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

Abstract

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.

Importance of vegetation classes in modeling CH4 emissions from boreal and subarctic wetlands in Finland

“…The present regional or global scale model simulations of CH 4 fluxes often lumped the effects of all vegetation together, which may induce large uncertainties in the estimates (Melton et al, 2013;Meng et al, 2015;Petrescu et al, 2010;Zhu et al, 2015). In addition, most previous process-based models have concentrated on a single vegetation type (e.g., graminoids) and only a few considered the complex vegetation composition for model validation (Li et al, 2010;Meng et al, 2012;Walter and Heimann, 2000;Walter et al, 1996;Xu and Tian, 2012;Zhang et al, 2002;Zhu et al, 2014).…”

Modelling methane emissions from natural wetlands by development and application of the TRIPLEX-GHG model

“…These uncertainties are generally introduced from large temporal and spatial variations in CH 4 flux, with the complex processes that underlie CH 4 emissions and also the limited inherent range of field and laboratory measurements (Arneth et al, 2010;Wania et al, 2010;Spahni et al, 2011;Meng et al, 2012). Therefore, further development of process-based CH 4 emission models is critical (Walter and Heimann, 2000;Ito and Inatomi, 2012).…”

“…Each has its own strategy and features to deal with wetland system complexity and CH 4 flux processes (Li, 2000;Walter and Heimann, 2000;Zhuang et al, 2004;Meng et al, 2012). Cao et al (1995Cao et al ( , 1996 developed a CH 4 emission model for rice paddies based on C substrate level, soil organic matter (SOM) degradation and environmental control factors and improved it for global natural wetland simulation; but the model has no specific CH 4 emission process.…”

“…This model was then modified to simulate global net CH 4 emissions for northern peatlands, naturally inundated wetlands and rice agriculture soils (Spahni et al, 2011). To characterize uncertainties and feedbacks between CH 4 flux and climate, Riley et al (2011) developed a CH 4 biogeochemistry model (CLM4Me) and integrated it into the land component of the Community Earth System Model (CESM) and further analyses were conducted by Meng et al (2012), but specific plant functional types have not been considered in wetlands.…”

Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties

“…Improving our understanding of the role of wetlands in global greenhouse gas budgets requires a representation of wetlands and their biogeochemical processes in land-surface models (LSMs) to both hindcast observed past variations (Singarayer et al, 2011) and predict future trajectories in atmospheric CH 4 and terrestrial C balance (Ito and Inatomi, 2012;Meng et al, 2012;Spahni et al, 2011;Stocker et al, 2014;Zürcher et al, 2013). Dynamic wetland schemes in LSMs were based on conceptual theories and physical processes describing surface water processes (e.g., infiltration and evapotranspiration) and water movement in the soil column using probability distributions derived from subgrid topographic information (Beven and Kirkby, 1979) or using analytical functional parametric forms with fixed parameters (Liang et al, 1994).…”