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Shaohong Wu

Researcher at Chinese Academy of Sciences

Publications -  15
Citations -  284

Shaohong Wu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Precipitation & Climate change. The author has an hindex of 6, co-authored 15 publications receiving 156 citations.

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Interannual Variations in Growing-Season NDVI and Its Correlation with Climate Variables in the Southwestern Karst Region of China

TL;DR: It can be found that the reduced temperature and solar radiation, caused by the increase in cloud cover and precipitation, may play important roles in the vegetation cover change in the Karst region.
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A synthesis of ecosystem aboveground productivity and its process variables under simulated drought stress

TL;DR: A global meta‐analysis of ANPP and the four variables (610 observations from 78 studies) for drought duration, intensity and their combination finds forest‐ANPP shows high sensitivity to long‐term moderate drought, whereas grass‐AN PP is more responsive to short‐term drought.
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Identification of key industries of industrial sector with energy-related CO2 emissions and analysis of their potential for energy conservation and emission reduction in Xinjiang, China

TL;DR: This study proposed a method for identifying key industries with CO2 emissions in Xinjiang's industrial sector and analyzed their energy consumption and CO2 emission characteristics and recommended that the local government exploit the potential of key industries for EC&ER by improving their energy efficiency.
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Revealing the Fingerprint of Climate Change in Interannual NDVI Variability among Biomes in Inner Mongolia, China

TL;DR: The result showed that the greatest variability of the growing season NDVI occurred in typical steppe and desert steppe, with forest and desert most stable, and the large slope of NDVI variation to temperature for desert implied that desert tended to amplify temperature variations, whereas other biomes displayed a capacity to buffer climate fluctuations.
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Impact of spring phenology variation on GPP and its lag feedback for winter wheat over the North China Plain.

TL;DR: A multiple linear regression model was established to quantitatively determine the contributions of the time lag effects of hydrothermal variation on GUD, and showed that the rate of change in curvature algorithm (RCCmax) had better performance in capturing the spatiotemporal variation of winter wheat GUD relative to the other three methods.