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Enregistrement W4390974665 · doi:10.18822/edgcc375293

SPATIAL VARIABILITY OF METHANE EMISSIONS FROM SOILS OF WET FORESTS: A BRIEF REVIEW

2024· review· en· W4390974665 sur OpenAlexaboutno aff
R A Runkov, D. V. Ilyasov

Notice bibliographique

RevueEnvironmental Dynamics and Global Climate Change · 2024
Typereview
Langueen
DomaineEnvironmental Science
ThématiqueAtmospheric and Environmental Gas Dynamics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMethaneEnvironmental scienceSoil waterMethane emissionsGreenhouse gasAtmospheric sciencesHydrology (agriculture)Soil scienceGeologyEcologyOceanographyGeotechnical engineering

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,019

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0050,007
Études des sciences et des technologies0,0000,000
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,021
Tête enseignante GPT0,279
Écart entre enseignants0,258 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

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