Hot spots of methane emission in West Siberian middle taiga wetlands disturbed by petroleum extraction activities

Methane is one of the most important greenhouse gases that cause climate change [Karol and Kiselev, 2003]. An increase in the atmospheric concentration of methane contributes to an increase in the temperature on the Earth, because this gas absorbs outgoing thermal radiation from the Earth's surface [Berdin, 2004]. Methane has a much shorter atmospheric lifetime than carbon dioxide (CO2), but CH4 absorbs certain wavelengths of energy more efficiently than СО2. The global warming potential of CH4 is 28 times greater than that of CO2 over a 100-year period [IPCC, 2013]. Its contribution to the formation of the greenhouse effect is 30% of the value assumed for carbon dioxide (Bazhin, 2006). Methane is removed from the atmosphere by photochemical oxidation in the troposphere and, to a lesser extent, by microbial oxidation in soils (Kirschke et al., 2013). Methane sources can be both natural and anthropogenic. The latter includes, firstly, industrial processes: fuel use [Omara et al., 2018; Johnson et al., 2023] (if the fuel is not completely burned, then methane gas is emitted into the air, besides it can also be released during the extraction and transportation of natural gas [Hawken et al., 2017]); food production (eg CH4 can be generated from the fermentation of food residues that were not used in the production process [Stephan et al., 2006]); as a result of microbial activity during the processing of waste in landfills and compost heaps (for example, in the process of biological waste treatment, methane can be produced in large quantities if the process is not properly controlled [Singh et al., 2017]). Secondly, two types of agricultural production are anthropogenic sources: rice cultivation [Seiler et al., 1984; Dannenberg and Conrad, 1999; Wang et al. 1997; Wang et al., 1999]; cattle breeding [Gerber et al., 2013; Johnson et al., 2023; Ellis et al., 2007]. CH4 is formed as a result of the biological decomposition of organic matter in the absence of oxygen [Dlugokencky and Houweling, 2003]. The most significant natural sources of methane are wetlands. Besides, methane can be emitted from aquatic ecosystems such as lakes and rivers. The decomposition of organic wastes in the soil, such as plant residues and animal manure, is also a natural source of methane (Smith et al., 2014) if this decomposition occurs under anaerobic conditions. Of great interest is the study of wet forests [Glukhova et al., 2021], since their contribution to methane emission can be quite significant. It is generally recognized that forests are CH4 sinks [Lemer and Roger, 2001; Megonigal and Guenther, 2008; Smith et al., 2000]. Nevertheless, very high CH4 fluxes were detected during spot measurements in some wet forests [Lohila et al., 2016; Tathy et al., 1992], that were comparable to the fluxes observed in wetlands [Harriss et al., 1982; Sabrekov et al., 2011; Glagolev et al., 2012; Davydov et al., 2021] (Fig. 1). However, single measurements of fluxes at individual spatial sites are clearly not enough to assess the role of wet forests in the overall methane balance. This role can be assessed only by knowing the dynamics of emission in time and its distribution in space. A comprehensive study of the variability of methane emission (from soils in general) began at the end of the 20th century in countries with significant areas of waterlogged soils: Brazil, Canada, the USA, and Russia [Bartlett et al., 1988; Moore et al., 1990; Disse, 1993; Glagolev et al., 1999]. At present, the emission spatial variability is studied in almost all regions of the world, including Finland, Mexico, and China [Zhang et al., 2020; Gonzalez-Valencia et al., 2021; Que et al., 2023]. However, there is very little data on the spatial variability of methane emissions in wet forests. Therefore, it is evident that current research should be focused on assessing the spatial variability of emissions in different types of wet forests. Emission of methane in wet forests. The main works devoted to measurements of the specific flux of methane in wet forests are summarized in Table 1. 1-3. It can be seen from the tables (and Fig. 2) that there is no clear relationship between the specific flux and the geographic location of the wet forest: in the “north” (in the boreal zone - about 57-67oN), values of ~4÷9 mg∙h-1∙m-2 can be measured [Lohila et al., 2016; Mochenov et al., 2018], that are similar to those typical for the tropics (~3÷8 mg∙h-1∙m-2 [Devol et al., 1990; Tathy et al., 1992]). On the contrary, in the south, values 1 or even 0.1 mg∙h-1∙m-2 can be measured that are more typical for northern territories. There is no doubt, everything is determined by environmental factors. The results of [Ulah and Moor, 2011] show that changes in soil temperature and moisture can have a significant impact on CH4 fluxes from forest soils. This often leads to so-called "hotspots" such as peak emissions from poorly drained soils when the pore space is filled with water and to a lower CO2:CH4 emission ratio. However, these factors are likely to be unequal. In fact, the flow rate is determined rather by the degree of anaerobiosis, depending on the conditions of humidity, than the temperature (the formation of CH4 should be very active at both 40o and 20°C assuming that temperatures around 20°C are quite common for the summer period in the boreal zone). It is certain, under the same humidity conditions, based on the well-known van't Hoff low, one can expect that the rate of methane production in the tropics at 40°C should be approximately 4-9 times higher than that at 20°C under boreal conditions. Yet, if there is a very deep anaerobiosis in the boreal zone (due to the complete watering of the soil) but wet soil in the tropics, then the above mentioned ratio can be reversed. The extremely strong dependence of methane production on the degree of anaerobiosis (and, hence, on humidity conditions) provides a very wide spatial variability of the emission. It can be seen from the data in Table 1 that, for example, in three seasonally flooded forests in Western Siberia, located at a distance of only about 5-10 km from each other, the entire spectrum of possible specific CH4 fluxes was observed at the same time, from absorption at a level of ~0.1 mg h-1 m-2 to a very active emission of ~10 mg h-1 m-2 [Mochenov et al., 2018]. An even more contrasting picture is observed, for example, in the mountain forest in Brazil and in the tropical forest of the Congo: within the same forest, the specific flux varies from 0 to 54 mg∙h-1∙m-2 [Bartlett et al., 1988] and from -0.31 to 150 mg∙h-1∙m-2, respectively (see Table 3). However, it is not always possible to find out the dependence of the flow on certain factors. For example, the measurements reported in Tang et al. [2018] showed that CH4 flux from tropical peat forest was similar to that from other managed and natural wetland ecosystems, including those located in different climate zones. However, meteorological variability in the rainforest does not correlate well with CH4 flux. Such apparent lack of correlation can be explained by the small range of micrometeorological variables in the tropical peat ecosystem. Ambus and Christensen [1995] studied several ecosystems where temporary waterlogging was possible. They made the following important assumption: the calculation of the total flux for periodically waterlogged ecosystems should be performed taking into account the topography of the landscape. Indeed, a more accurate estimate of methane consumption and emission can be obtained in this way, but the correct estimations of the gas flow by the chamber method requires taking into account the relative water levels during flooding. Knowing the topography and hydrology of each site in the area makes it possible to determine how long and how often this site remains relatively wet or dry [Glagolev et al., 2018]. From the above data, it is clear that there is a need to improve the quantitative assessment of the global methane emission from the soils of wet forests. Despite the establishment of a complex infrastructure for monitoring greenhouse gases on a global scale (eg ICOS, GMB, etc.), ground-based observations in wet forests on various continents are still underrepresented. Therefore, the contribution of forests to the global atmospheric exchange of CH4 remains uncertain.

Spatial Variability of Methane Emissions From Soils of Wet Forests: A Brief Review

Runkov,

Ilyasov

2024

Mathematical Models of Methane Consumption by Soils: A Review

Glagolev,

Terentieva,

Sabrekov

et al. 2023

This review explores mathematical models that assess methane (CH4) uptake in aerated soils within terrestrial ecosystems. Methane, a potent greenhouse gas, is produced under anaerobic conditions. While substantial research has been dedicated to methane emissions from water-saturated soils over the past four decades, the absorption of CH4 by non-saturated soils, despite their expansive coverage, has received less focus. In tropical and subtropical soils, methane consumption constitutes less than 5% of the global uptake. However, there's limited data concerning methane consumption in temperate non-saturated soils, which are prevalent in forests, grasslands, steppes, and croplands. This data scarcity has resulted in estimate uncertainty: methane consumption ranges between 1% to 15% of the global methane sink attributed to photochemical degradation. The mechanism of methane uptake by soils primarily stems from the dominance of methanotrophy over methanogenesis. In aerated soils, methane production by methanogens is absent (or minimal), with the primary source being the atmosphere. Methanotrophs, active in the upper soil layer, uptake this atmospheric methane. This absorption rate is influenced by both microbial oxidation and the diffusion of methane into the soil. The diffusion rate is notably determined by the atmospheric concentration of CH4 and the porosity of the soil's aeration – the fewer the pores filled with water, the more rapid the diffusion. The rate of oxidation, on the other hand, is influenced by the soil's temperature and moisture levels. Just as neither extremely dry soil (where microbial activity is limited due to water scarcity) nor overly wet soil (where microorganisms are deprived of oxygen) offer optimal conditions; temperature extremes – whether too cold or too hot – can also negatively impact the methane oxidation process. Nowadays, direct measurements of both methane consumption and emission processes are routinely conducted using high-precision field gas analyzers. However, while CH4 emissions have garnered significant attention, data collection on methane consumption is still limited, particularly in remote locations. When in situ data are limited, mathematical models offer a reliable approach for extrapolating site-specific data to regional or global scales, enhancing our understanding of soil methane oxidation processes and how they respond to climatic shifts. In this study, we critically evaluates various mathematical models related to the topic, examining their strengths, limitations, and suitability for estimating large-scale methane consumption in aerated soils. The field of CH4 cycle modeling currently employed a diverse range of mathematical models. These can be broadly classified into two main categories: (1) empirical models, and (2) physics-based models. The choice between these models often depends on the research objectives. On the other hand, models of regional ecology can be grouped into interpolation-extrapolation, analytical, and numerical categories. The interpolation-extrapolation models relate specific ecosystem properties (e.g. emissions) with their spatial or temporal coordinates. Analytical models capture the underlying physics, though achieving analytical solutions often requires simplifications to address the complexity of the equations. In contrast, numerical models are intricate and rely on numerical methods for their solutions. The "simple inventory" is interpolation-extrapolation method that estimates methane uptake from soil-atmosphere interactions using basic formulations. Originally based on biome types, the accuracy of this method is relatively low but has been used in several global and regional methane studies. Recent approaches further classify soils into structural classes, linking methane absorption rates to these classifications. Dutaur and Verchot (2007) aimed to refine this method, investigating correlations with latitude, temperature, and precipitation. Their use of discrete categorization variables, like climate zones and ecosystem types, improved predictive accuracy of the model. However, extrapolating localized measurements to broader scales remains a challenge due to the limited data and ecosystem heterogeneity. Analytical models leverage an understanding of the underlying physical processes to create equation-based representations. Early research indicated that the rate of soil methane absorption from the atmosphere was predominantly constrained by atmospheric diffusion (e.g. [Born et al.,1990; Potteretal.,1996]). This is because the ability of methanotrophs to consume methane often surpasses the diffusion transport mechanism's capacity. As a result, the peak rate of soil methane absorption from the atmosphere is capped by diffusion. As research deepened into the factors affecting CH4 absorption in non-saturated soils, models grew in complexity. It became evident that microbial oxidation, alongside methane diffusion, played a pivotal role in determining methane consumption rates. For optimal methane oxidation, conditions must be warm and the soil should be neither too dry nor too wet. The relationship between nitrogen and methane absorption remains a topic of debate. Nitrogen fertilizers suppress methane oxidation, but these fertilizers also promote plant growth, affecting soil moisture and potentially influencing methane dynamics. The MeMo model [Murguia-Flores et al., 2018] stands out as one of the most comprehensive adaptation, building upon the models of Ridgwelletal.[1999] (“R99”) and Curry [2007] (“C07”). The MeMo model incorporates factors, such as biome type, atmospheric methane concentration, soil temperature, nitrogen input, soil density, clay content, and soil moisture. Crucial enhancements were made to the original designs: a holistic analytical solution in a porous medium, refined nitrogen inhibition of methanotrophy, biome-specific influences on methane oxidation rate, and consideration of indigenous soil CH4 sources on methane uptake from the atmosphere. These modifications have notably improved the model's alignment with observational data. Regarding numerical models, few are specifically designed for assessing methane consumption, with more models being general ones that describe the methane dynamics in soil (incorporating oxidation, methane production, and transport). Intricate numerical models potentially offer more versatility than empirical or semi-empirical analytical ones: e.g. some analytical models often inherently assuming swamp methane oxidation as zero, not reflecting reality. However, numerical models usually require numerous site-specific parameters, such as soil usage, root zone depth, or even particular metabolic data. Because they're so tailored to specific sites, their use on a larger scale can be limited. Thus, using these models for regional methane uptake estimations doesn't guarantee high-quality results today. A recent trend in modeling natural processes focus on the ensemble approach. This strategy involves averaging results from multiple independent models focused on a shared metric. Comparative analysis shows that the highest quality is usually demonstrated by the "ensemble average" model. This is due to the fact that systematic errors of different models do not depend on each other and can be mutually compensated when averaging over the ensemble. The success of this approach has been confirmed in regularly published IPCC reports. The use of ensembles of models is also used in the study of methane fluxes from soil, both in solving direct and inverse problems [Glagolev et al., 2014; Poulter et al., 2017; Bergamaschi et al., 2018], but this approach has apparently not yet been used directly to estimate methane uptake by soils. Mathematical models don't always align with experimental data for specific research sites, as noted by authors such as Ridgwell et al.[1999] and Murguia-Flores et al.[2018]. These models can sometimes overestimate or underestimate certain metrics. This inconsistency is further evident when different researchers identify similar parameters in their models but, based on various datasets, arrive at different values. For instance, while R99 utilized a value based on 13 measurements from diverse locations, С07's value was derived from a five-year observation in Colorado. Meanwhile, the MeMo model introduced values for four distinct biome types. Nevertheless, when these models are applied on a global scale, they provide reasonably accurate estimates of the planet's total methane uptake by soils. These estimates are in line with both basic inventories, like those from [Born et al., 1990], and more advanced methods, such as the inverse modeling by Hein et al. [1997]. This suggests that for larger regions, the models can still yield sensible CH4 absorption assessments, with overestimations in certain geographical areas being balanced out by underestimations in others.

Multi-model ensemble successfully predicted atmospheric methane consumption in soils across the complex landscape

Glagolev,

Il’yasov,

Sabrekov

et al. 2024

Methane consumption by soils is a crucial component of the CH4 and carbon cycle. It is essential to thoroughly investigate CH4 uptake by soils, particularly considering its anticipated increase by the end of the century [Zhuang et al., 2013]. Numerous mathematical models, both empirical and detailed biogeochemical [Glagolev et al., 2023], have been developed to quantify methane consumption by soils from the atmosphere. These models are instrumental in handling spatio-temporal variability and can offer reliable estimates of regional and global methane consumption by soils. Furthermore, they enhance our comprehension of the physical and biological processes that influence methanotrophy intensity. Consequently, we can forecast the response of CH4 consumption by soil to global climate shifts [Murguia-Flores et al., 2018], especially since many models consider the effects of atmospheric CH4 concentration changes on methanotrophy and ecosystem type [Zhuang et al., 2013]. In addition to the utilization of individual models, such as those cited by [Hagedorn et al., 2005; Glagolev et al., 2014; Ito et al., 2016; Silva et al., 2016], there has been extensive advancement in employing multiple models in an ensemble format. This approach aims to integrate as much a priori information as feasible [Lapko, 2002]. Throughout the 20th century, the concept of ensemble modeling evolved from merely drawing conclusions based on multiple independent experts (F. Sanders, 1963) to structured ensemble mathematical modeling [Hagedorn et al., 2005]. In this context, the term "ensemble" consistently refers to a collection containing more than one model. Complexities in describing the physiology and biochemistry of methanotrophic bacteria in natural environments [Bedard, Knowles, 1989; Hanson, Hanson, 1996; Belova et al., 2013; Oshkin et al., 2014] make it difficult to develop accurate biological models and determine their specific biokinetic parameters [Curry, 2007]. At the same time, broader and often empirical models, such as those by [Potter et al., 1996; Ridgwell et al., 1999; Curry, 2007; Murguia-Flores et al., 2018], demonstrate reasonable estimates of global methane consumption. Employing model ensembles could enhance accuracy, not just in global and large-scale modeling, but also at the granular level of local study sites. Nonetheless, ensemble modeling doesn't always ensure optimal outcomes, as all models within an ensemble might overlook a biological process or effect that significantly influences the dynamics of a real ecosystem [Ito et al., 2016]. For instance, no model considered anaerobic methane oxidation until this process was empirically identified [Xu et al., 2015]. Therefore, it's crucial to validate the realism of an ensemble against specific in situ data for every application. This study aimed to develop an ensemble model describing methane consumption by soils and to test its efficacy on a randomly selected study site. In our research, we closely examined and replicated the algorithms of four soil methane consumption models: the modification by Glagolev, Filippov [2011] of Dörr et al. [1993], Curry's model [2007], the CH4 consumption block from the DLEM model [Tian et al., 2010], and the MeMo model excluding autochthonous CH4 sources [Murguia-Flores et al., 2018]. Using these, we developed an ensemble of four models. For experimental in situ data, we utilized field measurements from the Kursk region in Russia. Additionally, we introduced a method to average the ensemble model's prediction by assigning weight coefficients to each model. This approach acknowledges the idea that the total available information doubles every few years. Thus, newer models were given higher weights, while older ones received lower weights. The model ensemble effectively predicted CH4 consumption based on in situ measurements, albeit with a notably broad confidence interval for the predictions. Notably, there was minimal variance between the standard averaging of model predictions and weighted averaging. As anticipated, individual models underperformed compared to the ensemble. We computed the Theil inconsistency coefficient for various types of means, such as quadratic mean, cubic mean, and biquadratic mean, among others [Gini, Barbensi, 1958], both for ensemble modeling results and individual models. The ensemble predictions, when averaged using diverse methods, yielded Theil inconsistency coefficients ranging from 0.156 to 0.267. The most favorable outcome (0.156) was derived from the power mean with a power index of 0.7. However, the power mean presents a challenge as its power index isn't predetermined but chosen to best fit the experimental data. A similar limitation exists for the exponential mean. While the experimental data allows for the selection of a parameter yielding a Theil coefficient of 0.157, pre-determining this optimal value (1.3) is not feasible. Regarding other estimations that don't necessitate selecting optimal parameters, it was surprising to find that one of the best results (Theil's coefficient = 0.166) came from the half-sum of extreme terms. Surprisingly, the median provided a less satisfactory result, with a Theil's coefficient of 0.222. The merit of the ensemble approach stems from P.D. Thompson's 1977 observation, which he stated assertively: "It is an indisputable fact that two or more inaccurate, but independent predictions of the same event can be combined in such a way that their "combined" forecast, on average, will be more accurate than any of these individual forecasts" [Hagedorn et al., 2005]. Examining our ensemble of models through this lens reveals a limitation, as the condition of independence isn't fully satisfied. The models by Dörr et al. [1993], Curry [2007], and MeMo [Murguia-Flores et al., 2018] share underlying similarities and can be seen as part of a cohesive cluster. Only DLEM, crafted on entirely distinct principles, stands apart from these models. To enhance the ensemble's robustness in future iterations, the inclusion of genuinely independent models, such as a modified version of MDM [Zhuang et al., 2013] and the model by Ridgwell et al. [1999], is recommended. The ensemble, comprising four models and implemented without specific parameter adjustments, effectively captured methane consumption across diverse sites in the Kursk region, such as fields and forests. On average, the relative simulation error for all these sites was 36%, with the experimental data displaying a variation of 26%. Notably, while the variation is modest for this dataset, methane absorption measurements generally tend to fluctuate by several tens of percent [Crill, 1991, Fig. 1; Ambus, Robertson, 2006, Fig. 3; Kleptsova et al., 2010; Glagolev et al., 2012]. Considering this broader perspective, the simulation error achieved is indeed favorable. Upon evaluating different methods for combining individual model results within the ensemble (specifically those methods that can be applied without prior parameter adjustments based on experimental data), it was found that the most straightforward operators yielded the best outcomes. This assessment was based on Theil's inequality coefficient criterion. Both the semi-sum of extreme terms and the arithmetic mean stood out in their performance. However, a significant drawback of the constructed ensemble is the extensive confidence interval for its predictions, averaging ±78% at a 90% probability level. We hypothesize that expanding the number of independent models within the ensemble could potentially narrow this interval.