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Discussion papers
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 03 Sep 2019

Submitted as: data description paper | 03 Sep 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

Spatial-and temporal-patterns of global soil heterotrophic respiration in terrestrial ecosystems

Xiaolu Tang1,2, Shaohui Fan3, Manyi Du4, Wenjie Zhang5,6, Sicong Gao6, Shibin Liu1, Guo Chen1, Zhen Yu7, Yitong Yao8, and Wunian Yang1 Xiaolu Tang et al.
  • 1College of Earth Science, Chengdu University of Technology, Chengdu 610059, Sichuan, P.R. China
  • 2State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu 610059, P. R. China
  • 3Key laboratory of Bamboo and Rattan, International Centre for Bamboo and Rattan, Beijing 100102, P.R. China
  • 4Experimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing 102300, China
  • 5State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, Chin
  • 6School of Life Science, University of Technology Sydney, NSW 2007, Australia
  • 7Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
  • 8Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, P.R. China

Abstract. Soil heterotrophic respiration (RH) is one of the largest and most uncertain components of the terrestrial carbon cycle, directly reflecting carbon loss from soil to the atmosphere. However, high variations and uncertainties of RH existing in global carbon cycling models require an urgent development of data-derived RH dataset. To fill this knowledge gap, this study applied Random Forest (RF) algorithm – a machine learning approach, to (1) develop a globally gridded RH dataset and (2) investigate its spatial- and temporal-patterns from 1980 to 2016 at the global scale by linking field observations from the Global Soil Respiration Database and global environmental drivers – temperature, precipitation, soil water content, etc. Finally, a globally gridded RH dataset was developed covering from 1980 to 2016 with a spatial resolution of half degree and a temporal resolution of one year. Globally, the average annual RH was 57.2 ± 0.6 Pg C a−1 from 1980 to 2016, with a significantly increasing trend of 0.036 ± 0.007 Pg C a−2. However, the temporal trend of the carbon loss from RH varied with climate zones that RH showed significant increasing trends in boreal and temperate areas, in contrast, such trend was absent in tropical regions. Temperature driven RH dominated 39 % of global land and was mainly distributed at a high latitude. While the areas dominated by precipitation and soil water content were mainly semi-arid and tropical areas, accounting for 36 % and 25 % of the global land, respectively, suggesting variations in the dominance of environmental controls on the spatial patterns of RH. The developed globally gridded RH dataset will further aid in understanding of the mechanisms of global soil carbon dynamics, serving as a benchmark to constrain global vegetation models. The dataset is publicly available at (Tang et al., 2019a).

Xiaolu Tang et al.
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Xiaolu Tang et al.
Data sets

A globally gridded heterotrophic respiration dataset based on field observations X. Tang, S. Fa, M. Du, W. Zhang, S. Gao, S. Liu, G. Che, Z. Yu, Y. Yao, and W. Yang

Xiaolu Tang et al.
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Short summary
Global soil heterotrophic respiration (RH) was modelled using Random Forest by linking published observations and globally gridded environmental variables. Globally, RH increased from 55.8 to 58.3 Pg C a−1 with an increasing trend of 0.036 ± 0.007 Pg C a−2 and an annual mean RH of 57.2 ± 0.6 Pg C a−1 over 1980–2016. The developed RH dataset has great potentials to serve as a benchmark to constrain global vegetation models.
Global soil heterotrophic respiration (RH) was modelled using Random Forest by linking published...