[B-CG05] Methane in terrestrial and aquatic ecosystems: from microbes to the atmosphere

[BCG05-04] Effect of agricultural management on soil greenhouse gases and model validation

*Syu-Ruei Jhang¹, Yo-Jin Shiau², Yi-Ying Chen³, Lingyi Hsu⁴, Qi-An YANG¹, Kai-Qing Zhuang¹, Yu Hsuan Li ¹ (1.Department of Safety,Health and Environmental Engineering, National United University, Miaoli City, Taiwan, 2. Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei City, Taiwan, 3.Research Center for Environmental Changes, Academia Sinica, Taipei City, Taiwan, 4.National Environmental Research Academy (NERA),Taoyuan City, Taiwan)

Keywords:Rice paddy field, DeNitrification-DeComposition (DNDC) model, Alternate wetting and drying (AWD), Continuously flooded (CF)

Rice cultivation is a significant source of greenhouse gas (GHG) emissions. In recent years, non-continuous flooding methods , such as alternate wetting and drying (AWD), have been introduced as potential mitigation strategies. This study aims to analyze the impact of agricultural management practices, particularly continuous flooding (CF) and AWD irrigation, on GHG emissions. Field chamber measurements were conducted to quantify CH₄ and CO₂ fluxes. Additionally, the process-based biogeochemical DeNitrification-DeComposition (DNDC) model was applied for further validation. The results show that cumulative CH₄ fluxes were higher under CF compared to AWD, suggesting that AWD reduced CH₄ emissions by 39.5%. Both measured and simulated results indicated a CO₂ sink in the CF treatment; however, a similar trend was not observed under AWD. In conclusion, the DNDC model effectively captured the temporal (daily-scale) CO₂ and CH₄ fluxes but exhibited some limitations: it underestimated cumulative CO₂ sink by 22.7% for CF and underestimated cumulative CH₄ fluxes by 13% and 90% for CF and AWD, respectively. These findings underscore the potential of AWD in mitigating GHG emissions and highlight the importance of further investigation for both treatment and model validation.