Modeling methane emissions from rice paddies

Cao, Mingkui; Dent, J. B.; Heal, O. W.

doi:10.1029/94gb03231

Cited by 131 publications

(98 citation statements)

References 69 publications

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“…Our estimate is in the middle of current estimates of 26-120 Tg CH 4 yr −1 (Fig. 13c) (Olivier et al, 2005;Cao et al, 1995;Yan et al, 2009;Chen and Prinn, 2006;Wuebbles and Hayhoe, 2002;Cao et al, 1998;Holzapfelpschorn and Seiler, 1986;Seiler et al, 1984;Scheehle et al, 2002;Sass, 1994). For instance, Cao et al (1998) estimated global emissions from rice paddies to be ∼53 Tg CH 4 yr −1 using MF rice paddy areas.…”

Section: Global Simulation Of Rice Paddiessupporting

confidence: 49%

“…Several studies have shown that methane can be produced in acidic conditions, e.g., at pH of 4.0 in northern bogs (Williams and Crawford, 1985;Valentine et al, 1994). Another difference between our function and that in Cao et al (1995) is the optimal pH for methanogenesis, which is 7.5 in Cao et al (1995) and 6.2 here. Fig.…”

Section: Soil Ph Effects On Methanogenesismentioning

confidence: 99%

“…1). Compared with other functions used to specify the pH dependence of methane emissions (Zhuang et al, 2004;Cao et al, 1995), the advantage of this new pH function is that it allows for small but finite methane production at acidic pH. Several studies have shown that methane can be produced in acidic conditions, e.g., at pH of 4.0 in northern bogs (Williams and Crawford, 1985;Valentine et al, 1994).…”

Section: Soil Ph Effects On Methanogenesismentioning

confidence: 99%

See 2 more Smart Citations

Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations

et al. 2012

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Abstract. Methane emissions from natural wetlands and rice paddies constitute a large proportion of atmospheric methane, but the magnitude and year-to-year variation of these methane sources are still unpredictable. Here we describe and evaluate the integration of a methane biogeochemical model (CLM4Me; Riley et al., 2011) into the Community Land Model 4.0 (CLM4CN) in order to better explain spatial and temporal variations in methane emissions. We test new functions for soil pH and redox potential that impact microbial methane production in soils. We also constrain aerenchyma in plants in always-inundated areas in order to better represent wetland vegetation. Satellite inundated fraction is explicitly prescribed in the model, because there are large differences between simulated fractional inundation and satellite observations, and thus we do not use CLM4-simulated hydrology to predict inundated areas. A rice paddy module is also incorporated into the model, where the fraction of land used for rice production is explicitly prescribed. The model is evaluated at the site level with vegetation cover and water table prescribed from measurements. Explicit site level evaluations of simulated methane emissions are quite different than evaluating the grid-cell averaged emissions against available measurements. Using a baseline set of parameter values, our model-estimated average global wetland emissions for the period 1993-2004 were 256 Tg CH 4 yr −1 (including the soil sink) and rice paddy emissions in the year 2000 were 42 Tg CH 4 yr −1 . Tropical wetlands contributed 201 Tg CH 4 yr −1 , or 78 % of the global wetland flux. Northern latitude (>50 N) systems contributed 12 Tg CH 4 yr −1 . However, sensitivity studies show a large range (150-346 Tg CH 4 yr −1 ) in predicted global methane emissions (excluding emissions from rice paddies). The large range is sensitive to (1) the amount of methane transported through aerenchyma, (2) soil pH (±100 Tg CH 4 yr −1 ), and (3) redox inhibition (±45 Tg CH 4 yr −1 ). Results are sensitive to biases in the CLMCN and to errors in the satellite inundation fraction. In particular, the high latitude methane emission estimate may be biased low due to both underestimates in the high-latitude inundated area captured by satellites and unrealistically low high-latitude productivity and soil carbon predicted by CLM4.

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Section: Global Simulation Of Rice Paddiessupporting

confidence: 49%

Section: Soil Ph Effects On Methanogenesismentioning

confidence: 99%

Section: Soil Ph Effects On Methanogenesismentioning

confidence: 99%

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Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations

et al. 2012

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“…In rice fields, emissions are also dependent on the conditions and agricultural practices employed during cultivation, such as climate, soil characteristics, and cultivation practices including water management, fertilizers, other additives, and different varieties of rice plants (e.g., Neue and Roger, 1993;Minami & Neue, 1994;Cao et al, 1995Cao et al, , 1996Khalil et al, 1998). Emissions are also determined by the number of crops per year and the total area under cultivation, which is a function of the demand for rice, and the amount of rice produced per hectare, known as the 'yield'.…”

Section: Termites 13%mentioning

confidence: 99%

Atmospheric Methane: Trends and Impacts

Wuebbles

Hayhoe

2000

Non-Co2 Greenhouse Gases: Scientific Understanding, Control and Implementation

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Abstract:The concentration of methane (CH 4 ), the most abundant organic trace gas in the atmosphere, has increased dramatically over the last few centuries, more than doubling its concentration. The increasing concentrations of methane are of special concern because of its effects on climate and atmospheric chemistry. On a per molecule basis, additional methane is much more effective as a greenhouse gas than additional CO 2 . Methane is also important to both tropospheric and stratospheric chemistry. Here, we examine past trends in the concentration of methane, the sources and sinks affecting its growth rate, and the factors that could affect its growth rate in the future. This study also examines the current understanding of the effects of methane on atmospheric chemistry and climate.

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“…However, the existing studies suggest that the model have necessarily simulated different results from the actual emission by averaging parameters associated with the crop calendar e.g., Bodegom et al, 2000 . The existing approaches have shared a common issue of obtaining a spatial information of crop management, which is essentially determined by each farmers depending on the climate, rice growth, and other conditions Bachelet and Neue, 1993 ; each region and each paddy field have their own characteristics in agricultural practices in response to several agrometeorological conditions. The regional-scale database relevant to soil profile, climate, and agricultural practices needs to be constructed Cao et al, 1995;Fumoto et al, 2010 , but there is no means to sufficiently collect each information emerging from farmers in practice.…”

Section: Introductionmentioning

confidence: 99%

Estimation of methane emission from rice paddy soils in Japan using the diagnostic ecosystem model

Sasai

Nakai²,

Ono

et al. 2017

J. Agric. Meteorol.

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AbstactIt is needed to accurately evaluate the methane emission from paddy fi elds at a national scale for both scientifi c and political purposes. The existing approaches have shared a common issue with obtaining the realistic information on spatiotemporal variations of crop management. Satellite sensor could possibly detect them, but the methodology to link the satellite observations to the methane emission has not been established. In this study, we enhanced the existing diagnostic satellite-driven paddy ecosystem model, the Biosphere model integrating Eco-physiological And Mechanistic approaches using satellite data for regional Cropland BEAMS-C Sasai et al., 2012 , by integrating the methane processes and examined the potential of this approach by comparing the estimated nation-wide methane emission with that by the existing approaches. The carbon fl ux estimations had good agreements with the measurements at the Mase paddy fl ux site. In regional-scale analyses in Japan, methane emission averaged from 2001 to 2010 was 15.0 gC m -2 year -1 , which was similar to the Tire-1 and -2 results. If we have a continuing improvement of the diagnostic approach, it could be one of the most effi cient tools for estimating the methane fl ux at national scales.

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Modeling methane emissions from rice paddies

Cited by 131 publications

References 69 publications

Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations

Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations

Atmospheric Methane: Trends and Impacts

Estimation of methane emission from rice paddy soils in Japan using the diagnostic ecosystem model

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