Model for Methane Emission from Rice Fields and Its Application in Southern China

Ding Aiju; Wang Mingxing

doi:10.1007/BF02656859

Impact Factor: 5.8

Volume 13 Issue 2

Apr. 1996

Article Contents

Article > ADVANCES IN ATMOSPHERIC SCIENCES > 1996 > 13(2): 159-168

Model for Methane Emission from Rice Fields and Its Application in Southern China

1.
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

doi: 10.1007/BF02656859

Abstract
References
Related papers
PDF

Abstract:
A process model has been developed. The model has been used to calculate the methane emission from rice fields. The influence of climate conditions, field water management, organic fertilizers and soil types on methane emission from rice fields are considered. There are three major segments which are highly interactive in nature in the model: rice growth, decomposition of soil organic matter and methane production, transport efficiency and methane emission rate. Explicit equations for modeling each segment mentioned above are given. The main results of the model are: 1. The seasonal variation of methane emission of the model output agrees with that of field experiments. The de-viation of seasonal average methane emission rate between modeled value and experimental data is about 10%. 2. In the whole rice growing period, model output is similar to experimental data in the seasonal variation of transport ability of rice plant. 3. Soil organic matter content and soil physics and chemistry are major factors that determine the total season average emission rate, while soil temperature controls the temporal variation of methane emission from rice fields.
- Methane,
- Model,
- Rice field,
- Rice growth
References

[1]	Wang Mingxing, Shangguan Xingjian, Shen Renxing, Wassmann Reiner, Seiler Wolfgang, 1993: Methane Production, Emission and Possible Control Measures in the Rice Agriculture, ADVANCES IN ATMOSPHERIC SCIENCES, 10, 307-314. doi: 10.1007/BF02658136
[2]	ZHANG Dingyuan, LIAO Hong, WANG Yuesi, 2014: Simulated Spatial Distribution and Seasonal Variation of Atmospheric Methane over China: Contributions from Key Sources, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 283-292. doi: 10.1007/s00376-013-3018-y
[3]	Zhang Renjian, Wang Mingxing, 1999: Modeling the Sudden Decrease in CH4 Growth Rate in 1992, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 242-250. doi: 10.1007/BF02973085
[4]	BI Yun, CHEN Yuejuan, ZHOU Renjun, YI Mingjian, DENG Shumei, 2011: Simulation of the Effect of an Increase in Methane on Air Temperature, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 129-138. doi: 10.1007/s00376-010-9197-x
[5]	Igor Oliveira RIBEIRO, Rodrigo Augusto Ferreira de SOUZA, Rita Valèria ANDREOLI, Mary Toshie KAYANO, Patrícia dos Santos COSTA, 2016: Spatiotemporal Variability of Methane over the Amazon from Satellite Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 852-864. doi: 10.1007/s00376-016-5138-7
[6]	Chuanjie YANG, Guang LI, Lijuan YAN, Weiwei MA, Jiangqi WU, Yan TAN, Shuainan LIU, Shikang ZHANG, 2022: Effects of Plant Community Type on Soil Methane Flux in Semiarid Loess Hilly Region, Central Gansu Province, China, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1360-1374. doi: 10.1007/s00376-022-1169-4
[7]	LIU Chunyan, Jirko HOLST, Nicolas BRUGGEMANN, Klaus BUTTERBACH-BAHL, YAO Zhisheng, HAN Shenghui, HAN Xingguo, ZHENG Xunhua, 2008: Effects of Irrigation on Nitrous Oxide, Methane and Carbon Dioxide Fluxes in an Inner Mongolian Steppe, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 748-756. doi: 10.1007/s00376-008-0748-3
[8]	Denghui JI, Minqiang ZHOU, Pucai WANG, Yang YANG, Ting WANG, Xiaoyu SUN, Christian HERMANS, Bo YAO, Gengchen WANG, 2020: Deriving Temporal and Vertical Distributions of Methane in Xianghe Using Ground-based Fourier Transform Infrared and Gas-analyzer Measurements, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 597-607. doi: 10.1007/s00376-020-9233-4
[9]	ZHENG Xunhua, LIU Chunyan, HAN Shenghui, 2008: Description and Application of a Model for Simulating Regional Nitrogen Cycling and Calculating Nitrogen Flux, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 181-201. doi: 10.1007/s00376-008-0181-7
[10]	Zhao Li, Zhao Sixiong, 1995: Numerical Experiments of Meiyu(Baiu) Rainfall by Quasi-Lagrangian Limited Area Model with Terrain, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 57-66. doi: 10.1007/BF02661287
[11]	SHEN Shuanghe, YANG Dong, XIAO Wei, LIU Shoudong, Xuhui LEE, 2014: Constraining Anthropogenic CH4 Emissions in Nanjing and the Yangtze River Delta, China, Using Atmospheric CO2 and CH4 Mixing Ratios, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1343-1352. doi: 10.1007/s00376-014-3231-3
[12]	Shutao CHEN, Jianwen ZOU, Zhenghua HU, Yanyu LU, 2019: Climate and Vegetation Drivers of Terrestrial Carbon Fluxes: A Global Data Synthesis, ADVANCES IN ATMOSPHERIC SCIENCES, , 679-696. doi: 10.1007/s00376-019-8194-y
[13]	WANG Aihui, ZENG Xubin, 2009: Improving the Treatment of the Vertical Snow Burial Fraction over Short Vegetation in the NCAR CLM3, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 877-886. doi: 10.1007/s00376-009-8098-3
[14]	Tido SEMMLER, Thomas JUNG, Marta A. KASPER, Soumia SERRAR, 2018: Using NWP to Assess the Influence of the Arctic Atmosphere on Midlatitude Weather and Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 5-13. doi: 10.1007/s00376-017-6290-4
[15]	Yang Xin, Wang Mingxing, Huang Yao, 2001: The Climatic-induced Net Carbon Sink by Terrestrial Biosphere over 1901-1995, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 1192-1206. doi: 10.1007/s00376-001-0033-1
[16]	S. PANCHEV, T. SPASSOVA, 2005: Simple General Atmospheric Circulation and Climate Models with Memory, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 765-769. doi: 10.1007/BF02918720
[17]	ZHAO Haikun, WU Liguang, ZHOU Weican, 2010: Assessing the Influence of the ENSO on Tropical Cyclone Prevailing Tracks in the Western North Pacific, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1361-1371. doi: 10.1007/s00376-010-9161-9
[18]	JIANG Dabang, DING Zhongli, Helge DRANGE, GAO Yongqi, 2008: Sensitivity of East Asian Climate to the Progressive Uplift and Expansion of the Tibetan Plateau Under the Mid-Pliocene Boundary Conditions, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 709-722. doi: 10.1007/s00376-008-0709-x
[19]	Gou Ji, Zheng Xunhua, Wang Mingxing, Li Changsheng, 1999: Modeling N2O Emissions from Agricultural Fields in Southeast China, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 581-592. doi: 10.1007/s00376-999-0033-0
[20]	XIE Baohua, ZHOU Zaixing, ZHENG Xunhua, ZHANG Wen, ZHU Jianguo, 2010: Modeling Methane Emissions from Paddy Rice Fields under Elevated Atmospheric Carbon Dioxide Conditions, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 100-114. doi: 10.1007/s00376-009-8178-4

PDF

Get Citation+

Export:

Article Metrics

Article Views: 1351 Times

PDF downloads: 1131 Times

Cited by: Times

Proportional views

Manuscript History

Manuscript received: 10 April 1996

Manuscript revised: 10 April 1996

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Model for Methane Emission from Rice Fields and Its Application in Southern China

1. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Manuscript received: 1996-04-10
Manuscript revised: 1996-04-10

Abstract: A process model has been developed. The model has been used to calculate the methane emission from rice fields. The influence of climate conditions, field water management, organic fertilizers and soil types on methane emission from rice fields are considered. There are three major segments which are highly interactive in nature in the model: rice growth, decomposition of soil organic matter and methane production, transport efficiency and methane emission rate. Explicit equations for modeling each segment mentioned above are given. The main results of the model are: 1. The seasonal variation of methane emission of the model output agrees with that of field experiments. The de-viation of seasonal average methane emission rate between modeled value and experimental data is about 10%. 2. In the whole rice growing period, model output is similar to experimental data in the seasonal variation of transport ability of rice plant. 3. Soil organic matter content and soil physics and chemistry are major factors that determine the total season average emission rate, while soil temperature controls the temporal variation of methane emission from rice fields.

Keywords: