Effects of climate change and human activities on vegetation coverage change in northern China considering extreme climate and time-lag and -accumulation effects

Time-Lag of Seasonal Effects of Extreme Climate Events on Grassland Productivity Across an Altitudinal Gradient in Tajikistan

Mountain grassland ecosystems around the globe are highly sensitive to seasonal extreme climate events, which thus highlights the critical importance of understanding how such events have affected vegetation dynamics over recent decades. However, research on the time-lag of the effects of seasonal extreme climate events on vegetation has been sparse. This study focuses on Tajikistan, which is characterized by a typical alpine meadow-steppe ecosystem, as the research area. The net primary productivity (NPP) values of Tajikistan's grasslands from 2001 to 2022 were estimated using the Carnegie-Ames-Stanford Approach (CASA) model. In addition, 20 extreme climate indices (including 11 extreme temperature indices and 9 extreme precipitation indices) were calculated. The spatiotemporal distribution characteristics of the grassland NPP and these extreme climate indices were further analyzed. Using geographic detector methods, the impact factors of extreme climate indices on grassland NPP were identified along a gradient of different altitudinal bands in Tajikistan. Additionally, a time-lag analysis was conducted to reveal the lag time of the effects of extreme climate indices on grassland NPP across different elevation levels. The results revealed that grassland NPP in Tajikistan exhibited a slight upward trend of 0.01 gC/(m2·a) from 2001 to 2022. During this period, extreme temperature indices generally showed an increasing trend, while extreme precipitation indices displayed a declining trend. Notably, extreme precipitation indices had a significant impact on grassland NPP, with the interaction between Precipitation anomaly (PA) and Max Tmax (TXx) exerting the most pronounced influence on the spatial variation of grassland NPP (q = 0.53). Additionally, it was found that the effect of extreme climate events on grassland NPP had no time-lag at altitudes below 500 m. In contrast, in mid-altitude regions (1000-3000 m), the effect of PA on grassland NPP had a significant time-lag of two months (p < 0.05). Knowing the lag times until the effects of seasonal extreme climate events on grassland NPP will appear in Tajikistan provides valuable insight for those developing adaptive management and restoration strategies under current seasonal extreme climate conditions.

Spatiotemporal changes of vegetation in the northern foothills of Qinling Mountains based on kNDVI considering climate time-lag effects and human activities

Vegetation is fundamental to regulating the climate system and ensuring carbon balance. Recognizing the effects of climate change (CC) and human activities (HA) is vital for understanding shifts in vegetation. However, climate time-lag effects are rarely measured, resulting in an inadequate assessment of CC's effects on vegetation dynamics. In this study, firstly, based on the Landsat image dataset, the spatiotemporal variations of the kernel Normalized Difference Vegetation Index (kNDVI) in the northern foothills of the Qinling Mountains (NQLM) from 1986 to 2022 were analyzed. Then, the multiple regression residuals method, accounting for time-lag effects, was employed to determine the effects of CC and HA on kNDVI change. Finally, six patterns of kNDVI change were obtained based on the kNDVI trend and the changes of CC and HA to kNDVI. Our research found: (1) Over the past 37 years, the vegetation has fluctuated upward at a rate of 0.0061/a, and most areas have experienced significant greening (84.82%) in the NQLM. Only 0.86% of the area has experienced vegetation degradation, and the stability of vegetation has been maintained. (2) The kNDVI exhibited a positive correlation with both precipitation and temperature, kNDVI response to precipitation with 1-month time lag and 0-month for temperature. (3) The contribution of CC to kNDVI change was 84%, temperature and precipitation drive kNDVI change rates with 0.0012/a and 0.0039/a, respectively. The contribution of HA to kNDVI change was only 16%. While the role of HA cannot be overlooked, these findings underscore the critical influence of CC on vegetation changes. (4) Among the six patterns of kNDVI change, CC and HA collectively contributed to kNDVI change, and the effect of CC alone was more significant than that of HA. These findings can help policymakers design more targeted interventions to enhance ecological resilience and support long-term environmental stability, which is critical for the development of informed, sustainable revegetation strategies in the NQLM.

Climate change enhances greening while human activities accelerate degradation in northern China's grasslands

Northern China's grasslands play a pivotal role in livestock production, energy utilization, and ecosystem balance, both domestically and globally. However, they exhibit pronounced temporal variability and marked spatial heterogeneity. Since most existing studies rely on single vegetation indices and regional-scale analyses, they may introduce biases in interpreting grassland dynamics and their underlying drivers. To address this gap, we integrated both functional and structural indices - Gross Primary Productivity (GPP), solar-Induced chlorophyll fluorescence (SIF), Normalized Difference Vegetation Index (NDVI), and Leaf Area Index (LAI) - to systematically investigate spatiotemporal trends across various grassland types in northern China. Using partial derivative analysis, we quantified the relative contributions of climate change and human activities to these observed vegetation trends. Results indicated that over 70 % of grassland areas, especially temperate grasslands, showed an overall increase in vegetation indices, while a decline was observed in the southwestern alpine grasslands. Climate change was the primary driver of grassland greening (56.55 %-63.83 %), primarily through increased precipitation in temperate grasslands and rising temperatures in alpine grasslands. Human activities contributed substantially to greening (36.17 %-43.45 %), especially in desertified temperate grasslands (e.g., Mu Us Sandy Land, Gansu, Ningxia, Xinjiang) and Qinghai alpine meadows, mainly through farmland restoration and desertification control. Conversely, human activities also served as the primary driver of grassland degradation (51.70 %-69.64 %) in certain alpine regions, where overgrazing and population growth - compounded by rising temperatures and declining soil moisture - led to significant vegetation losses. Moreover, 72.66 % of temperate grasslands demonstrated strong coupling between vegetation structure and function, whereas 57.59 % of alpine grasslands exhibited increasing GPP alongside declines in both LAI and SIF. Overall, these findings underscore the spatial heterogeneity of grassland responses to climatic and anthropogenic drivers, highlighting the necessity of employing multiple vegetation indices to guide targeted and effective grassland management strategies.

Investigation into the temporal impacts of drought on vegetation dynamics in China during 2000 to 2022

Quantifying vegetation's response to drought and understanding its mechanisms is crucial for mitigating the adverse effects of drought disasters. The asymmetric cumulative and lag effects of drought on vegetation growth are widespread, yet the responses of different vegetation types, climate zones, and elevations in China remain unclear. This study used the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) to analyze vegetation status and drought trends from 2000 to 2022, examining the differentiation and mechanisms of cumulative (CED) and lag effect of drought (LED) under various conditions. The main findings are as follows: (1) 85.1% of the study area is becoming greener, with an overall growth rate of 0.026 per decade. Annual drought levels fluctuate, with increasingly severe conditions in parts of southwestern and northwestern China. (2) CED affects 35.94% of vegetated areas, with 77.44% showing a positive correlation between SPEI and NDVI. Grasslands have the longest CED (5.90 months), while forests have the shortest (4.72 months). Temperate and Arid climate zones show higher CED, at 6.91 months and 6.77 months, respectively. The highest CED is found at elevations of 2000-2500 m (6.34 months), and the lowest at 3000-3500 m (4.28 months). (3) LED affects a larger area (39.22%) with an average duration of 6.42 months, greater than the average CED (5.56 months). Grasslands have the longest LED (7.72 months), while forests (6.78 months) and shrublands (6.48 months) are shorter. The Arid climate zone has the highest LED (8.35 months), and the Tropical zone the lowest (4.82 months). LED shows significant elevation differences, being smallest at low elevations (6.48 months). (4) Climate type and potential evapotranspiration explain 0.269 and 0.259 of CED, respectively. For LED, temperature and potential evapotranspiration are dominant (0.173 and 0.167). The combination of factors significantly enhances the explanatory power of temporal effects. (5) NDVI stability is negatively influenced by CED. This study enhances understanding of the vegetation-drought relationship in China and provides theoretical support for addressing drought risks under climate change.

Spatiotemporal evolution and driving factors analysis of fractional vegetation coverage in the arid region of northwest China

The arid region of northwest China (ARNC) is the most ecologically fragile region in China, and is characterized by harsh natural conditions, severe soil erosion, and poor soil fertility. Understanding long-term vegetation changes in this region is critical for effective environmental monitoring and climate change adaptation. Fractional vegetation coverage (FVC) is a key parameter for characterizing the ecological conditions of the ARNC. However, the reliance on low-resolution FVC and NDVI data due to the lack of medium-resolution data has limited our understanding of the environmental dynamics in this region. Therefore, this study addressed this gap by utilizing Landsat data to generate FVC data, enabling a detailed investigation of the spatial-temporal variations and driving factors of vegetation in the ARNC from 2000 to 2020. The results indicated the following: (1) The FVC was generally low, with an average of 0.191. The FVC was greater in the northwest and lower in the southeast in terms of spatial distribution features. The trend of FVC change in ARNC showed significant spatial variability, with degradation outweighing improvement. (2) The coefficient of variation of FVC was 0.377, indicating significant temporal fluctuations, with more stable conditions in the northwest than in the southeast. (3) The spatial differentiation of the FVC in ARNC was primarily driven by land cover types, evapotranspiration, and precipitation, with explanatory powers exceeding 30 % each. This study is significant because it provides a comprehensive understanding of vegetation dynamics in one of China's most vulnerable regions, offering critical insights for ecological restoration, desertification control, and sustainable development. The findings underscore the importance of targeted ecological governance to address the challenges posed by environmental degradation in the ARNC.

Strong positive direct impact of soil moisture on the growth of central asian grasslands

As the issue of global climate change becomes increasingly prominent, the grassland ecosystems in Central Asia are facing severe challenges posed by the impacts of climate change. However, the dominant factors, impact pathways, and cumulative and time-lagged effects of climate factors on various grassland indices remain to be explored. This study selected data from 1988 to 2019, including Fractional Vegetation Cover (FVC), Leaf Area Index (LAI), Net Primary Productivity (NPP), and Vegetation Optical Depth (VOD), to characterize grassland coverage, greenness, biomass accumulation, and water content features. Utilizing multiple linear regression, path analysis, and correlation analysis, this study investigated the dominant effects, direct impacts, indirect influences, and cumulative and time-lagged effects of climate factors on various grassland indices from spatial and climatic zone perspectives. The research findings indicate that over time, the grassland FVC and NPP exhibited increasing trends, while the LAI and VOD showed decreasing trends. Grassland indices are primarily influenced by precipitation and soil moisture (SM). The direct impact of SM on grassland indices was higher than precipitation. Vapour pressure deficit (VPD) has a direct negative impact on grassland indices. Grassland indices are subject to positive indirect effects from precipitation via SM and negative indirect effects from VPD via SM. Precipitation and SM mainly exhibited no cumulative and time-lagged effects on the impact of grassland VOD. VPD primarily demonstrated cumulative and time-lagged effects on grassland indices. The research findings offer valuable insights for conserving grassland ecosystems in Central Asia, as well as for shaping socioeconomic strategies and formulating climate policies.

Influences of climatic variation and human activities on vegetation photosynthesis dynamics in Southwest China

Photosynthesis is a direct route for carbon sequestration in vegetation, and is influenced by climatic variation (CV) and human activities (HA). Therefore, a quantitative assessment of their influence on vegetation photosynthesis dynamics is pivotal for formulating effective carbon neutrality strategies. Herein, based on the solar-induced chlorophyll fluorescence index (SIF), which reflects the vegetation photosynthesis intensity, and TerraClimate meteorological data, we refined the residual trend approach by incorporating more climatic variables and their time effects on vegetation to assess the influences of CV and HA on SIF dynamics in Southwest China. Our results revealed that an increasing rate of vegetation SIF across Southwest China of 0.0312 Wm-2μm-1sr-1/10a (p < 0.001) from 2000 to 2019, and over 90% of the region exhibited an increase in SIF. The influence of CV on SIF dynamics had time effects, including time-cumulative and time-lag effects. Nevertheless, these effects varied by climatic variables and vegetation types. The variable importance in projection demonstrated that temperature was the primary factor influencing SIF dynamics, followed by precipitation, potential evapotranspiration, downward surface solar radiation, vapor pressure deficit, and wind speed. Furthermore, both CV and HA collectively enhanced the vegetation photosynthesis intensity in the region, HA was the main driver of the SIF increase, contributing 0.0239 Wm-2μm-1sr-1/10a, while CV accounted for 0.0073 Wm-2μm-1sr-1/10a. Overall, we refined the previous residual trend approach and provided a new way for quantitatively assessing the influences of CV and HA on vegetation photosynthetic intensity.

Night lights observations significantly improve the explainability of intra-annual vegetation growth globally

Understanding the underlying mechanism of vegetation growth is of great significance to improve our knowledge of how vegetation growth responds to its surrounding environment, thereby benefiting the prediction of future vegetation growth and guiding environmental management. However, human impacts on vegetation growth, especially its intra-annual variability, still represent a knowledge gap. Night Lights (NL) have been demonstrated as an effective indicator to characterize human activities, but little is known about the potential improvement of intra-annual vegetation growth using seasonal NL observations. To address this gap, we investigated and quantified the explainability improvement of intra-annual vegetation growth by establishing a multiple linear regression model for vegetation growth (indicated by Normalized Difference Vegetation Index, NDVI) with human factor (indicated by NL observations here) and three climatic factors, i.e., temperature, water availability, and solar radiation using the Principal Components Regression (PCR) method. Results indicate that NL observations significantly improve our understanding of intra-annual vegetation growth globally. Model explainability, i.e., adjusted R2 metric of the PCR model, was comparatively improved by 54 % on average with a median value of 11 % when taking NL observations into consideration. Such improvement occurred in 82 % of the whole investigation pixels. We found that the improvement of model explanatory power was significant in regions where both NL and NDVI trends were large, except for the case where both of their trends were negative. At the country-level, the improvement of model explanatory power increases as GDP decreases, illustrating a greater improvement in a lower middle-income country than that in a high-income country. Our findings emphasize the importance of considering human activities (indicated by NL here) in vegetation growth, offering novel insights into the explanation of intra-annual vegetation growth.

Impact of high temperature and drought stress on the microbial community in wolf spiders

High temperatures and drought present significant abiotic challenges that can limit the survival of many arthropods, including wolf spiders, which are ectothermic and play a crucial role in controlling pest populations. However, the impact of these stress factors on the microbiota of spiders remains poorly understood. In this study, we utilized 16 S rRNA gene sequencing to explore the diversity and composition of bacterial communities within Pardosa pseudoannulata under conditions of high temperature and drought stress. We found that Firmicutes, Bacteroidetes, and Proteobacteria were the predominant bacterial phyla present. Analyses of alpha diversity indicated an increase in bacterial diversity under combined stress conditions, as reflected by various diversity indices such as Ace, Chao1, Shannon, and Simpson. Furthermore, co-occurrence network analysis highlighted intricate interactions among the microbial taxa (e.g., Enterobacter, Chitinophaga, and Eubacterium), revealing the adaptive complexity of the spider's microbiome to environmental stress. Functional prediction analysis suggested that combined stress conditions might enhance key metabolic pathways, particularly those related to oxidative phosphorylation and amino acid metabolism. Using Random Forest analysis, we determined that changes in three heat shock proteins were largely attributed to variations in bacterial communities, with Firmicutes being notably influential. Collectively, this in-depth analysis offers novel insights into the responses of microbial communities within spider microbiomes to combined abiotic stresses, providing valuable information for understanding extreme climate impacts and informing ecological management strategies.

Nonlinear time effects of vegetation response to climate change: Evidence from Qilian Mountain National Park in China

Vegetation responses to climate change are typically nonlinear with varied time effects, yet current research lacks comprehensiveness and precise definitions, hindering a deeper understanding of the underlying mechanisms. This study focuses on the mountain-type Qilian Mountain National Park (QMNP), investigating the characteristics and patterns of these nonlinear time effects using a generalized additive model (GAM) based on MODIS-NDVI, growing season temperature, and precipitation data. The results show that 1) The time effects of climate change on vegetation exhibit significant spatial variations, differing across vegetation types and topographic conditions. Accounting for optimal time effects can increase the explanatory power of climate on vegetation change by 6.8 %. Precipitation responses are mainly characterized by time-lag and time-accumulation effects, notably in meadows and steppes, while temperature responses are largely cumulative, especially in steppes. The altitude and slope significantly influence the pattern of vegetation response to climate, particularly in areas with high altitudes and steep slopes. 2) There is a significant nonlinear relationship between vegetation growth and both precipitation and temperature, with the nonlinear relationship between precipitation and vegetation being stronger than that with temperature, particularly in the western and central regions of the park. Different vegetation types exhibit significant variations in their response to climate change, with deserts and steppes being more sensitive to precipitation. 3) Precipitation is the primary driver of vegetation change in the QMNP, particularly for high-elevation vegetation and herbaceous vegetation. The complex temporal patterns of vegetation response to climate change in the QMNP not only deepen the understanding of the intricate relationship between regional vegetation and climate variability but also provide a methodological reference for global studies on vegetation responses to climate change.

Restoration of vegetation in the Yellow River Basin of Inner Mongolia is limited by geographic factors

Studying the relationships between vegetation cover and geography in the Mongolian region of the Yellow River Basin will help to optimize local vegetation recovery strategies and achieve harmonious human relations. Based on MOD13Q1 data, the spatial and temporal variations in fractional vegetation cover (FVC) in the Mongolian Yellow River Basin during 2000-2020 were investigated via trend and correlative analysis. The results are as follows: (1) From 2000 to 2020, the vegetation cover in the Mongolian section of the Yellow River Basin recovered well, the mean increase in the FVC was 0.001/a, the distribution of vegetation showed high coverage in the southeast and low coverage in the northwest, and 31.19% of the total area showed an extremely significant and significant increase in vegetation cover. (2) The explanatory power of each geographic factor significantly differed. Precipitation, soil type, air temperature, land use type and slope were the main driving factors influencing the spatial distribution of the vegetation cover, and for each factor, the explanatory power of its interaction with other factors was greater than that of the single factor. (3) The correlation coefficients between FVC and temperature and precipitation are mainly positive. The mean value of the FVC and its variation trend are characterized by differences in terrain and soil characteristics, population density and land use. Land use conversion can reflect the characteristics of human activities, and positive effects, such as returning farmland to forest and grassland and afforestation of unused land, promote the significant improvement of regional vegetation, while negative effects, such as urban expansion, inhibit the growth of vegetation.

Evaluating the relative influence of climate and human activities on recent vegetation dynamics in West Bengal, India

Assessing the relative importance of climate change and human activities is important in developing sustainable management policies for regional land use. In this study, multiple remote sensing datasets, i.e. CHIRPS (Climate Hazard Group InfraRed Precipitation with Station Data) precipitation, MODIS Land Surface Temperature (LST), Enhanced Vegetation Index (EVI), Potential Evapotranspiration (PET), Soil Moisture (SM), WorldPop, and nighttime light have been analyzed to investigate the effect that climate change (CC) and regional human activities (HA) have on vegetation dynamics in eastern India for the period 2000 to 2022. The relative influence of climate and anthropogenic factors is evaluated on the basis of non-parametric statistics i.e., Mann-Kendall and Sen's slope estimator. Significant spatial and elevation-dependent variations in precipitation and LST are evident. Areas at higher elevations exhibit increased mean annual temperatures (0.22 °C/year, p < 0.05) and reduced winter precipitation over the last two decades, while the northern and southwest parts of West Bengal witnessed increased mean annual precipitation (17.3 mm/year, p < 0.05) and a slight cooling trend. Temperature and precipitation trends are shown to collectively impact EVI distribution. While there is a negative spatial correlation between LST and EVI, the relationship between precipitation and EVI is positive and stronger (R2 = 0.83, p < 0.05). Associated hydroclimatic parameters are potent drivers of EVI, whereby PET in the southwestern regions leads to markedly lower SM. The relative importance of CC and HA on EVI also varies spatially. Near the major conurbation of Kolkata, and confirmed by nighttime light and population density data, changes in vegetation cover are very clearly dominated by HA (87%). In contrast, CC emerges as the dominant driver of EVI (70-85%) in the higher elevation northern regions of the state but also in the southeast. Our findings inform policy regarding the future sustainability of vulnerable socio-hydroclimatic systems across the entire state.

Spatial and temporal variation of net primary productivity of herbaceous marshes and its climatic drivers in China

Herbaceous marshes are widely distributed in China and are vital to regional ecological security and sustainable development. Vegetation net primary productivity (NPP) is a vital indicator of vegetation growth. Climatic change can significantly affect NPP, but variations in NPP of herbaceous marsh and their responses to climate change in China remain unclear. Using meteorological data and MODIS NPP data during 2000-2020, this study analyzed the spatial and temporal variations of NPP and their responses to climate change in Chinese herbaceous marshes. We found that the annual NPP of herbaceous marshes in China increased significantly at a rate of 3.34 g C/m2/a from 2000 to 2020, with an average value of 336.60 g C/m2. The increased annual total precipitation enhanced the national average NPP, whereas annual mean temperature had no significant effect on the national average NPP. Regionally, precipitation had a significant positive effect on the NPP in temperate semi-arid and arid and temperate semi-humid and humid marsh regions. For the first time, we discovered asymmetry effects of daytime and nighttime temperatures on NPP in herbaceous marshes of China. In temperate humid and semi-humid marsh regions, increased summer daytime temperature decreased the NPP while increased summer nighttime temperature increased the NPP. In the Tibetan Plateau, increased autumn daytime temperature, as well as summer daytime and nighttime temperatures could increase the NPP of herbaceous marshes. This study highlights the different influences of seasonal climate change on the NPP of herbaceous marshes in China and indicates that the differential effects of daytime and nighttime temperatures should be considering in simulating the NPP of herbaceous marshes in terrestrial ecosystem models, especially under the background of global asymmetric diurnal warming.

Analysis of spatial and temporal changes in vegetation cover and its drivers in the Aksu River Basin, China

Exploring vegetation dynamics in arid areas and their responses to different natural and anthropogenic factors is critical for understanding ecosystems. Based on the monthly MOD13Q1 (250 m) remote sensing data from 2000 to 2019, this study analyzed spatio-temporal changes in vegetation cover in the Aksu River Basin and predicted future change trends using one-dimensional linear regression, the Mann-Kendall test, and the Hurst index. Quantitative assessment of the magnitude of anthropogenic and natural drivers was performed using the Geodetector model. Eleven natural and anthropogenic factors were quantified and analyzed within five time periods. The influence of the driving factors on the changes in the normalized difference vegetation index (NDVI) in each period was calculated and analyzed. Four main results were found. (1) The overall vegetation cover in the region significantly grew from 2000 to 2019. The vegetation cover changes were dominated by expected future improvements, with a Hurst index average of 0.45. (2) Land use type, soil moisture, surface temperature, and potential vapor dispersion were the main drivers of NDVI changes, with annual average q-values above 0.2. (3) The driving effect of two-factor interactions was significantly greater than that of single factors, especially land use type interacts with other factors to a greater extent on vegetation cover. (4) The magnitude of the interaction between soil moisture and potential vapor dispersion and the magnitude of the interaction between anthropogenic factors and other factors showed an obvious increasing trend. Current soil moisture and human activities had a positive influence on the growth of vegetation in the area. The findings of this study are important for ecological monitoring and security as well as land desertification control.

Alpine grassland greening on the Northern Tibetan Plateau driven by climate change and human activities considering extreme temperature and soil moisture

Alpine grassland is among the world's most vulnerable ecosystems, characterized by a high sensitivity to climate change (CC) and human activities (HA). Quantifying the relative contributions of CC and HA to grassland change plays a crucial role in safeguarding grassland ecological security and devising sustainable grassland management strategies. Although there were adequate studies focusing on the separate impacts of CC and HA on alpine ecosystem, insufficient attention has been given to investigating the effects of extreme temperatures and soil moisture. In this study, the spatiotemporal variations of alpine grassland were analyzed based on MODIS NDVI during the growing season from 2000 to 2020 in Naqu, using partial least squares regression and residual analysis methods to analyze the importance of climate factors and the impacts of CC and HA on grassland change. The results show that the NDVI during the growing season in Naqu exhibited an increasing trend of 0.0046/10a. At the biome scale, the most significant and rapid increase was observed in alpine desert and alpine desert grassland. Extreme temperature and soil moisture (SM) exerted a more significant importance on alpine grassland at whole scale. SM always showed a significant importance at biome and grid scale. The contributions of CC and HA to the change during the growing season were calculated as 0.0032/10a and 0.0015/10a, respectively, accounting for 68.05 % and 31.05 %. CC dominated the increase in NDVI during the growing season; HA contributed positively to NDVI in most areas of Naqu. The results are expected to enhance our understanding of grassland variations under CC and HA and provide a scientific basis for future ecological conservation in alpine regions.

Impacts of climate change on fractional vegetation coverage of temperate grasslands in China from 1982 to 2015

The vegetation coverage of temperate grasslands in China has changed substantially due to climate change during the past decades, which significantly affects the function of grassland ecosystems. To appropriately carry out adaptive management and protect temperate grassland vegetation, it is important to understand the variations in fractional vegetation coverage (FVC) of China's temperate grasslands and how they are responding to climate change. Using the GIMMS NDVI and climatic datasets, this study explored the dynamics of FVC and their climatic drivers across the temperate grassland region of China during 1982∼2015. The results showed that the growing season mean FVC increased by 0.12% per year during 1982∼2015. The increases in precipitation and minimum temperature in the growing-season (especially in spring) could enhance the FVC of various vegetation types. In summer, the FVC of temperate steppe and desert steppe could drastically increase with increasing precipitation. In addition, this study found that the impacts of daytime and night-time warming on the FVC of temperate grasslands were asymmetric. Daytime warming can moderately increase FVC of temperate grasslands, while night-time warming could significantly increase it. Furthermore, the increase in summer daytime and night-time temperatures leads to a weak decrease and a moderate increase in FVC, respectively. This asymmetric effect was more evident for the temperate steppe and desert steppe in the central area. In autumn, the temperatures increase had significant positive impacts on the FVC of temperate meadows and steppes. This study highlights the differences in the impacts of climate change at different time scales on the FVC of grasslands with various vegetation types, and indicates that the asymmetric influences of daytime and night-time temperatures in different seasons on FVC must be included in calculating the vegetation coverage of China's temperate grasslands. The results could provide information for maintaining grassland ecosystem functions and managing environmental systems.

Assembly of typical steppe community and functional groups along the precipitation gradient from 1985 to 2022

Long-term observations have shown that structure and function of grasslands have changed due to climate change over the past decades. However, little is known about how grasslands respond to climate change along the precipitation gradient, and potential mechanisms remain elusive. Here, we utilize a long-term experiment in typical steppe to explore universal and differential mechanisms of community and functional groups assembly along the precipitation gradient. Our results indicated that the sensitivity of community and functional groups assembly to climate change was related to local precipitation. The strength of the positive effects of climate change on aboveground biomass, species richness, and their relationship of community decreased modestly with local precipitation. The mechanism behind this was the change in plant community composition of the precipitation-induced, annuals that was more responsive to climate change decreased as increased local precipitation. Furthermore, current and past climate both drove community and functional group assembly, and the role of past climate diminished with increasing local precipitation. Among them, climate fluctuation, average climate and current climate were the most critical climate indicators affecting community and functional groups assembly in low, medium and high precipitation sites, respectively. In conclusion, climatic change do not always exert identical effects on grasslands along the precipitation gradient. This could be critical importance for improving our ability to predict future changes in grassland ecosystems.

Shifts bidirectional dependency between vegetation greening and soil moisture over the past four decades in China

Soil moisture (SM) has changed significantly over the past 40 years in China, while NDVI has varied dramatically, leading to increasing regional conflict between vegetation growth and water resource use. Quantifying the bidirectional dependency between SM and NDVI is essential for understanding the balance between land vegetation and water resources. However, few studies have reported their mutual feedback and spatiotemporal bidirectional dependency. This paper aims to reveal the bidirectional dependency between SM and NDVI using Granger causality test to show spatiotemporal tendency coupling patterns through trend coupling analysis, wavelet transform, and lag correlation. The Results indicated that a coupling relationship existed between SM and NDVI over most of China. The unidirectional Granger effect between SM on NDVI was 58 %, the unidirectional Granger effect of NDVI on SM was 26 %, and the bidirectional Granger relationship between SM and NDVI was 16 %. The Granger relationship is different for different soil layers or land cover types. SM and NDVI increased together in 36 % of the land cover areas, but SM increased and NDVI decreased in 12 %, and the SM decreased and NDVI increased in 27 %. The trend coupling between SM and NDVI has spatial heterogeneity. There is no change rule of coupling relationship with drought variation, but SM and NDVI increased together with more overlapping ecological restoration projects. SM decreased with the increase of NDVI from 1982 to 2010 but has reversed since 2011. NDVI and SM co-increased significantly with the implementation of ecological restoration projects during 2011-2022. The coupling relationship has a time lag effect of 1-3 months, and the time lag of NDVI to SM of deep soil layers mainly occurred in Southern China. This study illustrated the coupling framework and feedback analysis between SM and vegetation greening, which is helpful for the scientific implementing ecological restoration projects and the management of ecosystem carbon and water cycles.

Land use significantly improved grassland degradation and desertification states in China over the last two decades

China possesses extensive grasslands primarily located in dryland regions, which are highly susceptible and fragile to climate change and human intervention, making them prone to degradation and desertification. In recent decades, China has implemented numerous ecological projects to improve the states of ecosystems. Additionally, recent studies have revealed the critical roles of rising CO2 on dryland greening. However, it is still limited to understand the contributions of anthropogenic recovery and CO2 fertilization, as well as other climate factors, to the dynamics of grassland degradation and desertification in China. To address these gaps, we employed a two-step approach to differentiate between grassland degradation and desertification as distinct processes across the grasslands and sparsely vegetated lands in China. This involved assessing degradation within existing grassland areas and identifying the conversion of grasslands into desert regions. The study period of 2000-2020 was examined to determine the occurrence of grassland desertification, which suggested a significant decrease in the desertification area in China. Subsequently, the time series of the Enhanced Vegetation Index (EVI) during the growing season was analyzed to track vegetation dynamics. Since the beginning of the 21st century, a significant greening trend has been observed in approximately 97% of the study area. Furthermore, we quantified the effects of anthropogenic climate change (ACC), climate change, and land use change on grassland degradation and desertification in China. The analyses indicated that 50.7% of the observed vegetation changes in the study area from 2000 to 2020 were primarily driven by land use, followed by the effects of rising CO2, accounting for 42.1% of the changes. These findings provided some insights on developing regionally-targeted strategies for grassland conservation in China.

Quantifying climate variability and regional anthropogenic influence on vegetation dynamics in northwest India

To explore the spatio-temporal dynamics and mechanisms underlying vegetation cover in Haryana State, India, and implications thereof, we obtained MODIS EVI imagery together with CHIRPS rainfall and MODIS LST at annual, seasonal and monthly scales for the period spanning 2000 to 2022. Additionally, MODIS Potential Evapotranspiration (PET), Ground Water Storage (GWS), Soil Moisture (SM) and nighttime light datasets were compiled to explore their spatial relationships with vegetation and other selected environmental parameters. Non-parametric statistics were applied to estimate the magnitude of trends, along with correlation and residual trend analysis to quantify the relative influence of Climate Change (CC) and Human Activities (HA) on vegetation dynamics using Google Earth Engine algorithms. The study reveals regional contrasts in trends that are evidently related to elevation. An annual increasing trend in rainfall (21.3 mm/decade, p < 0.05), together with augmented vegetation cover and slightly cooler (-0.07 °C/decade) LST is revealed in the high-elevation areas. Meanwhile, LST in the plain regions exhibit a warming trend (0.02 °C/decade) and decreased in vegetation and rainfall, accompanied by substantial reductions in GWS and SM related to increased PET. Linear regression demonstrates a strongly significant relationship between rainfall and EVI (R2 = 0.92), although a negative relationship is apparent between LST and vegetation (R2 = -0.83). Additionally, increased LST in the low-elevation parts of the study area impacted PET (R2 = 0.87), which triggered EVI loss (R2 = 0.93). Moreover, increased HA resulted in losses of 25.5 mm GSW and 1.5 mm SM annually. The relative contributions of CC and HA are shown to vary with elevation. At higher elevations, CC and HA contribute respectively 85% and 15% to the increase in EVI. However, at lower elevations, reduced EVI is largely (79%) due to human activities. This needs to be considered in managing the future of vulnerable socio-ecological systems in the state of Haryana.

Considering Climatic Factors, Time Lag, and Cumulative Effects of Climate Change and Human Activities on Vegetation NDVI in Yinshanbeilu, China

Climate and human activities are the basic driving forces that control and influence the spatial distribution and change of vegetation. Using trend analysis, the Hurst index, correlation analysis, the Moran index, path analysis, residual analysis, and other methods, the effects of human activities and climate factors on vegetation change were analyzed. The results show that: (1) The research area's normalized difference vegetation index (NDVI) exhibited a substantial upward trend from 2001 to 2020, increasing at a rate of 0.003/a, and the vegetation cover was generally healthy. The generally constant NDVI region made up 78.45% of the entire area, and the grassland, cultivated land, and forest land showed the most visible NDVI aggregation features. (2) The Vegetation is mainly promoted by water and heat, particularly precipitation, have a major impact on plants, with the direct influence of precipitation on vegetation growth being much greater than the indirect effect through the temperature. (3) The trend of NDVI residuals showed obvious spatial variability, presenting a distribution characteristic of high in the south and low in the north. The results of this study can provide a basis for the scientific layout of ecological protection and restoration projects in the Yinshanbeilu area.