Quantifying soil moisture impacts on light use efficiency across biomes

Soil resource acquisition strategy modulates global plant nutrient and water economics

Natural selection favors growth by selecting a combination of plant traits that maximize photosynthetic CO2 assimilation at the lowest combined carbon costs of resource acquisition and use. We quantified how soil nutrient availability, plant nutrient acquisition strategies, and aridity modulate the variability in plant costs of nutrient acquisition relative to water acquisition (β). We used an eco-evolutionary optimality framework and a global carbon isotope dataset to quantify β. Under low soil nitrogen-to-carbon (N : C) ratios, a mining strategy (symbioses with ectomycorrhizal and ericoid mycorrhizal fungi) reduced β by mining organic nitrogen, compared with a scavenging strategy (symbioses with arbuscular mycorrhizal fungi). Conversely, under high N : C ratios, scavenging strategies reduced β by effectively scavenging soluble nitrogen, compared with mining strategies. N2-fixing plants did not exhibit reduced β under low N : C ratios compared with non-N2-fixing plants. Moisture increased β only in plants using a scavenging strategy, reflecting direct impacts of aridity on the carbon costs of maintaining transpiration in these plants. Nitrogen and phosphorus colimitation further modulated β. Our findings provide a framework for simulating the variability of plant economics due to plant nutrient acquisition strategies in earth system models.

Susceptibility to Photosynthesis Suppression From Extreme Storms Is Highly Site-Dependent

Extreme storms are becoming more intense and frequent under climate change. Although these extreme wet events are smaller in extent and duration than drought events, recent evidence suggests the global impact of both extremes is similar. However, the impact of individual extreme storms on photosynthesis-and therefore on vegetation and the carbon cycle-remains difficult to predict, as photosynthesis may be suppressed via waterlogging or increased by the alleviation of moisture stress. Here, we use random forest models to calculate daily photosynthesis anomalies attributable to extreme soil moisture using data from 54 FLUXNET sites across the globe. We hypothesize that photosynthesis' response to a given extreme event is primarily controlled by storm intensity, and to a lesser degree by site vegetation, climate, soil, and topography. However, we find instead that photosynthesis responses are better explained by site characteristics (soil texture, climate, topography, and vegetation density) than by storm intensity, such that the likelihood of waterlogging from a given storm is heavily site-dependent. Although storms that induce waterlogging are roughly as common as those that induce stress alleviation overall, photosynthesis rarely declines at sites not prone to waterlogging. Instead, photosynthesis anomalies at these sites show a much weaker relationship with storm intensity. Increasingly intense storms are therefore unlikely to impact all locations equally. This highlights the potential to use site characteristics to enhance prediction of storm effects on ecosystems and the land carbon sink.

Analysis and prediction of carbon storage changes on the Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau, a crucial global carbon reservoir, plays an essential role in the carbon cycle. This study used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to analyze land use and carbon storage changes from 2000 to 2020, and the Patch-generating Land Use Simulation (PLUS) model to predict land use trends and carbon storage for 2030 and 2040 under various scenarios, combining carbon density data. The impact of driving factors on carbon storage and spatial heterogeneity were assessed using the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. Results showed a fluctuating increase in carbon storage, mainly from grasslands and forests, with soil organic carbon as the largest pool. Positive factors included Digital Elevation Model (DEM), temperature, proximity to railways, roads, and Normalized Difference Vegetation Index (NDVI), while aridity was negative. Predictions suggest carbon storage will rise across all scenarios, with ecological protection showing the largest increase. This study comprehensively analyzes the impact of climate and land use changes on carbon storage in the Qinghai-Tibet Plateau, enhances understanding of the plateau's ecosystem sustainability, and supports policy-making.

Plant traits shape global spatiotemporal variations in photosynthetic efficiency

Photosynthetic efficiency (PE) quantifies the fraction of absorbed light used in photochemistry to produce chemical energy during photosynthesis and is essential for understanding ecosystem productivity and the global carbon cycle, particularly under conditions of vegetation stress. However, nearly 60% of the global spatiotemporal variance in terrestrial PE remains unexplained. Here we integrate remote sensing and eco-evolutionary optimality theory to derive key plant traits, alongside explainable machine learning and global eddy covariance observations, to uncover the drivers of daily PE variations. Incorporating plant traits into our model increases the explained daily PE variance from 36% to 80% for C3 vegetation and from 54% to 84% for C4 vegetation compared with using climate data alone. Key plant traits-including chlorophyll content, leaf longevity and leaf mass per area-consistently emerge as important factors across global biomes and temporal scales. Water availability and light conditions are also critical in regulating PE, underscoring the need for an integrative approach that combines plant traits with climatic factors. Overall, our findings demonstrate the potential of remote sensing and eco-evolutionary optimality theory to capture principal PE drivers, offering valuable tools for more accurately predicting ecosystem productivity and improving Earth system models under climate change.

Greening but enhanced vegetation water stress in the Yellow River Basin: A holistic perspective

The Yellow River Basin (YRB) has emerged as a focal point of global vegetation greening due to climate change and human activities. Given its ecological vulnerability and intense human activities, environmental sustainability has become an urgent concern for scholars. Current research on the hydrological effects of vegetation greening, from a reductionist perspective, still struggle to answer the crucial question that whether vegetation water stress is increasing or decreasing. Towards that, we adopt a holistic perspective to explore the relationships between monthly vegetation dynamics and multiple water stress indicators in the YRB from 1982 to 2018. Using statistical methods and the random forest model, we revealed that both gross primary productivity and water use efficiency showed an increasing trend, with rates of 5.83 g Cm-2 and 0.01 g Cmm-1m-2 per year, respectively. We identified that with increasing climatic aridity, the water stress factors for vegetation transition from monthly scale water conditions (vapor pressure deficit, VPD) to 1-2 months scale (soil water content, SWC) and seasonal scale (standardized precipitation evapotranspiration index-3, SPEI-3) water balance status. And with an aridity index of 0.35 as the threshold, the response of vegetation to water stress factors exhibits marked spatial differentiation. Furthermore, since 2000, despite a persistent greening trend in the YRB, there has been a noticeable expansion in the spatial range of intermediate and long-term water stress factors (SWC, SPEI-3), indicating an enhancing vegetation water stress. This suggests that a serious attention should be paid to the future ecological security of the YRB under the intensified climate change.

Exploring the multifaceted reason for deficits in soil water within different soil layers in China's drylands

Soil water regulates the hydrological cycle and provides the water required for vegetation growth in drylands. However, existing studies have rarely investigated the reason for changes in soil water within different layers and compared differences in the contribution of driving factors from spatial and inter-annual perspectives. This study analyzed the dynamics of soil water content (SWC) at shallow (0-28 cm), intermediate (28-100 cm), and deep (100-289 cm) layers. The individual and interactive effects of different environmental factors on the spatial heterogeneity of SWC were investigated using the geographical detector. By selecting evapotranspiration (ET), precipitation, air temperature, land surface temperature (LST), and the Normalized Difference Vegetation Index (NDVI), we determined the influences of these factors on SWC dynamics for each layer as well as their contributions using ridge regression. Meanwhile, we also analyzed the influence of extreme events and lag effects of climatic factors on SWC. The results showed that SWC in each layer primarily exhibited a downward trend. In different layers, all interactive factors (two factors) had stronger effects on the spatial heterogeneity of SWC compared to any individual factor. Precipitation was the largest contributor (25.03 ± 20.66%) to SWC dynamics in shallow layer but its contribution decreased with increasing soil depth. NDVI mainly controlled the SWC in intermediate and deep layers with the largest contribution and exerted a dual effect on SWC. The contributions of air temperature and LST were the smallest. The lagged months of precipitation and air temperature on SWC increased with soil depth. Precipitation extremes with long duration and warm temperature extremes had large contributions and exceeded that of the average climate changes. These findings can provide a deeper insight into soil water dynamics and their environmental factors and scientific guidance for maintaining the hydrological cycle and sustainable development of vegetation in drylands.

Heat and Drought Events Alter Biogenic Capacity to Balance CO2 Budget in South-Western Europe

Heat and drought events are increasing in frequency and intensity, posing significant risks to natural and agricultural ecosystems with uncertain effects on the net ecosystem CO2 exchange (NEE). The current Vegetation Photosynthesis and Respiration Model (VPRM) was adjusted to include soil moisture impacts on the gross ecosystem exchange (GEE) and respiration (R ECO) fluxes to assess the temporal variability of NEE over south-western Europe for 2001-2022. Warming temperatures lengthen growing seasons, causing an increase in GEE, which is mostly compensated by a similar increment in R ECO. As a result, there is a modest increase in the net carbon sink of 0.69 gC m-2 yr-1 but with high spatial and annual variability. The heatwave of 2022 reduced net carbon uptake by 91.7 TgC, a 26.4% decrease from the mean. The interannual variability of NEE is more influenced by drought in temperate humid regions than in Mediterranean semi-arid regions. These results emphasize the vulnerability of the net carbon sink as drying trends could revert the NEE trends, as it is happening for croplands in the French Central Massif.

Snowmelt decreases light use efficiency in Qinghai-Tibetan plateau between 2000 and 2017

Due to the impact of climate change, significant alterations in snowmelt have already occurred, which have been demonstrated to play a crucial role in photosynthetic carbon sequestration processes in vegetation. However, the effect of changes in snowmelt on light use efficiency (LUE) of grassland remain largely unknown in the permafrost region of Qinghai-Tibetan Plateau (QTP). By utilizing remote sensing data from 2000 to 2017, we conducted an analysis on the spatial and temporal patterns of LUE for various types of permafrost and grassland on the QTP. The LUE of the growing season was 1.1588 g C·MJ-1, displaying variations among different ecosystems: alpine steppe of seasonally frozen ground (ASS) > alpine meadow of seasonally frozen ground (AMS) > alpine meadow of permafrost (AMP) > alpine steppe of permafrost (ASP). Furthermore, our study demonstrated that decreasing snowmelt during the growing season had a negative impact on LUE through meteorological factors, elucidating its influence on LUE for approximately 40.65%, 34.06%, 41.05%, and 32.68% of ecosystems studied. Reduced snowmelt indirectly affects LUE by lowering air temperatures, vapor pressure deficit and solar radiation, while replenishing soil moisture. Additionally, changes in snowmelt can directly affect LUE by reducing the insulating properties of snow cover. Therefore, when estimating gross primary productivity (GPP) using remote sensing data based on LUE, it is essential to consider the impact of snowmelt. This will better represent vegetation phenology's response to climate change.

Evidence for widespread thermal acclimation of canopy photosynthesis

Plants acclimate to temperature by adjusting their photosynthetic capacity over weeks to months. However, most evidence for photosynthetic acclimation derives from leaf-scale experiments. Here we address the scarcity of evidence for canopy-scale photosynthetic acclimation by examining the correlation between maximum photosynthetic rates (Amax,2,000) and growth temperature (T air ¯ ) across a range of concurrent temperatures and canopy foliage quantity, using data from >200 eddy covariance sites. We detect widespread thermal acclimation of canopy-scale photosynthesis, demonstrated by enhanced Amax,2,000 under higherT air ¯ , across flux sites with adequate water availability. A 14-day period is identified as the most relevant timescale for acclimation across all sites, with a range of 12-25 days for different plant functional types. The mean apparent thermal acclimation rate across all ecosystems is 0.41 (-0.38-1.04 for 5th-95th percentile range) µmol m-2 s-1 °C-1, with croplands showing the largest acclimation rates and grasslands the lowest. Incorporating an optimality-based prediction of leaf photosynthetic capacities into a biochemical photosynthesis model is shown to improve the representation of thermal acclimation. Our results underscore the critical need for enhanced understanding and modelling of canopy-scale photosynthetic capacity to accurately predict plant responses to warmer growing seasons.

PSInet: a new global water potential network

Given the pressing challenges posed by climate change, it is crucial to develop a deeper understanding of the impacts of escalating drought and heat stress on terrestrial ecosystems and the vital services they offer. Soil and plant water potential play a pivotal role in governing the dynamics of water within ecosystems and exert direct control over plant function and mortality risk during periods of ecological stress. However, existing observations of water potential suffer from significant limitations, including their sporadic and discontinuous nature, inconsistent representation of relevant spatio-temporal scales and numerous methodological challenges. These limitations hinder the comprehensive and synthetic research needed to enhance our conceptual understanding and predictive models of plant function and survival under limited moisture availability. In this article, we present PSInet (PSI-for the Greek letter Ψ used to denote water potential), a novel collaborative network of researchers and data, designed to bridge the current critical information gap in water potential data. The primary objectives of PSInet are as follows. (i) Establishing the first openly accessible global database for time series of plant and soil water potential measurements, while providing important linkages with other relevant observation networks. (ii) Fostering an inclusive and diverse collaborative environment for all scientists studying water potential in various stages of their careers. (iii) Standardizing methodologies, processing and interpretation of water potential data through the engagement of a global community of scientists, facilitated by the dissemination of standardized protocols, best practices and early career training opportunities. (iv) Facilitating the use of the PSInet database for synthesizing knowledge and addressing prominent gaps in our understanding of plants' physiological responses to various environmental stressors. The PSInet initiative is integral to meeting the fundamental research challenge of discerning which plant species will thrive and which will be vulnerable in a world undergoing rapid warming and increasing aridification.

Atmospheric drought dominates changes in global water use efficiency

Water use efficiency (defined as the ratio of gross primary productivity to plant transpiration, WUET) describes the tradeoff between ecosystem carbon uptake and water loss. However, a comprehensive understanding of the impact of soil and atmospheric moisture deficits on WUET across large regions remains incomplete. Solar-induced chlorophyll fluorescence (SIF) serves as an effective signal for measuring both terrestrial vegetation photosynthesis and transpiration, thereby enabling a rapid response to changes in the physiological status of plants under water stress. The objectives of this study were to: 1) mechanistically calculate WUET using top-of-canopy SIF data and meteorological information by using the revised mechanistic light response model and the Penman-Monteith equation; 2) analyze the effects of atmospheric and soil water deficits on SIF-based WUET by using decoupled soil water content (SWC) and vapor pressure deficit (VPD); 3) evaluate estimated SIF-based WUET against data from 28 eddy covariance (EC) flux sites representing eight different vegetation types. Results indicated that the model performed well in ecosystems with dense canopies, explaining 56 % of the daily variability in EC tower-based WUET. For the years 2019-2020, the global average WUET derived from SIF was 3.49 g C/kg H2O. Notably, this value exceeded 4 g C/kg H2O in tropical rainforest regions near the equator and went beyond 5 g C/kg H2O in the high-latitude regions of the Northern Hemisphere. We found that SIF-based WUET was primarily influenced by VPD rather than SWC in over 90 % of the global vegetated area. The model used in this study increased our ability to mechanistically estimate WUET with SIF at the global scale, thereby highlighting the significance of the global response of SIF-based WUET to water stress, and also enhancing our understanding of the water‑carbon cycle in terrestrial ecosystems.

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.

Spatial and temporal variations of ecosystem water use efficiency and its response to soil moisture drought in a water-limited watershed of northern China

Drought synchronously affects the water cycle and interferes with the carbon cycle in terrestrial ecosystems. Ecosystem water use efficiency (WUE), serving as a vital metric for assessing the interplay between water and carbon cycles, has found extensively use in exploring how ecosystems responses to drought. However, the effects of soil moisture drought on WUE are still poorly recognized. Taking Ziya River Basin as an example, the spatial-temporal variations of WUE from 2001 to 2020 were estimated by the Penman-Monteith-Leuning Version 2 (PML-V2) data. Based on the Standardized Soil Moisture Index (SSI) calculated from Soil Moisture of China by in situ data, version 1.0 (SMCI1.0) data, the sensitivity and thresholds of different vegetation WUE to drought magnitudes were investigated, and the influences of both lagged and cumulative effects of drought on WUE were further analyzed. Results showed that the annual mean WUE was 2.160 ± 0.975 g C kg-1 H2O-1 in the Ziya River Basin, with a significant increasing trend of 0.037 g C kg-1 H2O-1 yr-1 (p < 0.05). For all the vegetation types, the WUE reached the maximum value at a certain drought threshold (SSI = -1.5 ± 0.1). The dominant factor controlling WUE sensitivity to drought changed from evapotranspiration (ET) to gross primary production (GPP) when severe drought transformed into extreme drought. Significant lagged and cumulative effects were found in the response of WUE to drought in nearly 58.64 % (72.94 %) of the study area, with an average time scale of 6.65 and 2.11 months (p < 0.05) respectively. Drought resistance in descending order was: forest > shrub > grassland > cropland. Our findings enrich the understanding of the coupled carbon and water cycle processes in terrestrial ecosystems and their response to soil moisture drought in the context of global climate change.

Changes in vegetation in China's drylands are closely related to afforestation compared with climate change

The response of vegetation to climate change and human activities has attracted considerable attention. However, quantitative studies on the effects of climate change and human activities on dryland vegetation in different seasons remain unclear. This study investigated the impacts of precipitation, temperature, soil water storage (SWS) (top [0-7 cm], shallow [7-28 cm], and middle [28-100 cm] layers), vapor pressure deficit (VPD), and afforestation on vegetation as well as their relative contribution rates during the rainy season ([RS], June to September), dry season ([DS], November to April), transition season ([TS], May and October), and all year period (AY) in China's drylands from 2001 to 2020 using the first-difference method. Areas with precipitation and SWS showing significant positive correlation with dryland vegetation (p < 0.05) were found to be larger in RS than in DS and TS, and the positive effect of SWS increased with soil depth in the 0-28 cm interval. Increasing VPD induced a significant negative effect on vgetation during RS but it was not predominant in DS and TS. Afforestation showed an extremely significant positive correlated with dryland vegetation across >60 % of China's dryland areas (p < 0.01), but this improvement was found to be limited to regions with the highest afforestation area. Moreover, dryland vegetation dynamics were driven by afforestation in all seasons, with contribution rates of 64.23 %-71.46 %. The effects of SWS and VPD on vegetation driven by precipitation and temperature exceeded the direct effects of precipitation and temperature. Among climatic factors, VPD showed a major regulating effect on dryland vegetation at the top and shallow soil layers in almost all seasons, whereas the relative contribution rate of SWS increased with soil layer. The findings can provide a scientific reference for the sustainable development and protection of drylands under global warming.

Dryness stress weakens the sustainability of global vegetation cooling

Dryness stress can limit vegetation growth, and the cooling potential of vegetation will also be strongly influenced. However, it is still unclear how dryness stress feedback weakens the sustainability of vegetation-based cooling. Based on the long-time series of multi-source remote sensing product data for the period 2001-2020, the relative contribution rate, and the method of decoupling and boxing, we determined that greening will likely mitigate global warming by 0.065 ± 0.009 °C/a, but nearly 47 % of the area is unsustainable. This phenomenon is strongly related to dryness stress. The restricted area of soil moisture (SM: 68.35 %) to vegetation is larger than that of the atmospheric vapor pressure deficit (VPD: 34.19 %). With the decrease in SM, vegetation will decrease by an average of 14.9 %, and with the increase in VPD, vegetation will decrease by 3.8 %. With the continuous increase in the dryness stress area, the sustainability of the vegetation cooling effect will be threatened in an area of about 21.03 million km2, which is equivalent to the area of North America. Specifically, we found that with the decrease in SM and the increase in VPD, the contribution of vegetation to the cooling effect has been weakened by 10.8 %. This conclusion confirms that dryness stress will threaten the sustainability of vegetation-based climate cooling and provides further insight into the effect of dryness stress on vegetation cooling.

Vapour pressure deficit was not a primary limiting factor for gas exchange in an irrigated, mature dryland Aleppo pine forest

Climate change is often associated with increasing vapour pressure deficit (VPD) and changes in soil moisture (SM). While atmospheric and soil drying often co-occur, their differential effects on plant functioning and productivity remain uncertain. We investigated the divergent effects and underlying mechanisms of soil and atmospheric drought based on continuous, in situ measurements of branch gas exchange with automated chambers in a mature semiarid Aleppo pine forest. We investigated the response of control trees exposed to combined soil-atmospheric drought (low SM, high VPD) during the rainless Mediterranean summer and that of trees experimentally unconstrained by soil dryness (high SM; using supplementary dry season water supply) but subjected to atmospheric drought (high VPD). During the seasonal dry period, branch conductance (gbr ), transpiration rate (E) and net photosynthesis (Anet ) decreased in low-SM trees but greatly increased in high-SM trees. The response of E and gbr to the massive rise in VPD (to 7 kPa) was negative in low-SM trees and positive in high-SM trees. These observations were consistent with predictions based on a simple plant hydraulic model showing the importance of plant water potential in the gbr and E response to VPD. These results demonstrate that avoiding drought on the supply side (SM) and relying on plant hydraulic regulation constrains the effects of atmospheric drought (VPD) as a stressor on canopy gas exchange in mature pine trees under field conditions.

Increased photosynthesis during spring drought in energy-limited ecosystems

Drought is often thought to reduce ecosystem photosynthesis. However, theory suggests there is potential for increased photosynthesis during meteorological drought, especially in energy-limited ecosystems. Here, we examine the response of photosynthesis (gross primary productivity, GPP) to meteorological drought across the water-energy limitation spectrum. We find a consistent increase in eddy covariance GPP during spring drought in energy-limited ecosystems (83% of the energy-limited sites). Half of spring GPP sensitivity to precipitation was predicted solely from the wetness index (R2 = 0.47, p < 0.001), with weaker relationships in summer and fall. Our results suggest GPP increases during spring drought for 55% of vegetated Northern Hemisphere lands ( >30° N). We then compare these results to terrestrial biosphere model outputs and remote sensing products. In contrast to trends detected in eddy covariance data, model mean GPP always declined under spring precipitation deficits after controlling for air temperature and light availability. While remote sensing products captured the observed negative spring GPP sensitivity in energy-limited ecosystems, terrestrial biosphere models proved insufficiently sensitive to spring precipitation deficits.

Diagnosing evapotranspiration responses to water deficit across biomes using deep learning

Accounting for water limitation is key to determining vegetation sensitivity to drought. Quantifying water limitation effects on evapotranspiration (ET) is challenged by the heterogeneity of vegetation types, climate zones and vertically along the rooting zone. Here, we train deep neural networks using flux measurements to study ET responses to progressing drought conditions. We determine a water stress factor (fET) that isolates ET reductions from effects of atmospheric aridity and other covarying drivers. We regress fET against the cumulative water deficit, which reveals the control of whole-column moisture availability. We find a variety of ET responses to water stress. Responses range from rapid declines of fET to 10% of its water-unlimited rate at several savannah and grassland sites, to mild fET reductions in most forests, despite substantial water deficits. Most sensitive responses are found at the most arid and warm sites. A combination of regulation of stomatal and hydraulic conductance and access to belowground water reservoirs, whether in groundwater or deep soil moisture, could explain the different behaviors observed across sites. This variety of responses is not captured by a standard land surface model, likely reflecting simplifications in its representation of belowground water storage.

Widespread and complex drought effects on vegetation physiology inferred from space

The response of vegetation physiology to drought at large spatial scales is poorly understood due to a lack of direct observations. Here, we study vegetation drought responses related to photosynthesis, evaporation, and vegetation water content using remotely sensed data, and we isolate physiological responses using a machine learning technique. We find that vegetation functional decreases are largely driven by the downregulation of vegetation physiology such as stomatal conductance and light use efficiency, with the strongest downregulation in water-limited regions. Vegetation physiological decreases in wet regions also result in a discrepancy between functional and structural changes under severe drought. We find similar patterns of physiological drought response using simulations from a soil-plant-atmosphere continuum model coupled with a radiative transfer model. Observation-derived vegetation physiological responses to drought across space are mainly controlled by aridity and additionally modulated by abnormal hydro-meteorological conditions and vegetation types. Hence, isolating and quantifying vegetation physiological responses to drought enables a better understanding of ecosystem biogeochemical and biophysical feedback in modulating climate change.

Disentangling the effects of vapor pressure deficit on northern terrestrial vegetation productivity

The impact of atmospheric vapor pressure deficit (VPD) on plant photosynthesis has long been acknowledged, but large interactions with air temperature (T) and soil moisture (SM) still hinder a complete understanding of the influence of VPD on vegetation production across various climate zones. Here, we found a diverging response of productivity to VPD in the Northern Hemisphere by excluding interactive effects of VPD with T and SM. The interactions between VPD and T/SM not only offset the potential positive impact of warming on vegetation productivity but also amplifies the negative effect of soil drying. Notably, for high-latitude ecosystems, there occurs a pronounced shift in vegetation productivity's response to VPD during the growing season when VPD surpasses a threshold of 3.5 to 4.0 hectopascals. These results yield previously unknown insights into the role of VPD in terrestrial ecosystems and enhance our comprehension of the terrestrial carbon cycle's response to global warming.

Decline in stability of forest productivity in the tropics as determined by canopy water content

The impacts of low soil moisture (SM) and high vapour pressure deficit (VPD) on tree's photosynthesis and productivity are ultimately realized by changing water content in the canopy leaves. In this study, variations in canopy water content (CWC) that can be detected from microwave remotely sensed vegetation optical depth (VOD) have been proposed as a promising measure of vegetation water status, and we first reported that the regulation of CWC on productivity stability is universally applicable for global forests. Results of structural equation model (SEM) also confirmed the significant negative effect of CWC on coefficient of variation (CV) of productivity, indicating that the decrease in CWC could inevitably induce the instability of forest productivity under climate change. The most significant decrease (p < 0.01) of CWC is observed primarily in evergreen broadleaf forest in the tropics, implying an increasing instability of the most important carbon sink in terrestrial ecosystem.

Satellite solar-induced chlorophyll fluorescence tracks physiological drought stress development during 2020 southwest US drought

Monitoring and estimating drought impact on plant physiological processes over large regions remains a major challenge for remote sensing and land surface modeling, with important implications for understanding plant mortality mechanisms and predicting the climate change impact on terrestrial carbon and water cycles. The Orbiting Carbon Observatory 3 (OCO-3), with its unique diurnal observing capability, offers a new opportunity to track drought stress on plant physiology. Using radiative transfer and machine learning modeling, we derive a metric of afternoon photosynthetic depression from OCO-3 solar-induced chlorophyll fluorescence (SIF) as an indicator of plant physiological drought stress. This unique diurnal signal enables a spatially explicit mapping of plants' physiological response to drought. Using OCO-3 observations, we detect a widespread increasing drought stress during the 2020 southwest US drought. Although the physiological drought stress is largely related to the vapor pressure deficit (VPD), our results suggest that plants' sensitivity to VPD increases as the drought intensifies and VPD sensitivity develops differently for shrublands and grasslands. Our findings highlight the potential of using diurnal satellite SIF observations to advance the mechanistic understanding of drought impact on terrestrial ecosystems and to improve land surface modeling.

The ecosystem wilting point defines drought response and recovery of a Quercus-Carya forest

Soil and atmospheric droughts increasingly threaten plant survival and productivity around the world. Yet, conceptual gaps constrain our ability to predict ecosystem-scale drought impacts under climate change. Here, we introduce the ecosystem wilting point (Ψ_EWP ), a property that integrates the drought response of an ecosystem's plant community across the soil-plant-atmosphere continuum. Specifically, Ψ_EWP defines a threshold below which the capacity of the root system to extract soil water and the ability of the leaves to maintain stomatal function are strongly diminished. We combined ecosystem flux and leaf water potential measurements to derive the Ψ_EWP of a Quercus-Carya forest from an "ecosystem pressure-volume (PV) curve," which is analogous to the tissue-level technique. When community predawn leaf water potential (Ψ_pd ) was above Ψ_EWP (=-2.0 MPa), the forest was highly responsive to environmental dynamics. When Ψ_pd fell below Ψ_EWP , the forest became insensitive to environmental variation and was a net source of carbon dioxide for nearly 2 months. Thus, Ψ_EWP is a threshold defining marked shifts in ecosystem functional state. Though there was rainfall-induced recovery of ecosystem gas exchange following soaking rains, a legacy of structural and physiological damage inhibited canopy photosynthetic capacity. Although over 16 growing seasons, only 10% of Ψ_pd observations fell below Ψ_EWP , the forest is commonly only 2-4 weeks of intense drought away from reaching Ψ_EWP , and thus highly reliant on frequent rainfall to replenish the soil water supply. We propose, based on a bottom-up analysis of root density profiles and soil moisture characteristic curves, that soil water acquisition capacity is the major determinant of Ψ_EWP , and species in an ecosystem require compatible leaf-level traits such as turgor loss point so that leaf wilting is coordinated with the inability to extract further water from the soil.

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.

Environmental controls on the light use efficiency of terrestrial gross primary production

Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf-level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy-level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C₃ species; however, additional assumptions are required to "scale up" from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed-effects model. The same model design was fitted to site-based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple "goodness of fit" comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.

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.

Interannual and seasonal relationships between photosynthesis and summer soil moisture in the Ili River basin, Xinjiang, 2000-2018

Soil moisture (SM) is essential for controlling terrestrial carbon uptake, as it directly provides moisture for photosynthesis, especially in arid and semiarid regions. We selected the arid and semiarid Ili River basin (IRB) of Xinjiang as the study area, and investigated the spatial and temporal characteristics and interrelationships with SM and photosynthesis from 2000 to 2018 using the ERA5 products and solar-induced chlorophyll fluorescence (SIF). SM and photosynthesis showed a decreasing trend during the study period. Compared with those in spring and autumn, the variation of summer SM and SIF was more consistent with the interannual variation. Anomaly analysis showed that negative SM anomalies were most profound in 2012-2015, 2008, and 2014. Additionally, we quantified the effect of seasonal SM deficits on photosynthesis by performing model-based experiments. The results indicated that the gross primary productivity (GPP) simulated by the P-model could capture the characteristics of photosynthesis in the IRB, which had a high correlation with SIF (R² = 0.82, p < 0.001). In 2012-2015, 2008, and 2014, SM deficits caused more GPP reduction in the summers than in the springs or the autumns. The trends were mainly visible in the northern IRB, where GPP was below 40 % of the multi-year mean, and SM was below 23 %. GPP decreased more significantly in grassland than in the forest under the influence of SM deficit. This study reveals seasonal differences in the effects of SM deficit on photosynthesis and emphasizes that the summer SM deficit was the main factor responsible for decreases in GPP in the IRB during the study period. These findings contribute to a better understanding of the relationships between photosynthesis and environmental factors, and provide a reference for an accurate assessment of the regional carbon cycle.

Assessing the impact of drought-land cover change on global vegetation greenness and productivity

Drought-land cover change (D-LCC) is considered to be an important stress factor that affects vegetation greenness and productivity (VG&P) in global terrestrial ecosystems. Understanding the effects of D-LCC on VG&P benefits the development of terrestrial ecosystem models and the prediction of ecosystem evolution. However, till today, the mechanism remains underexploited. In this study, based on the Theil-Sen median estimator and Mann-Kendall test, Hurst exponent evaluation and rescaled range analysis (R/S), Pearson and Partial correlation coefficient analyses, we explore the spatiotemporal distribution characteristics and future trends of Leaf area index (LAI), Net primary productivity (NPP), Solar-induced chlorophyll fluorescence (SIF), Standardized precipitation evapotranspiration index (SPEI), Soil moisture (SM), Land cover type (LC), and the impact mechanism of D-LCC on global VG&P. Our results provide four major insights. First, three independent satellite observations consistently indicate that the world is experiencing an increasing trend of VG&P: LAI (17.69 %), NPP (20.32 %) and SIF (16.46 %). Nonetheless, productivity-reducing trends are unfolding in some tropical regions, notably the Amazon rainforest and the Congo basin. Second, from 2001 to 2020, the frequency, severity, duration, and scope of global droughts have been increasing. Third, the impact of land cover change on global VG&P is region-dependent. Finally, our results indicate that the continuous growth of VG&P in the global vegetation area is likely to become more difficult to maintain.

Towards a unified theory of plant photosynthesis and hydraulics

The global carbon and water cycles are governed by the coupling of CO₂ and water vapour exchanges through the leaves of terrestrial plants, controlled by plant adaptations to balance carbon gains and hydraulic risks. We introduce a trait-based optimality theory that unifies the treatment of stomatal responses and biochemical acclimation of plants to environments changing on multiple timescales. Tested with experimental data from 18 species, our model successfully predicts the simultaneous decline in carbon assimilation rate, stomatal conductance and photosynthetic capacity during progressive soil drought. It also correctly predicts the dependencies of gas exchange on atmospheric vapour pressure deficit, temperature and CO₂. Model predictions are also consistent with widely observed empirical patterns, such as the distribution of hydraulic strategies. Our unified theory opens new avenues for reliably modelling the interactive effects of drying soil and rising atmospheric CO₂ on global photosynthesis and transpiration.

Exceptional heat and atmospheric dryness amplified losses of primary production during the 2020 U.S. Southwest hot drought

Earth's ecosystems are increasingly threatened by "hot drought," which occurs when hot air temperatures coincide with precipitation deficits, intensifying the hydrological, physiological, and ecological effects of drought by enhancing evaporative losses of soil moisture (SM) and increasing plant stress due to higher vapor pressure deficit (VPD). Drought-induced reductions in gross primary production (GPP) exert a major influence on the terrestrial carbon sink, but the extent to which hotter and atmospherically drier conditions will amplify the effects of precipitation deficits on Earth's carbon cycle remains largely unknown. During summer and autumn 2020, the U.S. Southwest experienced one of the most intense hot droughts on record, with record-low precipitation and record-high air temperature and VPD across the region. Here, we use this natural experiment to evaluate the effects of hot drought on GPP and further decompose those negative GPP anomalies into their constituent meteorological and hydrological drivers. We found a 122 Tg C (>25%) reduction in GPP below the 2015-2019 mean, by far the lowest regional GPP over the Soil Moisture Active Passive satellite record. Roughly half of the estimated GPP loss was attributable to low SM (likely a combination of record-low precipitation and warming-enhanced evaporative depletion), but record-breaking VPD amplified the reduction of GPP, contributing roughly 40% of the GPP anomaly. Both air temperature and VPD are very likely to continue increasing over the next century, likely leading to more frequent and intense hot droughts and substantially enhancing drought-induced GPP reductions.

Widespread increasing vegetation sensitivity to soil moisture

Global vegetation and associated ecosystem services critically depend on soil moisture availability which has decreased in many regions during the last three decades. While spatial patterns of vegetation sensitivity to global soil water have been recently investigated, long-term changes in vegetation sensitivity to soil water availability are still unclear. Here we assess global vegetation sensitivity to soil moisture during 1982-2017 by applying explainable machine learning with observation-based leaf area index (LAI) and hydro-climate anomaly data. We show that LAI sensitivity to soil moisture significantly increases in many semi-arid and arid regions. LAI sensitivity trends are associated with multiple hydro-climate and ecological variables, and strongest increasing trends occur in the most water-sensitive regions which additionally experience declining precipitation. State-of-the-art land surface models do not reproduce this increasing sensitivity as they misrepresent water-sensitive regions and sensitivity strength. Our sensitivity results imply an increasing ecosystem vulnerability to water availability which can lead to exacerbated reductions in vegetation carbon uptake under future intensified drought, consequently amplifying climate change.

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.

Estimation of Global Cropland Gross Primary Production from Satellite Observations by Integrating Water Availability Variable in Light-Use-Efficiency Model

Satellite-based models have been widely used to estimate gross primary production (GPP) of terrestrial ecosystems. Although they have many advantages for mapping spatiotemporal variations of regional or global GPP, the performance in agroecosystems is relatively poor. In this study, a light-use-efficiency model for cropland GPP estimation, named EF-LUE, driven by remote sensing data, was developed by integrating evaporative fraction (EF) as limiting factor accounting for soil water availability. Model parameters were optimized first using CO2 flux measurements by eddy covariance system from flux tower sites, and the optimized parameters were further spatially extrapolated according to climate zones for global cropland GPP estimation in 2001–2019. The major forcing datasets include the fraction of absorbed photosynthetically active radiation (FAPAR) data from the Copernicus Global Land Service System (CGLS) GEOV2 dataset, EF from the ETMonitor model, and meteorological forcing variables from ERA5 data. The EF-LUE model was first evaluated at flux tower site-level, and the results suggested that the proposed EF-LUE model and the LUE model without using water availability limiting factor, both driven by flux tower meteorology data, explained 82% and 74% of the temporal variations of GPP across crop sites, respectively. The overall KGE increased from 0.73 to 0.83, NSE increased from 0.73 to 0.81, and RMSE decreased from 2.87 to 2.39 g C m−2 d−1 in the estimated GPP after integrating EF in the LUE model. These improvements may be largely attributed to parameters optimized for different climatic zones and incorporating water availability limiting factor expressed by EF into the light-use-efficiency model. At global scale, the verification by GPP measurements from cropland flux tower sites showed that GPP estimated by the EF-LUE model driven by ERA5 reanalysis meteorological data and EF from ETMonitor had overall the highest R2, KGE, and NSE and the smallest RMSE over the four existing GPP datasets (MOD17 GPP, revised EC-LUE GPP, GOSIF GPP and PML-V2 GPP). The global GPP from the EF-LUE model could capture the significant negative GPP anomalies during drought or heat-wave events, indicating its ability to express the impacts of the water stress on cropland GPP.

Assessment of the importance of increasing temperature and decreasing soil moisture on global ecosystem productivity using solar-induced chlorophyll fluorescence

The accurate assessment of the global gross primary productivity (GPP) of vegetation is the key to estimating the global carbon cycle. Temperature (Ts) and soil moisture (SM) are essential for vegetation growth. It is acknowledged that the global Ts has shown an increasing trend, yet SM has shown a decreasing trend. However, the importance of SM and Ts changes on the productivity of global ecosystems remains unclear, as SM and Ts are strongly coupled through soil-atmosphere interactions. Using solar-induced chlorophyll fluorescence (SIF) as a proxy for GPP and by decoupling SM and Ts changes, our investigation shows Ts plays a more important role in SIF in 60% of the vegetation areas. Overall, increased Ts promotes SIF by mitigating the resistance from SM's reduction. However, the importance of SM and Ts varies, given different vegetation types. The results show that in the humid zone, the variation of Ts plays a more important role in SIF, but in the arid and semi-arid zones, the variation of SM plays a more important role; in the semi-humid zone, the disparity in the importance of SM and Ts is difficult to unravel. In addition, our results suggest that SIF is very sensitive to aridity gradients in arid and semi-arid ecosystems. By decoupling the intertwined SM-Ts impact on SIF, our study provides essential evidence that benefits future investigation on the factors the influence ecosystem productivity at regional or global scales.

Confronting the water potential information gap

Water potential directly controls the function of leaves, roots, and microbes, and gradients in water potential drive water flows throughout the soil-plant-atmosphere continuum. Notwithstanding its clear relevance for many ecosystem processes, soil water potential is rarely measured in-situ, and plant water potential observations are generally discrete, sparse, and not yet aggregated into accessible databases. These gaps limit our conceptual understanding of biophysical responses to moisture stress and inject large uncertainty into hydrologic and land surface models. Here, we outline the conceptual and predictive gains that could be made with more continuous and discoverable observations of water potential in soils and plants. We discuss improvements to sensor technologies that facilitate in situ characterization of water potential, as well as strategies for building new networks that aggregate water potential data across sites. We end by highlighting novel opportunities for linking more representative site-level observations of water potential to remotely-sensed proxies. Together, these considerations offer a roadmap for clearer links between ecohydrological processes and the water potential gradients that have the 'potential' to substantially reduce conceptual and modeling uncertainties.

Atmospheric dryness reduces photosynthesis along a large range of soil water deficits

Both low soil water content (SWC) and high atmospheric dryness (vapor pressure deficit, VPD) can negatively affect terrestrial gross primary production (GPP). The sensitivity of GPP to soil versus atmospheric dryness is difficult to disentangle, however, because of their covariation. Using global eddy-covariance observations, here we show that a decrease in SWC is not universally associated with GPP reduction. GPP increases in response to decreasing SWC when SWC is high and decreases only when SWC is below a threshold. By contrast, the sensitivity of GPP to an increase of VPD is always negative across the full SWC range. We further find canopy conductance decreases with increasing VPD (irrespective of SWC), and with decreasing SWC on drier soils. Maximum photosynthetic assimilation rate has negative sensitivity to VPD, and a positive sensitivity to decreasing SWC when SWC is high. Earth System Models underestimate the negative effect of VPD and the positive effect of SWC on GPP such that they should underestimate the GPP reduction due to increasing VPD in future climates.

Effects of Environmental Factors on the Changes in MODIS NPP along DEM in Global Terrestrial Ecosystems over the Last Two Decades

Global warming has exerted widespread impacts on the terrestrial ecosystem in the past three decades. Vegetation is an important part of the terrestrial ecosystem, and its net primary productivity (NPP) is an important variable in the exchange of materials and energy in the terrestrial ecosystem. However, the effect of climate variation on the spatial pattern of zonal distribution of NPP has remained unclear over the past two decades. Therefore, we analyzed the spatiotemporal patterns and trends of MODIS NPP and environmental factors (temperature, radiation, and soil moisture) derived from three sets of reanalysis data. The moving window method and digital elevation model (DEM) were used to explore their changes along elevation gradients. Finally, we explored the effect of environmental factors on the changes in NPP and its elevation distribution patterns. Results showed that nearly 60% of the global area exhibited an increase in NPP with increasing elevation. Soil moisture has the largest uncertainty either in the spatial pattern or inter-annual variation, while temperature has the smallest uncertainty among the three environmental factors. The uncertainty of environmental factors is also reflected in its impact on the elevation distribution of NPP, and temperature is still the main dominating environmental factor. Our research results imply that the carbon sequestration capability of vegetation is becoming increasingly prominent in high-elevation regions. However, the quantitative evaluation of its carbon sink (source) functions needs further research under global warming.

Resilience of the Central Indian Forest Ecosystem to Rainfall Variability in the Context of a Changing Climate

Understanding the spatio-temporal pattern of natural vegetation helps decoding the responses to climate change and interpretation on forest resilience. Satellite remote sensing based data products, by virtue of their synoptic and repetitive coverage, offer to study the correlation and lag effects of rainfall on forest growth in a relatively longer time scale. We selected central India as the study site. It accommodates tropical natural vegetation of varied forest types such as moist and dry deciduous and evergreen and semi-evergreen forests that largely depend on the southwest monsoon. We used the MODIS derived NDVI and CHIRPS based rainfall datasets from 2001 to 2018 in order to analyze NDVI and rainfall trend by using Sen’s slope and standard anomalies. The study observed a decreasing rainfall trend over 41% of the forests, while the rest of the forest area (59%) demonstrated an increase in rainfall. Furthermore, the study estimated drought conditions during 2002, 2004, 2009, 2014 and 2015 for 98.2%, 92.8%, 89.6%, 90.1% and 95.8% of the forest area, respectively; and surplus rainfall during 2003, 2005, 2007, 2011, 2013 and 2016 for 69.5%, 63.9%, 71.97%, 70.35%, 94.79% and 69.86% of the forest area, respectively. Hence, in the extreme dry year (2002), 93% of the forest area showed a negative anomaly, while in the extreme wet year (2013), 89% of forest cover demonstrated a positive anomaly in central India. The long-term vegetation trend analysis revealed that most of the forested area (>80%) has a greening trend in central India. When we considered annual mean NDVI, the greening and browning trends were observed over at 88.65% and 11.35% of the forested area at 250 m resolution and over 93.01% and 6.99% of the area at 5 km resolution. When we considered the peak-growth period mean NDVI, the greening and browning trends were as follows: 81.97% and 18.03% at 250 m and 88.90% and 11.10% at 5 km, respectively. The relative variability in rainfall and vegetation growth at five yearly epochs revealed that the first epoch (2001–2005) was the driest, while the third epoch (2011–2015) was the wettest, corresponding to the lowest vegetation vigour in the first epoch and the highest in the third epoch during the past two decades. The study reaffirms that rainfall is the key climate variable in the tropics regulating the growth of natural vegetation, and the central Indian forests are dominantly resilient to rainfall variation.

Dynamic Changes in Plant Resource Use Efficiencies and Their Primary Influence Mechanisms in a Typical Desert Shrub Community

Understanding plant resource use efficiencies (RUEs) and their tradeoffs in a desert shrub community, particularly as it concerns the usage of water, light, and nitrogen, remains an ecological imperative. Plant RUEs have been widely used as indicators to understand plant acclimation processes to unfavorable environmental conditions. This study aimed to examine seasonal dynamics in RUEs in two widely distributed plant species in a typical desert shrub community (i.e., Artemisia ordosica and Leymus secalinus) based on in-situ measurements of leaf photosynthesis, specific leaf area (SLA), leaf nitrogen concentration (i.e., Nmass + Narea), and several site-related abiotic factors. Both species exhibited significant seasonal variation in RUEs, with a coefficient of variation (CV) >30% and seasonal divergence among the various RUEs. Seasonal divergence was largely controlled by variation in stomatal conductance (Gs), which was in turn influenced by variation in soil water content (SWC) and water vapor pressure deficit (VPD). RUEs between species converged, being positively correlated, yielding: (i) r2 = 0.40 and p < 0.01 for WUE; (ii) r2 = 0.18 and p < 0.01 for LUE; and (iii) r2 = 0.25 and p < 0.01 for NUE. RUEs for A. ordosica were mostly larger than those for L. secalinus, but less reactive to drought. This suggests A. ordosica was more conservative in its usage of available resources and was, therefore, better able to adapt to arid conditions. Resource use strategies between species differed in response to drought. Desert shrubs are projected to eventually replace grasses, as drought severity and duration increase with sustained regional climate change.

Global climate and nutrient controls of photosynthetic capacity

There is huge uncertainty about how global exchanges of carbon between the atmosphere and land will respond to continuing environmental change. A better representation of photosynthetic capacity is required for Earth System models to simulate carbon assimilation reliably. Here we use a global leaf-trait dataset to test whether photosynthetic capacity is quantitatively predictable from climate, based on optimality principles; and to explore how this prediction is modified by soil properties, including indices of nitrogen and phosphorus availability, measured in situ. The maximum rate of carboxylation standardized to 25 °C (Vcmax25) was found to be proportional to growing-season irradiance, and to increase-as predicted-towards both colder and drier climates. Individual species' departures from predicted Vcmax25 covaried with area-based leaf nitrogen (Narea) but community-mean Vcmax25 was unrelated to Narea, which in turn was unrelated to the soil C:N ratio. In contrast, leaves with low area-based phosphorus (Parea) had low Vcmax25 (both between and within communities), and Parea increased with total soil P. These findings do not support the assumption, adopted in some ecosystem and Earth System models, that leaf-level photosynthetic capacity depends on soil N supply. They do, however, support a previously-noted relationship between photosynthesis and soil P supply.

Soil microbial CO2 fixation plays a significant role in terrestrial carbon sink in a dryland ecosystem: A four-year small-scale field-plot observation on the Tibetan Plateau

Assessment of the global terrestrial carbon (C) sink remains uncertain, and the uncertainty is largely derived from dryland ecosystems. Here we investigated the uncertainty and dynamics of gross primary productivity (GPP) by distinguishing the contributions of soil microbial primary producers and plants to CO₂ fixation during four sequential growing seasons in a fragile dry grassland on the Tibetan Plateau. The results demonstrated that soil microbial GPP consistently accounted for a high proportion of plant GPP (18.2%), and both exhibited similar seasonal patterns during the four-year observation. Soil microbial GPP demonstrated a much greater interannual variation (76.1%) than plant GPP (15.1%), indicating that the interannual GPP uncertainty could be largely from microbial primary producers. Regression analysis indicated that plant GPP had higher sensitivity (demonstrated by slope) than soil microbial GPP to both soil water content and temperature. The GPP ratio of soil microbes to plants also demonstrated a clear seasonal change, and peaked in July in the four-year observation, with a minimum interannual variation (6.8%). The GPP ratio enhanced with increasing soil water content (P < 0.001), but did not correlate with soil temperature. Our findings suggest the great potential of soil microbial GPP, and challenge the plant-oriented models of terrestrial C estimation, which account for plant GPP but ignore soil microbial GPP. Thus, a more robust framework needs to incorporate both soil microbial and plant GPPs for accurately assessing C balance.

Climate-Driven Variability and Trends in Plant Productivity Over Recent Decades Based on Three Global Products

Variability in climate exerts a strong influence on vegetation productivity (gross primary productivity; GPP), and therefore has a large impact on the land carbon sink. However, no direct observations of global GPP exist, and estimates rely on models that are constrained by observations at various spatial and temporal scales. Here, we assess the consistency in GPP from global products which extend for more than three decades; two observation-based approaches, the upscaling of FLUXNET site observations (FLUXCOM) and a remote sensing derived light use efficiency model (RS-LUE), and from a suite of terrestrial biosphere models (TRENDYv6). At local scales, we find high correlations in annual GPP among the products, with exceptions in tropical and high northern latitudes. On longer time scales, the products agree on the direction of trends over 58% of the land, with large increases across northern latitudes driven by warming trends. Further, tropical regions exhibit the largest interannual variability in GPP, with both rainforests and savannas contributing substantially. Variability in savanna GPP is likely predominantly driven by water availability, although temperature could play a role via soil moisture-atmosphere feedbacks. There is, however, no consensus on the magnitude and driver of variability of tropical forests, which suggest uncertainties in process representations and underlying observations remain. These results emphasize the need for more direct long-term observations of GPP along with an extension of in situ networks in underrepresented regions (e.g., tropical forests). Such capabilities would support efforts to better validate relevant processes in models, to more accurately estimate GPP.

Soil moisture dominates dryness stress on ecosystem production globally

Dryness stress can limit vegetation growth and is often characterized by low soil moisture (SM) and high atmospheric water demand (vapor pressure deficit, VPD). However, the relative role of SM and VPD in limiting ecosystem production remains debated and is difficult to disentangle, as SM and VPD are coupled through land-atmosphere interactions, hindering the ability to predict ecosystem responses to dryness. Here, we combine satellite observations of solar-induced fluorescence with estimates of SM and VPD and show that SM is the dominant driver of dryness stress on ecosystem production across more than 70% of vegetated land areas with valid data. Moreover, after accounting for SM-VPD coupling, VPD effects on ecosystem production are much smaller across large areas. We also find that SM stress is strongest in semi-arid ecosystems. Our results clarify a longstanding question and open new avenues for improving models to allow a better management of drought risk.

Remote sensing of the impact of flash drought events on terrestrial carbon dynamics over China

BACKGROUND

Flash drought poses a great threat to terrestrial ecosystems and influences carbon dynamics due to its unusually rapid onset and increasing frequency in a warming climate. Understanding the response of regional terrestrial carbon dynamics to flash drought requires long-term observations of carbon fluxes and soil moisture at a large scale. Here, MODIS satellite observations of ecosystem productivity and ERA5 reanalysis modeling of soil moisture are used to detect the response of ecosystems to flash drought over China.

RESULTS

The results show that GPP, NPP, and LAI respond to 79-86% of the flash drought events over China, with highest and lowest response frequency for NPP and LAI, respectively. The discrepancies in the response of GPP, NPP, and LAI to flash drought result from vegetation physiological and structural changes. The negative anomalies of GPP, NPP, and LAI occur within 19 days after the start of flash drought, with the fastest response occurring over North China, and slower responses in southern and northeastern China. Water use efficiency (WUE) is increased in most regions of China except for western regions during flash drought, illustrating the resilience of ecosystems to rapid changes in soil moisture conditions.

CONCLUSIONS

This study shows the rapid response of ecosystems to flash drought based on remote-sensing observations, especially for northern China with semiarid climates. Besides, NPP is more sensitive than GPP and LAI to flash drought under the influence of vegetation respiration and physiological regulations. Although the mean WUE increases during flash drought over most of China, western China shows less resilience to flash drought with little changes in WUE during the recovery stage. This study highlights the impacts of flash drought on ecosystems and the necessity to monitor rapid drought intensification.