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Unveiling uncertainties in soil organic carbon modeling : the critical role of climate response functions

Li, Huiwen ; Cao, Yue ; Xiao, Jingfeng ; Zhang, Wenxin LU orcid ; Wu, Yiping ; Ali, Arshad and Yuan, Zuoqiang (2025) In Environmental Modelling and Software 192.
Abstract

Accurately simulating soil organic carbon (SOC) dynamics is essential for carbon-related assessments. Process-oriented SOC models employ temperature (f(T)) and soil moisture (f(W)) response functions derived from specific conditions to simulate SOC responses to climate change, yet are widely applied in regional and global-scale studies. How these functions affect regional SOC simulations remains unclear. We evaluated the impacts of ten f(T) and nine f(W) functions using the Double Layer Carbon Model (DLCM) in the Qinling Mountains from 1982 to 2018. After calibration by Particle Swarm Optimization, DLCM estimated initial SOC with high spatial consistency (R2 > 0.9) and less than 1 % bias against machine learning based... (More)

Accurately simulating soil organic carbon (SOC) dynamics is essential for carbon-related assessments. Process-oriented SOC models employ temperature (f(T)) and soil moisture (f(W)) response functions derived from specific conditions to simulate SOC responses to climate change, yet are widely applied in regional and global-scale studies. How these functions affect regional SOC simulations remains unclear. We evaluated the impacts of ten f(T) and nine f(W) functions using the Double Layer Carbon Model (DLCM) in the Qinling Mountains from 1982 to 2018. After calibration by Particle Swarm Optimization, DLCM estimated initial SOC with high spatial consistency (R2 > 0.9) and less than 1 % bias against machine learning based baseline maps over 85 % of the area. Different functions led to large SOC variations (up to 37 % in topsoil and 30 % in subsoil). Their combined impacts vary significantly under climate fluctuations, highlighting the need for accurate functions to improve SOC prediction in a changing climate.

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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Climate change, Response functions, SOC dynamics, SOC modeling, Soil carbon model
in
Environmental Modelling and Software
volume
192
article number
106537
publisher
Elsevier
external identifiers
  • scopus:105005736858
ISSN
1364-8152
DOI
10.1016/j.envsoft.2025.106537
language
English
LU publication?
yes
id
013e3291-366c-4f2f-8728-4007da70e764
date added to LUP
2025-07-15 11:19:02
date last changed
2025-07-15 11:19:40
@article{013e3291-366c-4f2f-8728-4007da70e764,
  abstract     = {{<p>Accurately simulating soil organic carbon (SOC) dynamics is essential for carbon-related assessments. Process-oriented SOC models employ temperature (f(T)) and soil moisture (f(W)) response functions derived from specific conditions to simulate SOC responses to climate change, yet are widely applied in regional and global-scale studies. How these functions affect regional SOC simulations remains unclear. We evaluated the impacts of ten f(T) and nine f(W) functions using the Double Layer Carbon Model (DLCM) in the Qinling Mountains from 1982 to 2018. After calibration by Particle Swarm Optimization, DLCM estimated initial SOC with high spatial consistency (R<sup>2</sup> &gt; 0.9) and less than 1 % bias against machine learning based baseline maps over 85 % of the area. Different functions led to large SOC variations (up to 37 % in topsoil and 30 % in subsoil). Their combined impacts vary significantly under climate fluctuations, highlighting the need for accurate functions to improve SOC prediction in a changing climate.</p>}},
  author       = {{Li, Huiwen and Cao, Yue and Xiao, Jingfeng and Zhang, Wenxin and Wu, Yiping and Ali, Arshad and Yuan, Zuoqiang}},
  issn         = {{1364-8152}},
  keywords     = {{Climate change; Response functions; SOC dynamics; SOC modeling; Soil carbon model}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Environmental Modelling and Software}},
  title        = {{Unveiling uncertainties in soil organic carbon modeling : the critical role of climate response functions}},
  url          = {{http://dx.doi.org/10.1016/j.envsoft.2025.106537}},
  doi          = {{10.1016/j.envsoft.2025.106537}},
  volume       = {{192}},
  year         = {{2025}},
}