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An improved estimate of soil carbon pool and carbon fluxes in the Qinghai-Tibetan grasslands using data assimilation with an ecosystem biogeochemical model

Zhao, Ruiying LU ; Zhang, Wenxin LU orcid ; Duan, Zheng LU ; Chen, Songchao and Shi, Zhou (2023) In Geoderma 430.
Abstract

The accurate estimation of soil carbon (C) pool and fluxes is a prerequisite to better understand the terrestrial C feedback to climate change. However, recent studies showed considerable uncertainties in soil C estimates. To provide a reliable C estimate in the grasslands of the Qinghai-Tibet Plateau (QTP), we calibrated key parameters in a process-based ecosystem model (the CENTURY model) through data assimilation based on 570 soil samples and 21 sites of eddy covariance measurements. Two assimilating strategies (Opt1 – assimilating C pool observations; Opt2 –assimilating both C pool and C flux) were examined. Compared to default parameterization, our results showed both Opt1 and Opt2 improved the soil organic carbon density (SOCD)... (More)

The accurate estimation of soil carbon (C) pool and fluxes is a prerequisite to better understand the terrestrial C feedback to climate change. However, recent studies showed considerable uncertainties in soil C estimates. To provide a reliable C estimate in the grasslands of the Qinghai-Tibet Plateau (QTP), we calibrated key parameters in a process-based ecosystem model (the CENTURY model) through data assimilation based on 570 soil samples and 21 sites of eddy covariance measurements. Two assimilating strategies (Opt1 – assimilating C pool observations; Opt2 –assimilating both C pool and C flux) were examined. Compared to default parameterization, our results showed both Opt1 and Opt2 improved the soil organic carbon density (SOCD) estimation, with R2 increasing from 0.59 to 0.75 and 0.73, respectively. Opt2 was superior to Opt1 in constraint of parameters dominating aboveground processes and yield a better estimation of net ecosystem production (NEP). Based on different parameterization, the spatial variability of SOCD and NEP across the QTP grassland were generated. Both Opt1 and Opt2 ameliorated the overestimation of SOCD by the default model, estimating a total soil C of 6.63 Pg and 6.48 Pg C for the topsoil (0–30 cm) of the QTP grasslands, respectively. Opt2 showed lower uncertainties in the NEP estimation and predicted a net sink of 14.33 Tg C annually. Compared with existing datasets, our study provided a more reliable estimation of carbon storage and fluxes in the QTP grassland with the calibrated ecosystem model. The results highlight that data assimilation with multiple observational data sets is promising to constrain process-based ecosystem models and increase the robustness of model predictions for terrestrial C cycle feedback to future climate change.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Climate change, Data assimilation, Ecosystem model, Qinghai-Tibet Plateau, Soil organic carbon
in
Geoderma
volume
430
article number
116283
publisher
Elsevier
external identifiers
  • scopus:85149845498
ISSN
0016-7061
DOI
10.1016/j.geoderma.2022.116283
language
English
LU publication?
yes
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Funding Information: We thank Y. Luo and his colleagues for the sharing code of data assimilation used in the present study and all colleagures from Z. Shi’s team for collecting the soil samples.This study is financially supported by the National Natural Science Foundation of China (grant no. 41930754 ) and the China Scholarship Council (CSC). W.Z. was supported by the grants from the Swedish Research Council VR 2020-05338 and State Key Laboratory of Frozen Soil Engineering Opend Fund (SKLFSE202002). The authors would also like to thank the Center for Scientific and Technical Computing at Lund University (LUNARC) for proving resources of computation and storage within the Swedish National Infrastructure for Computing project (LU 2021/2-115 and SNIC 2021/6-341). The authors also thank three anonymous reviewers for their constructive feedback. Funding Information: We thank Y. Luo and his colleagues for the sharing code of data assimilation used in the present study and all colleagures from Z. Shi's team for collecting the soil samples.This study is financially supported by the National Natural Science Foundation of China (grant no. 41930754) and the China Scholarship Council (CSC). W.Z. was supported by the grants from the Swedish Research Council VR 2020-05338 and State Key Laboratory of Frozen Soil Engineering Opend Fund (SKLFSE202002). The authors would also like to thank the Center for Scientific and Technical Computing at Lund University (LUNARC) for proving resources of computation and storage within the Swedish National Infrastructure for Computing project (LU 2021/2-115 and SNIC 2021/6-341). The authors also thank three anonymous reviewers for their constructive feedback. The EC measurements can be obtained from Wei et al (2021). Soil samples are available from https://www.resdc.cn/data.aspx?DATAID = 185 or on request from the corresponding author. The model input data can be download from https://www.climatologylab.org/terraclimate.html and http://globalchange.bnu.edu.cn/research/data. The calibrated parameters and model outputs are publicly available from Zenodo at https://doi.org/10.5281/zenodo.6570584 (Zhao, 2022). Code for carrying out data assimilation and model simulation can also be obtained through this website. Publisher Copyright: © 2022
id
fc65a9b7-2d97-4c51-b0c3-0a8da668f38e
date added to LUP
2023-03-27 11:10:53
date last changed
2023-03-27 16:14:08
@article{fc65a9b7-2d97-4c51-b0c3-0a8da668f38e,
  abstract     = {{<p>The accurate estimation of soil carbon (C) pool and fluxes is a prerequisite to better understand the terrestrial C feedback to climate change. However, recent studies showed considerable uncertainties in soil C estimates. To provide a reliable C estimate in the grasslands of the Qinghai-Tibet Plateau (QTP), we calibrated key parameters in a process-based ecosystem model (the CENTURY model) through data assimilation based on 570 soil samples and 21 sites of eddy covariance measurements. Two assimilating strategies (Opt1 – assimilating C pool observations; Opt2 –assimilating both C pool and C flux) were examined. Compared to default parameterization, our results showed both Opt1 and Opt2 improved the soil organic carbon density (SOCD) estimation, with R<sup>2</sup> increasing from 0.59 to 0.75 and 0.73, respectively. Opt2 was superior to Opt1 in constraint of parameters dominating aboveground processes and yield a better estimation of net ecosystem production (NEP). Based on different parameterization, the spatial variability of SOCD and NEP across the QTP grassland were generated. Both Opt1 and Opt2 ameliorated the overestimation of SOCD by the default model, estimating a total soil C of 6.63 Pg and 6.48 Pg C for the topsoil (0–30 cm) of the QTP grasslands, respectively. Opt2 showed lower uncertainties in the NEP estimation and predicted a net sink of 14.33 Tg C annually. Compared with existing datasets, our study provided a more reliable estimation of carbon storage and fluxes in the QTP grassland with the calibrated ecosystem model. The results highlight that data assimilation with multiple observational data sets is promising to constrain process-based ecosystem models and increase the robustness of model predictions for terrestrial C cycle feedback to future climate change.</p>}},
  author       = {{Zhao, Ruiying and Zhang, Wenxin and Duan, Zheng and Chen, Songchao and Shi, Zhou}},
  issn         = {{0016-7061}},
  keywords     = {{Climate change; Data assimilation; Ecosystem model; Qinghai-Tibet Plateau; Soil organic carbon}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Geoderma}},
  title        = {{An improved estimate of soil carbon pool and carbon fluxes in the Qinghai-Tibetan grasslands using data assimilation with an ecosystem biogeochemical model}},
  url          = {{http://dx.doi.org/10.1016/j.geoderma.2022.116283}},
  doi          = {{10.1016/j.geoderma.2022.116283}},
  volume       = {{430}},
  year         = {{2023}},
}