Articles | Volume 10, issue 9
https://doi.org/10.5194/gmd-10-3277-2017
https://doi.org/10.5194/gmd-10-3277-2017
Development and technical paper
 | 
05 Sep 2017
Development and technical paper |  | 05 Sep 2017

SUPECA kinetics for scaling redox reactions in networks of mixed substrates and consumers and an example application to aerobic soil respiration

Jin-Yun Tang and William J. Riley

Related authors

Technical note: A modified formulation of dynamic energy budget theory for faster computation of biological growth
Jinyun Tang and William J. Riley
Biogeosciences, 22, 1809–1819, https://doi.org/10.5194/bg-22-1809-2025,https://doi.org/10.5194/bg-22-1809-2025, 2025
Short summary
Impacts of tile drainage on hydrology, soil biogeochemistry, and crop yield in the U.S. Midwestern agroecosystems
Zewei Ma, Kaiyu Guan, Bin Peng, Wang Zhou, Robert Grant, Jinyun Tang, Murugesu Sivapalan, Ming Pan, Li Li, and Zhenong Jin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-340,https://doi.org/10.5194/hess-2024-340, 2024
Revised manuscript under review for HESS
Short summary
Deriving a Transformation Rate Map of Dissolved Organic Carbon over the Contiguous U.S.
Lingbo Li, Hong-Yi Li, Guta Abeshu, Jinyun Tang, L. Ruby Leung, Chang Liao, Zeli Tan, Hanqin Tian, Peter Thornton, and Xiaojuan Yang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-43,https://doi.org/10.5194/essd-2024-43, 2024
Revised manuscript accepted for ESSD
Short summary
A chemical kinetics theory for interpreting the non-monotonic temperature dependence of enzymatic reactions
Jinyun Tang and William J. Riley
Biogeosciences, 21, 1061–1070, https://doi.org/10.5194/bg-21-1061-2024,https://doi.org/10.5194/bg-21-1061-2024, 2024
Short summary
KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments
Licheng Liu, Shaoming Xu, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, Matthew D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar, and Zhenong Jin
Geosci. Model Dev., 15, 2839–2858, https://doi.org/10.5194/gmd-15-2839-2022,https://doi.org/10.5194/gmd-15-2839-2022, 2022
Short summary

Related subject area

Biogeosciences
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025,https://doi.org/10.5194/gmd-18-3131-2025, 2025
Short summary
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025,https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025,https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025,https://doi.org/10.5194/gmd-18-2521-2025, 2025
Short summary
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025,https://doi.org/10.5194/gmd-18-2509-2025, 2025
Short summary

Cited articles

Achat, D. L., Augusto, L., Gallet-Budynek, A., and Loustau, D.: Future challenges in coupled C-N-P cycle models for terrestrial ecosystems under global change: a review, Biogeochem., 131, 173–202, https://doi.org/10.1007/s10533-016-0274-9, 2016.
Aksnes, D. L. and Egge, J. K.: A theoretical-model for nutrient-uptake in phytoplankton, Mar. Ecol. Prog. Ser., 70, 65–72, 1991.
Allison, S. D.: A trait-based approach for modelling microbial litter decomposition, Ecol. Lett., 15, 1058–1070, 2012.
Armstrong, R. A.: Nutrient uptake rate as a function of cell size and surface transporter density: A Michaelis-like approximation to the model of Pasciak and Gavis, Deep-Sea Res. Pt. I, 55, 1311–1317, 2008.
Download
Short summary
We proposed the SUPECA kinetics to scale from single biogeochemical reactions to a network of mixed substrates and consumers. The framework for the first time represents single-substrate reactions, two-substrate reactions, and mineral surface sorption reactions in a scaling consistent manner. This new theory is theoretically solid and outperforms existing theories, particularly for substrate-limiting systems. The test with aerobic soil respiration showed its strengths for pragmatic application.
Share