An ecosystem simulation model for methane production and emission from wetlands

Potter, Christopher

doi:10.1029/97gb02302

Cited by 105 publications

(95 citation statements)

References 66 publications

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“…Other process-based models soon followed but were not applied on a global scale, at least initially (Walter et al, 1996;Potter, 1997;Walter and Heimann, 2000). These initial papers included mechanistic modelling of such processes as diffusive, aerenchymal, and ebullition gas and oxygen transport.…”

Section: Introductionmentioning

confidence: 99%

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Wania

Melton²,

Hodson³

et al. 2013

Geosci. Model Dev.

189

180

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Abstract. The Wetland and Wetland CH 4 Intercomparison of Models Project (WETCHIMP) was created to evaluate our present ability to simulate large-scale wetland characteristics and corresponding methane (CH 4 ) emissions. A multi-model comparison is essential to evaluate the key uncertainties in the mechanisms and parameters leading to methane emissions. Ten modelling groups joined WETCHIMP to run eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO 2 ) forcing datasets. We reported the main conclusions from the intercomparison effort in a companion paper (Melton et al., 2013). Here we provide technical details for the six experiments, which included an equilibrium, a transient, and an optimized run plus three sensitivity experiments (temperature, precipitation, and atmospheric CO 2 concentration). The diversity of approaches used by the models is summarized through a series of conceptual figures, and is used to evaluate the wide range of wetland extent and CH 4 fluxes predicted by the models in the equilibrium run. We discuss relationships among the various approaches and patterns in consistencies of these model predictions. Within this group of models, there are three broad classes of methods used to estimate wetland extent: prescribed based on wetland distribution maps, prognostic relationships between hydrological states based on satellite observations, and explicit hydrological mass balances. A larger variety of approaches was used to estimate the net CH 4 fluxes from wetland systems. Even though modelling of wetland extent and CH 4 emissions has progressed significantly over recent decades, large uncertainties still exist when estimating CH 4 emissions: there is little consensus on model structure or complexity due to knowledge gaps, different aims of the models, and the range of temporal and spatial resolutions of the models.

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Section: Introductionmentioning

confidence: 99%

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Wania

Melton²,

Hodson³

et al. 2013

Geosci. Model Dev.

189

180

View full text Add to dashboard Cite

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“…More processbased modelling of CH 4 production, oxidation, and transport soon followed (Christensen and Cox, 1995;Christensen et al, 1996;Cao et al, 1996;Walter et al, 1996;Potter, 1997;Walter and Heimann, 2000), with much recent work devoted to improving these parameterizations (Segers and Leffelaar, 2001;van Bodegom and Goudriaan, 2001;van Bodegom et al, 2001;Zhuang et al, 2006) and using models to investigate the recent past (Ringeval et al, 2010;Hodson et al, 2011;Spahni et al, 2011), more distant past climates (Kaplan, 2002;van Huissteden, 2004;Valdes et al, 2005;Kaplan et al, 2006;Beerling et al, 2011;Hopcroft et al, 2011;Singarayer et al, 2011), and to project responses to future climate change (Shindell et al, 2004;Gedney et al, 2004;Bohn et al, 2007;Bohn and Lettenmaier, 2010;Ringeval, 2011).…”

Section: J R Melton Et Al: Wetchimp Conclusion 755mentioning

confidence: 99%

Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP)

et al. 2013

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Global wetlands are believed to be climate sensitive, and are the largest natural emitters of methane (CH4). Increased wetland CH4 emissions could act as a positive feedback to future warming. The Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP) investigated our present ability to simulate large-scale wetland characteristics and corresponding CH4 emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO2) forcing datasets. The WETCHIMP experiments were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO2 concentration). Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location, with some models simulating wetland area prognostically, while other models relied on remotely sensed inundation datasets, or an approach intermediate between the two. Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH4 emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH4 emissions, but large variation between the models remains. For annual global CH4 emissions, the models vary by ±40% of the all-model mean (190 Tg CH4 yr−1). Second, all models show a strong positive response to increased atmospheric CO2 concentrations (857 ppm) in both CH4 emissions and wetland area. In response to increasing global temperatures (+3.4 °C globally spatially uniform), on average, the models decreased wetland area and CH4 fluxes, primarily in the tropics, but the magnitude and sign of the response varied greatly. Models were least sensitive to increased global precipitation (+3.9 % globally spatially uniform) with a consistent small positive response in CH4 fluxes and wetland area. Results from the 20th century transient simulation show that interactions between climate forcings could have strong non-linear effects. Third, we presently do not have sufficient wetland methane observation datasets adequate to evaluate model fluxes at a spatial scale c...

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“…Process-based methane emission models have been previously used to estimate the global methane budget (Zhuang et al, 2004;Walter et al, 2001;Potter, 1997;Christensen et al, 1996;Wania et al, 2010;Cao et al, 1996). Due to the complexity of wetland systems and the paucity of field and laboratory measurements to constrain process representations, these models used different approaches to simulate the methane emissions.…”

Section: Introductionmentioning

confidence: 99%

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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An ecosystem simulation model for methane production and emission from wetlands

Cited by 105 publications

References 66 publications

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP)

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

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