A study led by Prof. ZHANG Nannan from the Xinjiang Institute of Ecology and Geography of the Chinese Academy of Sciences has introduced an innovative geological knowledge-constrained method for extracting entities and relationships from textual data.
A research team led by Prof. MAO Miaohua at the Yantai Institute of Coastal Zone Research of the Chinese Academy of Sciences, has developed a method for predicting storm surges. This innovative approach enhances the quality of typhoon wind field modeling through the use of a hybrid wind field. The researchers created four Machine Learning models to predict storm surges, significantly improving forecasting accuracy when integrated with the Finite Volume Community Ocean Model.
A recent study reveals that global ocean patterns can act as early warning signals for extreme summer rainfall in China. The study identifies how six major oceanic modes influence Summer Extreme Persistent Precipitation—a phenomenon characterized by prolonged heavy rainfall that can lead to severe flooding.
A recent study offers a comprehensive analysis of how updates to the baseline influence the detection of extreme climate events across China. The study highlights that modifying the baseline significantly alters the intensity, trends, and timing of detectable climate signals across the Chinese mainland.
A recent study led by CHEN Yaning from the Xinjiang Institute of Ecology and Geography reveals that the frequency, intensity, and duration of extreme precipitation events in arid Northwest China have significantly increased due to global warming. These changes pose a serious challenge to the region's water resources and disaster management strategies.
A new study has revealed how different types of mycorrhizal fungi associated with trees influence soil organic carbon (SOC) storage in temperate forests. The study, led by researchers from the Institute of Applied Ecology, offers new insights into the important role of tree mycorrhizal type in determining SOC stocks at local scales in a temperate forest.
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