Methane Emission Model Research Articles

Direct and Indirect Measurements and Modeling of Methane Emissions in Indianapolis, Indiana.

This paper describes process-based estimation of CH4 emissions from sources in Indianapolis, IN and compares these with atmospheric inferences of whole city emissions. Emissions from the natural gas distribution system were estimated from measurements at metering and regulating stations and from pipeline leaks. Tracer methods and inverse plume modeling were used to estimate emissions from the major landfill and wastewater treatment plant. These direct source measurements informed the compilation of a methane emission inventory for the city equal to 29 Gg/yr (5% to 95% confidence limits, 15 to 54 Gg/yr). Emission estimates for the whole city based on an aircraft mass balance method and from inverse modeling of CH4 tower observations were 41 ± 12 Gg/yr and 81 ± 11 Gg/yr, respectively. Footprint modeling using 11 days of ethane/methane tower data indicated that landfills, wastewater treatment, wetlands, and other biological sources contribute 48% while natural gas usage and other fossil fuel sources contribute 52% of the city total. With the biogenic CH4 emissions omitted, the top-down estimates are 3.5-6.9 times the nonbiogenic city inventory. Mobile mapping of CH4 concentrations showed low level enhancement of CH4 throughout the city reflecting diffuse natural gas leakage and downstream usage as possible sources for the missing residual in the inventory.

Model estimates of global and regional atmospheric methane emissions of wetland ecosystems

Estimates of the changes of atmospheric methane emissions from wetland ecosystems for different regions and the Earth as a whole are performed. The new version of the model of methane emissions from soil in a global climate model of intermediate complexity developed at the A.M. Obukhov Institute of atmospheric physics is used. Numerical experiments in accordance with the conditions of WETCHIMP (Wetland and Wetland CH4 Inter-comparison of Models Project) have been performed. The model is capable of realistically reproducing global and regional characteristics of methane emissions. The general trend of increases in methane flows from wetland ecosystems into the atmosphere due to global warming is noted. According to the results of calculations, the global natural methane emissions from wetlands have increased in the 20th century by 9 MtCH4/year. The sensitivity of global methane emissions to changes in global near-surface temperature over the land was estimated to be equal to 16 MtCH4/year/K (approximately 10%/K). With continuing warming, we can expect a significant increase in methane emissions, primarily from the high latitudes of the northern hemisphere.

Seasonal and interannual variability in wetland methane emissions simulated by CLM4Me' and CAM-chem and comparisons to observations of concentrations

Abstract. Understanding the temporal and spatial variation of wetland methane emissions is essential to the estimation of the global methane budget. Our goal for this study is three-fold: (i) to evaluate the wetland methane fluxes simulated in two versions of the Community Land Model, the Carbon-Nitrogen (CN; i.e., CLM4.0) and the Biogeochemistry (BGC; i.e., CLM4.5) versions using the methane emission model CLM4Me' so as to determine the sensitivity of the emissions to the underlying carbon model; (ii) to compare the simulated atmospheric methane concentrations to observations, including latitudinal gradients and interannual variability so as to determine the extent to which the atmospheric observations constrain the emissions; (iii) to understand the drivers of seasonal and interannual variability in atmospheric methane concentrations. Simulations of the transport and removal of methane use the Community Atmosphere Model with chemistry (CAM-chem) model in conjunction with CLM4Me' methane emissions from both CN and BGC simulations and other methane emission sources from literature. In each case we compare model-simulated atmospheric methane concentration with observations. In addition, we simulate the atmospheric concentrations based on the TransCom wetland and rice paddy emissions derived from a different terrestrial ecosystem model, Vegetation Integrative Simulator for Trace gases (VISIT). Our analysis indicates CN wetland methane emissions are higher in the tropics and lower at high latitudes than emissions from BGC. In CN, methane emissions decrease from 1993 to 2004 while this trend does not appear in the BGC version. In the CN version, methane emission variations follow satellite-derived inundation wetlands closely. However, they are dissimilar in BGC due to its different carbon cycle. CAM-chem simulations with CLM4Me' methane emissions suggest that both prescribed anthropogenic and predicted wetlands methane emissions contribute substantially to seasonal and interannual variability in atmospheric methane concentration. Simulated atmospheric CH4 concentrations in CAM-chem are highly correlated with observations at most of the 14 measurement stations evaluated with an average correlation between 0.71 and 0.80 depending on the simulation (for the period of 1993–2004 for most stations based on data availability). Our results suggest that different spatial patterns of wetland emissions can have significant impacts on Northern and Southern hemisphere (N–S) atmospheric CH4 concentration gradients and growth rates. This study suggests that both anthropogenic and wetland emissions have significant contributions to seasonal and interannual variations in atmospheric CH4 concentrations. However, our analysis also indicates the existence of large uncertainties in terms of spatial patterns and magnitude of global wetland methane budgets, and that substantial uncertainty comes from the carbon model underlying the methane flux modules.

Entropy based statistical inference for methane emissions released from wetland

We concentrate on the paradigm question how much stochasticity and how much chaos are present in the methane emission model. In particular we analyze the residua from the process of methane emissions from wetlands in the sedge-grass marsh, in South Bohemia, Czech Republic. Relation to entropy and a specific version of Kullback–Leibler divergence will be given. A graphical tool to assess the amount of entropy in the system is developed and illustrated on real data from the sedge-grass marsh methane emission.

Modeling of methane emissions using artificial neural network approach

The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN) with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN) and a General Regression Neural Network (GRNN). A conventional multiple linear regression (MLR) model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies.

Environmental drivers of methane fluxes from an urban temperate wetland park

AbstractMethane (CH4) emissions were measured at the Wilma H. Schiermeier Olentangy River Wetland Research Park (ORWRP) over three summers and two winters using an eddy covariance system. We used an empirical model to determine the main environmental drivers of methane emissions. Methane emissions covary strongly with water vapor fluxes, CO2 fluxes, and soil temperature. We adjust our models to account for the heterogeneous environment of the wetland by including the flux footprint distribution among different microsites as a predictive variable in the methane model. We used a forward linear stepwise model in combination with an Akaike information criteria‐based model selection process and neural network modeling to determine which environmental variables are most effective in modeling methane emissions in our site. Different models and environmental variables best represented methane fluxes in the winter and summer and also during the day or night within each season. We parameterized an optimal empirical model for methane emissions from the ORWRP that is used for gap filling of site‐level methane fluxes over 2 years. Some of the most effective variables for modeling methane were carbon, water vapor, and heat fluxes, all of which typically have the same data gaps as the time series of methane flux. In order to determine if these variables were useful for modeling methane despite the additional gap‐filling error, we determined through an error propagation experiment that eddy covariance gap‐filling models for methane may be best developed by including other gap‐filled fluxes as predictors, despite the high level of shared gaps and subsequent gap‐fill error propagation.

Evaluation of a plot-scale methane emission model using eddy covariance observations and footprint modelling

Abstract. Most plot-scale methane emission models – of which many have been developed in the recent past – are validated using data collected with the closed-chamber technique. This method, however, suffers from a low spatial representativeness and a poor temporal resolution. Also, during a chamber-flux measurement the air within a chamber is separated from the ambient atmosphere, which negates the influence of wind on emissions. Additionally, some methane models are validated by upscaling fluxes based on the area-weighted averages of modelled fluxes, and by comparing those to the eddy covariance (EC) flux. This technique is rather inaccurate, as the area of upscaling might be different from the EC tower footprint, therefore introducing significant mismatch. In this study, we present an approach to validate plot-scale methane models with EC observations using the footprint-weighted average method. Our results show that the fluxes obtained by the footprint-weighted average method are of the same magnitude as the EC flux. More importantly, the temporal dynamics of the EC flux on a daily timescale are also captured (r2 = 0.7). In contrast, using the area-weighted average method yielded a low (r2 = 0.14) correlation with the EC measurements. This shows that the footprint-weighted average method is preferable when validating methane emission models with EC fluxes for areas with a heterogeneous and irregular vegetation pattern.

Improving a plot-scale methane emission model and its performance at a northeastern Siberian tundra site

Abstract. In order to better address the feedbacks between climate and wetland methane (CH4) emissions, we tested several mechanistic improvements to the wetland CH4 emission model Peatland-VU with a longer Arctic data set than any other model: (1) inclusion of an improved hydrological module, (2) incorporation of a gross primary productivity (GPP) module, and (3) a more realistic soil-freezing scheme. A long time series of field measurements (2003–2010) from a tundra site in northeastern Siberia is used to validate the model, and the generalized likelihood uncertainty estimation (GLUE) methodology is used to test the sensitivity of model parameters. Peatland-VU is able to capture both the annual magnitude and seasonal variations of the CH4 flux, water table position, and soil thermal properties. However, detailed daily variations are difficult to evaluate due to data limitation. Improvements due to the inclusion of a GPP module are less than anticipated, although this component is likely to become more important at larger spatial scales because the module can accommodate the variations in vegetation traits better than at plot scale. Sensitivity experiments suggest that the methane production rate factor, the methane plant oxidation parameter, the reference temperature for temperature-dependent decomposition, and the methane plant transport rate factor are the most important parameters affecting the data fit, regardless of vegetation type. Both wet and dry vegetation cover are sensitive to the minimum water table level; the former is also sensitive to the runoff threshold and open water correction factor, and the latter to the subsurface water evaporation and evapotranspiration correction factors. These results shed light on model parameterization and future improvement of CH4 modelling. However, high spatial variability of CH4 emissions within similar vegetation/soil units and data quality prove to impose severe limits on model testing and improvement.

A mathematical model for a positive permafrost carbon-climate feedback

The permafrost methane emission problem is the focus of attention on different climate models. Here, we present a mathematical model for permafrost lake methane emission and its influence on the climate system. We model this process using the theory of non-linear phase transitions. Further, we find that a climate catastrophe possibility depends on a value of feedback connecting the methane concentration in the atmosphere and temperature, and on the tundra permafrost methane pool. We note that the permafrost lake model that we developed for the methane emission positive feedback loop problem is a conceptual climate model.

A stochastic model for the polygonal tundra based on Poisson–Voronoi diagrams

Abstract. Subgrid processes occur in various ecosystems and landscapes but, because of their small scale, they are not represented or poorly parameterized in climate models. These local heterogeneities are often important or even fundamental for energy and carbon balances. This is especially true for northern peatlands and in particular for the polygonal tundra, where methane emissions are strongly influenced by spatial soil heterogeneities. We present a stochastic model for the surface topography of polygonal tundra using Poisson–Voronoi diagrams and we compare the results with available recent field studies. We analyze seasonal dynamics of water table variations and the landscape response under different scenarios of precipitation income. We upscale methane fluxes by using a simple idealized model for methane emission. Hydraulic interconnectivities and large-scale drainage may also be investigated through percolation properties and thresholds in the Voronoi graph. The model captures the main statistical characteristics of the landscape topography, such as polygon area and surface properties as well as the water balance. This approach enables us to statistically relate large-scale properties of the system to the main small-scale processes within the single polygons.

Towards an inventory of methane emissions from manure management that is responsive to changes on Canadian farms

Methane emissions from manure management represent an important mitigation opportunity, yet emission quantification methods remain crude and do not contain adequate detail to capture changes in agricultural practices that may influence emissions. Using the Canadian emission inventory methodology as an example, this letter explores three key aspects for improving emission quantification: (i) obtaining emission measurements to improve and validate emission model estimates, (ii) obtaining more useful activity data, and (iii) developing a methane emission model that uses the available farm management activity data. In Canada, national surveys to collect manure management data have been inconsistent and not designed to provide quantitative data. Thus, the inventory has not been able to accurately capture changes in management systems even between manure stored as solid versus liquid. To address this, we re-analyzed four farm management surveys from the past decade and quantified the significant change in manure management which can be linked to the annual agricultural survey to create a continuous time series. In the dairy industry of one province, for example, the percentage of manure stored as liquid increased by 300% between 1991 and 2006, which greatly affects the methane emission estimates. Methane emissions are greatest from liquid manure, but vary by an order of magnitude depending on how the liquid manure is managed. Even if more complete activity data are collected on manure storage systems, default Intergovernmental Panel on Climate Change (IPCC) guidance does not adequately capture the impacts of management decisions to reflect variation among farms and regions in inventory calculations. We propose a model that stays within the IPCC framework but would be more responsive to farm management by generating a matrix of methane conversion factors (MCFs) that account for key factors known to affect methane emissions: temperature, retention time and inoculum. This MCF matrix would be populated using a mechanistic emission model verified with on-farm emission measurements. Implementation of these MCF values will require re-analysis of farm surveys to quantify liquid manure emptying frequency and timing, and will rely on the continued collection of this activity data in the future. For model development and validation, emission measurement campaigns will be needed on representative farms over at least one full year, or manure management cycle (whichever is longer). The proposed approach described in this letter is long-term, but is required to establish baseline data for emissions from manure management systems. With these improvements, the manure management emission inventory will become more responsive to the changing practices on Canadian livestock farms.

CH4 Emission Model from Bos Primigenius Waste in Fish-Water: Implications for Integrated Livestock-Fish Farming Systems

This paper studies a methane (CH4) emission model from the waste of cattle (B. primigenius) based on trends in integrated livestock-fish farming adoption by farmers in Nigeria. Dung of B. primigenius was employed as substrate in fish-water, obtained from a fish-rearing farm, as a matrix medium for simulating a low-oxygen wastewater environment of an agriculture-aquaculture system. A substrate to fish-water mass ratio of 1:3 was used, developed in a laboratory-size digesting reactor system. Volumetric readings, at ambient temperature conditions and with a retention time of thirty-two days, were then subjected to the logistic probability density function, and tested against correlation coefficient and Nash-Sutcliffe coefficient of efficiency criteria. The readings show that a volume of CH4-containing gas as high as 65.3 x 10 −3 dm 3 was produced on the 13th day from the B. primigenius substrate. Also, production of 234.59 x 10 −3 dm 3 /kg CH4-containing gas, totaling 703.76 x 10 −3 dm 3 , was observed through the studied retention time. The 60% CH4 constituent model of the measured gas generation showed a potency of 2.0664 kg emission per animal, which is equivalent to 43.3944 CO2eq of global warming potential (GWP) annually per animal. This bears environmental and climate change implications, and therefore alternative sustainable practices for integrated livestock-fish farming adoption are suggested.

Improved methodology to assess modification and completion of landfill gas management in the aftercare period

Municipal solid waste landfills represent the dominant option for waste disposal in many parts of the world. While some countries have greatly reduced their reliance on landfills, there remain thousands of landfills that require aftercare. The development of cost-effective strategies for landfill aftercare is in society’s interest to protect human health and the environment and to prevent the emergence of landfills with exhausted aftercare funding. The Evaluation of Post-Closure Care (EPCC) methodology is a performance-based approach in which landfill performance is assessed in four modules including leachate, gas, groundwater, and final cover. In the methodology, the objective is to evaluate landfill performance to determine when aftercare monitoring and maintenance can be reduced or possibly eliminated. This study presents an improved gas module for the methodology. While the original version of the module focused narrowly on regulatory requirements for control of methane migration, the improved gas module also considers best available control technology for landfill gas in terms of greenhouse gas emissions, air quality, and emissions of odoriferous compounds. The improved module emphasizes the reduction or elimination of fugitive methane by considering the methane oxidation capacity of the cover system. The module also allows for the installation of biologically active covers or other features designed to enhance methane oxidation. A methane emissions model, CALMIM, was used to assist with an assessment of the methane oxidation capacity of landfill covers.

Source attribution of the changes in atmospheric methane for 2006–2008

Abstract. The recent increase of atmospheric methane is investigated by using two atmospheric inversions to quantify the distribution of sources and sinks for the 2006–2008 period, and a process-based model of methane emissions by natural wetland ecosystems. Methane emissions derived from the two inversions are consistent at a global scale: emissions are decreased in 2006 (−7 Tg) and increased in 2007 (+21 Tg) and 2008 (+18 Tg), as compared to the 1999–2006 period. The agreement on the latitudinal partition of the flux anomalies for the two inversions is fair in 2006, good in 2007, and not good in 2008. In 2007, a positive anomaly of tropical emissions is found to be the main contributor to the global emission anomalies (~60–80%) for both inversions, with a dominant share attributed to natural wetlands (~2/3), and a significant contribution from high latitudes (~25%). The wetland ecosystem model produces smaller and more balanced positive emission anomalies between the tropics and the high latitudes for 2006, 2007 and 2008, mainly due to precipitation changes during these years. At a global scale, the agreement between the ecosystem model and the inversions is good in 2008 but not satisfying in 2006 and 2007. Tropical South America and Boreal Eurasia appear to be major contributors to variations in methane emissions consistently in the inversions and the ecosystem model. Finally, changes in OH radicals during 2006–2008 are found to be less than 1% in inversions, with only a small impact on the inferred methane emissions.

Numerical modeling of methane emissions from lakes in the permafrost zone

A brief review of published observations of methane fluxes to the atmosphere from bogs and lakes in the permafrost zone is presented. Approaches to modeling the emission of methane from bogs are consid� ered, and their advantages and shortcomings, in particular, from the point of view of their coupling to climate models, are outlined. A onedimensional model develope d by the authors for methane generation, transport, and sink in the ground-water body system and coupled to a hydrothermodynamic model of a water body is described. The approaches used in analogous models for bogs as well as new parametrizations describing lake� specific processes are applied. A parametrization of methane generation in vicinity the lower boundary of the thawed ground zone underneath a water body (talik) is suggested. The results of calibrating this model against available observations of methane emission from the thermokarst Shuchi Lake in northeastern Siberia are dis� cussed.

Estimating historical landfill quantities to predict methane emissions

There are no observations for methane emissions from landfill waste in Ireland. Methane emissions are imputed from waste data. There are intermittent data on waste sent to landfill. We compare two alternative ways to impute the missing waste “data” and evaluate the impact on methane emissions. We estimate Irish historical landfill quantities from 1960–2008 and Irish methane emissions from 1968–2006. A model is constructed in which waste generation is a function of income, price of waste disposal and, household economies of scale. A transformation ratio of waste to methane is also included in the methane emissions model. Our results contrast significantly with the Irish Environmental Protection Agency’s (EPA) figures due to the differences in the underlying assumptions. The EPA’s waste generation and methane emission figures are larger than our estimates from the early 1990s onwards. Projections of the distance to target show that the EPA overestimates the required policy effort.

Modeling Methane Emission from Wetlands in North-Eastern New South Wales, Australia Using Landsat ETM+

Natural wetlands constitute a major source of methane emission to the atmosphere, accounting for approximately 32 ± 9.4% of the total methane emission. Estimation of methane emission from wetlands at both local and national scale using process-based models would improve our understanding of their contribution to global methane emission. The aim of the study is to estimate the amount of methane emission from the coastal wetlands in north-eastern New South Wales (NSW), Australia, using Landsat ETM+ and to estimate emission with a temperature increase. Supervised wetland classification was performed using the Maximum Likelihood Standard algorithm. The temperature dependent factor was obtained through land surface temperature (LST) estimation algorithms. Measurements of methane fluxes from the wetlands were performed using static chamber techniques and gas chromatography. A process-based methane emission model, which included productivity factor, wetland area, methane flux, precipitation and evaporation ratio, was used to estimate the amount of methane emission from the wetlands. Geographic information system (GIS) provided the framework for analysis. The variability of methane emission from the wetlands was high, with forested wetlands found to produce the highest amount of methane, i.e., 0.0016 ± 0.00009 teragrams (Tg) in the month of June, 2001. This would increase to 0.0022 ± 0.0001 Tg in the month of June with a 1 °C rise in mean annual temperature by the year 2030 in north-eastern NSW, Australia.

Sensitivity analysis of a wetland methane emission model based on temperate and arctic wetland sites

Abstract. Modelling of wetland CH4 fluxes using wetland soil emission models is used to determine the size of this natural source of CH4 emission on local to global scale. Most process models of CH4 formation and soil-atmosphere CH4 transport processes operate on a plot scale. For large scale emission modelling (regional to global scale) upscaling of this type of model requires thorough analysis of the sensitivity of these models to parameter uncertainty. We applied the GLUE (Generalized Likelihood Uncertainty Analysis) methodology to a well-known CH4 emission model, the Walter-Heimann model, as implemented in the PEATLAND-VU model. The model is tested using data from two temperate wetland sites and one arctic site. The tests include experiments with different objective functions, which quantify the fit of the model results to the data. The results indicate that the model 1) in most cases is capable of estimating CH4 fluxes better than an estimate based on the data avarage, but does not clearly outcompete a regression model based on local data; 2) is capable of reproducing larger scale (seasonal) temporal variability in the data, but not the small-scale (daily) temporal variability; 3) is not strongly sensitive to soil parameters, 4) is sensitive to parameters determining CH4 transport and oxidation in vegetation, and the temperature sensitivity of the microbial population. The GLUE method also allowed testing of several smaller modifications of the original model. We conclude that upscaling of this plot-based wetland CH4 emission model is feasible, but considerable improvements of wetland CH4 modelling will result from improvement of wetland vegetation data.

A hierarchical model for estimating methane emission from wetlands using MODIS data and ARIMA modeling

A three-step hierarchical Semi Automated Empirical Methane Emission Model (SEMEM) has been used to estimate methane emission from wetlands and waterlogged areas in India using Moderate Resolution Imagine Spectroradiometer (MODIS) sensor data onboard Terra satellite. Wetland Surface Temperature (WST), methane emission fluxes and wetland extent have been incorporated as parameters in order to model the methane emission. Analysis of monthly MODIS data covering the whole of India from November 2004 to April 2006 was carried out and monthly methane emissions have been estimated. Interpolation techniques were adopted to fill the data gaps due to cloudy conditions during the monsoon period. AutoRegressive Integrated Moving Average (ARIMA) model has been fitted to estimate the emitted methane for the months of May 2006 to August 2006 using SPSS software.

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.