Depth-dependent hydrological and substrate dynamics enhance methane modeling and inform water management in rice systems

Authors: TANG, RONGYUN - University Of Arkansas; LI, BONAN - Oregon State University; MORENO-GARCIA, BEATRIZ - University Of Arkansas; TAJFAR, ELAHE - University Of Arkansas; Reba, Michele; GUAN, KAIYU - University Of Illinois; PENG, BIN - University Of Illinois; OIKAWA, PATTY - California State University; RUNKLE, BENJAMIN - University Of Arkansas

Submitted to: Global Change Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/20/2026
Publication Date: 5/24/2026
Citation: Tang, R., B. Li, B. Moreno-García, E. Tajfar, M.L. Reba, K. Guan, B. Peng, P. Oikawa, and B.R.K. Runkle. Depth-dependent hydrological and substrate dynamics enhance methane modeling and inform water management in rice systems. Agricultural and Forest Meteorology. Volume 386, 111241. https://doi.org/10.1016/j.agrformet.2026.111241. 2026.
DOI: https://doi.org/10.1016/j.agrformet.2026.111241

Interpretive Summary: Rice paddies release methane, a powerful greenhouse gas, which contributes to the changing climate and potentially affect the agricultural industry. To better understand and reduce these emissions, we developed a numerical model that simulates how carbon and methane move through rice plants, soil, and water. We found that water management and plant growth strongly influence methane emissions. With this model, we can predict when, where, and the amount these gases are released. We can also understand the major driving mechanisms behind these gas releases. This research can help farmers, scientists, and policymakers develop strategies to grow rice more sustainably while sustaining crop yield.

Technical Abstract: Methane emissions from rice paddies are a critical contributor to the global greenhouse gas budget, driven by complex interactions among plant, soil, and microbial communities under hydrological processes and agricultural management. Accurately simulating methane emissions under managed water regimes remains challenging for effective carbon accounting in rice paddies. Building on the PEPRMT peatland carbon model, we developed the PEPRMT-Rice model with enhanced parameterizations to account for depth-dependent substrate availability and hydrological dynamics in governing methane production, oxidation, and transport. The refined model was calibrated and evaluated against eddy covariance flux data from 12 site-seasons, showing marked improvements in daily CH4 simulations, with R² values increasing from 0.23 to 0.78 compared to the original model. It also performed well in simulating carbon fluxes, achieving average R² values of 0.83 for daily Gross Primary Production (GPP), 0.93 for Net Ecosystem Exchange (NEE), and 0.78 for ecosystem respiration (Reco). Simulations revealed mechanistic differences in methane cycling under different water management strategies, particularly alternate wetting and drying (AWD) and continuous flooding (CF) irrigation. Notably, AWD, as a methane mitigation strategy, consistently reduced methane emissions compared to CF. However, scenarios with additional drainage show that frequent drainage near the anaerobic–aerobic transition zone can lead to subtle, unexpected increases in emissions. These findings highlight the importance of optimizing AWD practices to maximize mitigation benefits. The PEPRMT-Rice model can serve as an efficient tool for advancing climate-smart rice farming in developing strategies to reduce emissions while sustaining productivity.