poorly understood, which hinders effective establishment of drought warning systems and prevention measures. Here, propagation thresholds were assessed by firstly identifying drought events during 1961-2016 in the Yellow River Basin, China, subsequently pooling, excluding, and matching them, and finally assessing their threshold conditions by using a combined Copula function and transition rate (T_r) analysis. These results show that response time changed according to variations in drought duration and watershed characteristics. Importantly, response times increased according to the timescales over which they were studied; for example, the Wenjiachuan watershed recorded response times of 8, 10, 10, and 13 months when examined at 1-, 3-, 6-, and 12-month timescales, respectively. Additionally, the severity and duration of meteorological and hydrological drought events both increased when events were combined rather than studied individually. These effects were also amplified for matched meteorological and hydrological droughts by factors of 1.67 (severity) and 1.45 (duration), respectively. Shorter response times were identified in the Linjiacun (LJC) and Zhangjiashan (ZJS) watersheds, and correlated with their relatively small T_r values of 43 % and 47 %, respectively. Higher propagation thresholds for drought characteristics (e.g., 1.81 and 1.95 for drought severity in the LJC and ZJS watersheds, respectively) imply that shorter response times tended to have greater effects on hydrological drought events and lowered their T_r, and vice versa. These results provide new insight into propagation thresholds used for water resource planning and management, and may help to mitigate the effects of future climate change.

Recent Declines in Nutrient Concentrations and Fluxes in the Lower Changjiang River

To elucidate nutrient variation patterns and trends over various timescales under combined effects of human activities and climate change, nutrient concentrations were monitored monthly in Lower Changjiang (Yangtze) River from November 2016 to August 2020. They were also monitored daily during an extreme flood in July 2020. Over daily and seasonal timescales, the Changjiang River discharges had a dominant influence on nutrient concentrations. By combining existing data over recent decades with those from the current study, we found that turning points for concentration trends for most nutrients emerged in the recent decade (2010-2020), i.e., 2012 for NO3-, PO43-, and NH4+ and 2014 for SiO32-. After these turning point years, NO3-, SiO32-, and PO43- concentrations decreased at annual rates of 2.953, 3.746, and 0.108 μM/year, respectively. Regarding NO3- and PO43-, their concentrations and fluxes increased from 1960s to 2012, similar to the increasing trends of anthropogenic N and P fertilizer inputs from the drainage basin. After 2012, concentrations and fluxes of NO3- and PO43- showed significant decreasing trends, largely due to the control of N and P fertilizer usage. A comparison among eight rivers in East and South China (including the Changjiang River) indicated that basin latitudes were essential to determining areal nutrient yields, implying that latitude-related factors, such as temperature, precipitation, and areal population density, significantly impacted nutrient fluxes. This study emphasized that the deteriorating Changjiang River aquatic environment (which lasted from 1960s to 2010) has been successfully terminated over the last 10 years in 2010s.

Coarse spatial resolution remote sensing data with AVHRR and MODIS miss the greening area compared with the Landsat data in Chinese drylands

The warming-wetting climates in Chinese drylands, together with a series of ecological engineering projects, had caused apparent changes to vegetation therein. Regarding the vegetation greening trend, different remote sensing data had yielded distinct findings. It was critical to evaluate vegetation dynamics in Chinese drylands using a series of remote sensing data. By comparing the three most commonly used remote sensing datasets [i.e., MODIS, Advanced Very High Resolution Radiometer (AVHRR), and Landsat], this study comprehensively investigated vegetation dynamics for Chinse drylands. All three remote sensing datasets exhibited evident vegetation greening trends from 2000 to 2020 in Chinese drylands, especially in the Loess Plateau and Northeast China. However, Landsat identified the largest greening areas (89.8%), while AVHRR identified the smallest greening area (58%). The vegetation greening areas identified by Landsat comprise more small patches than those identified by MODIS and AVHRR. The MODIS data exhibited a higher consistency with Landsat than with AVHRR in terms of detecting vegetation greening areas. The three datasets exhibited high consistency in identifying vegetation greening in Northeast China, Loess Plateau, and Xinjiang. The percentage of inconsistent areas among the three datasets was 39.56%. The vegetation greening areas identified by Landsat comprised more small patches. Sensors and the atmospheric effect are the two main reasons responsible for the different outputs from each NDVI product. Ecological engineering projects had a great promotion effect on vegetation greening, which can be detected by the three NDVI datasets in Chinese drylands, thereby combating desertification and reducing dust storms.

Spatial-temporal variation of the carbon sequestration rate of afforestation in China: Implications for carbon trade and planning

Afforestation and reforestation (A&R) are nature-based and cost-effective solutions for enhancing terrestrial carbon sinks and facilitating faster carbon neutrality. However, the lack of hierarchical spatial-temporal maps for the carbon sequestration rate (CSR) from A&R at the national scale impedes the scientific implementation of forest management planning to a large extent. Here, we assessed the spatial-temporal CSR per area for A&R at the provincial, prefectural, and county levels in China using a forest carbon sequestration model under three climate scenarios. Results showed that the CSR of vegetation (CSR_Veg), soil (CSR_Soil), and the ecosystem (CSR_Eco) significantly varied across space and time. In China, the CSR_Veg, CSR_Soil, and CSR_Eco were primarily regulated by the spatial variations in temperature and precipitation. Additionally, CSR_Veg was found to be positively influenced by precipitation and temperature, whereas temperature had a negative influence on CSR_Soil. Therefore, the differences between the CSR_Veg and CSR_Soil should be emphasized in the future. These information on the spatiotemporal variation of CSR of A&R (vegetation, soil, and ecosystem) on unit area basis and at levels of province, prefecture, and county in China, can be used as a comparable protocol to estimate the carbon sinks of A&R at different scales. Overall, these hierarchical spatiotemporal maps for CSR on A&R may help to identify priority areas of forest management planning and carbon trade policy to achieve faster carbon neutrality for China in the future.

Restored vegetation is more resistant to extreme drought events than natural vegetation in Southwest China

Large scale Ecosystem restoration projects (ERPs) have been implemented to restore vegetation and increase carbon stocks across China. However, whether restored vegetation is strongly resistant to Extreme drought events (EDEs) remains unclear, especially when compared to natural vegetation. Therefore, we used the standardized anomaly of 3-month Standard Precipitation-Evapotranspiration Index (SPEI) to characterize the spatial-temporal trends of EDEs, and figured out the capacity of restored vegetation to withstand the strongest EDE in Southwest China by analyzing their changes of Gross Primary Productivity (GPP) and Water Use Efficiency (WUE). The results showed that Southwest China had experienced six typical EDEs with increasing frequency and severity from 1982 to 2017, particularly the EDE during 2009-2010 (EDE 2009/2010) which had the longest duration and strongest severity. Overall, the EDE 2009/2010 substantially suppressed the vegetation GPP and ecosystem WUE in both restored and natural vegetation area. Compared with natural vegetation, the GPP and WUE of restored vegetation was relative higher and moreover, their GPP decreased more slowly during the EDE 2009/2010 and increased more quickly during the recovery period. This indicates that restored vegetation had a higher drought resistance to the EDE than natural vegetation. Additionally, karst landforms have a stronger negative impact on vegetation GPP and WUE during the EDE. Furthermore, the reduction in the afforestation areas was more obviously observed than that in natural forest areas. Therefore, we suggest that vegetation suitable for regional characteristics should be selected during vegetation restoration, such as afforestation in the non-karst areas.

Reversal of soil moisture constraint on vegetation growth in North China

The response of vegetation growth to soil moisture varies greatly from space and time under climate change and anthropogenic activities. As an important grain producer in China, the vegetation growth and grain production of North China are constrained by the region's water resources. With the significant increase in vegetation greenness in North China over the last 40 years, it is essential to explore the changes in soil moisture constraints on vegetation growth to water management. However, to what degree vegetation growth responds to soil moisture and how the response varies spatiotemporally in North China remain unclear. In this study, the response patterns of vegetation growth to soil moisture at different depths and the spatiotemporal trend patterns of their relationships were explored thoroughly based on long time series remote sensing data in North China over the past 40 years. The results showed that compared to forests, the growth of grasslands and crops with one maturity per year and two maturity per year in North China was more constrained by soil moisture. Due to the combined effects of climatic conditions and human activities, vegetation growth in North China has been significantly less constrained by soil moisture over the last 40 years. This was especially seen in one maturity per year crop and natural vegetation in Shanxi and central Shandong. However, with the significant increase in temperature, potential evapotranspiration and water demand of the crop, the moisture constraints on vegetation growth in North China have begun to show an increasing trend since the early 2000s, especially for irrigated crop in central and southern North China. These findings highlight a comprehensive understanding of the vegetation response to soil moisture from the time-varying perspective and provide a theoretical basis for water management and appropriate planning of agricultural water use in North China.

Land cover change in global drylands: A review

As a sensitive region, identifying land cover change in drylands is critical to understanding global environmental change. However, the current findings related to land cover change in drylands are not uniform due to differences in data and methods among studies. We compared and judged the spatial and temporal characteristics, driving forces, and ecological effects by identifying the main findings of land cover change in drylands at global and regional scales (especially in China) to strengthen the overall understanding of land cover change in drylands. Four main points were obtained. First, while most studies found that drylands were experiencing vegetation greening, some evidence showed decreases in vegetation and large increases in bare land due to inconsistencies in the datasets and the study phases. Second, the dominant factors affecting land cover change in drylands are precipitation, agricultural activities, and urban expansion. Third, the impact of land cover change on the water cycle, especially the impact of afforestation on water resources in drylands, is of great concern. Finally, drylands experience severe land degradation and require dataset matching (classification standards, resolution, etc.) to quantify the impact of human activities on land cover.

Earlier leaf senescence dates are constrained by soil moisture

The unprecedented warming that has occurred in recent decades has led to later autumn leaf senescence dates (LSD) throughout the Northern Hemisphere. Yet, great uncertainties still exist regarding the strength of these delaying trends, especially in terms of how soil moisture affects them. Here we show that changes in soil moisture in 1982-2015 had a substantial impact on autumn LSD in one-fifth of the vegetated areas in the Northern Hemisphere (>30° N), and how it contributed more to LSD variability than either temperature, precipitation or radiation. We developed a new model based on soil-moisture-constrained cooling degree days (CDD_SM ) to characterize the effects of soil moisture on LSD and compared its performance with the CDD, Delpierre and spring-influenced autumn models. We show that the CDD_SM model with inputs of temperature and soil moisture outperformed the three other models for LSD modelling and had an overall higher correlation coefficient (R), a lower root mean square error and lower Akaike information criterion (AIC) between observations and model predictions. These improvements were particularly evident in arid and semi-arid regions. We studied future LSD using the CDD_SM model under two scenarios (SSP126 and SSP585) and found that predicted LSD was 4.1 ± 1.4 days and 5.8 ± 2.8 days earlier under SSP126 and SSP585, respectively, than other models for the end of this century. Our study therefore reveals the importance of soil moisture in regulating autumn LSD and, in particular, highlights how coupling this effect with LSD models can improve simulations of the response of vegetation phenology to future climate change.

Climate-driven vegetation greening further reduces water availability in drylands

Climate change alters surface water availability (WA; precipitation minus evapotranspiration, P - ET) and consequently impacts agricultural production and societal water needs, leading to increasing concerns on the sustainability of water use. Although the direct effects of climate change on WA have long been recognized and assessed, indirect climate effects occurring through adjustments in terrestrial vegetation are more subtle and not yet fully quantified. To address this knowledge gap, here we investigate the interplay between climate-induced changes in leaf area index (LAI) and ET and quantify its ultimate effect on WA during the period 1982-2016 at the global scale, using an ensemble of data-driven products and land surface models. We show that ~44% of the global vegetated land has experienced a significant increase in growing season-averaged LAI and climate change explains 33.5% of this greening signal. Such climate-induced greening has enhanced ET of 0.051 ± 0.067 mm year^-2 (mean ± SD), further amplifying the ongoing increase in ET directly driven by variations in climatic factors over 36.8% of the globe, and thus exacerbating the decline in WA prominently in drylands. These findings highlight the indirect impact of positive feedbacks in the land-climate system on the decline of WA, and call for an in-depth evaluation of these phenomena in the design of local mitigation and adaptation plans.

Unintended consequences of combating desertification in China

Since the early 2000s, China has carried out extensive "grain-for-green" and grazing exclusion practices to combat desertification in the desertification-prone region (DPR). However, the environmental and socioeconomic impacts of these practices remain unclear. We quantify and compare the changes in fractional vegetation cover (FVC) with economic and population data in the DPR before and after the implementation of these environmental programmes. Here we show that climatic change and CO₂ fertilization are relatively strong drivers of vegetation rehabilitation from 2001-2020 in the DPR, and the declines in the direct incomes of farmers and herders caused by ecological practices exceed the subsidies provided by governments. To minimize economic hardship, enhance food security, and improve the returns on policy investments in the DPR, China needs to adapt its environmental programmes to address the potential impacts of future climate change and create positive synergies to combat desertification and improve the economy in this region.

Spatial and temporal variations of grassland vegetation on the Mongolian Plateau and its response to climate change

The Mongolian Plateau is an arid and semi-arid region with grassland as its main vegetation. It has a fragile ecosystem and is a sensitive area for global warming. The study is based on MODIS NDVI data and growth season meteorological data from 2000 to 2018, this study examined the spatial and temporal variation characteristics of grassland vegetation on the Mongolian Plateau during the growing season using trend analysis, partial correlation analysis, and residual analysis, and it explores the dual response of NDVI changes to climate and human activities. The study’s findings demonstrated that the growing season average NDVI of grassland vegetation on the plateau gradually increased from southwest to northeast during the growing season; the growing season average NDVI demonstrated a significant overall increase of 0.023/10a (p &lt; 0.05) from 2000 to 2018, with an increase rate of 0.030/10a in Inner Mongolia and 0.019/10a in Mongolia; the area showing a significant increase in NDVI during the growing season accounted for 91.36% of the entire study area. In Mongolian Plateau grasslands during the growing season of 2000–2018, precipitation and downward surface shortwave radiation grew significantly at rates of 34.83mm/10a and 0.57 W/m2/10a, respectively, while average air temperature decreased slightly at a rate of −0.018°C/10a. Changes in meteorological factors of grassland vegetation varied by region as well, with Inner Mongolia seeing higher rates of precipitation, lower rates of average air temperature, and lower rates of downward surface shortwave radiation than Mongolia. On the Mongolian Plateau, the NDVI of grassland vegetation in the growing season showed a significant positive correlation with precipitation (0.31) and a significant negative correlation with average air temperature (−0.09) and downward surface shortwave radiation (−0.19), indicating that increased in NDVI was driven by an increase in precipitation paired with a decrease in air temperature and a decrease in surface shortwave radiation. The overall increase in NDVI caused by human activity in the grasslands of the Mongolian Plateau was primarily positive, with around 18.37% of the region being beneficial. Climate change and human activity both affect NDVI variations in Mongolian Plateau grasslands, which are spatially heterogeneous. Moderate ecological engineering and agricultural production activities are crucial for vegetation recovery. This work is crucial to further understanding surface–atmosphere interactions in arid and semi-arid regions in the context of global climate change.

No Signs of Long-term Greening Trend in Western Mongolian Grasslands

Trends for increased vegetation greenness based on satellite-derived data have been repeatedly published for the temperate grassland biome (including forest steppes) of eastern Inner Asia since 1982. Although this greening trend has been attenuated or partially reversed by drought in the early twenty-first century, linear increases in the Normalized Difference Vegetation Index (NDVI) or other parameters of vegetation greenness are nevertheless evident when the period since 1982 is regarded. However, the question arises whether these trends are part of a long-term trend driven by climate change, as simultaneously forests in the region show widespread drought-induced growth reductions and mortality outbreaks. Therefore, we hypothesized that the post-1982 greening trend was neither part of a long-term trend nor unprecedented. To test this hypothesis, we analyzed monthly maximum NDVI data from AVHRR time series and correlated these data with standardized tree-ring data of Larix sibirica from two regions of western Mongolia. We used linear regression to model the NDVI from tree-ring anomalies and to reconstruct the NDVI since 1940. These reconstructions show that the availability of satellite-based NDVI data coincidentally began during a dry period of low vegetation greenness in the early 1980s and was followed by a wet phase in the 1990s, producing the linear greening trend. No positive long-term trend in the reconstructed NDVI was observed from 1940 to 2010. This result rules out a recent climate change-driven greening trend for the grasslands and forest steppes of western Mongolia and calls into question its existence for all of eastern Inner Asia.

Future climate imposes pressure on vulnerable ecological regions in China

Ecological regions of medium fragility account for 55 % of China's land. Large-scale afforestation and land reclamation have been carried out in these areas over the past few decades. However, how future climate change poses risks and challenges to them remains unclear. By establishing a multi-algorithm framework combining machine learning algorithms with multi-source dataset, our work predicts Normalized Difference Vegetation Index (NDVI, a proxy for vegetation greenness) and its variations in the 21st century under different climate scenarios. We find that vegetation greening (i.e., NDVI increase) in northern and southwestern China is unstable over four 20-year periods from 2020 to 2100. However, a strikingly prominent greening is expected to occur on the Qinghai-Tibet Plateau until the end of this century. Future warming can not only exacerbate the difficulties of vegetation conservation and restoration in vulnerable ecological regions, also threaten these new croplands, stymieing ambitions to increase crop production in China. Our results underscore the crucible that a warming climate presents to current restoration projects. We highlight the urgency of adapting to climate change to achieve ambitious goals of carbon sequestration and food security in China.

Drylands contribute disproportionately to observed global productivity increases

Drylands cover about 40% of the terrestrial surface and are sensitive to climate change, but their relative contributions to global vegetation greening and productivity increase in recent decades are still poorly known. Here, by integrating satellite data and biosphere modeling, we showed that drylands contributed more to global gross primary productivity (GPP) increase (65% ± 16%) than to Earth greening (33% ± 15%) observed during 1982-2015. The enhanced productivity per unit leaf area, i.e., light-use efficiency (LUE), was the mechanism behind this pattern. We also found that LUE was more sensitive to soil moisture than to atmospheric vapor pressure deficit (VPD) in drylands, while the opposite was observed (i.e., LUE was more sensitive to VPD) in humid areas. Our findings suggest the importance of using different moisture stress metrics in projecting the vegetation productivity changes of dry versus humid regions and highlight the prominent role of drylands as key controllers of the global carbon cycle.

The Nonlinear Impact of Mobile Human Activities on Vegetation Change in the Guangdong-Hong Kong-Macao Greater Bay Area

Vegetation is essential for ecosystem function and sustainable urban development. In the context of urbanization, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), as the typical urban-dominated region, has experienced a remarkable increase in social and economic activities. Their impact on vegetation is of great significance but unclear, as interannual flow data and linear methods have limitations. Therefore, in this study, we used human and vehicle flow data to build and simulate the indices of mobile human activity. In addition, we used partial least squares regression (PLSR), random forest (RF), and geographical detector (GD) models to analyze the impact of mobile human activities on vegetation change. The results showed that indices of mobile human and vehicle flow increased by 1.43 and 7.68 times from 2000 to 2019 in the GBA, respectively. Simultaneously, vegetation increased by approximately 64%, whereas vegetation decreased mainly in the urban areas of the GBA. Vegetation change had no significant linear correlation with mobile human activities, exhibiting a regression coefficient below 0.1 and a weight of coefficients of PLSR less than 40 between vegetation change and all the factors of human activities. However, a more significant nonlinear relationship between vegetation change and driving factors were obtained. In the RF regression model, vegetation decrease was significantly affected by mobile human activity of vehicle flow, with an importance score of 108.11. From the GD method, vegetation decrease was found to mainly interact with indices of mobile human and vehicle inflow, and the highest interaction force was 0.82. These results may support the attainment of sustainable social-ecological systems and global environmental change.

Enhancing sustainability of vegetation ecosystems through ecological engineering: A case study in the Qinghai-Tibet Plateau

Ecological engineering is an important measure to promote ecosystem adaptation and restoration to deal with environmental change and human disturbance. To assess the effectiveness of ecological construction and analyze the influencing factors of ecosystem changes in the Qinghai-Tibet Plateau (QTP), this study detected the spatial changes and dynamic hotspots of vegetation ecosystems in the ecological construction regions of the QTP (QTPE) and regions without ecological construction (QTPWE) using hot spot analysis and comprehensive dynamic degree model. Then the random forest (RF) model and geographical weighted regression model were used to study the degree and spatial heterogeneity of impacts of climate and human activities on normalized difference vegetation index (NDVI). Results showed that the vegetation restoration of the QTPE was obvious during 2001-2018 as the area of the increasing NDVI accounted for 74.15%. In addition, the effects of climate and human activities on NDVI of vegetation ecosystem showed significant spatial heterogeneity. The RF model showed that population density was the most significant factor affecting ecosystem vegetation in the QTPE, and its relative importance was between 26.1-32.6%, followed by downward shortwave radiation (7.9-16.8%). However, climate factors still had the greatest impact in the QTPWE, with the relative importance of precipitation and temperature being 45% and 15%, respectively. These findings provide a scientific basis for the restoration and management of vegetation on the QTP, and are of great significance for the deployment of future ecological engineering projects.

The Carbon Sink Potential of Southern China After Two Decades of Afforestation

Afforestation and land use changes that sequester carbon from the atmosphere in the form of woody biomass have turned southern China into one of the largest carbon sinks globally, which contributes to mitigating climate change. However, forest growth saturation and available land that can be forested limit the longevity of this carbon sink, and while a plethora of studies have quantified vegetation changes over the last decades, the remaining carbon sink potential of this area is currently unknown. Here, we train a model with multiple predictors characterizing the heterogeneous landscapes of southern China and predict the biomass carbon carrying capacity of the region for 2002-2017. We compare observed and predicted biomass carbon density and find that during about two decades of afforestation, 2.34 PgC have been sequestered between 2002 and 2017, and a total of 5.32 Pg carbon can potentially still be sequestrated. This means that the region has reached 73% of its aboveground biomass carbon carrying capacity in 2017, which is 12% more than in 2002, equal to a decrease of 0.77% per year. We identify potential afforestation areas that can still sequester 2.39 PgC, while old and new forests have reached 87% of their potential with 1.85 PgC remaining. Our work locates areas where vegetation has not yet reached its full potential but also shows that afforestation is not a long-term solution for climate change mitigation.

Impact of climate on vegetation in Pindari watershed of Western Himalayas, Kumaun, India, using spatiotemporal analysis: 1972-2018

Vegetation dynamics is an important aspect for determining climate change trends. The present study delineates to examine spatiotemporal changes of vegetation cover in Pindari valley (Kumaun Himalaya) from the 1972 to 2018 timeline. The study includes the calculation of vegetation spectral indices of normalized vegetation index (NDVI), extraction of different vegetation classes, and statistical analysis of the Mann-Kendall (MK) test on historical metrological data (especially precipitation and temperature) of the study site. For the statistical analysis of metrological data, the power data access viewer datasets have been used. The central feature classes of the study are grassland, scrubland, and forest cover. The results revealed that the region's forest cover significantly decreased by 24.74 sq. km from 1972 to 2018, increased in grassland cover by 17.84 sq. km, respectively, and a slight increase in scrubland class by 3.13 sq. km for the study period. The calculated NDVI shows significant changes over the study location; it has been noticed that the maximum values of the NDVI decreased by 0.24, and the minimum values show growth of about 0.047. The analysis indicates that climatic parameters such as precipitation and temperature are the main limiting factors affecting vegetation growth. The annual mean maximum temperature showed a decreasing trend. The estimated results show an increase in annual rainfall and annual minimum temperature, while the decreasing trend is observed in the case of maximum annual temperature. Objectives of the study are (1) spatiotemporal analysis of the vegetation cover, (2) identification of the main causes of change in the vegetation cover, and (3) statistical trend analysis of long-term metrological data. The outcome of the presented research work would be beneficial for the proper management and monitoring of the forest ecosystem.

Processes and mechanisms of vegetation ecosystem responding to climate and ecological restoration in China

Vegetation is an essential component of the earth's surface system and its dynamics is a clear indicator of global climate change. However, the vegetation trends of most studies were based on time-unvarying methods, cannot accurately detect the long-term nonlinear characteristics of vegetation changes. Here, the ensemble empirical mode decomposition and the Breaks for Additive Seasonal and Trend algorithm were applied to reconstruct the the normalized difference vegetation index (NDVI) data and diagnose spatiotemporal evolution and abrupt changes of long-term vegetation trends in China during 1982-2018. Residual analysis was used to separate the influence of climate and human activities on NDVI variations, and the effect of specific human drivers on vegetation growth was obtained. The results suggest that based on the time-varying analysis, high vegetation browning was masked by overall vegetation greening. Vegetation growth in China experienced an abrupt change in the 1990s and 2000s, accounting for 50% and 33.6% of the whole China respectively. Of the area before the breakpoint, 45.4% showed a trend of vegetation decrease, which was concentrated mainly in east China, while 43% of the area after the breakpoint also showed vegetation degradation, mainly in northwest China. Climate was an important driving force for vegetation change in China. It played a positive role in south China, but had a negative effect in northwest China. The impact of human activities on vegetation growthchanged from an initial negative influence to a positive one. In terms of human activities, an inverted-U-shaped relation was detected between CO2 emissions and vegetation growth; that is, the fertilization effect of CO2 had a certain threshold. Once that threshold was exceeded, it would hinder vegetation growth. Population density had a slight constraint on vegetation growth, and the implementation of ecological restoration projects (e.g., the Grain for Green Program) can promote vegetation growth to a certain extent.

Ecological restoration programs dominate vegetation greening in China

Many ecological restoration programs have been implemented in China during the last two decades. At the same time, the vegetation has turned green significantly in China. However, few studies have directly evaluated the contribution of the ecological restoration programs to vegetation greening in comparison with the contribution of climate change using high-resolution data of afforestation areas at the national scale. We used newly compiled high-resolution data on yearly forest plantation and mountain closure, the daily climate data from the 2480 meteorological stations and GIMMS 3g NDVI data. We used a multiple linear regression model to compare the influence of temperature, precipitation, and ecological restoration programs on NDVI dynamics. We then used the hierarchical variance partitioning method to evaluate the relative contribution of temperature, precipitation, and ecological restoration programs on NDVI dynamics. We found a significant greening trend in China from 1999 to 2015 with an annual increase rate of 0.0017 yr^-1 in the mean growing season NDVI. The ecological restoration programs dominated the vegetation greening in northern China and the southern coastal regions, indicating a good performance of restoration programs in these regions. In contrast, temperature or precipitation dominated the vegetation greening in southwestern China, Inner Mongolia and the implementation regions of several ecological restoration programs in northeastern China. Among the ecological restoration programs except the Three-North Shelterbelt Forest Program, the effect of ecological restoration programs on vegetation greening was stronger than the total effects of temperature and precipitation changes. Our study presents a systematic assessment on the contribution of ecological restoration programs to the vegetation greening in China, accessed the role on vegetation greening of different ecosystem restoration programs. We analyzed the reasons for the differences in the contribution of different ecological restoration programs to vegetation greening and provided insights facilitating policy makers to prioritize future restoration planning.

Analysing the spatio-temporal patterns of vegetation dynamics and their responses to climatic parameters in Meghalaya from 2001 to 2020

Quantification of the spatio-temporal trends in vegetation dynamics and its drivers is crucial to ensure sustainable management of ecosystems. The north-eastern state of Meghalaya possessing an idiosyncratic climatic regime has been undergoing tremendous pressure in the past decades considering the recent climate change scenario. A robust trend analysis has been performed using the MODIS NDVI (MOD13Q1) data (2001-2020) along with multi-source gridded climate data (precipitation and temperature) to detect changes in the vegetation dynamics and corresponding climatic variables by employing the Theil-Sen Median trend test and Mann-Kendall test (τ). The spatial variability of trends was gauged with respect to 7 major forest types, administrative boundaries and different elevational gradients found in the area. Results revealed a large positive inter-annual trend (85.48%) with a minimal negative trend (14.52%) in the annual mean NDVI. Mean Annual Precipitation presents a negative trend in 66.97% of the area mainly concentrated in the eastern portion of the state while the western portion displays a positive trend in about 33.03% of the area. Temperature exhibits a 98% positive trend in Meghalaya. Pettitt Change Point Detection revealed three major breakpoints viz., 2010, 2012 and 2014 in the NDVI values from 2001 to 2020 over the forested region of Meghalaya. A consistent future vegetation trend (87.78%) in Meghalaya was identified through Hurst Exponent. A positive correlation between vegetation and temperature was observed in about 82.81% of the area. The western portion of the state was seen to reflect a clear correlation between NDVI and rainfall as compared to the eastern portion where NDVI is correlated more with temperature than rainfall. A gradual deviation of rainfall towards the west was identified which might be feedback of the increasing significant greening observed in the state in the recent decades. This study, therefore, serves as a decadal archive of forest dynamics and also provides an insight into the long-term impact of climate change on vegetation which would further help in investigating and projecting the future ecosystem dynamics in Meghalaya.

Vegetation patterns governing the competitive relationship between runoff and evapotranspiration using a novel water balance model at a semi-arid watershed

Vegetation patterns play an important role in precipitation partitioning into hydrological components, especially evapotranspiration and runoff. However, few studies focus on their competitive relationship and the influence of the vegetation on them. In this study, a vegetation threshold was postulated to prevent further decrease of runoff by determining a new hydrological component continuing evapotranspiration (partitioned from total and initial evapotranspiration) through a novel model coupled with the Budyko model (dimensional form) and two-stage partitioning model (nondimensional form) in the semi-arid watershed. The results showed significant correlations between model parameters ε (underlying surface index), λ (ratio of initial evapotranspiration) and vegetation coverage (M) (R² = 0.95 and 0.97, p < 0.01) b Based on the modified Budyko model and λ. The nondimensional model showed high-precise estimations of K_H (Horton index Fraction), K_B (Baseflow Fraction), K_V (evapotranspiration Fraction), K_R (runoff Fraction), and K_C (continuing evapotranspiration Fraction) (R² > 0.98, p < 0.01) as a function of a new aridity index φ. K_H, K_B, K_V, K_R, showed symmetrical patterns correlated with φ both at between-subwatershed and between-year scale based on the dimensional model and λ. However, K_C showed asymmetrical different correlation with M³ and φ (K_C/M³ ∼ φ: in between-subwatershed: R² = 0.92, p < 0.01; and between-year scale: R² = 0.74, p < 0.01). Based on the solution of continuing evapotranspiration, the vegetation threshold has been solved with M = 0.73 (+0.09/-0.02) to prevent further decreasing runoff. The framework presented can be applied in other semi-arid watersheds worldwide to better protect the sustainability of the hydro-ecosystems.

Vegetation Changing Patterns and Its Sensitivity to Climate Variability across Seven Major Watersheds in China

Climate changes have profound impacts on vegetation and further alter hydrological processes through transpiration, interception, and evaporation. This study investigated vegetation's changing patterns and its sensitivity to climate variability across seven major watersheds in China based on a hybrid regionalization approach and a novel, empirical index-Vegetation Sensitivity Index (VSI). Vegetation showed linearly increasing trends in most of the seven watersheds, while decreases in vegetation were mostly found in the source regions of the Yangtze River Basin (YZRB) and Yellow River Basin (YRB), the forest and grassland areas of the Songhua River Basin (SHRB) and Liao River Basin (LRB), the Yangtze River Delta, and the Pearl River Delta during the growing season. The selected watersheds can be categorized into 11 sub-regions, and the regionalization result was consistent with the topography and vegetation types; the characteristics of vegetation dynamics were more homogeneous among sub-regions. Vegetation types such as forests and shrubland in the central parts of the YZRB were relatively more vulnerable to climate variations than the grasslands and alpine meadows and tundra (AMT) in the source regions of the YZRB and YRB and the Loess Plateau of the YRB. In arid and semi-arid regions, precipitation had a profound impact on vegetation, while, at low latitudes, solar radiation was the main controlling factor. Such comprehensive investigations of the vegetation-climate relationship patterns across various watersheds are expected to provide a foundation for the exploration of future climate change impacts on ecosystems at the watershed scale.