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Revealing how plant architecture affects the responses of canopy temperature depression (CTD), water use (WU), and grain yield to population density (PD) in wheat (Triticum aestivum L.) would help explore a water‐saving pathway related to managing population. This study was conducted over three consecutive years under rainfed and supplemental irrigation conditions. The flat‐leafed Jinmai 47 and upright‐leafed Jing 411 genotypes were grown at high population density (HD), medium population density (MD), and low population density (LD); seeding rates were 450, 675, and 900 seeds m−2, respectively). CTD peaked at MD in Jinmai 47 but reached the highest at HD in Jing 411. WU generally increased with increasing PD but remained unchanged from LD to MD in Jinmai 47 under the two water conditions, while it remained unchanged from MD to HD under supplemental irrigation condition in Jing 411. Yield increased with increasing density in both genotypes under the two water conditions. It is thus clear that Jinmai 47 gained higher yield but did not consume more water at MD than at LD under the two water conditions; Jing 411 obtained higher yield at the expense of similar amount of water at HD than at MD under supplemental irrigation condition. The grain yield and WU responses of wheat crops to PD implied that through appropriately adjusting PD in combination with plant architecture and water condition, the dual goals of optimizing yield and effectively saving irrigation water could be realized synchronously. Core Ideas: Plant architecture influences canopy temperature and water use responses to population density in wheat.Canopy temperature depression reached its peak and then declined with increasing population density in wheat.Water use and grain yield generally increased as sowing density increased from 450 to 900 seeds m−2.Water use did not change at sowing density of 450–675 seeds m−2 for Jinmai 47 and 675–900 seeds m−2 for Jing 411. [ABSTRACT FROM AUTHOR]

Assessing the terrestrial vegetation carbon density (TVCD) is crucial for evaluating the national carbon balance. However, current national‐scale TVCD assessments show strong disparities, despite the good estimation method of their underlying models. Here, we attribute this contradiction to a flaw in the methods of using multimodel simulation results, which ignore the connections between results, leading to an overoptimistic evaluation of the multimodel ensemble mean (MMEM) method.Thus, using the state‐of‐the‐art multimodel random forest ensemble (MMRFE) method to integrate the results of 10 models, we reproduced Chinese TVCD data during 1982–2010.Compared with the nationally averaged TVCD field investigation data (27 ± 26 Mg C/ha), we found that the results of five models were overestimated by 7.4%–85.2%, and the remaining models were underestimated by 3.7%–77.8%. The MMEM TVCD method produced an overestimation of 2%, but the MMRFE method produced an underestimation of only 0.2%. Additionally, the summary Taylor diagrams of the TVCD at the national and ecosystem (forest, shrub, grass and crop ecosystems) scales all showed that the MMRFE TVCD produced the smallest standard deviations and root mean square deviations and the highest correlation coefficients. Furthermore, the MMRFE TVCDs were all significantly positively correlated with the normalized difference vegetation index (NDVI), and they had the same increasing trend, but an opposite variation trend from the MMEM TVCD and NDVI. This result implied that the spatiotemporal variation modes of the MMRFE TVCD were consistent with those of the NDVI. The results suggested that compared with the traditional MMEM method, the MMRFE TVCD and its spatiotemporal variation modes were more similar to the real TVCD.In conclusion, the MMRFE method can effectively improve the accuracy of national‐scale TVCD estimation, and effectively reduce the uncertainty of large‐scale terrestrial vegetation carbon estimation processes. Notably, we provide a new method that uses a machine learning approach to mine multimodel terrestrial carbon information to reduce the uncertainty in the estimation of terrestrial ecosystem carbon components. [ABSTRACT FROM AUTHOR]

Predicting the change in carbon storage in regions of high carbon uptake and those under highly intensive human disturbance is crucial for regional ecosystem management to promote sustainable development of the economy and ecology in the future. We use a process‐based model to estimate the terrestrial carbon storage in Yangtze River Economic Belt (YREB) and to predict the change of carbon storage over the next 100 years. The results show that the vegetation carbon (VC) and soil organic carbon (SOC) storage were 8.97 and 28.85 Pg C in the YREB from 1981 to 2005, respectively. The highest VC density is distributed in the southern region of the YREB, and the highest SOC density distributes in subalpine and alpine area of the western region of the YREB. Carbon storage in the YREB continued to increase from 1981 to 2005 and in future projections, under both the representative concentration pathway 4.5 (RCP4.5) and the RCP8.5 scenarios. The increased rate of carbon storage in the YREB under the RCP8.5 scenario is higher than that under the RCP4.5 scenario. Under the RCP4.5 scenario, the increasing trend of VC storage tends to be reduced after the 2060s; conversely, the increase of both VC and SOC is accelerated after the 2050s under the RCP8.5 scenario. The SOC density in Western Sichuan will decrease in the future, especially under the RCP8.5 scenario. Western Sichuan has the highest SOC density in the YREB; therefore, it is important to manage the ecosystems there in order to cope with significant warming. The positive impact of warming and the CO2 fertilization effect on vegetation growth and carbon uptake will be predominantly attributed to the increase of terrestrial carbon storage in the YREB. However, warming will stimulate the decomposition of soil organic carbon, contributing directly to reducing SOC storage in high‐altitude regions (e.g., alpine and subalpine regions of Western Sichuan). [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems provide multiple services interacting in complex ways. However, most ecosystem services (ESs) models (e.g., InVEST and ARIES) ignored the relationships among ESs. Process‐based models can overcome this limitation, and the integration of ecological models with remote sensing data could greatly facilitate the investigation of the complex ecological processes. Therefore, based on the Carbon and Exchange between Vegetation, Soil, and Atmosphere (CEVSA) models, we developed a process‐based ES model (CEVSA‐ES) integrating remotely sensed leaf area index to evaluate four important ESs (i.e., productivity provision, carbon sequestration, water retention, and soil retention) at annual timescale in China. Compared to the traditional terrestrial biosphere models, the main innovation of CEVSA‐ES model was the consideration of soil erosion processes and its impact on carbon cycling. The new version also improved the carbon‐water cycle algorithms. Then, the Sobol and DEMC methods that integrated the CEVSA‐ES model with nine flux sites comprising 39 site‐years were used to identify and optimize parameters. Finally, the model using the optimized parameters was validated at 26 field sites comprising 135 site‐years. Simulation results showed good fits with ecosystem processes, explaining 95%, 92%, 76%, and 65% interannual variabilities of gross primary productivity, ecosystem respiration, net ecosystem productivity, and evapotranspiration, respectively. The CEVSA‐ES model performed well for productivity provision and carbon sequestration, which explained 96% and 81% of the spatial‐temporal variations of the observed annual productivity provision and carbon sequestration, respectively. The model also captured the interannual trends of water retention and soil erosion for most sites or basins. Plain Language Summary: Terrestrial ecosystems simultaneously provide multiple ecosystem services (ESs). The common environmental drivers and internal mechanisms lead to nonlinear and dynamic relationships among ESs. Assessing the spatiotemporal changes of ESs have recently emerged as an element of ecosystem management and environmental policies. However, appropriate methods linking ESs to biogeochemical and biophysical processes are still lacking. In this study, we developed a process‐based model Carbon and Exchange between Vegetation, Soil, and Atmosphere (CEVSA‐ES) that integrates remote sensing data for evaluating ESs. We first described the model framework and detailed algorithms of the processes related to ESs. Then a model‐fusion method was applied to optimize parameters to which the model was sensitive and to improve model performance based on multi‐source observational data. The calibrated CEVSA‐ES model showed good performance for carbon and water fluxes (i.e., gross primary productivity, ecosystem respiration, net ecosystem productivity, and evapotranspiration). The CEVSA‐ES model performed well for productivity provision, and carbon sequestration. It also captured the interannual trends of water retention and soil erosion for most sites or basins in Chinese terrestrial ecosystems. The CEVSA‐ES model not only has the potential to improve the accuracy of simulated ESs, but also can capture the relationships among ESs, which could support the trade‐offs and synergies among ESs. Key Points: We developed an ecosystem service model Carbon and Exchange between Vegetation, Soil, and Atmosphere‐ecosystem services (CEVSA‐ES) that integrates ecosystem processes with satellite‐based dataAccounting for soil retention/erosion and its impact on carbon cycling was the main difference from other process‐based modelsThe CEVSA‐ES model with optimized parameters explained 47%–96% of the spatial and temporal variations of four ecosystem services in China [ABSTRACT FROM AUTHOR]

Great understanding of the genotypic difference in diurnal stomatal conductance (gs) pattern and the key determinants of the pattern is important for saving water by adopting cultivars appropriately. Fifteen wheat genotypes were studied under different soil conditions and various meteorological conditions with pot cultivation in rain shelter for two years. Genotype and air humidity were found to be key determinants of diurnal gs pattern. All genotypes under low relative humidity (LRH) and most genotypes under high relative humidity (HRH) displayed a gradual decline pattern from morning through the afternoon. Under moderate relative humidity (MRH), all genotypes present a single‐peak curve pattern, but they differed in peak time, which may lead to unreliable gs comparison between genotypes and get ostensible contrasting materials. The stomatal conductance was significantly different among genotypes under LRH and the increased gs magnitude is also significantly different when it was compared between LRH and HRH. The present results provide new thinking for selecting and adopting appropriate cultivars with specific stomata traits for the area with various meteorological conditions. [ABSTRACT FROM AUTHOR]

Changes in vegetation phenology directly reflect the response of vegetation growth to climate change. In this study, using the Normalized Difference Vegetation Index dataset from 1982 to 2015, we extracted start date of vegetation growing season (SOS), end date of vegetation growing season (EOS), and length of vegetation growing season (LOS) in the middle and eastern Eurasia region and evaluated linear trends in SOS, EOS, and LOS for the entire study area, as well as for four climatic zones. The results show that the LOS has significantly increased by 0.27 days/year, mostly due to a significantly advanced SOS (−0.20 days/year) and a slightly delayed EOS (0.07 days/year) over the entire study area from 1982 to 2015. The vegetation phenology trends in the four climatic zones are not continuous throughout the 34‐year period. Furthermore, discrepancies in the shifting patterns of vegetation phenology trend existed among different climatic zones. Turning points (TP) of SOS trends in the Cold zone, Temperate zone, and Tibetan Plateau zone occurred in the mid‐ or late 1990s. The advanced trends of SOS in the Cold zone, Temperate zone, and Tibetan Plateau zone exhibited accelerated, stalled, and reversed patterns after the corresponding TP, respectively. The TP did not occurred in Cold‐Temperate zone, where the SOS showed a consistent and continuous advance. TPs of EOS trends in the Cold zone, Cold‐Temperate zone, Temperate zone, and Tibetan Plateau zone occurred in the late 1980s or mid‐1990s. The EOS in the Cold zone, Cold‐Temperate zone, Temperate zone, and Tibetan Plateau zone showed weak advanced or delayed trends after the corresponding TP, which were comparable with the delayed trends before the corresponding TP. The shift patterns of LOS trends were primarily influenced by the shift patterns of SOS trends and were also heterogeneous within climatic zones. [ABSTRACT FROM AUTHOR]

An anomalous reduction in tree growth indices has been detected in tree ring records from many circumpolar northern latitude sites in recent years, which was known as the divergence phenomenon. In order to understand whether the divergence phenomenon appeared at altitudinal treelines in east edge of Tibetan Plateau, we analyzed the correlation coefficients between tree growth and climate variables before and after recent climate warming, respectively. The results showed that divergence phenomenon took place and varied with sites. Compared with those in 1955-1982, the correlation coefficient of growth to mean temperature in June declined during 1983-2012. In Songpan site, more than half of the sampled trees showed an increasing positive correlation with temperature from July to September, while others turned to be a negative correlation. The positive responses to the mean temperature in previous-year November and December declined in Markang site. Only in Miyaluo site, the positive correlation between tree ring index and temperature from July to September increased. Both the anomalous reduction or disappearance of the responses to the mean temperature and the divergent growth responses suggested that the divergence phenomenon was universal at altitudinal treelines in east edge of Tibetan Plateau, which had potentially significant influences on the estimation of forest productivity and climatic reconstructions based on tree ring. [ABSTRACT FROM AUTHOR]

The response of ecosystems to different magnitudes of climate warming and corresponding precipitation changes during the last few decades may provide an important reference for predicting the magnitude and trajectory of net primary productivity ( NPP) in the future. In this study, a process-based ecosystem model, Carbon Exchange between Vegetation, Soil and Atmosphere ( CEVSA), was used to investigate the response of NPP to warming at both national and subregional scales during 1961-2010. The results suggest that a 1.3°C increase in temperature stimulated the positive changing trend in NPP at national scale during the past 50 years. Regardless of the magnitude of temperature increase, warming enhanced the increase in NPP; however, the positive trend of NPP decreased when warming exceeded 2°C. The largest increase in NPP was found in regions where temperature increased by 1-2°C, and this rate of increase also contributed the most to the total increase in NPP in China's terrestrial ecosystems. Decreasing precipitation depressed the positive trend in NPP that was stimulated by warming. In northern China, warming depressed the increasing trend of NPP and warming that was accompanied by decreasing precipitation led to negative changing trends in NPP in large parts of northern China, especially when warming exceeded 2°C. However, warming stimulated the increase in NPP until warming was greater than 2°C, and decreased precipitation helped to increase the NPP in southern China. [ABSTRACT FROM AUTHOR]