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Global change has substantially shifted vegetation phenology, with important implications in the carbon and water cycles of terrestrial ecosystems. Various vegetation phenology datasets have been developed using remote sensing data; however, the significant uncertainties in these datasets limit our understanding of ecosystem dynamics in terms of phenology. It is therefore crucial to generate a reliable large-scale vegetation phenology dataset, by fusing various existing vegetation phenology datasets, to provide comprehensive and accurate estimation of vegetation phenology with fine spatiotemporal resolution. In this study, we merged four widely used vegetation phenology datasets to generate a new dataset using the Reliability Ensemble Averaging fusion method. The spatial resolution of the new dataset is 0.05° and its temporal scale spans 1982–2022. The evaluation using the ground-based PhenoCam dataset from 280 sites indicated that the accuracy of the newly merged dataset was improved substantially. The start of growing season and the end of growing season in the newly merged dataset had the largest correlation (0.84 and 0.71, respectively) and accuracy in terms of the root mean square error (12 and 17 d, respectively). Using the new dataset, we found that the start of growing season exhibits a significant (p < 0.01) advanced trend with a rate of approximately 0.24 d yr−1, and that the end of growing season exhibits a significant (p < 0.01) delayed trend with a rate of 0.16 d yr−1 over the period 1982–2022. This dataset offers a unique and novel source of vegetation phenology data for global ecology studies. [ABSTRACT FROM AUTHOR]

Vegetation phenology and hydrological cycles are closely interacted from leaf and species levels to watershed and global scales. As one of the most sensitive biological indicators of climate change, plant phenology is essential to be simulated accurately in hydrological models. Despite the Soil and Water Assessment Tool (SWAT) has been widely used for estimating hydrological cycles, its lack of integration with the phenology module has led to substantial uncertainties. In this study, we developed a process-based vegetation phenology module and coupled it with the SWAT-Carbon model to investigate the effects of vegetation dynamics on runoff in the upper reaches of Jinsha River watershed in China. The modified SWAT-Carbon model showed reasonable performance in phenology simulation, with root mean square error (RMSE) of 9.89 days for the start-of-season (SOS) and 7.51 days for the end-of-season (EOS). Simulations of both vegetation dynamics and runoff were also substantially improved compared to the original model. Specifically, the simulation of leaf area index significantly improved with the coefficient of determination (R2) increased by 0.62, the Nash–Sutcliffe efficiency (NSE) increased by 2.45, and the absolute percent bias (PBIAS) decreased by 69.0 % on average. Additionally, daily runoff simulation also showed notably improvement, particularly noticeable in June and October, with R2 rising by 0.22 and NSE rising by 0.43 on average. Our findings highlight the importance of integrating vegetation phenology into hydrological models to enhance modeling performance. [ABSTRACT FROM AUTHOR]

Vegetation phenological shifts impact the terrestrial carbon and water cycle and affect the local climate system through biophysical and biochemical processes. Dynamic global vegetation models (DGVMs), serving as pivotal simulation tools for investigating climate impacts on terrestrial ecosystem processes, incorporate representations of vegetation phenological processes. Nevertheless, it is still a challenge to achieve an accurate simulation of vegetation phenology in the DGVMs. Here, we developed and implemented spring and autumn phenology algorithms into one of the DGVMs, LPJ-GUESS. The new phenology modules are driven by temperature and photoperiod and are parameterized for deciduous trees and shrubs by using remotely sensed phenological observations and the reanalysis data from ERA5. The results show that the LPJ-GUESS with the new phenology modules substantially improved the accuracy in capturing the start and end dates of growing seasons. For the start of the growing season, the simulated RMSE for deciduous trees and shrubs decreased by 8.04 and 17.34 d, respectively. For the autumn phenology, the simulated RMSE for deciduous trees and shrubs decreased by 22.61 and 17.60 d, respectively. Interestingly, we have also found that differences in the simulated start and end of the growing season also alter the simulated ecological niches and competitive relationships among different plant functional types (PFTs) and subsequentially influence the terrestrial carbon and water cycles. Hence, our study highlights the importance of accurate phenology estimation to reduce the uncertainties in plant distribution and terrestrial carbon and water cycling. [ABSTRACT FROM AUTHOR]

Over the past decades, global warming has led to a lengthening of the time window during which temperatures remain favorable for carbon assimilation and tree growth, resulting in a lengthening of the green season. The extent to which forest green seasons have tracked the lengthening of this favorable period under climate warming, however, has not been quantified to date. Here, we used remote sensing data and long‐term ground observations of leaf‐out and coloration for six dominant species of European trees at 1773 sites, for a total of 6060 species–site combinations, during 1980–2016 and found that actual green season extensions (GS: 3.1 ± 0.1 day decade−1) lag four times behind extensions of the potential thermal season (TS: 12.6 ± 0.1 day decade−1). Similar but less pronounced differences were obtained using satellite‐derived vegetation phenology observations, that is, a lengthening of 4.4 ± 0.13 and 7.5 ± 0.13 day decade−1 for GS and TS, respectively. This difference was mainly driven by the larger advance in the onset of the thermal season compared to the actual advance of leaf‐out dates (spring mismatch: 7.2 ± 0.1 day decade−1), but to a less extent caused by a phenological mismatch between GS and TS in autumn (2.4 ± 0.1 day decade−1). Our results showed that forest trees do not linearly track the new thermal window extension, indicating more complex interactions between winter and spring temperatures and photoperiod and a justification of demonstrating that using more sophisticated models that include the influence of chilling and photoperiod is needed to accurately predict spring phenological changes under warmer climate. They urge caution if such mechanisms are omitted to predict, for example, how vegetative health and growth, species distribution and crop yields will change in the future. [ABSTRACT FROM AUTHOR]