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首页> 外文期刊>Current Science: A Fortnightly Journal of Research >Modelling of methane emissions from rice-based production systems in India with the denitrification and decomposition model: field validation and sensitivity analysis.
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Modelling of methane emissions from rice-based production systems in India with the denitrification and decomposition model: field validation and sensitivity analysis.

机译：使用反硝化和分解模型对印度基于水稻的生产系统的甲烷排放进行建模：现场验证和敏感性分析。

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The DNDC (DeNitrification and DeComposition) model was calibrated and tested against data on CH4 emission from rice fields obtained through an experiment conducted at the Central Rice Research Institute, Cuttack, Orissa, India, during the kharif and rabi seasons of 1996, 1997, 1999 and 2001. There was good agreement between the simulated and observed values of grain yield, total biomass, N uptake and seasonal CH4 emission. Overall, the model satisfactorily simulated the seasonal variations in CH4 emission from flooded rice paddy. However, some discrepancies were evident between observed and simulated seasonal patterns of CH4 emission. Large discrepancies between simulated and observed seasonal fluxes occurred at sites that used manual chamber flux measurements. Sensitivity test results indicated that soil texture and pH had significant effects on CH4 emission. Changes in organic C content had a moderate effect on CH4 emission at this site. Variation in the quantity of aboveground biomass returning to the soil was predicted to have slight effects on short-term seasonal simulations. Increasing the length of mid-season aeration reduced CH4 emissions significantly, while the addition of sulfate fertilizer reduced CH4 emissions. With continuous modifications and calibration, DNDC can become a powerful tool for the estimation of greenhouse gas emissions, forecasting yield trends and studying the impact of climate change and policy formulations..
机译：DNDC（反硝化和分解）模型经过校准，并根据1996年，1997年和1999年卡里夫和狂犬病季节在印度奥里萨邦库塔克市中央水稻研究所进行的实验获得的稻田CH4排放数据进行了测试。和2001年。谷物产量，总生物量，氮吸收和季节性CH4排放的模拟值与观测值之间有很好的一致性。总体而言，该模型令人满意地模拟了淹水稻田CH4排放量的季节性变化。但是，在观测到的和模拟的CH4排放的季节性模式之间存在一些差异。在模拟和观测到的季节性通量之间存在很大差异，这发生在使用手动室通量测量的地点。敏感性测试结果表明，土壤质地和pH值对CH4排放有显着影响。有机碳含量的变化对该站点的CH4排放有中等影响。预测返回土壤的地上生物量的变化会对短期季节性模拟产生轻微影响。增加季中通气时间会显着减少CH4排放，而添加硫酸盐肥料则可减少CH4排放。通过不断的修改和校准，DNDC可以成为估算温室气体排放，预测产量趋势以及研究气候变化和政策制定的强大工具。

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