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

2018 ◽  
Vol 11 (3) ◽  
pp. 1199-1228 ◽  
Author(s):  
Jouni Susiluoto ◽  
Maarit Raivonen ◽  
Leif Backman ◽  
Marko Laine ◽  
Jarmo Makela ◽  
...  
Keyword(s):  
Methane Emission ◽  
Process Model ◽  
Hierarchical Modeling ◽  
Net Primary Production ◽  
Measurement Data ◽  
Flux Measurement ◽  
Transport Processes ◽  
Emission Model ◽  
Ch4 Production ◽  
Emission Models

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.

scholarly journals Calibrating a wetland methane emission model with hierarchical modeling and adaptive MCMC

2017 ◽  
Author(s):  
Jouni Susiluoto ◽  
Maarit Raivonen ◽  
Leif Backman ◽  
Marko Laine ◽  
Jarmo Mäkelä ◽  
...  
Keyword(s):  
Gas Transport ◽  
Hierarchical Modeling ◽  
Measurement Data ◽  
Flux Measurement ◽  
Ch4 Emission ◽  
Ch4 Oxidation ◽  
Adaptive Mcmc ◽  
Measurement Site ◽  
Ch4 Production ◽  
Plant Transport

Abstract. Methane (CH4) emission estimation for natural wetlands is complex and the estimates contain large uncertainties. The models used for the task are typically heavily parametrized and the parameter values are not well known. In this study we perform a Bayesian model calibration for a new wetland CH4 model to improve 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 MCMC techniques. For the estimation, the analysis utilizes measurement data from the Siikaneva flux measurement site in Southern Finland. The model parameters are calibrated using six different modeled peat column depths, and the hierarchical modeling allows us to assess the effect of the parameters on an annual basis. The results of the calibration and their cross validation suggest that the early spring net primary production and soil temperatures could be used to predict the annual methane emissions. The modeled peat column depth has an effect on how much the plant transport pathway dominates the gas transport, and the optimization moved most of the gas transport from the diffusive pathway to plant transport. This is in line with other research, highlighting the usefulness of algorithmic calibration of biogeochemical models. Modeling only 70 cm of the peat column gives the best flux estimates at the flux measurement site, while the estimates are worse for a column deeper than one meter or shallower than 50 cm. The posterior parameter distributions depend on the modeled peat depth. At the process level, the flux measurement data is able to constrain CH4 production and gas transport processes, but for CH4 oxidation, which is an important constituent of the total CH4 emission, the determining parameter is not identifiable.

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

2014 ◽  
Vol 11 (17) ◽  
pp. 4651-4664 ◽  
Author(s):  
A. Budishchev ◽  
Y. Mi ◽  
J. van Huissteden ◽  
L. Belelli-Marchesini ◽  
G. Schaepman-Strub ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Methane Emission ◽  
Average Method ◽  
Temporal Dynamics ◽  
Weighted Average ◽  
Ambient Atmosphere ◽  
Emission Model ◽  
Closed Chamber ◽  
Weighted Average Method ◽  
Emission Models

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.

scholarly journals Evaluation of a plot scale methane emission model at the ecosystem scale using eddy covariance observations and footprint modelling

2014 ◽  
Vol 11 (3) ◽  
pp. 3927-3961 ◽  
Author(s):  
A. Budishchev ◽  
Y. Mi ◽  
J. van Huissteden ◽  
L. Belelli-Marchesini ◽  
G. Schaepman-Strub ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Methane Emission ◽  
Average Method ◽  
Temporal Dynamics ◽  
Weighted Average ◽  
Ambient Atmosphere ◽  
Emission Model ◽  
Closed Chamber ◽  
Weighted Average Method ◽  
Emission Models

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 closed-chamber measurements, 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 time scale 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 and an underestimation of methane emissions by 27.4%. 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.

scholarly journals Evaluation of the Effective Microstructure Parameter of the Microwave Emission Model of Layered Snowpack for Multiple-Layer Snow

2021 ◽  
Vol 13 (10) ◽  
pp. 2012
Author(s):  
Yue Yu ◽  
Jinmei Pan ◽  
Jiancheng Shi
Keyword(s):  
Process Model ◽  
Mean Squared Error ◽  
Weighted Average ◽  
Single Layer ◽  
Microwave Emission ◽  
Emission Model ◽  
Snow Layer ◽  
Optimal Method ◽  
Microstructure Parameter ◽  
Multiple Layer

Natural snow, one of the most important components of the cryosphere, is fundamentally a layered medium. In forward simulation and retrieval, a single-layer effective microstructure parameter is widely used to represent the emission of multiple-layer snowpacks. However, in most cases, this parameter is fitted instead of calculated based on a physical theory. The uncertainty under different frequencies, polarizations, and snow conditions is uncertain. In this study, we explored different methods to reduce the layered snow properties to a set of single-layer values that can reproduce the same brightness temperature (TB) signal. A validated microwave emission model of layered snowpack (MEMLS) was used as the modelling tool. Multiple-layer snow TB from the snow’s surface was compared with the bulk TB of single-layer snow. The methods were tested using snow profile samples from the locally validated and global snow process model simulations, which follow the natural snow’s characteristics. The results showed that there are two factors that play critical roles in the stability of the bulk TB error, the single-layer effective microstructure parameter, and the reflectivity at the air–snow and snow–soil boundaries. It is important to use the same boundary reflectivity as the multiple-layer snow case calculated using the snow density at the topmost and bottommost layers instead of the average density. Afterwards, a mass-weighted average snow microstructure parameter can be used to calculate the volume scattering coefficient at 10.65 to 23.8 GHz. At 36.5 and 89 GHz, the effective microstructure parameter needs to be retrieved based on the product of the snow layer transmissivity. For thick snow, a cut-off threshold of 1/e is suggested to be used to include only the surface layers within the microwave penetration depth. The optimal method provides a root mean squared error of bulk TB of less than 5 K at 10.65 to 36.5 GHz and less than 10 K at 89 GHz for snow depths up to 130 cm.

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

2009 ◽  
Vol 6 (12) ◽  
pp. 3035-3051 ◽  
Author(s):  
J. van Huissteden ◽  
A. M. R. Petrescu ◽  
D. M. D. Hendriks ◽  
K. T. Rebel
Keyword(s):  
Temporal Variability ◽  
Global Scale ◽  
Transport Processes ◽  
Small Scale ◽  
Soil Parameters ◽  
Wetland Soil ◽  
Emission Model ◽  
Ch4 Emission ◽  
Model Based ◽  
Ch4 Transport

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.

scholarly journals Microstructure representation of snow in coupled snowpack and microwave emission models

2016 ◽  
Author(s):  
Melody Sandells ◽  
Richard Essery ◽  
Nick Rutter ◽  
Leanne Wake ◽  
Leena Leppänen ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Microstructure Evolution ◽  
Microwave Emission ◽  
Model Systems ◽  
Research Centre ◽  
Emission Model ◽  
Mean Error ◽  
Scale Factors ◽  
Wide Range ◽  
Emission Models

Abstract. This is the first study to encompass a wide range of coupled snow evolution and microwave emission models in a common modelling framework in order to generalise the link between snowpack microstructure predicted by the snow evolution models and microstructure required to reproduce observations of brightness temperature as simulated by snow emission models. Brightness temperatures at 18.7 and 36.5 GHz were simulated by 1323 ensemble members, formed from 63 Jules Investigation Model snowpack simulations, three microstructure evolution functions and seven microwave emission model configurations. Two years of meteorological data from the Sodankylä Arctic Research Centre, Finland were used to drive the model over the 2011–2012 and 2012–2013 winter periods. Comparisons between simulated snow grain diameters and field measurements with an IceCube instrument showed that the evolution functions from SNTHERM simulated snow grain diameters that were too large (mean error 0.12 to 0.16 mm), whereas MOSES and SNICAR microstructure evolution functions simulated grain diameters that were too small (mean error −0.16 to −0.24 mm for MOSES, and −0.14 to −0.18 mm for SNICAR). No model (HUT, MEMLS or DMRT-ML) provided a consistently good fit across all frequencies and polarizations. The smallest absolute values of mean bias in brightness temperature over a season for a particular frequency and polarization ranged from 0.9 to 7.2 K. Optimal scaling factors for the snow microstructure were presented to compare compatibility between snowpack model microstructure and emission model microstructure. Scale factors ranged between 0.3 for the SNTHERM-Empirical MEMLS model combination (2011–2012), and 5.0 or greater when considering non-sticky particles in DMRT-ML in conjunction with MOSES or SNICAR microstructure (2012–2013). Differences in scale factors between microstructure models were generally greater than the differences between microwave emission models, suggesting that more accurate simulations in coupled snowpack-microwave model systems will be achieved primarily through improvements in the snowpack microstructure representation, followed by improvements in the emission models. Other snowpack parameterisations in the snowpack model, mainly densification, led to a mean brightness temperature difference of 11 K when the JIM ensemble was applied to the MOSES microstructure and empirical MEMLS emission model for the 2011–2012 season. Consistency between snowpack microstructure and microwave emission models, and the choice of snowpack densification algorithms should be considered in the design of snow mass retrieval systems and microwave data assimilation systems.

scholarly journals Wetland restoration and methanogenesis: the activity of microbial populations and competition for substrates at different temperatures

2009 ◽  
Vol 6 (6) ◽  
pp. 1127-1138 ◽  
Author(s):  
V. Jerman ◽  
M. Metje ◽  
I. Mandić-Mulec ◽  
P. Frenzel
Keyword(s):  
Methane Emission ◽  
Methane Production ◽  
Iron Reduction ◽  
Carbon Mineralization ◽  
Marsh Soil ◽  
Ch4 Production ◽  
Different Temperatures ◽  
Acetoclastic Methanogenesis ◽  
Diverse Community ◽  
Water Saturated

Abstract. Ljubljana marsh in Slovenia is a 16 000 ha area of partly drained fen, intended to be flooded to restore its ecological functions. The resultant water-logging may create anoxic conditions, eventually stimulating production and emission of methane, the most important greenhouse gas next to carbon dioxide. We examined the upper layer (~30 cm) of Ljubljana marsh soil for microbial processes that would predominate in water-saturated conditions, focusing on the potential for iron reduction, carbon mineralization (CO2 and CH4 production), and methane emission. Methane emission from water-saturated microcosms was near minimum detectable levels even after extended periods of flooding (>5 months). Methane production in anoxic soil slurries started only after a lag period of 84 d at 15°C and a minimum of 7 d at 37°C, the optimum temperature for methanogenesis. This lag was inversely related to iron reduction, which suggested that iron reduction out-competed methanogenesis for electron donors, such as H2 and acetate. Methane production was observed only in samples incubated at 14–38°C. At the beginning of methanogenesis, acetoclastic methanogenesis dominated. In accordance with the preferred substrate, most (91%) mcrA (encoding the methyl coenzyme-M reductase, a key gene in methanogenesis) clone sequences could be affiliated to the acetoclastic genus Methanosarcina. No methanogens were detected in the original soil. However, a diverse community of iron-reducing Geobacteraceae was found. Our results suggest that methane emission can remain transient and low if water-table fluctuations allow re-oxidation of ferrous iron, sustaining iron reduction as the most important process in terminal carbon mineralization.

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

2018 ◽  
Vol 15 (3) ◽  
pp. 937-951 ◽  
Author(s):  
Olli Peltola ◽  
Maarit Raivonen ◽  
Xuefei Li ◽  
Timo Vesala
Keyword(s):  
Temporal Variability ◽  
Model Comparison ◽  
Technical Note ◽  
Scale Model ◽  
Emission Model ◽  
Ch4 Emission ◽  
Ch4 Emissions ◽  
Emission Models ◽  
Modelling Approach ◽  
Modelling Approaches

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.

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