CH4 Emission Model Research Articles

Detection of inland water bodies under dense biomass by CYGNSS

The reliable detection of inland water bodies under dense biomass is not possible with traditional spaceborne remote sensing systems. In this investigation, we demonstrate the capability of the National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission to detect and quantify the extent of small water bodies under dense biomass up to 400 ton/ha in complex heterogenous scenes such as tropical rainforests. A novel inland water body product based on L1 CYGNSS data is developed using a combination of peak surface reflectivity Γp and its spread with respect to time delay and Doppler shift, as characterized by the power ratio Pratio. Only L1 CYGNSS mission data has the potential to study Earth's surface water dynamics because raw Intermediate Frequency (IF) data availability is limited. Several coherence indices are used to verify that the L-band Global Positioning System (GPS) signals are being coherently reflected by surface water bodies and attenuated by the intervening vegetation canopy. These indices include full and computationally fast versions of the entropy associated with Delay Doppler Maps (DDMs), Efull and Efast, as well as the time derivative of the phase φpeak of the reflected GPS signal. The capability to accurately resolve surface water extent under dense vegetation has been shown to improve the estimation by current state-of-art surface water extension datasets used by global methane CH4 emission models in wetlands.

High-Resolution Estimation of Methane Emissions from Boreal and Pan-Arctic Wetlands Using Advanced Satellite Data

This paper investigates the use of soil moisture data from satellites and a hydrological model as inputs to a simplified CH4 emission model (MeSMOD) for estimating CH4 emissions from boreal and pan-Arctic regions between 2015 and 2021. MeSMOD is calibrated using FLUXNET—CH4 sites and the predictive performance is evaluated using several metrics, including the Nash-Sutcliffe efficiency (NSE). Using satellite soil moisture with 100 m resolution, MeSMOD has the highest performance (NSE = 0.63) compared with using satellite soil moisture of 10 km and hydrological model soil moisture of 10 km and 50 km (NSE = 0.59, 0.56, and 0.53, respectively) against site-level CH4 flux. This study has upscaled the estimates to the pan-Arctic region using MeSMOD, resulting in comparable mean annual estimates of CH4 emissions using satellite soil moisture of 10 km (33 Tg CH4 yr−1) and hydrological model soil moisture of 10 km (39 Tg CH4 yr−1) compared with previous studies using random forest technique for upscaling (29.5 Tg CH4 yr−1), LPJ-wsl process model (30 Tg CH4 yr−1), and CH4 CAMS inversion (34 Tg CH4 yr−1). MeSMOD has also accurately captured the high methane emissions observed by LPJ-wsl and CAMS in 2016 and 2020 and effectively caught the interannual variability of CH4 emissions from 2015 to 2021. The study emphasizes the importance of using high-resolution satellite soil moisture data for accurate estimation of CH4 emissions from wetlands, as these data directly reflect soil moisture conditions and lead to more reliable estimates. The approach adopted in this study helps to reduce errors and improve our understanding of wetlands’ role in CH4 emissions, ultimately reducing uncertainties in global CH4 budgets.

The Importance of Spatial Resolution in the Modeling of Methane Emissions from Natural Wetlands

An important uncertainty in the modeling of methane (CH4) emissions from natural wetlands is the wetland area. It is difficult to model wetlands’ CH4 emissions because of several factors, including its spatial heterogeneity on a large range of scales. In this study, we investigate the impact of model resolution on the simulated wetland methane emission for the Fennoscandinavian Peninsula. This is carried out using a high-resolution wetland map (100 × 100 m2) and soil carbon map (250 × 250 m2) in combination with a highly simplified CH4 emission model that is coarsened in five steps from 0.005° to 1°. We find a strong relation between wetland emissions and resolution, which is sensitive, however, to the sub-grid treatment of the wetland fraction. In our setup, soil carbon and soil moisture are positively correlated at a high resolution, with the wetland location leading to increasing CH4 emissions with increasing resolution. Keeping track of the wetland fraction reduces the impact of resolution. However, uncertainties in CH4 emissions remain high because of the large uncertainty in the representation of wetland the area, as demonstrated using the output of the WetChimp intercomparison over our study domain. Because of wetland mapping uncertainties, existing models are unlikely to realistically represent the correlation between soil moisture and soil carbon availability. The correlation is positive in our simplified model but may be different in more complex models depending on their method of representing substrate availability. Therefore, depending on the correlation, CH4 emissions may be over- or underestimated. As increasing the model resolution is an effective approach to mitigate the problem of accounting for the correlation between soil moisture and soil carbon and to improve the accuracy of models, the main message of this study is that increasing the resolution of global wetland models, and especially the input datasets that are used, should receive high priority.

Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates.

The recent rise in atmospheric methane (CH4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking systemto examine the relationship between wetland CH4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year-1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Relevance of sward structure and forage nutrient contents in explaining methane emissions from grazing beef cattle and sheep

Forage nutrient contents are an important factor explaining the dry matter intake (DMI), average daily gain (ADG), and methane emissions (CH4) of ruminants fed indoors. However, for grazing animals, the forage nutrient contents might be limited in explaining such response variables. We aimed to verify the explanatory power of forage nutrient contents and sward structure on daily intake, performance, and CH4 emissions by sheep and beef cattle grazing different grassland types in southern Brazil. We analyzed data from five grazing trials using sheep and beef cattle grazing on Italian ryegrass (Lolium multiflorum), mixed Italian ryegrass and black oat (Lolium multiflorum + Avena strigosa), pearl millet (Pennisetum americanum), and multispecies native grassland. We used mixed models, including the forage nutrient contents [crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF)], sward structure (sward height and herbage mass) and their interactions, as fixed effects and trial, season, methodologies, animal species, grassland type, and paddock, as random effects. The model for DMI (kg DM/LW0.75) had an adjusted coefficient of determination (R2adj) of 71.6 %, where 11.3, 23.1, and 37.2 % of the R2adj were explained by the forage nutrient contents, sward structure, and their interaction, respectively. The ADG (kg/LW0.75) model presented an R2adj of 74.2 %, with 12.5 % explained by forage nutrient contents, 29.3 % by sward structure, and 32.4 % by their interaction. The daily CH4 emission (g/LW0.75) model had a lower adjusted coefficient of determination (R2adj = 47.6 %), with 16.8 % explained by forage nutrient contents and 30.8 % explained by sward structure, but no effect of the interaction. Our results show that in grazing ecosystems, the forage nutrient contents explain a small fraction, and the greater explanatory power for DMI, ADG, and CH4 emissions models is related to sward structure descriptors, such as sward height and herbage mass. Moreover, the interaction between these variables explains most of the variation. In conclusion, forage nutrient contents and sward structure have different influences on DMI, ADG, and CH4 emissions by grazing ruminants. Because of its relevance to daily CH4 emissions, offering an optimal sward structure to grazing animals is a major climate-smart strategy to improve animal production and mitigate CH4 emissions in pastoral ecosystems.

Enteric methane emission models for diverse beef cattle feeding systems in South-east Asia: A meta-analysis

Prediction models for enteric methane (CH4) emissions from beef cattle proposed by various groups may not perform with similar accuracy for the low- and middle-income countries in South-east Asia (SE-Asia) because beef cattle in these countries are raised under different climatic conditions with diverse feeding systems, and have different CH4 emission characteristics. The objectives of this study were to: i) predict CH4 emission (g d−1 animal−1), yield [g kg−1 dry matter intake; DMI)−1], intensity [g kg−1 average daily gain)−1], and CH4 conversion factor (Ym) using an intercountry database of individual animal records from SE-Asia; ii) evaluate the impact of different dietary forage contents (all-, high- and low-forage) representing the diverse feeding systems on CH4 emission, yield, intensity and Ym in SE-Asia; and iii) cross-validate equations from this study with published data. A total of 398 individual animal observations of beef cattle from SE-Asia were used for this analysis. Linear models developed by incrementally adding covariates revealed that CH4 emission model using only DMI fitted to all data had a root mean square prediction error (RMSPE) of 16.9%. Subsets containing data with 100% forage in the diet (all-forage), 50–85% (high-forage) and < 50% (low-forage) had an RMSPE of 16.5%, 14.7%, and 17.4%, respectively. Linear multiple equation based on DMI and dietary NDF concentration (DMI + NDF_C, RMSPE = 15.2%; all-data) improved prediction accuracy over that of DMI alone. The DMI + NDF_C models for all-forage (RMSPE = 14.6%) and high-forage subsets (RMSPE = 13.3%) except for low-forage (RMSPE = 16.4%), improved the precision and accuracy of CH4 emission prediction. Methane yield and CH4 emission intensity could not be reliably modelled with the current database. The present study provides improved CH4 prediction models for beef cattle managed under diverse feeding systems in SE-Asia and affirmed that region-specific models are needed to reliably predict beef cattle CH4 emission at national or regional levels, particularly for low- and middle-income countries.

Estimating Drivers and Pathways for Hydroelectric Reservoir Methane Emissions Using a New Mechanistic Model

AbstractHydroelectric reservoirs can emit significant quantities of methane, particularly through degassing at turbine outlets. Improved understanding of processes affecting hydroelectric reservoir CH4 emissions is thus important as the world economy transitions to renewable forms of energy production. Here we develop and evaluate a new mechanistic model of CH4 emissions: ResME ([Res]ervoir [M]ethane [E]missions), which estimates carbon inputs and methanogenesis to predict CH4 release via ebullition and diffusion, plant emissions, and downstream emissions. ResME results demonstrate that the relative importance of allochthonous and autochthonous carbon input to methane emissions varies by latitude, with allochthonous carbon contributions typically being higher in tropical reservoirs. Results also demonstrate that total reservoir emissions are highly dependent on turbine intake depths, which are not typically reported. Potential maximum degassing emissions from existing hydroelectric reservoirs are estimated as 11 ± 4 Tg C/yr, if all reservoirs had deep turbine intakes and stratified for 5 months per year. In comparison, the estimated diffusive, ebullitive, and plant CH4 emissions are estimated to be 2.8 ± 0.2 Tg C/yr (where the true uncertainty is much higher than the model standard error). Future work should focus on improving estimates of reservoir carbon inputs and decomposition rates, as well as surveying turbine intake depths. Satellite measurements from missions such as TROPOMI may also help constrain hydropower methane emissions.

Large Methane Emissions From the Pantanal During Rising Water‐Levels Revealed by Regularly Measured Lower Troposphere CH4 Profiles

AbstractThe Pantanal region of Brazil is the largest seasonally flooded tropical grassland and, according to local chamber measurements, a substantial CH4 source. CH4 emissions from wetlands have recently become of heightened interest because global atmospheric 13CH4 data indicate they may contribute to the resumption of atmospheric CH4 growth since 2007. We have regularly measured vertical atmospheric profiles for 2 years in the center of the Pantanal with the objectives to obtain an estimate of CH4 emissions using an atmospheric approach, and provide information about flux seasonality and its relation to controlling factors. Boundary layer‐free troposphere differences observed in the Pantanal are large compared to other wetlands. Total emissions based on a planetary boundary layer budgeting technique are 2.0–2.8 TgCH4 yr−1 (maximum flux ∼0.4 gCH4 m−2 d−1) while those based on a Bayesian inversion using an atmospheric transport model are ∼3.3 TgCH4 yr−1. Compared to recent estimates for Amazonia (∼41 ± 3 TgCH4 yr−1, maximum flux ∼0.3 gCH4 m−2 d−1) these emissions are not that large. Our Pantanal data suggest a clear flux seasonality with CH4 being released in large amounts just after water levels begin to rise again after minimum levels have been reached. CH4 emissions decline substantially once the maximum water level has been reached. While predictions with prognostic wetland CH4 emission models agree well with the magnitude of the fluxes, they disagree with the phasing. Our approach shows promise for detecting and understanding longer‐term trends in CH4 emissions and the potential for future wetlands CH4 emissions climate feedbacks.

Numerical modeling of methane oxidation and emission from landfill cover soil coupling water-heat-gas transfer: Effects of meteorological factors

CH4 emissions from landfills present temporal and spatial variation due to the changing meteorological conditions. The combined effect of meteorological parameter on CH4 transport, oxidation and emission are not fully understood. In this study a water-heat-gas transfer model embedding a meteorological module and CH4 oxidation was built to elucidate the CH4 transport, oxidation and emissions in landfill cover soils. A series of parametric studies are carried out to investigate the influence of barometric pressure, air temperature and rainfall on CH4 transport, oxidation and emissions in landfill cover soil. Finally, a real-time climatic condition was conducted to predict the daily CH4 oxidation and emissions from different climatic zones. The simulated results indicated that a low atmospheric pressure would convert the dominant gas transport mechanism from diffusion to advection and therefore result in a lower CH4 oxidation efficiency. In a landfill, the cover soil would also have a higher CH4 oxidation capacity because of the heat conduction from the waste layer under cold conditions. A significant decrease of CH4 emission flux was observed during the rainfall. After rainfall event, the CH4 emission flux was significantly positively correlated with rain intensity. This study proved that building a real-time landfill CH4 emission model considering varying climatic conditions is necessary to improve the accuracy of model predicting.

Exploring constraints on a wetland methane emission ensemble (WetCHARTs) using GOSAT observations

Abstract. Wetland emissions contribute the largest uncertainties to the current global atmospheric CH4 budget, and how these emissions will change under future climate scenarios is also still poorly understood. Bloom et al. (2017b) developed WetCHARTs, a simple, data-driven, ensemble-based model that produces estimates of CH4 wetland emissions constrained by observations of precipitation and temperature. This study performs the first detailed global and regional evaluation of the WetCHARTs CH4 emission model ensemble against 9 years of high-quality, validated atmospheric CH4 observations from GOSAT (the Greenhouse Gases Observing Satellite). A 3-D chemical transport model is used to estimate atmospheric CH4 mixing ratios based on the WetCHARTs emissions and other sources. Across all years and all ensemble members, the observed global seasonal-cycle amplitude is typically underestimated by WetCHARTs by −7.4 ppb, but the correlation coefficient of 0.83 shows that the seasonality is well-produced at a global scale. The Southern Hemisphere has less of a bias (−1.9 ppb) than the Northern Hemisphere (−9.3 ppb), and our findings show that it is typically the North Tropics where this bias is the worst (−11.9 ppb). We find that WetCHARTs generally performs well in reproducing the observed wetland CH4 seasonal cycle for the majority of wetland regions although, for some regions, regardless of the ensemble configuration, WetCHARTs does not reproduce the observed seasonal cycle well. In order to investigate this, we performed detailed analysis of some of the more challenging exemplar regions (Paraná River, Congo, Sudd and Yucatán). Our results show that certain ensemble members are more suited to specific regions, due to either deficiencies in the underlying data driving the model or complexities in representing the processes involved. In particular, incorrect definition of the wetland extent is found to be the most common reason for the discrepancy between the modelled and observed CH4 concentrations. The remaining driving data (i.e. heterotrophic respiration and temperature) are shown to also contribute to the mismatch with observations, with the details differing on a region-by-region basis but generally showing that some degree of temperature dependency is better than none. We conclude that the data-driven approach used by WetCHARTs is well-suited to producing a benchmark ensemble dataset against which to evaluate more complex process-based land surface models that explicitly model the hydrological behaviour of these complex wetland regions.

Development of a dynamic energy-partitioning model for enteric methane emissions and milk production in goats using energy balance data from indirect calorimetry studies

The main objective of this study was to develop a dynamic energy balance model for dairy goats to describe and quantify energy partitioning between energy used for work (milk) and that lost to the environment. Increasing worldwide concerns regarding livestock contribution to global warming underscore the importance of improving energy efficiency utilization in dairy goats by reducing energy losses in feces, urine and methane (CH4). A dynamic model of CH4 emissions from experimental energy balance data in goats is proposed and parameterized (n = 48 individual animal observations). The model includes DM intake, NDF and lipid content of the diet as explanatory variables for CH4 emissions. An additional data set (n = 122 individual animals) from eight energy balance experiments was used to evaluate the model. The model adequately (root MS prediction error, RMSPE) represented energy in milk (E-milk; RMSPE = 5.6%), heat production (HP; RMSPE = 4.3%) and CH4 emissions (E-CH4; RMSPE = 11.9%). Residual analysis indicated that most of the prediction errors were due to unexplained variations with small mean and slope bias. Some mean bias was detected for HP (1.12%) and E-CH4 (1.27%) but was around zero for E-milk (0.14%). The slope bias was zero for HP (0.01%) and close to zero for E-milk (0.10%) and E-CH4 (0.22%). Random bias was >98% for E-CH4, HP and E-milk, indicating non-systematic errors and that mechanisms in the model are properly represented. As predicted energy increased, the model tended to underpredict E-CH4 and E-milk. The model is a first step toward a mechanistic description of nutrient use by goats and is useful as a research tool for investigating energy partitioning during lactation. The model described in this study could be used as a tool for making enteric CH4 emission inventories for goats.

CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; budgets and global warming potential modifications in &amp;lt;i&amp;gt;Sphagnum&amp;lt;/i&amp;gt;-dominated peat mesocosms invaded by &amp;lt;i&amp;gt;Molinia caerulea&amp;lt;/i&amp;gt;

Abstract. Plant communities play a key role in regulating greenhouse gas (GHG) emissions in peatland ecosystems and therefore in their ability to act as carbon (C) sinks. However, in response to global change, a shift from Sphagnum-dominated to vascular-plant-dominated peatlands may occur, with a potential alteration in their C-sink function. To investigate how the main GHG fluxes (CO2 and CH4) are affected by a plant community change (shift from dominance of Sphagnum mosses to vascular plants, i.e., Molinia caerulea), a mesocosm experiment was set up. Gross primary production (GPP), ecosystem respiration (ER) and CH4 emission models were used to estimate the annual C balance and global warming potential under both vegetation covers. While the ER and CH4 emission models estimated an output of, respectively, 376±108 and 7±4 g C m−2 yr−1 in Sphagnum mesocosms, this reached 1018±362 and 33±8 g C m−2 yr−1 in mesocosms with Sphagnum rubellum and Molinia caerulea. Annual modeled GPP was estimated at -414±122 and -1273±482 g C m−2 yr−1 in Sphagnum and Sphagnum + Molinia plots, respectively, leading to an annual CO2 and CH4 budget of −30 g C m−2 yr−1 in Sphagnum plots and of −223 g C m−2 yr−1 in Sphagnum + Molinia ones (i.e., a C sink). Even if CH4 emissions accounted for a small part of the gaseous C efflux (ca. 3 %), their global warming potential value makes both plant communities have a climate warming effect. The shift of vegetation from Sphagnum mosses to Molinia caerulea seems beneficial for C sequestration at a gaseous level. However, roots and litter of Molinia caerulea could provide substrates for C emissions that were not taken into account in the short measurement period studied here.

Spatial methane emission modelling from wetlands using geospatial tools

ABSTRACTWetland ecosystems have acquired importance among the scientific community because of their role in biogeochemical cycling and as source and sink of greenhouse gas emissions particularly methane (CH4) in addition to the ecosystem services that they provide. To estimate the CH4 emission from wetlands in spatial domain, models incorporating the geospatial tools are required. Accordingly, main focus of this study is to demonstrate the utility of geospatial techniques in assessing the spatial CH4 emission variability from four different regions of Uttar Pradesh (UP), India, namely, Western, Central, Bundelkhand, and Eastern regions deploying Semi-Automated Empirical CH4 emission Model (SEMEModel) using Moderate Resolution Imaging Spectro-radiometer data of 2010–2012. SEMEModel is a three-tier model which determines the CH4 emissions in spatial domain as a function of remote sensing (RS) and Geographic Information System (GIS) derived wetland components including wetland area and corresponding temperature factors coupled with point CH4 emission coefficients developed via field measurements. Results of the study have shown that eastern region of UP exhibited maximum estimated/modelled CH4 emissions (43.10 Gg yr−1) as compared to other regions due to more area being under wetlands whereas central region was found to be the least contributor (0.266 Gg yr−1) due to the fact that it has minimum wetland area (0.40%) among all the regions. It was observed that estimated/modelled CH4 emissions depicted an increase by 4.96 orders of magnitude in 2010–2011 and 4.04 orders of magnitude in 2011–2012 when estimated by applying literature-based global CH4 emission coefficients for UP in place of CH4 flux values derived in field. It signifies that the upscaling of CH4 flux values using literature-based CH4 flux values of one region to another region may not reflect actual values. Therefore, this study not only helps to improve accuracy of CH4 emission estimates from wetlands but also credibly adjudges that integration of CH4 flux field measurements with modern tools of RS and GIS will immensely assist to reduce the uncertainties in CH4 emission predictions done over larger regions.

Particle-scale visualization of the evolution of methanogens and methanotrophs and its correlation with CH4 emissions during manure aerobic composting

Methane (CH4) emissions are a major environmental concern in composting facilities. Therefore, this study initially visualized the dynamic distribution and quantity of methanogens and methanotrophs in composting particles during manure aerobic composting using fluorescence in situ hybridization–confocal laser scanning microscopy (FISH–CLSM) and quantified their correlation with CH4 emissions. The visualization results showed that methanogens existed inside the particles, while methanotrophs clustered in the outer layer; a facultative anaerobic zone existed in between. The quantification results of integral optical density of methanogens and methanotrophs per unit particle area (Ugen and Uoxi, respectively) indicated that, in the cooling phase, CH4 generation and oxidation could still be high and could strike a balance if the initial organic matter content of composting materials is high, while both could be extremely low if the content is low. A strong linearity between Ugen obtained by FISH–CLSM and methyl-coenzyme M reductase copy number obtained by quantitative polymerase chain reaction analysis (R2 = 0.88) was observed, which justified the effectiveness of the FISH–CLSM method and demonstrated that macro-scale CH4 emissions were essentially an accumulation of particle-scale CH4 emissions. CH4 emissions were equal to 3.3297 × 107Ugen – 3.1814 × 106Uoxi – 3902.9900 (R2 = 0.98). Overall, the results showed that methanogens exerted more influence on CH4 emissions than methanotrophs. Combining these results with CH4-generation and -oxidation kinetics may help illustrate CH4-emission mechanisms, improve particle-scale CH4-emission models, and thereby provide theoretical guidance for operation optimization and emission reduction in composting processes.

Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC

Abstract. Estimating methane (CH4) emissions from natural wetlands is complex, and the estimates contain large uncertainties. The models used for the task are typically heavily parameterized and the parameter values are not well known. In this study, we perform a Bayesian model calibration for a new wetland CH4 emission model to improve the quality of the predictions and to understand the limitations of such models.The detailed process model that we analyze contains descriptions for CH4 production from anaerobic respiration, CH4 oxidation, and gas transportation by diffusion, ebullition, and the aerenchyma cells of vascular plants. The processes are controlled by several tunable parameters. We use a hierarchical statistical model to describe the parameters and obtain the posterior distributions of the parameters and uncertainties in the processes with adaptive Markov chain Monte Carlo (MCMC), importance resampling, and time series analysis techniques. For the estimation, the analysis utilizes measurement data from the Siikaneva flux measurement site in southern Finland. The uncertainties related to the parameters and the modeled processes are described quantitatively. At the process level, the flux measurement data are able to constrain the CH4 production processes, methane oxidation, and the different gas transport processes. The posterior covariance structures explain how the parameters and the processes are related. Additionally, the flux and flux component uncertainties are analyzed both at the annual and daily levels. The parameter posterior densities obtained provide information regarding importance of the different processes, which is also useful for development of wetland methane emission models other than the square root HelsinkI Model of MEthane buiLd-up and emIssion for peatlands (sqHIMMELI). The hierarchical modeling allows us to assess the effects of some of the parameters on an annual basis. The results of the calibration and the cross validation suggest that the early spring net primary production could be used to predict parameters affecting the annual methane production. Even though the calibration is specific to the Siikaneva site, the hierarchical modeling approach is well suited for larger-scale studies and the results of the estimation pave way for a regional or global-scale Bayesian calibration of wetland emission models.

Technical note: Comparison of methane ebullition modelling approaches used in terrestrial wetland models

Abstract. Emission via bubbling, i.e. ebullition, is one of the main methane (CH4) emission pathways from wetlands to the atmosphere. Direct measurement of gas bubble formation, growth and release in the peat–water matrix is challenging and in consequence these processes are relatively unknown and are coarsely represented in current wetland CH4 emission models. In this study we aimed to evaluate three ebullition modelling approaches and their effect on model performance. This was achieved by implementing the three approaches in one process-based CH4 emission model. All the approaches were based on some kind of threshold: either on CH4 pore water concentration (ECT), pressure (EPT) or free-phase gas volume (EBG) threshold. The model was run using 4 years of data from a boreal sedge fen and the results were compared with eddy covariance measurements of CH4 fluxes.Modelled annual CH4 emissions were largely unaffected by the different ebullition modelling approaches; however, temporal variability in CH4 emissions varied an order of magnitude between the approaches. Hence the ebullition modelling approach drives the temporal variability in modelled CH4 emissions and therefore significantly impacts, for instance, high-frequency (daily scale) model comparison and calibration against measurements. The modelling approach based on the most recent knowledge of the ebullition process (volume threshold, EBG) agreed the best with the measured fluxes (R2 = 0.63) and hence produced the most reasonable results, although there was a scale mismatch between the measurements (ecosystem scale with heterogeneous ebullition locations) and model results (single horizontally homogeneous peat column). The approach should be favoured over the two other more widely used ebullition modelling approaches and researchers are encouraged to implement it into their CH4 emission models.

Greenhouse gas emissions from liquid dairy manure: Prediction and mitigation

The handling and use of manure on livestock farms contributes to emissions of the greenhouse gases (GHG) CH4 and N2O, especially with liquid manure management. Dairy farms are diverse with respect to manure management, with practices ranging from daily spreading to long-term storage for more efficient recycling of manure nutrients for crop production. Opportunities for GHG mitigation will depend on the baseline situation with respect to handling and storage, and therefore prediction and mitigation at the farm level requires a dynamic description of housing systems and storage conditions, and use of treatment technologies. Also, effects of treatment and handling on the properties of field-applied manure must be taken into account. Storage conditions and manure composition importantly define carbon and nitrogen transformations, and the resulting emissions of CH4 and N2O, as well as CO2 and NH3, which are all important for the GHG balance. Currently, inventories for CH4 and N2O emissions from manure are based on emission factors for a limited number of production systems, together with average annual temperature, but the inherent uncertainty of this approach is a barrier toward prediction and mitigation. Although more representative emission factors may be determined at country level, this is both challenging and costly, and effects of management changes for GHG mitigation are not easily quantified. An empirical model of CH4 emissions during storage is discussed that is based on daily time steps, and a parameterization based on measurements. A distinction between emissions from manure in barns and outside storage facilities is important for assessing effects of treatment technologies, such as anaerobic digestion, where only posttreatment emissions are affected. Upon field application, manure and soil together define the equilibrium distribution of labile carbon and nitrogen between bulk soil and manure hotspots. This introduces heterogeneity with respect to potential for N2O emissions, which is not represented in existing prediction models. Manure treatment and management options for GHG mitigation are discussed with emphasis on effects on manure volatile solids and N availability. Anaerobic digestion and acidification represent treatment technologies that are relevant for GHG mitigation on dairy farms.

A simulation model for methane emissions from landfills with interaction of vegetation and cover soil

Global climate change and ecological problems brought about by greenhouse gas effect have become a severe threat to humanity in the 21st century. Vegetation plays an important role in methane (CH4) transport, oxidation and emissions from municipal solid waste (MSW) landfills as it modifies the physical and chemical properties of the cover soil, and transports CH4 to the atmosphere directly via their conduits, which are mainly aerenchymatous structures. In this study, a novel 2-D simulation CH4 emission model was established, based on an interactive mechanism of cover soil and vegetation, to model CH4 transport, oxidation and emissions in landfill cover soil. Results of the simulation model showed that the distribution of CH4 concentration and emission fluxes displayed a significant difference between vegetated and non-vegetated areas. CH4 emission flux was 1–2 orders of magnitude higher than bare areas in simulation conditions. Vegetation play a negative role in CH4 emissions from landfill cover soil due to the strong CH4 transport capacity even though vegetation also promotes CH4 oxidation via changing properties of cover soil and emitting O2 via root system. The model will be proposed to allow decision makers to reconsider the actual CH4 emission from vegetated and non-vegetated covered landfills.

Remotely sensed MODIS wetland components for assessing the variability of methane emissions in Indian tropical/subtropical wetlands

Temperature and area fluctuations in wetlands greatly influence its various physico-chemical characteristics, nutrients dynamic, rates of biomass generation and decomposition, floral and faunal composition which in turn influence methane (CH4) emission rates. In view of this, the present study attempts to up-scale point CH4 flux from the wetlands of Uttar Pradesh (UP) by modifying two-factor empirical process based CH4 emission model for tropical wetlands by incorporating MODIS derived wetland components viz. wetland areal extent and corresponding temperature factors (Ft). This study further focuses on the utility of remotely sensed temperature response of CH4 emission in terms of Ft. Ft is generated using MODIS land surface temperature products and provides an important semi-empirical input for up-scaling CH4 emissions in wetlands. Results reveal that annual mean Ft values for UP wetlands vary from 0.69 (2010–2011) to 0.71(2011–2012). The total estimated area-wise CH4 emissions from the wetlands of UP varies from 66.47Ggyr−1with wetland areal extent and Ft value of 2564.04km2 and 0.69 respectively in 2010–2011 to 88.39Ggyr−1with wetland areal extent and Ft value of 2720.16km2 and 0.71 respectively in 2011–2012. Temporal analysis of estimated CH4 emissions showed that in monsoon season estimated CH4 emissions are more sensitive to wetland areal extent while in summer season sensitivity of estimated CH4 emissions is chiefly controlled by augmented methanogenic activities at high wetland surface temperatures.

A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0)

Abstract. Wetland emissions remain one of the principal sources of uncertainty in the global atmospheric methane (CH4) budget, largely due to poorly constrained process controls on CH4 production in waterlogged soils. Process-based estimates of global wetland CH4 emissions and their associated uncertainties can provide crucial prior information for model-based top-down CH4 emission estimates. Here we construct a global wetland CH4 emission model ensemble for use in atmospheric chemical transport models (WetCHARTs version 1.0). Our 0.5° × 0.5° resolution model ensemble is based on satellite-derived surface water extent and precipitation reanalyses, nine heterotrophic respiration simulations (eight carbon cycle models and a data-constrained terrestrial carbon cycle analysis) and three temperature dependence parameterizations for the period 2009–2010; an extended ensemble subset based solely on precipitation and the data-constrained terrestrial carbon cycle analysis is derived for the period 2001–2015. We incorporate the mean of the full and extended model ensembles into GEOS-Chem and compare the model against surface measurements of atmospheric CH4; the model performance (site-level and zonal mean anomaly residuals) compares favourably against published wetland CH4 emissions scenarios. We find that uncertainties in carbon decomposition rates and the wetland extent together account for more than 80 % of the dominant uncertainty in the timing, magnitude and seasonal variability in wetland CH4 emissions, although uncertainty in the temperature CH4 : C dependence is a significant contributor to seasonal variations in mid-latitude wetland CH4 emissions. The combination of satellite, carbon cycle models and temperature dependence parameterizations provides a physically informed structural a priori uncertainty that is critical for top-down estimates of wetland CH4 fluxes. Specifically, our ensemble can provide enhanced information on the prior CH4 emission uncertainty and the error covariance structure, as well as a means for using posterior flux estimates and their uncertainties to quantitatively constrain the biogeochemical process controls of global wetland CH4 emissions.