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Reference carbon cycle dataset for typical Chinese forests via colocated observations and data assimilation.

Honglin HeRong GeXiaoli RenLi ZhangQingqing ChangQian XuGuoyi ZhouZongqiang XieSilong WangHuimin WangQibin ZhangAnzhi WangZe-Xin FanYiping ZhangWeijun ShenHuajun YinLuxiang LinMathew WilliamsGui-Rui Yu
Published in: Scientific data (2021)
Chinese forests cover most of the representative forest types in the Northern Hemisphere and function as a large carbon (C) sink in the global C cycle. The availability of long-term C dynamics observations is key to evaluating and understanding C sequestration of these forests. The Chinese Ecosystem Research Network has conducted normalized and systematic monitoring of the soil-biology-atmosphere-water cycle in Chinese forests since 2000. For the first time, a reference dataset of the decadal C cycle dynamics was produced for 10 typical Chinese forests after strict quality control, including biomass, leaf area index, litterfall, soil organic C, and the corresponding meteorological data. Based on these basic but time-discrete C-cycle elements, an assimilated dataset of key C cycle parameters and time-continuous C sequestration functions was generated via model-data fusion, including C allocation, turnover, and soil, vegetation, and ecosystem C storage. These reference data could be used as a benchmark for model development, evaluation and C cycle research under global climate change for typical forests in the Northern Hemisphere.
Keyphrases
  • climate change
  • human health
  • electronic health record
  • big data
  • quality control
  • air pollution
  • machine learning
  • plant growth