Severe hypoxia was observed in the submarine canyon to the east of the Changjiang estuary in July 14, 2015, two days after typhoon Chan-hom. The oxygen concentration reached as low as 2.0 mg/L and occupied a water column of about 25 m. A ROMS model was configured to explore the underlying physical processes causing the formation of hypoxia. Chan-hom passed through the Changjiang estuary during the neap tide. The stratification was completely destroyed in the shallow nearshore region when typhoon passing. However, it was maintained in the deep canyon, though the surface mixed layer was largely deepened. The residual water in the deep canyon is considered to be the possible source of the later hypoxia. After Chan-hom departure, not only the low salinity plume water spread further off shore, but also the sea surface temperature (SST) rewarmed quickly. Both changes helped strengthen the stratification and facilitate the formation of hypoxia. It was found that the surface heat flux, especially the solar short wave radiation dominated the surface re-warming, the off shore advection of the warmer Changjiang Diluted Water (CDW) also played a role. In addition to the residual water in the deep canyon, the Taiwan Warm Current (TWC) was found to flow into the deep canyon pre- and soon post- Chan-hom, which was considered to be the original source of the hypoxia water.
Plant phenology is the study of the timing of recurrent biological events and the causes of their timing with regard to biotic and abiotic forces. Plant phenology affects the structure and function of terrestrial ecosystems and determines vegetation feedback to the climate system by altering the carbon, water and energy fluxes between the vegetation and near-surface atmosphere. Therefore, an accurate simulation of plant phenology is essential to improve our understanding of the response of ecosystems to climate change and the carbon, water and energy balance of terrestrial ecosystems. Phenological studies have developed rapidly under global change conditions, while the research of phenology modeling is largely lagged. Inaccurate phenology modeling has become the primary limiting factor for the accurate simulation of terrestrial carbon and water cycles.Understanding the mechanism of phenological response to climate change and building process-based plant phenology models are thus important frontier issues. In this review, we first summarized the drivers of plant phenology and overviewed the development of plant phenology models. Finally, we addressed the challenges in the development of plant phenology models and highlighted that coupling machine learning and Bayesian calibration into process-based models could be a potential approach to improve the accuracy of phenology simulation and prediction under future global change conditions. 相似文献