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Abstract The sensitivity of tree growth to precipitation regulates their responses to drought, and is a crucial metric for predicting ecosystem dynamics and vulnerability. Sensitivity may be changing with continuing climate change, yet a comprehensive assessment of its change is still lacking. We utilized tree ring measurements from 3,044 sites, climate data and CO2 concentrations obtained from monitoring stations, combined with dynamic global vegetation models to investigate spatiotemporal changes in the sensitivity over the past century. We observed an increasing sensitivity since around 1950. This increased sensitivity was particularly pronounced in arid biomes due to the combined effect of increased precipitation and elevated CO2. While elevated CO2 reduced the sensitivity of the humid regions, the intensified water pressure caused by decreased precipitation still increased the sensitivity. Our findings suggest an escalating vulnerability of tree growth to precipitation change, which may increase the risk of tree mortality under future intensified drought.

Abstract Ecosystem‐scale photosynthetic efficiency (EPE) is proposed as an effective indicator to quantify gross primary productivity (GPP), but how the coupling between EPE and GPP varies as vegetation resilience decreases has not been evaluated. Here, we quantified forest resilience with optimized Bayesian models. With the use of multisource satellite and modeling data, our study revealed that forests on the Loess Plateau and in the Qinba Mountains in China are experiencing rapid resilience loss and are already facing mortality warnings after 2010. Reductions in resilience also drove the marked decoupling of GPP from EPE. Notably, the decline in resilience was accompanied by a decrease in EPE in about 74% of the forests while GPP increased. The mechanism underlying this decoupling could be attributed to enhanced atmospheric water demand and soil water constraints. The dynamic relationships found here could help to improve forest mortality models and enhance photosynthesis‐based GPP evaluation.

Enhancing forest cover is important for effective climate change mitigation. Studies suggest that drylands are promising areas for expanding forests, but conflicts arise with increased forest area and water consumption. Recent tree mortality in drylands raises concerns about carbon sequestration potential in tree plantations. Using Chinese dryland tree plantations as an example, we compared their growth with natural forests. Our results suggested plantation trees grew 1.6–2.1 times faster in juvenile phases, significantly shortening time to maturity (13.5 vs. 30 years) compared to natural forests, potentially stemming from simple plantation age structures. Different from natural forests, 74% of trees in plantations faced growth decline, indicating a short “prime period” for carbon sequestration and even a short lifespan. Additionally, a negative relationship between evapotranspiration and tree growth was observed in tree plantations since maturity, leading to high sensitivities of trees to vapor pressure deficit and soil water. However, this was not observed in natural forests. To address this, we suggest afforestation in drylands should consider complex age structures, ensuring a longer prime period for carbon sequestration and life expectancy in tree plantations.

Extreme climate events can have severe impacts on both vegetation and the carbon cycle. However, whether land management exacerbates or mitigates the effects of extreme climate events on vegetation remains unknown. We investigated the case of an extreme precipitation event that occurred in a region dominated by double–cropping (DC) systems located in the Yangtze–Huai plain in China. Waterlogging disasters were triggered by an extreme precipitation event in October 2016, which severely affected the sowing and seedling emergence of winter crops (mainly winter wheat). The lack of sowing and low seedling emergence rate subsequently led to months of low growth, as evidenced by negative enhanced vegetation index (EVI) anomalies, especially from March to May 2017. Local agricultural meteorological monthly reports, government announcements, and winter wheat data based on statistics and remote sensing confirmed the reduced yields. The influence on vegetation ended in June 2017, when summer crops were planted. Our results demonstrate that, in such a DC system, when extreme events occur during the key sowing period, the impacts will continue through the entire crop growth period, until the next sowing. More generally, agricultural land management could extend the duration and magnitude of the impacts of extreme climate events on vegetation.

Abstract Forest protection and afforestation have been identified as a means to partially offset anthropogenic CO2 emissions. Yet, increasingly frequent observations of drought‐induced tree mortality are reported. Here, we applied a risk analysis framework for global drought‐induced forest mortality by examining extreme reductions in greenness and water content of forest canopies during past mortality events as well as growth recovery of surviving individual trees following stand‐scale mortality events. We defined a drought‐induced mortality risk index (DMR) that explains 80% of documented tree mortality. Rising CO2 alleviated the increase of DMR with short‐term drought, however, the observed DMR increases with long‐term drought no matter whether considering plant responses to CO2. DMR in sites where tree mortality has been observed significantly increased since the 1980s. More than that, drought exposure threatened 0.28 billion hectares of forested areas. Our framework highlights how climate change‐induced drought, especially hotter‐droughts, threatens the sustainability of global forests.

Forest stand structure is not only a crucial factor for regulating forest functioning but also an important indicator for sustainable forest management and ecosystem services. Although there exists a few national/global structure databases for natural forests, a country-wide synthetic structure database for plantation forests over China, the world’s largest player in plantation forests, has not been achieved. In this study, we built a country-wide synthetic stand structure database by surveying more than 600 peer-reviewed literature. The database covers tree species, mean stand age, mean tree height, stand density, canopy coverage, diameter at breast height, as well as the associated ancillary in-situ topographical and soil properties. A total of 594 published studies concerning diverse forest stand structure parameters were compiled for 46 tree species. This first synthesis for stand structure of plantation forests over China supports studies on the evolution/health of plantation forests in response to rapid climate change and intensified disturbances, and benefits country-wide sustainable forest management, future afforestation or reforestation planning. Potential users include those studying forest community dynamics, regional tree growth, ecosystem stability, and health, as well as those working with conservation and sustainable management. This dataset is freely accessible at http://www.doi.org/10.11922/sciencedb.j00076.00091.

Abstract Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is

Abstract Tree resistance to extreme droughts and post‐drought recovery are sensitive to the drought timing. However, how the bioclimatic sensitivity of tree growth may vary with the timing and order of extreme droughts and wetness is still poorly understood. Here, we quantified the bioclimatic sensitivity of tree growth in the period of 1951–2013 under different seasonal extreme drought/wetness regimes over the extra‐tropical Northern Hemisphere, using 1,032 tree ring chronologies from 121 gymnosperm and angiosperm species. We found a negative asymmetry in tree growth under regimes with seasonal extreme droughts. With extreme drought, tree growth in arid and temperate dry regions is more negatively impacted by pre‐growing‐season (PGS) extreme droughts. Clade‐wise, angiosperms are more sensitive to PGS water availability, and gymnosperms to legacy effects of the preceding tree growth conditions in temperate dry and humid regions. Our finding of divergent bioclimatic legacy effects underscores contrasting trends in forest responses to drought across different ecoregions and functional groups in a more extreme climate.

Drought-related tree mortality has become a major concern worldwide due to its pronounced negative impacts on the functioning and sustainability of forest ecosystems. However, our ability to identify the species that are most vulnerable to drought, and to pinpoint the spatial and temporal patterns of mortality events, is still limited. Model is useful tools to capture the dynamics of vegetation at spatiotemporal scales, yet contemporary land surface models (LSMs) are often incapable of predicting the response of vegetation to environmental perturbations with sufficient accuracy, especially under stressful conditions such as drought. Significant progress has been made regarding the physiological mechanisms underpinning plant drought response in the past decade, and plant hydraulic dysfunction has emerged as a key determinant for tree death due to water shortage. The identification of pivotal physiological events and relevant plant traits may facilitate forecasting tree mortality through a mechanistic approach, with improved precision. In this review, we (1) summarize current understanding of physiological mechanisms leading to tree death, (2) describe the functionality of key hydraulic traits that are involved in the process of hydraulic dysfunction, and (3) outline their roles in improving the representation of hydraulic function in LSMs. We urge potential future research on detailed hydraulic processes under drought, pinpointing corresponding functional traits, as well as understanding traits variation across and within species, for a better representation of drought-induced tree mortality in models.

Plants are known to be influenced by climate, topsoil conditions, and ecology, but the impact of features deeper in the Earth system are poorly understood. Here, the authors show that primary productivity is controlled in part by deep bedrock characteristics far below the roots.

Increased extreme wet and dry years and forest growth loss from drought legacy effect lead to a question whether wetness events can conversely compensate for this loss. Here the authors report substantial growth enhancement after extreme wetness compensating for drought-induced growth loss globally.

Remotely sensed vegetation indices (VIs) have been widely used to estimate the aboveground biomass (AGB) carbon stock of coastal wetlands by establishing Vis-related linear models. However, these models always have high uncertainties due to the large spatial variation and fragmentation of coastal wetlands. In this paper, an efficient coastal wetland AGB model for the Bohami Rim coastal wetlands was presented based on multiple data sets. The model was developed statistically with 7 independent variables from 23 metrics derived from remote sensing, topography, and climate data. Compared to previous models, it had better performance, with a root mean square error and r value of 188.32 g m−2 and 0.74, respectively. Using the model, we firstly generated a regional coastal wetland AGB map with a 10 m spatial resolution. Based on the AGB map, the AGB carbon stock of the Bohai Rim coastal wetland was 2.11 Tg C in 2019. The study demonstrated that integrating emerging high spatial resolution multi-remote sensing data and several auxiliary metrics can effectively improve VIs-based coastal wetland AGB models. Such models with emerging freely available data sets will allow for the rapid monitoring and better understanding of the special role that “blue carbon” plays in global carbon cycle.

Seasonal non-structural carbohydrate (NSC) dynamics in different organs can indicate the strategies trees use to cope with water stress; however, these dynamics remain poorly understood along a large precipitation gradient. In this study, we hypothesized that the correlation between water availability and NSC concentrations in different organs might be strengthened by decreasing precipitation in Pinus tabulaeformis Carr. forests in temperate China. Our results show that the concentrations of soluble sugars were lower in stems and coarse roots, and starch was higher in branches in the early growing season at drier sites. Throughout the growing season, the concentrations of soluble sugars increased in drier sites, especially for leaves, and remained stable in wetter sites, while starch concentrations were relatively stable in branches and stems at all sites. The NSC concentrations, mainly starch, decreased in coarse roots along the growing season at drier sites. Trees have a faster growth rate with an earlier cessation in active stem growth at drier sites. Interestingly, we also found a divergent relationship between NSCs in different organs and mean growing season water availability, and a stronger correlation was observed in drier sites. These results show that pine forests in arid and semi-arid regions of northern China exhibit different physiological responses to water availability, improving our understanding of the adaptive mechanisms of trees to water limitations in a warmer and drier climate.

The surface–subsurface architecture of the critical zone (CZ) regulates the hydrologic and geochemical processes that drive the CZ function. Due to the complex heterogeneity, highly coupled processes, and the dynamic controlling mechanisms across scales, CZ science faces grand challenges on interface coupling, process integration, and scaling. Carbon–water cycles are important physical-geochemical processes in the CZ. However, the key factors that govern the carbon–water processes across spatiotemporal scales under natural conditions are poorly understood. Especially, such research in the CZ of the alpine region, e.g., the Qinghai-Tibet Plateau (QTP), has not been reported yet is challenging to conduct. The Qinghai Lake Basin Critical Zone Observatory (QLBCZO) is being established to study alpine hydrology, carbon–water processes, and ecological functions on the northeastern QTP. Previous studies in the QLBCZO mainly focused on ecohydrologic processes and the water budget. Currently, the QLBCZO is designed to integrate state-of the-art observation techniques (such as including deep coring, ground-penetrating radar, electrical resistance tomography, isotope technique, and remote sensing) and frontier modeling approaches to investigate: (i) the three-dimensional architecture of the subsurface environment (vegetation–soil–rock–microbial community) as well as the carbon and water storage, (ii) multiscale carbon–water processes and carbon–water budget, (iii) interactions between freeze–thaw and carbon–water processes, and (iv) carbon sequestration and water resources. The establishment of the QLBCZO and its associated research would be crucial for advancing both theory and methodology for the CZ and Earth system science research on the QTP and alpine regions in general.

Rapid climate warming, with much higher warming rates in winter and spring, could affect the vernalization fulfillment, a critical process for induction of crop reproductive growth and consequent grain filling in temperate winter crops. However, regional observational evidence of the effects of historical warming-mediated vernalization variations on temperate winter crop yields is lacking. Here, we statistically quantified the interannual sensitivity of winter wheat yields to vernalization degree days (VDD) during 1975–2009 and its spatial relationship with multi-year mean VDD over temperate Europe (TE), using EUROSTAT crop yield statistics, observed and simulated crop phenology data and gridded daily climate data. Our results revealed a pervasively positive interannual sensitivity of winter wheat yields to variations in VDD (γVDD) over TE, with a mean γVDD of 2.8 ± 1.5 kg ha−1 VDD−1. We revealed a significant (p < 0.05) negative exponential relationship between γVDD and multi-year mean VDD for winter wheat across TE, with higher γVDD in winter wheat planting areas with lower multi-year mean VDD. Our findings shed light on potential vulnerability of winter wheat yields to warming-mediated vernalization variations over TE, particularly considering a likely future warmer climate.

With the increasing temperature and intensified drought, global climate change has profound impacts on tree growth in temperate regions, which consequently regulates terrestrial-atmosphere biogeochemical processes and biophysical feedbacks. Thus, increasing numbers of studies have addressed the long-term annual trends in tree growth and their response to climate change at diverse spatial scales. However, the potential divergence in tree growth trends and growth variability (represented by coefficient of variance) in different seasons across large-scale climate gradients remains poorly understood. Here, we investigated the tree growth trends and growth variability in different seasons across diverse drought conditions in forested regions over northeastern China during the period 1982–2015, using both remote sensing observations and in situ tree-ring measurements. We found clear seasonal divergence in tree growth trends during 1982–2015, and the apparent increase was mainly observed in spring and autumn, attributed mainly to the increase in spring temperature and autumn solar radiation, respectively, but not in summer. The magnitudes of increasing trends in tree growth decrease with the increase of the multi-year average dryness index (MAI) in semi-arid areas (1.5 < MAI < 4.0) in all seasons. We further revealed that the interannual variability in tree growth was much larger in the semi-arid regions than in the humid and semi-humid regions in all seasons, and tree growth variability was significantly and negatively correlated with the variations in temperature and water deficit. Our findings improve our understanding of seasonal divergence in tree growth trends and provide new insights into spatial patterns in forest vulnerability in a warmer and drier climate.

Mapping vegetation cover is critical for understanding and monitoring ecosystem functions in semi-arid biomes. As existing estimates tend to underestimate the woody cover in areas with dry deciduous shrubland and woodland, we present an approach to improve the regional estimation of woody and herbaceous fractional cover in the East Asia steppe. This developed approach uses Random Forest models by combining multiple remote sensing data—training samples derived from high-resolution image in a tailored spatial sampling and model inputs composed of specific metrics from MODIS sensor and ancillary variables including topographic, bioclimatic, and land surface information. We emphasize that effective spatial sampling, high-quality classification, and adequate geospatial information are important prerequisites of establishing appropriate model inputs and achieving high-quality training samples. This study suggests that the optimal models improve estimation accuracy (NMSE 0.47 for woody and 0.64 for herbaceous plants) and show a consistent agreement with field observations. Compared with existing woody estimate product, the proposed woody cover estimation can delineate regions with subshrubs and shrubs, showing an improved capability of capturing spatialized detail of vegetation signals. This approach can be applicable over sizable semi-arid areas such as temperate steppes, savannas, and prairies.

Hemi-boreal forests, which make up the transition from temperate deciduous forests to boreal forests in southern Siberia, have experienced significant warming without any accompanying increase in precipitation during the last 80 years. This climatic change could have a profound impact on tree growth and on the stability of forest ecosystems in this region, but at present evidence for these impacts is lacking. In this study, we report a recent dramatic decline in the growth of hemi-boreal forests, based on ring width measurements from three dominant tree-species (Pinus sylvestris, Larix sibirica and Larix gmelinii), sampled from eight sites in the region. We found that regional tree growth has become increasingly limited by low soil water content in the pre- and early-growing season (from October of the previous year to July of the current year) over the past 80 years. A warming-induced reduction in soil water content has also increased the climate sensitivity of these three tree species. Beginning in the mid-1980s, a clear decline in growth is evident for both the pine forests and the larch forests, although there are increasing trends in the proxy of soil water use efficiencies. Our findings are consistent with those from other parts of the world and provide valuable insights into the regional carbon cycle and vegetation dynamics, and should be useful for devising adaptive forest management strategies.

Based on radial tree growth measurements in nine plots of area 625 m ^2 (369 trees in total) and climate data, we explored the possibly changing effects of climate on regional tree growth in the temperate continental semi-arid mountain forests in the Tianshan Mountains in northwest China during 1933–2005. Tree growth in our study region is generally limited by the soil water content of pre- and early growing season (February–July). Remarkably, moving correlation functions identified a clear temporal change in the relationship between tree growth and mean April temperature. Tree growth showed a significant ( p