Modeling greenhouse gas emissions from rice‐based production systems: Sensitivity and upscaling

TLDR: In this paper, a biogeochemical model, denitrification-decomposition (DNDC), was modified to enhance its capacity of predicting greenhouse gas (GHG) emissions from paddy rice ecosystems.

Abstract: [1] A biogeochemical model, Denitrification-Decomposition (DNDC), was modified to enhance its capacity of predicting greenhouse gas (GHG) emissions from paddy rice ecosystems. The major modifications focused on simulations of anaerobic biogeochemistry and rice growth as well as parameterization of paddy rice management. The new model was tested for its sensitivities to management alternatives and variations in natural conditions including weather and soil properties. The test results indicated that (1) varying management practices could substantially affect carbon dioxide (CO2), methane (CH4), or nitrous oxide (N2O) emissions from rice paddies; (2) soil properties affected the impacts of management alternatives on GHG emissions; and (3) the most sensitive management practices or soil factors varied for different GHGs. For estimating GHG emissions under certain management conditions at regional scale, the spatial heterogeneity of soil properties (e.g., texture, SOC content, pH) are the major source of uncertainty. An approach, the most sensitive factor (MSF) method, was developed for DNDC to bring the uncertainty under control. According to the approach, DNDC was run twice for each grid cell with the maximum and minimum values of the most sensitive soil factors commonly observed in the grid cell. The simulated two fluxes formed a range, which was wide enough to include the “real” flux from the grid cell with a high probability. This approach was verified against a traditional statistical approach, the Monte Carlo analysis, for three selected counties or provinces in China, Thailand, and United States. Comparison between the results from the two methods indicated that 61-99% of the Monte Carlo-produced GHG fluxes were located within the MSA-produced flux ranges. The result implies that the MSF method is feasible and reliable to quantify the uncertainties produced in the upscaling processes. Equipped with the MSF method, DNDC modeled emissions of CO2, CH4, and N2O from all of the rice paddies in China with two different water management practices, i.e., continuous flooding and midseason drainage, which were the dominant practices before 1980 and in 2000, respectively. The modeled results indicated that total CH4 flux from the simulated 30 million ha of Chinese rice fields ranged from 6.4 to 12.0 Tg CH4-C per year under the continuous flooding conditions. With the midseason drainage scenario, the national CH4 flux from rice agriculture reduced to 1.7–7.9 Tg CH4-C. It implied that the water management change in China reduced CH4 fluxes by 4.2–4.7 Tg CH4-C per year. Shifting the water management from continuous flooding to midseason drainage increased N2O fluxes by 0.13–0.20 Tg N2O-N/yr, although CO2 fluxes were only slightly altered. Since N2O possesses a radiative forcing more than 10 times higher than CH4, the increase in N2O offset about 65% of the benefit gained by the decrease in CH4 emissions.