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

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 (CO 2 ), methane (CH 4 ), or nitrous oxide (N 2 O) 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 MSAproduced 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 CO 2 , CH 4 , and N 2 O 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 CH 4 flux from the simulated 30 million ha of Chinese rice fields ranged from 6.4 to 12.0 Tg CH 4 -C per year under the continuous flooding conditions. With the midseason drainage scenario, the national CH 4 flux from rice agriculture reduced to 1.7-7.9 Tg CH 4 -C. It implied that the water management change in China reduced CH 4 fluxes by 4.2-4.7 Tg CH 4 -C per year. Shifting the water management from continuous flooding to midseason drainage increased N 2 O fluxes by 0.13-0.20 Tg N 2 O-N/yr, although CO 2 fluxes were only slightly altered. Since N 2 O possesses a radiative forcing more than 10 times higher than CH 4 , the increase in N 2 O offset about 65% of the benefit gained by the decrease in CH 4 emissions.INDEX TERMS: 0315 Atmospheric Composition and Structure: Biosphere/atmosphere

Crop residues exacerbate the negative effects of extreme flooding on soil quality

“…Flooding has been shown to either promote or reduce CO 2 emissions depending upon land management regime (Aulakh et al 2001;Li et al 2004;Loeb et al 2008). Although a reduction in the production of CO 2 was expected due to the increase in the salinity of soil solution observed when the flooded soil was amended with maize residues (high ratio C/ N; Hasbullah and Marschner 2015), in our experiment, inundation caused little change in net CO 2 emissions.…”

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

“…Many studies (Khalil and Butenhoff, 2008;Li et al, 2004;Matthews et al, 2000;Van Bodegom et al, 2002) have suggested that a significant proportion of the uncertainty in regional rice paddy methane emissions arises from data scarcity, especially with regard to SAND, OM, GY, W ptn and VI. The CH4MOD sensitivity analysis similarly indicates the importance of these five factors in methane emissions (Table B1 in Appendix B).…”

“…Most of these previous studies focused on organic matter application and water regimes in their estimations of uncertainty (Table 5) because of data scarcity in these two factors. Taking into consideration the tremendous spatial heterogeneity of soil characteristics, Li et al (2004) believed that these were the most sensitive factors accounting for uncertainties, and that the uncertainty was between 2.3-10.5 Tg yr −1 (1.7-7.9 Tg yr −1 C) for mid-season drainage irrigation and 8.5-16.0 Tg yr −1 (6.4-12.0 Tg yr −1 C) when continuous flooding was applied.…”

“…1 represent procedures for estimating national methane emissions, and the hollow arrows describe the uncertainty assessments accompanying the model upscaling. Although many studies have demonstrated how to upscale a model to make regional estimations from various baseline scenarios (Matthews et al, 2000;Li et al, 2004;Ogle et al, 2010), the primary focus of the present study is the aggregation of the uncertainties in model estimations due to data scarcity.…”

“…Extrapolating a site-specific model to a regional or global scale is usually referred to as "model upscaling" (King et al, 1991;van Bodegom et al, 2000). A common framework for this upscaling involves partitioning a large region into smaller, individual areas and running the model for each area (Matthews et al, 2000;Li et al, 2004;Yu et al, 2012).…”