Spatial patterns in temperature sensitivity of soil respiration in China: Estimation with inverse modeling

Tao Zhou, Pei Jun Shi, Da Feng Hui, Yi Qi Luo

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Temperature sensitivity of soil respiration (Q10) is an important parameter in modeling the effects of global warming on ecosystem carbon release. Experimental studies of soil respiration have ubiquitously indicated that Q10 has high spatial heterogeneity. However, most biogeochemical models still use a constant Q10 in projecting future climate change and no spatial pattern of Q10 values at large scales has been derived. In this study, we conducted an inverse modeling analysis to retrieve the spatial pattern of Q10 in China at 8 km spatial resolution by assimilating data of soil organic carbon into a process-based terrestrial carbon model (CASA model). The results indicate that the optimized Q10 values are spatially heterogeneous and consistent to the values derived from soil respiration observations. The mean Q10 values of different soil types range from 1.09 to 2.38, with the highest value in volcanic soil, and the lowest value in cold brown calcic soil. The spatial pattern of Q10 is related to environmental factors, especially precipitation and top soil organic carbon content. This study demonstrates that inverse modeling is a useful tool in deriving the spatial pattern of Q10 at large scales, with which being incorporated into biogeochemical models, uncertainty in the projection of future carbon dynamics could be potentially reduced.

Original languageEnglish (US)
Pages (from-to)982-989
Number of pages8
JournalScience in China, Series C: Life Sciences
Issue number10
StatePublished - 2009
Externally publishedYes


  • Global warming
  • Inverse analysis
  • Q
  • Soil respiration
  • Temperature sensitivity

ASJC Scopus subject areas

  • General Energy
  • General Environmental Science
  • Energy Engineering and Power Technology
  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences
  • Management, Monitoring, Policy and Law
  • Fuel Technology
  • Renewable Energy, Sustainability and the Environment
  • Modeling and Simulation


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