A high‐resolution GIS‐based inventory of the west Siberian peat carbon pool

Sheng, Yongwei; Smith, L. C.; MacDonald, Glen M.; Kremenetski, K. V.; Frey, Karen E.; Величко, А.А.; Lee, Mary; Beilman, David W.; Dubinin, Peter

doi:10.1029/2003gb002190

Cited by 176 publications

(227 citation statements)

References 14 publications

Supporting

Mentioning

214

Contrasting

Unclassified

Order By: Relevance

“…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

934

733

View full text Add to dashboard Cite

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.

show abstract

Section: Wetlandsmentioning

confidence: 99%

The global methane budget 2000–2012

Saunois

Bousquet

Poulter

et al. 2016

Earth Syst. Sci. Data

934

733

View full text Add to dashboard Cite

show abstract

“…1), (ii) northern peatland distribution from NCSCD (Northern Circumpolar Soil Carbon Database; Tarnocai et al, 2007Tarnocai et al, , 2009 (Fig. 2), and (iii) peatland distribution for the West Siberian Lowlands (Sheng et al, 2004) (Fig. 3).…”

Section: Soil and Wetland Distribution Datamentioning

confidence: 99%

“…For these simulations, each grid cell was separated into two parts: an upland fraction, underlain by mineral soils, with soil textures supplied by the FAO Digital Soil Map of the World (Batjes, 1997); and a lake/wetland fraction, underlain by peat soils, with peat depths given by the database of Sheng et al (2004) and other characteristics taken from Letts et al (2000). Simulations were run separately for each portion of the grid cell.…”

Section: Wetchimp Set-upmentioning

confidence: 99%

“…Simulations were run separately for each portion of the grid cell. The lake/wetland portion of each grid cell was determined as the superset of the Sheng et al (2004) peatland map; wetlands, wet tundra, and croplands (so that nearby lakes could have a surrounding catchment) given by the Bartalev et al (2003) land cover classification; and lakes given by the Global Lake and Wetland Database (GLWD; Lehner and Döll, 2004). Bathymetries for the lake/wetlands were estimated by combining lake size distributions from the GLWD; average lake depths from literature for bog pools, Arctic thaw lakes, and other boreal lakes; and topography of surrounding wetlands from the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) (Hayakawa et al, 2008) and STRM (Shuttle Topography Radar Mission; Digital Elevation Model ) (Farr and Kobrick, 2000) DEMs.…”

Section: Wetchimp Set-upmentioning

confidence: 99%

“…The UW-VIC model is the most complex model and takes advantage of all of the features included in Fig. 6, using the fractional peatland cover by Sheng et al (2004) only as maximal boundaries, rather than as a fixed map.…”

Section: Ch 4 Producing Areamentioning

confidence: 99%

See 2 more Smart Citations

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Wania

Melton²,

Hodson³

et al. 2013

Geosci. Model Dev.

187

180

View full text Add to dashboard Cite

Abstract. The Wetland and Wetland CH 4 Intercomparison of Models Project (WETCHIMP) was created to evaluate our present ability to simulate large-scale wetland characteristics and corresponding methane (CH 4 ) emissions. A multi-model comparison is essential to evaluate the key uncertainties in the mechanisms and parameters leading to methane emissions. Ten modelling groups joined WETCHIMP to run eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO 2 ) forcing datasets. We reported the main conclusions from the intercomparison effort in a companion paper (Melton et al., 2013). Here we provide technical details for the six experiments, which included an equilibrium, a transient, and an optimized run plus three sensitivity experiments (temperature, precipitation, and atmospheric CO 2 concentration). The diversity of approaches used by the models is summarized through a series of conceptual figures, and is used to evaluate the wide range of wetland extent and CH 4 fluxes predicted by the models in the equilibrium run. We discuss relationships among the various approaches and patterns in consistencies of these model predictions. Within this group of models, there are three broad classes of methods used to estimate wetland extent: prescribed based on wetland distribution maps, prognostic relationships between hydrological states based on satellite observations, and explicit hydrological mass balances. A larger variety of approaches was used to estimate the net CH 4 fluxes from wetland systems. Even though modelling of wetland extent and CH 4 emissions has progressed significantly over recent decades, large uncertainties still exist when estimating CH 4 emissions: there is little consensus on model structure or complexity due to knowledge gaps, different aims of the models, and the range of temporal and spatial resolutions of the models.

show abstract

Evaluating approaches for estimating peat depth

Parry

West

Holden

et al. 2014

J. Geophys. Res. Biogeosci.

View full text Add to dashboard Cite

Estimates of peat depth are required to inform understanding of peatland development, functioning, and ecosystem services such as carbon storage. However, there is a considerable lack of peat depth data at local, national, and global scales. Recent studies have attempted to address this knowledge deficit by using manual probing and ground-penetrating radar (GPR) to estimate depth. Despite increasing application, little consideration has been given to the accuracy of either of these techniques. This study examines the accuracy of probing and GPR for measuring peat depth. Corresponding GPR and probing surveys were carried out at a catchment scale in a blanket peatland. GPR depth estimations, calibrated using common midpoint (CMP) surveys, were found to be on average 35% greater than probe measurements. The source of disagreement was found to be predominantly caused by depth probes becoming obstructed by artifacts within the peat body, although occasionally probing rods also penetrated sediments underlying the peat. Using the Complex Refractive Index Model, it was found that applying a single velocity of 0.036 m ns À1 across a single site may also result in À8 to +17% error in estimation of peat depth due to spatial variability in water content and porosity. It is suggested that GPR calibrated at each site using CMP surveys may provide a more accurate method for measuring peat depth.

show abstract

A high‐resolution GIS‐based inventory of the west Siberian peat carbon pool

Cited by 176 publications

References 14 publications

The global methane budget 2000–2012

The global methane budget 2000–2012

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Evaluating approaches for estimating peat depth

Contact Info

Product

Resources

About