Methane Emission Model Research Articles

Validation of revised DNDC model for methane emissions from irrigated rice fields in Thailand and sensitivity analysis of key factors

The original Denitrification–Decomposition (DNDC) model and a revised version were tested against data from field observations of methane (CH4) emissions from irrigated rice fields in Thailand. The revised DNDC model, which was modified for use in Japanese rice fields by revising the crop growth and soil biogeochemical submodels, yielded better simulation results than the original model. In most cases, daily CH4 fluxes predicted by the revised DNDC model agreed well with observations. Seasonal CH4 emissions simulated by the revised model showed significantly higher correlation with observations than those obtained with the original model. Errors in the simulation appear to have resulted from uncertainties in both the input parameters and the model descriptions. Sensitivity analysis revealed that the revised DNDC model is highly sensitive to the concentration of reducible soil Fe, the rate of rice straw incorporation, and rice root biomass. Therefore, uncertainties in these factors may strongly affect the prediction of CH4 emissions. These results suggest that for reliable prediction of CH4 emissions from Thai rice fields, further work is needed to improve the estimates of reducible soil Fe, to quantify the rate of straw incorporation, and to parameterize the crop submodel for the dominant rice varieties grown in Thailand.

Methane emission from the oligotrophic bog massif in the Northwestern Russia

Results of measuring methane emissions from the Lammin-Suo oligotrophic bog massif are considered. It is shown that emission intensity depends on the methane transport from the active layer of the peat bed. The highest emission intensity is observed in the sedge-sphagnum microlandscape and over swampy hollows of the hummock-ridge complex. It is found that the methane flux intensity approaches zero when the wetland level drops by 30–35 cm from the bog surface. Spatial methane emission variability is estimated within dominating bog landscapes. The methane emission reaches its maximum values (207%) in microlandscapes with oriented microrelief (hummock-ridge complex); in the central bog (sphagnum-suffrutescent-cottongrass landscape afforested with pine), it reaches its lowest level (76%). A model of methane emissions from bogs is developed. The model has been verified from the observational data. The comparison of model calculations with experimental data is indicative of their good agreement, which makes it possible to use the model in different calculations and assessments of the influence of natural factors on the methane emission intensity.

Four‐dimensional variational data assimilation for inverse modeling of atmospheric methane emissions: Analysis of SCIAMACHY observations

Recent observations from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument aboard ENVISAT have brought new insights in the global distribution of atmospheric methane. In particular, the observations showed higher methane concentrations in the tropics than previously assumed. Here, we analyze the SCIAMACHY observations and their implications for emission estimates in detail using a four‐dimensional variational (4D‐Var) data assimilation system. We focus on the period September to November 2003 and on the South American continent, for which the satellite observations showed the largest deviations from model simulations. In this set‐up the advantages of the 4D‐Var approach and the zooming capability of the underlying TM5 atmospheric transport model are fully exploited. After application of a latitude‐dependent bias correction to the SCIAMACHY observations, the assimilation system is able to accurately fit those observations, while retaining consistency with a network of surface methane measurements. The main emission increments resulting from the inversion are an increase in the tropics, a decrease in South Asia, and a decrease at northern hemispheric high latitudes. The SCIAMACHY observations yield considerable additional emission uncertainty reduction, particularly in the (sub‐)tropical regions, which are poorly constrained by the surface network. For tropical South America, the inversion suggests more than a doubling of emissions compared to the a priori during the 3 months considered. Extensive sensitivity experiments, in which key assumptions of the inversion set‐up are varied, show that this finding is robust. Independent airborne observations in the Amazon basin support the presence of considerable local methane sources. However, these observations also indicate that emissions from eastern South America may be smaller than estimated from SCIAMACHY observations. In this respect it must be realized that the bias correction applied to the satellite observations does not take into account potential regional systematic errors, which – if identified in the future – will lead to shifts in the overall distribution of emission estimates.

Modeling and prediction of ventilation methane emissions of U.S. longwall mines using supervised artificial neural networks

Abstract Methane emissions from a longwall ventilation system are an important indicator of how much methane a particular mine is producing and how much air should be provided to keep the methane levels under statutory limits. Knowing the amount of ventilation methane emission is also important for environmental considerations and for identifying opportunities to capture and utilize the methane for energy production. Prediction of methane emissions before mining is difficult since it depends on a number of geological, geographical, and operational factors. This study proposes a principle component analysis (PCA) and artificial neural network (ANN)-based approach to predict the ventilation methane emission rates of U.S. longwall mines. Ventilation emission data obtained from 63 longwall mines in 10 states for the years between 1985 and 2005 were combined with corresponding coalbed properties, geographical information, and longwall operation parameters. The compiled database resulted in 17 parameters that potentially impacted emissions. PCA was used to determine those variables that most influenced ventilation emissions and were considered for further predictive modeling using ANN. Different combinations of variables in the data set and network structures were used for network training and testing to achieve minimum mean square errors and high correlations between measurements and predictions. The resultant ANN model using nine main input variables was superior to multilinear and second-order non-linear models for predicting the new data. The ANN model predicted methane emissions with high accuracy. It is concluded that the model can be used as a predictive tool since it includes those factors that influence longwall ventilation emission rates.

A process-based model for methane emission from flooded rice paddy systems

Methane is the second most important greenhouse gas after carbon dioxide. Rice paddy soils release approximately 15–20% of total methane emitted to the atmosphere. A process-based methane emission model was developed for rice paddy systems that highlights plant mediated methane transport. Sequential utilization of alternative electron acceptors such as oxygen, nitrate, Mn(IV), Fe(III) and sulfate in flooded soils is included and permits examination of the effects of fertilizer application and field drainage on methane emissions. Acetate and hydrogen, two representative electron donors produced from the biologically mediated decomposition of solid organic matter, are assumed to be the substrates driving the electron transfer processes. Effects of temperature on reaction kinetics and diffusion processes are based on empirical relationships observed in the laboratory and field. Other processes considered include the exudation of organic carbon and radial release of oxygen from roots, the infiltration flow induced by plant transpiration, the growth dynamics of rice plants, the vertical distribution of soil organic carbon and root biomass, dieback of roots, and loss of gaseous species through ebullition. The performance of the model is evaluated using methane flux data collected in Chongqing and Sichuan, China. Model simulations reveal that although hybrid rice cultivars are several times more efficient in mediating methane transport than traditional tall cultivars at seedling stage, the development of methane transport capacity over the growing season leads to a relatively small difference in total seasonal methane flux (∼15%) among fields planted with tall and hybrid cultivars. Application of nitrate fertilizer at a rate of 64 kg N/ha (about 50% of total nitrogen applied at the Chongqing site) could reduce methane emission by 7%. By converting both iron and manganese to oxidized forms, pre-season drainage is found to be able to reduce methane emissions by 8–10%. A 1-week drainage of a rice field during the growing season could further reduce the methane emission by 22–23% and might be a very promising methane-emission mitigation technique, since such drainage practices can also conserve water and improve rice yields. This model will be implemented on a national scale to establish national methane emission inventories and to evaluate the feasibility and cost-effectiveness of various mitigation options that could vary from site to site.

Spatiotemporal dynamics of methane emission from rice fields at global scale

Rice fields are one of the anthropogenic sources of methane emission with largest uncertainty. Precise global estimation from different sites is difficult due to large spatial and temporal variabilities in climate, soil properties, duration and pattern of flooding, rice cultivars and crop growth, organic amendments, fertilization, and cultural practices. The temporal dynamics of methane emission from rice fields represented by ordinary differential equations is coupled with its spatial dynamics by connecting the temporal methane emission model with soil–climate model. This model includes detailed biogeochemical processes of methane emission, which is capable to identify emission sources and can be used to address the natural/artificial control and feedback issues. A complete computational scheme for identifying out-busting emission sources and for simulating and reducing methane emission from rice fields has been proposed in this paper with respect to space and time.

Field Validation of DNDC Model for Methane and Nitrous Oxide Emissions from Rice-based Production Systems of India

The DNDC (DeNitrification and DeComposition) model was tested against experimental data on CH4 and N2O emissions from rice fields at different geographical locations in India. There was a good agreement between the simulated and observed values of CH4 and N2O emissions. The difference between observed and simulated CH4 emissions in all sites ranged from −11.6 to 62.5 kg C ha−1 season−1. Most discrepancies between simulated and observed seasonal fluxes were less than 20% of the field estimate of the seasonal flux. The relative deviation between observed and simulated cumulative N2O emissions ranged from −237.8 to 28.6%. However, some discrepancies existed between observed and simulated seasonal patterns of CH4 and N2O emissions. The model simulated zero N2O emissions from continuously flooded rice fields and poorly simulated CH4 emissions from Allahabad site. For all other simulated cases, the model satisfactorily simulated the seasonal variations in greenhouse gas emission from paddy fields with different land management. The model also simulated the C and N balances in all the sites, including other gas fluxes, viz. CO2, NO, NO2, N2 and NH3 emissions. Sensitivity tests for CH4 indicate that soil texture and pH significantly influenced the CH4 emission. Changes in organic C content had a moderate influence on CH4 emission on these sites. Introducing the mid-season drainage reduced CH4 emissions significantly. Process-based biogeochemical modeling, as with DNDC, can help in identifying strategies for optimizing resource use, increasing productivity, closing yield gaps and reducing adverse environmental impacts.

Evaluation of methane emissions from Taiwanese paddies

The main greenhouse gases are carbon dioxide, methane and nitrous oxide. Methane is the most important because the warming effect of methane is 21 times greater than that of carbon dioxide. Methane emitted from rice paddy fields is a major source of atmospheric methane. In this work, a methane emission model (MEM), which integrates climate change, plant growth and degradation of soil organic matter, was applied to estimate the emission of methane from rice paddy fields in Taiwan. The estimated results indicate that much methane is emitted during the effective tillering and booting stages in the first crop season and during the transplanting stage in the second crop season in a year. Sensitivity analysis reveals that the temperature is the most important parameter that governs the methane emission rate. The order of the strengths of the effects of the other parameters is soil pH, soil water depth (SWD) and soil organic matter content. The masses of methane emitted from rice paddy fields of Taiwan in the first and second crop seasons are 28,507 and 350,231 tons, respectively. The amount of methane emitted during the second crop season is 12.5 times higher than that emitted in the first crop season. With a 12% reduction in planted area during the second crop season, methane emission could be reduced by 21%. In addition, removal of rice straw left from the first crop season and increasing the depth of flooding to 25 cm are also strategies that could help reduce annual emission by up to 18%.

A Process-Based Model for Methane Emissions from Irrigated Rice Fields: Experimental Basis and Assumptions

In this paper, we review the process-level studies that the authors have performed in rice fields of Texas since 1989 and the development of a semi-empirical model based on these studies. In this model, it is hypothesized that methanogenic substrates are primarily derived from rice plants and added organic matter. Rates of methane (CH4) production in flooded rice soils are determined by the availability of methanogenic substrates and the influence of climate, soil, and agronomic factors. Rice plant growth and added carbon control the fraction of CH4 emitted. The amount of CH4 transported from the soil to the atmosphere is determined by the rates of production and the emitted fraction. Model calibration against observations from a single rice-growing season in Texas, USA, without organic amendments and with continuous irrigation demonstrated that the seasonal variation of CH4 emission is regulated by rice biomass and cultivar type. A further validation of the model against measurements from irrigated rice paddy soils in various regions of the world, including Italy, China, Indonesia, Philippines, and the United States, suggests that CH4 emission can be predicted from rice net productivity, cultivar character, soil texture, temperature, and organic matter amendments.

An ecosystem simulation model for methane production and emission from wetlands

Previous experimental studies suggest that methane emission from wetlands is influenced by multiple interactive pathways of gas production and transport through soil and sediment layers to the atmosphere. The objective of this study is to evaluate a new simulation model of methane production and emission in wetland soils that was developed initially to help identify key processes that regulate methanogenesis and net flux of CH4 to the air but which is designed ultimately for regional simulation using remotely sensed inputs for land cover characteristics. The foundation for these computer simulations is based on a well‐documented model (Carnegie‐Ames‐Stanford Approach, CASA) of ecosystem production and carbon cycling in the terrestrial biosphere. Modifications to represent flooded wetland soils and anaerobic decomposition include three new submodels for (1) layered soil temperature and water table depth (WTD) as a function of daily climate drivers, (2) CH4 production within the anoxic soil layer as a function of WTD and CO2 production under poorly drained conditions, and (3) CH4 gaseous transport pathways (molecular diffusion, ebullition, and plant vascular transport) as a function of WTD and ecosystem type. The model was applied and tested using climate and ecological data to characterize tundra wetland sites near Fairbanks, Alaska, studied previously by Whalen and Reeburgh [1992]. Comparison of model predictions to measurements of soil temperature and thaw depth, water table depth, and CH4 emissions over a 2‐year period suggest that intersite differences in soil physical conditions and methane fluxes could be reproduced accurately for selected periods. Day‐to‐day comparison of predicted emissions to measured CH4 flux rates reveals good agreement during the early part of the thaw season, but the model tends to underestimate production of CH4 during the months of July and August in both test years. Important seasonal effects, including that of falling WTD during these periods, are apparently overlooked in the model formulation. Nevertheless, reasonably close agreement was achieved between the model's mean daily and seasonal estimates of CH4 flux and observed emission rates for northern wetland ecosystems. Several features of the model are identified as crucial to more accurate prediction of wetland methane emission, including the capacity to incorporate influences of localized topographic and hydrologic features on site‐specific soil temperature and WTD dynamics and mechanistic simulation of methane emission transport pathways from within the soil profile.

Global carbon exchange and methane emissions from natural wetlands: Application of a process‐based model

Wetlands are one of the most important sources of atmospheric methane (CH4), but the strength of this source is still highly uncertain. To improve estimates of CH4 emission at the regional and global scales and predict future variation requires a process‐based model integrating the controls of climatic and edaphic factors and complex biological processes over CH4 flux rates. This study used a methane emission model based on the hypothesis that plant primary production and soil organic matter decomposition act to control the supply of substrate needed by methanogens; the rate of substrate supply and environmental factors, in turn, control the rate of CH4 production, and the balance between CH4 production and methanotrophic oxidation determines the rate of CH4 emission into the atmosphere. Coupled to data sets for climate, vegetation, soil, and wetland distribution, the model was used to calculate spatial and seasonal distributions of CH4 emissions at a resolution of 1° latitude × 1° longitude. The calculated net primary production (NPP) of wetlands ranged from 45 g C m−2 yr−1 for northern bogs to 820 g C m−2 yr−1 for tropical swamps. CH4 emission rates from individual gridcells ranged from 0.0 to 661 mg CH4 m−2 d−1, with a mean of 40 mg CH4 m−2 d−1 for northern wetland, 150 mg CH4 m−2 d−1 for temperate wetland, and 199 mg CH4 m−2 d−1 for tropical wetland. Total CH4 emission was 92 Tg yr−1. Sensitivity analysis showed that the response of CH4 emission to climate change depends upon the combined effects of soil carbon storage, rate of decomposition, soil moisture and activity of methanogens.

Model for methane emission from rice fields and its application in southern china

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 deviation 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 emissions from China's paddyland

China has the world's largest area of rice paddy. As such, emissions of methane (CH 4) from China are of interest in understanding the global CH 4 budget. Using a simplified process-based methane emission model and a georeferenced database, CH 4 emission rates from 329 defined agricultural zones in China, where rice production occurred, were estimated in this study. The calculated daily CH 4 emission rates ranged from 0.12 g m −2 to 0.70 g m −2 with an average value of 0.44 g m −2. The annual CH 4 emissions varied between 25 g m −2 and 80 g m −2. On average across the whole rice growing area in China, 59.9 g m −2 CH 4 was released annually. Total CH 4 emission was 16.2 Tg year −1 (1 Tg = 10 12 g). Although the model and supporting database used had some uncertainties, these results demonstrated the ability to assess CH 4 emissions at the regional level within the framework of climate-plant-soil interactions.

Modeling methane emissions from rice paddies

The quantification of methane (CH4) emissions from global rice paddies is still highly uncertain. The extrapolation of CH4 release rates from point measurements to regional level or empirically correlating methane emission rates with a few factors (e.g., temperature and rice primary production) is unlikely to yield reliable results. Reducing the uncertainties in estimates of current CH4 emission and predicting its future change require a process model to simulate CH4 emissions from various paddyland environments. CH4 emission is an ecosystem process closely coupled to plant growth and soil organic matter decomposition. In this study a methane emission model was developed based on supplies of carbon substrate for methanogens by rice primary production and soil organic matter degradation, direct environmental controls on methanogenesis, and the balance between CH4 production and consumption by methanotrophic oxidation. A validation of the model indicated its reasonable ability to calculate CH4 emission rates and their seasonal variations. The model, when coupled with supporting data sets, would complement spatial and dynamic analysis of CH4 emissions from rice paddies in the framework of climate‐plant‐soil interactions.