Extreme climate events, particularly droughts, pose significant threats to vegetation, severely impacting ecosystem functionality and resilience. However, the limited temporal resolution of current satellite data hinders accurate monitoring of vegetation's diurnal responses to these events. To address this challenge, we leveraged the advanced satellite ECOSTRESS, combining its high-resolution evapotranspiration (ET) data with a LightGBM model to generate the hourly continuous ECOSTRESS-based ET (HC-ETECO) for the middle and lower reaches of the Yangtze River Basin (YRB) from 2015 to 2022. This dataset showed strong agreement with both ground-based and satellite observations. Utilizing the SPEI, we identified the significant drought period: September to November 2019 and August to September 2022. By integrating hourly Solar-Induced Chlorophyll Fluorescence (SIF) data, we observed that during drought period, the typical afternoon peak in SIF was absent. In contrast to non-drought period, morning photosynthesis and SIF-based Water Use Efficiency (WUESIF) anomalies were primarily driven by high Vapor Pressure Deficit (VPD), while the afternoon reductions were influenced by both high VPD and low Soil Moisture (SM) as the drought progressed. Our simulated HC-ETECO data revealed that ET in the middle and lower reaches of the YRB was consistently lower than normal during drought period. Attribution analysis indicated that this reduction was primarily driven by midday temperature increases and high VPD, suggesting that vegetation in the region copes with drought stress predominantly by limiting water loss. These findings highlight the utility of the generated high-resolution ET dataset in advancing our understanding of vegetation dynamics under drought climate conditions. This work provides critical insights for enhancing climate adaptation strategies and enhancing ecosystem management practices in the face of increasing climate variability.

The Amazon, the Earth's largest tropical forest, plays a critical role in the global carbon cycle, acting as a significant carbon sink. Recent studies, however, indicate a decline in its carbon sequestration capacity due to climate variability, intensive deforestation, and fires. This study aims to examine the impacts of these factors on the carbon dynamics of the Amazon over a centennial scale based on dynamic global vegetation models (DGVMs) of Trendy-v11. It was found that the Amazon region exhibited significant spatiotemporal variations in net land carbon (C) fluxes, and was a net C sink (40.02 ± 242.64 Tg C yr-1) during 1901-2021. The Amazonian net biome productivity (NBP) showed a 6-decades-scale shift from a decreasing trend (-3.78 Tg C yr-2) during 1901-1959 to an increasing trend (2.39 Tg C yr-2) during 1960-2021. The Amazonian NBP was negatively related to air temperature while positively related to dry-season precipitation during 1901-2021. Furthermore, the increase of atmospheric CO2 concentration during 1901-2020 enhanced Amazonian NBP by 36.40 ± 8.39 Pg C, which was largely offset by land use change (-18.84 ± 12.02 Pg C) and climate change (-10.03 ± 5.00 Pg C). Our findings underscore the critical need for sustainable management practices in the Amazon to enhance its C sink and preserve its function in the global climate system.

Observations of CH4 fluxes from 27 sites across the wetland and grassland on the QTP.

China's forests play a vital role in the global carbon cycle through the absorption of atmospheric CO2 to mitigate climate change caused by the increase of anthropogenic CO2. It is essential to evaluate the carbon sink potential (CSP) of China's forest ecosystem. Combining NDVI, field-investigated, and vegetation and soil carbon density data modeled by process-based models, we developed the state-of-the-art learning ensembles model of process-based models (the multi-model random forest ensemble (MMRFE) model) to evaluate the carbon stocks of China's forest ecosystem in historical (1982-2021) and future (2022-2081, without NDVI-driven data) periods. Meanwhile, we proposed a new carbon sink index (CSindex) to scientifically and accurately evaluate carbon sink status and identify carbon sink intensity zones, reducing the probability of random misjudgments as a carbon sink. The new MMRFE models showed good simulation results in simulating forest vegetation and soil carbon density in China (significant positive correlation with the observed values, r = 0.94, P < 0.001). The modeled results show that a cumulative increase of 1.33 Pg C in historical carbon stocks of forest ecosystem is equivalent to 48.62 Bt CO2, which is approximately 52.03% of the cumulative increased CO2 emissions in China from 1959 to 2018. In the next 60 years, China's forest ecosystem will absorb annually 1.69 (RCP45 scenario) to 1.85 (RCP85 scenario) Bt CO2. Compared with the carbon stock in the historical period, the cumulative absorption of CO2 by China's forest ecosystem in 2032-2036, 2062-2066, and 2077-2081 are approximately 11.25-39.68, 110.66-121.49 and 101.31-111.11 Bt CO2, respectively. In historical and future periods, the medium and strong carbon sink intensity regions identified by the historical CSindex covered 65% of the total forest area, cumulative absorbing approximately 31.60 and 65.83-72.22 Bt CO2, respectively. In the future, China's forest ecosystem has a large CSP with a non-continuous increasing trend. However, the CSP should not be underestimated. Notably, the medium carbon sink intensity region should be the priority for natural carbon sequestration action. This study not only provides an important methodological basis for accurately estimating the future CSP of forest ecosystem but also provides important decision support for future forest ecosystem carbon sequestration action.

Being China’s first comprehensive space solar observatory, ASO-S (Advanced Space-based Solar Observatory) has been launched into orbit at 7:43 Beijing Time on October 9, 2022. A very brief introduction on ASO-S is presented here, including the background, scientific goals, payload deployments, mission assembly, and organizations. A short prospect is made for the operation of the mission and the future scientific output.

As a trace gas involved in hydration during plant photosynthesis, carbonyl sulfide (COS) and its leaf relative uptake rate (LRU) is used to reduce the uncertainties in simulations of gross primary productivity (GPP). In this study, 101 independent observations were collected from 22 studies. We extracted the LRU, stomatal conductance (gs), canopy COS and carbon dioxide (CO2) fluxes, and relevant environmental conditions (i.e., light, temperature, and humidity), as well as the atmospheric COS and CO2 concentrations (Ca,COS and Ca,CO2). Although no evidence was found showing that gs regulates LRU, they responded in opposite ways to diurnal variations of environmental conditions in both mixed forests (LRU: Hedges’d = −0.901, LnRR = −0.189; gs: Hedges’d = 0.785, LnRR = 0.739) and croplands dominated by C3 plants (Hedges’d = −0.491, LnRR = −0.371; gs: Hedges’d = 1.066, LnRR = 0.322). In this process, the stomata play an important role in COS assimilation (R2 = 0.340, p = 0.020) and further influence the interrelationship of COS and CO2 fluxes (R2 = 0.650, p = 0.000). Slight increases in light intensity (R2 = 1, p = 0.002) and atmospheric drought (R2 = 0.885, p = 0.005) also decreased the LRU. The LRU saturation points of Ca,COS and Ca,CO2 were observed when ΔCa,COS ≈ 13 ppt (R2 = 0.580, p = 0.050) or ΔCa,CO2 ≈ −18 ppm (R2 = 0.970, p = 0.003). This study concluded that during plant photosynthesis and COS assimilation, light and water conditions co-regulated the stomata and LRU.

Forest ecosystems play a significant role in climate change mitigation and uptake a larger amount of atmospheric CO2 than other terrestrial ecosystems via photosynthesis process in form of gross primary production (GPP). The photosynthesis or GPP is largely determined by the photosynthetic capacity of vegetation (i.e., maximum rate of carboxylation, V-cmax) in ecosystem models. However, considerable uncertainties of V-cmax estimates may limit our potential to address scientific issues of GPP related to the increasing emission of atmospheric CO2. Recently, solar-induced chlorophyll fluorescence (SIF) signals have been used as a proxy for resolving photosynthesis. In this study, the biochemical and structural parameters were retrieved from hyperspectral reflectance and fluorescence quantum efficiency (FQEs) was retrieved from ground-based SIF. Then, retrieved parameters were incorporated into the Soil Canopy Observation Photosynthesis and Energy (SCOPE) model to explore the potential of ground-based SIF to track V-cmax variability for a subtropical evergreen mixed forest. Then, SIF-derived V-cmax was used to parameterize Boreal Ecosystem Production Simulator (BEPS) model to simulate the GPP. With retrieved vegetative parameters and FQEs, the ground-based SIF was strongly correlated with the model-based SIF simulation at O-2-B and O-2-A bands, demonstrating that the coefficient of determination (R-2) improved from 0.15 (constant values) to 0.60 (retrieved values) for SIFB and from 0.79 to 0.94 for SIFA simulation. Using SIF-derived V-cmax, the R-2 value of simulated GPP against eddy covariance-based measurements substantially increased from 0.18 (constant V-cmax) to 0.38 (SIF-derived V-cmax) for dry season and from 0.56 to 0.67 for wet season respectively. The utilization of SIF-derived V-cmax with its corrected temperature response function reduced the relative error in annual GPP simulations by 24.9%. Our results support the significant references toward reducing unbiased SIF simulation and highlighting the potential of ground SIF in deriving V-cmax at the site scale for defining forest management options.

Solar‐induced chlorophyll fluorescence (SIF) provides remotely sensible signals for monitoring gross primary production (GPP). Ground‐based multiangle observations of both red and far‐red SIF above wheat and maize canopies were conducted to examine angular effects on SIF. With these new measurements, we were able for the first time to refine and apply an algorithm developed for angular normalization of both red and far‐red SIF measurements. The angular normalization improved the correlation of SIF with GPP derived from eddy covariance measurements at the instantaneous scale (1 min), with increases of the diurnal coefficients of determination (of sunlit SIF with GPP) up to 0.21 for far‐red SIF and 0.3 for red SIF based on analysis on 6 sunny days. The improvement was slightly smaller for far‐red SIF than for red SIF, attributing to that the observed angular variation of SIF in the red band was greater than that in the far‐red band due to weaker multiple scattering in the red band in the canopy. In addition, at the hourly time scale, far‐red sunlit SIF shows its advantage to track GPP for heterogonous canopies, while angular normalization of red SIF is effective for homogeneous canopies. In comparison with another angular normalization method based on the escape ratio using datasets over both wheat and maize canopies, the two kinds of method show similar ability to improve the correlation between SIF and GPP, while the results suggest a limitation of SIF in estimating GPP for dense canopies where the fraction of shaded leaves are large.

The Moderate Resolution Imaging Radiometer (MODIS) is a primary instrument in the NASA Earth Observing System (EOS) which was designed for monitoring global terrestrial vegetation. MODIS provides global estimates of 8-day mean gross primary productivity (GPP) at 1-km spatial resolution. In this study, the MODIS GPP algorithm using light use efficiency (LUE) approach and the Integrated Carbon-Canadian Land Surface Scheme (IC-CLASS) based on Farquhar photosynthetic model and a sunlit and shaded leaf separation scheme was evaluated against eddy covariance (EC) measured GPP in a variety of ecosystems in Canada. Although GPP simulated by the two models agreed well when they were averaged over Canadian landmass, there were systematic differences between them in spatial distribution patterns. These differences were due to inherent shortcomings of the LUE modeling approach. When a constant maximum LUE value is specified for each biome type, this simplification cannot appropriately deal with the shaded leaf contribution to total canopy GPP. When GPP was simulated by IC-CLASS with the separation of sunlit and shaded leaves, the biases were minimized. Compared with daily and annual GPP derived from EC flux data at 7 Fluxnet Canada sites, IC-CLASS performed better than the MODIS GPP algorithm. The differences between IC-CLASS and MODIS GPP were larger in more clumped canopies (i.e. forests), resulting from the increase in the fraction of shaded leaves. Thus, the LUE models should be improved to consider different LUEs in sunlit and shaded portions of the canopy for their effective and reliable estimation of GPP at regional scale.

Aim: Geographical variation of range size is thought to be linked to climate seasonality and Quaternary glacial-interglacial oscillations. In addition, the importance of long-term climate stability is expected to be modulated by species' migration abilities. For plants, growth forms integrate traits that affect migration ability. Hence, we assess the relative importance of short- and long-term climate stability for range size patterns in plants and whether long-term stability is more important for more poorly migrating growth forms. Location: China. Time period: Last 21 ka. Major taxa studied: Vascular plants. Methods: A dataset including >30,000 vascular plants was used to quantify geographical patterns of range size across China for all species and for eight growth forms separately. Spatial and non-spatial regressions with information-theoretical multi-model selection were performed to estimate the explanatory importance of climate seasonality and climate-change velocity from the Last Glacial Maximum to the present. The proportion of endemism for each growth form was also calculated. Results: Large geographical range sizes were generally observed in regions with strong climate seasonality and high climate-change velocity. The association between range size and velocity was stronger for perennial herbs and shrubs than for ferns, annual herbs, climbers and trees and stronger for small than for large trees; that is, more important for the growth forms with lower migration capacities. In line with this interpretation, growth forms strongly influenced by velocity also tend to have high proportions of endemism, which is also consistent with a relatively low ability to migrate. Main conclusions: Range size patterns of vascular plants in China are shaped by both climate seasonality and long-term climate stability, even though the region had a relatively mild influence from the Pleistocene glaciations, with a stronger influence of palaeoclimate stability on more poorly migrating groups. The differential influences of palaeoclimate stability across growth forms suggest that it needs further attention in research and management and that different growth forms are likely to respond in different ways to future climate changes.

The soil water stress factor (f(w)) and the maximum photosynthetic carboxylation rate at 25 degrees C (V-cmax) are two of the most important parameters for estimating evapotranspiration and carbon uptake of vegetation. Ecologically these two parameters have different temporal variations and thus their optimization in ecosystem models poses a challenge. To minimize the temporal scale effect, we propose a three-stage approach to optimize these two parameters using an ensemble Kalman filter (EnKF), based on observations of latent heat (LE) and gross primary productivity (GPP) fluxes at three flux tower sites in 2009. First, the EnKF is applied daily to obtain precursor estimates of V-cmax and f(w). Then, V-cmax is optimized at different time scales, assuming f(w) is unchanged from the first step. The best temporal period is then determined by analyzing the coefficient of determination (R-2) of GPP and LE between simulation and observation. Finally, the daily f(w) value is optimized for rain-free days corresponding to the V-cmax curve from the best temporal period. We found that the variations of optimized f(w) are largely explained by soil water content in the summer. In the spring, the optimized f(w) shows a smooth increase following the rise of soil temperature, indicating that f(w) may respond to the development of fine roots, which is related to the amount of accumulated heat in the soil. The optimized V-cmax generally follows a pattern of a rapid increase at the leaf expansion stage in the spring, small variation in summer, and an abrupt decrease at foliage senescence. With eddy covariance fluxes data, data assimilation with a EnKF can retrieve the seasonal variations of water uptake and photosynthetic parameters in an ecosystem model, and such gives clues on how to model forest responses to water stress. (C) 2014 Elsevier B.V. All rights reserved.

Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among four process models (ecosys, C-CLASS, Can-IBIS and 3PG) and an inventory model (CBM-CFS3) over a 2500 ha landscape in the Oyster River (OR) area of British Columbia, Canada. The process models used local weather data to simulate net primary productivity (NPP), net ecosystem productivity (NEP) and net biome productivity (NBP) from 1920 to 2005. Other inputs used by the process and inventory models were generated from soil, land cover and disturbance records. During a period of intense disturbance from 1928 to 1943, simulated NBP diverged considerably among the models. This divergence was attributed to differences among models in the sizes of detrital and humus C stocks in different soil layers to which a uniform set of soil C transformation coefficients were applied during disturbances. After the disturbance period, divergence in modelled NBP among models was much smaller, and attributed mainly to differences in simulated NPP caused by different approaches to modelling weather effects on productivity. In spite of these differences, age-detrended variation in annual NPP and NEP of closed canopy forest stands was negatively correlated with mean daily maximum air temperature during July September (Tamax) in all process models (R=0.4-0.6), indicating that these correlations were robust. The negative correlation between Tamax and NEP was attributed to different processes in different models, which were tested by comparing CO2 fluxes from these models with those measured by eddy covariance (EC) under contrasting air temperatures (Ta). The general agreement in sensitivity of annual NPP to Tamax among the process models led to the development of a generalized