Map‐based inventory of wetland biomass and net primary production in western Siberia

Peregon, Anna; Maksyutov, Shamil; Kosykh, Natalia P.; Миронычева-Токарева, Н. П.

doi:10.1029/2007jg000441

Cited by 68 publications

(78 citation statements)

References 28 publications

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“…The largest wetland areas in the SWAMPS-GLWD are in Amazonia, the Congo Basin, and the western Siberian lowlands, which in previous studies have appeared to be strongly underestimated by several inventories . However, wetlands above 70 • N appear under-represented in GLWD as compared to Sheng et al (2004) and Peregon et al (2008). Indeed, approximately half of the global natural wetland area lies in the boreal zone between 50 and 70 • N, while 35 % can be found in the tropics, between 20 • N and 30 • S (Matthews and Fung, 1987;Aselmann and Crutzen, 1989).…”

Section: Wetlandsmentioning

confidence: 99%

The global methane budget 2000–2012

Saunois

Bousquet

Poulter

et al. 2016

Earth Syst. Sci. Data

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906

733

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Abstract. The global methane (CH 4 ) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH 4 over the past decade. Emissions and concentrations of CH 4 are continuing to increase, making CH 4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH 4 sources that overlap geographically, and from the destruction of CH 4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (∼ biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). . Top-down inversions consistently infer lower emissions in China (∼ 58 Tg CH 4 yr −1 , range 51-72, −14 %) and higher emissions in Africa (86 Tg CH 4 yr −1 , range 73-108, +19 %) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models.The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40 % on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions.

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Section: Wetlandsmentioning

confidence: 99%

The global methane budget 2000–2012

Saunois

Bousquet

Poulter

et al. 2016

Earth Syst. Sci. Data

Self Cite

906

733

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“…We considered four wetland map products over the WSL, all of which have been used in high-latitude wetland carbon studies. Two of them are regional maps specific to the WSL: Sheng et al (2004), denoted by "Sheng2004", and Peregon et al (2008), denoted by "Peregon2008". Both Sheng 2004 and Peregon2008 used the 1 : 2500 000-scale map of Romanova (1977): Peregon2008 was entirely based on the Romanova map, while Sheng2004 used the Romanova map north of 65 • N and used the 1 : 100 000-scale maps of Markov (1971) and Matukhin and Danilov (2000) elsewhere.…”

Section: Terminologymentioning

confidence: 99%

“…Until recently, the evaluation of large-scale wetland CH 4 emissions models has been difficult, due to the sparseness of in situ and atmospheric CH 4 observations. However, observations from the West Siberian Lowland (WSL) now offer the opportunity to assess model performance, thanks to recent intensive field campaigns , aircraft profiles (Umezawa et al, 2012), tall-tower observations (Sasakawa et al, 2010;Winderlich et al, 2010), and high-resolution wetland inventories (Sheng et al, 2004;Peregon et al, 2008Peregon et al, , 2009.…”

Section: Introductionmentioning

confidence: 99%

WETCHIMP-WSL: intercomparison of wetland methane emissions models over West Siberia

et al. 2015

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Abstract. Wetlands are the world's largest natural source of methane, a powerful greenhouse gas. The strong sensitivity of methane emissions to environmental factors such as soil temperature and moisture has led to concerns about potential positive feedbacks to climate change. This risk is particularly relevant at high latitudes, which have experienced pronounced warming and where thawing permafrost could potentially liberate large amounts of labile carbon over the next 100 years. However, global models disagree as to the magnitude and spatial distribution of emissions, due to uncertainties in wetland area and emissions per unit area and a scarcity of in situ observations. Recent intensive field campaigns across the West Siberian Lowland (WSL) make this an ideal region over which to assess the perPublished by Copernicus Publications on behalf of the European Geosciences Union. T. J. Bohn et al.: Intercomparison of wetland methane emissions modelsformance of large-scale process-based wetland models in a high-latitude environment. Here we present the results of a follow-up to the Wetland and Wetland CH 4 Intercomparison of Models Project (WETCHIMP), focused on the West Siberian Lowland (WETCHIMP-WSL). We assessed 21 models and 5 inversions over this domain in terms of total CH 4 emissions, simulated wetland areas, and CH 4 fluxes per unit wetland area and compared these results to an intensive in situ CH 4 flux data set, several wetland maps, and two satellite surface water products. We found that (a) despite the large scatter of individual estimates, 12-year mean estimates of annual total emissions over the WSL from forward models (5.34 ± 0.54 Tg CH 4 yr −1 ), inversions (6.06 ± 1.22 Tg CH 4 yr −1 ), and in situ observations (3.91 ± 1.29 Tg CH 4 yr −1 ) largely agreed; (b) forward models using surface water products alone to estimate wetland areas suffered from severe biases in CH 4 emissions; (c) the interannual time series of models that lacked either soil thermal physics appropriate to the high latitudes or realistic emissions from unsaturated peatlands tended to be dominated by a single environmental driver (inundation or air temperature), unlike those of inversions and more sophisticated forward models; (d) differences in biogeochemical schemes across models had relatively smaller influence over performance; and (e) multiyear or multidecade observational records are crucial for evaluating models' responses to long-term climate change.

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“…Романовой, как она описана в [Peregon et al, 2008]); S ik (м 2 ) -площадь болотных комплексов i-го типа в k-ой природной зоне (для подсчета площадей использовали электронную карту болотных комплексов Западной Сибири [Peregon et al, 2008]); T k (час/год) -период эмиссии метана в k-ой природной зоне (тундра -2424, лесотундра -2832, северная тайга -3240, средняя тайга -3912, южная тайга -4056, подтайга -4392, лесостепь -4704, степь -5112); α ijk -доля ландшафта j-го вида (j = 1, 2…m, m = 8: j = 1 -приозерные сплавины, j = 2 -мерзлые бугры, j = 3 -гряды, j = 4 -олиготрофные мочажины, j = 5 -мезотрофные болота, j = 6 -эутрофные болота, j = 7 -внутриболотные озера, j = 8 -рямы в соответствии с [Peregon et al, 2009]) на болотах i-го типа в k-ой природной зоне (численные значения α ijk взяты из [Peregon et al, 2008] 1) And -Andromeda polifolia, Bet -Betula nana, Cha -Chamaedaphne calyculata, Eri -Eriophorum vaginatum, Led -Ledum palustris, Pin -Pinus sylvestris, Rub -Rubus chamaemorus, Spa -Sphagnum angustifolium, Spf -Sphagnum fuscum, Sps -Sphagnum sp. ; 2) Положительные и отрицательные величины обозначают ситуации, когда уровень болотных вод (УБВ) соответственно ниже и выше усредненной поверхности мха; 3) h1 = -15 см, h2 = -5 см, h3 = 25 см; 4) h1 = 0 см, h2 = 10 см, h3 = 40 см; 5) h1 = 0 см, h2 = 10 см, h3 = 25 см.…”

Section: примечанияunclassified

“…Таблица 1. Относительные площади типов болот и слагающих их элементов микрорельефа в соответствии с картой Peregon et al [2008] …”

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Methane emission from middle taiga ridges and ryams of Western Siberia

Kleptsova¹,

Glagolev²,

Filippov³

et al. 2010

Environmental Dynamics and Global Climate Change

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В статье приводятся экспериментальные данные по потокам метана с рямов и гряд средней тайги Западной Сибири. Эти данные объединяются в рамках «стандартной модели» Bc7, включающей в себя медианы распределений потоков метана с шести типов микроландшафтов, их площади в ячейках географической сетки 0.5°×0.5° и продолжительность периода эмиссии метана для данной зоны. На основании модели Вс7 годовая эмиссия СН4 из средней тайги Западной Сибири оценена величиной 0.69 МтСН4/год, что составляет 21.6% региональной эмиссии.

show abstract

Map‐based inventory of wetland biomass and net primary production in western Siberia

Cited by 68 publications

References 28 publications

The global methane budget 2000–2012

The global methane budget 2000–2012

WETCHIMP-WSL: intercomparison of wetland methane emissions models over West Siberia

Methane emission from middle taiga ridges and ryams of Western Siberia

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