Development of a global dataset of Wetland Area and Dynamics for
Methane Modeling (WAD2M)

Zhang, Zhen; Fluet‐Chouinard, Etienne; Jensen, Katherine; McDonald, K. C.; Hugelius, Gustaf; Gumbricht, Thomas; Carroll, M.; Prigent, Catherine; Bartsch, Annett; Poulter, Benjamin

doi:10.5194/essd-2020-262

Cited by 18 publications

(26 citation statements)

References 38 publications

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“…In particular, small ponds and lakes, streams and rivers, and coastal wetlands are difficult to separate from freshwater wetlands using coarse-to-moderate spatial resolution optical and radar remote sensing. Recent wetland area mapping aims to reduce the problem of double counting by explicitly removing inland-waters from remote-sensing based surface inundation data 40 . However, there remains a need for finer spatial resolution approaches that would permit better mapping and counting of both small ponds and streams to partition these from vegetated wetlands.…”

Section: Uncertainties In Aquatic Methane Sourcesmentioning

confidence: 99%

Half of global methane emissions come from highly variable aquatic ecosystem sources

et al. 2021

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Atmospheric methane is a potent greenhouse gas that plays a major role in controlling the Earth's climate. The causes of the renewed increase of methane concentration since 2007 are uncertain given the multiple sources and complex biogeochemistry. Here, we present a meta-data analysis of methane fluxes from all major natural, impacted and human-made aquatic ecosystems. Our revised bottom-up global aquatic methane emissions combine diffusive, ebullitive and plant-mediated and/or fluxes from several sediment-water-air interfaces. We emphasize the high variability of methane fluxes within and between aquatic ecosystems and a positively skewed distribution of empirical data, making global estimates sensitive to statistical assumptions and sampling design. We find aquatic ecosystems contribute (median) 41% or (mean) 53% of total global methane emissions from anthropogenic and natural sources. We show that methane emissions increase from natural to impacted aquatic ecosystems, and from coastal to freshwater ecosystems. We argue that aquatic emissions will likely increase due to urbanization, eutrophication and positive climate-feedbacks, and suggest changes in land-use management as potential mitigation strategies to reduce aquatic methane emissions. Main text:Methane (CH4) is the second most important greenhouse gas after carbon dioxide (CO2), accounting for 16 to 25% of atmospheric warming to date 1,2 . Atmospheric methane nearly tripled since pre-industrial times with a steady rise between 1984 and 2000 (8.4 ± 0.6 ppb yr -1 ) 3 , little or no growth between 2000 and 2006 (0.5 ± 0.5 ppb yr -1 ) 3 , and a renewed growth to present day (2007 to 2020: 7.3 ± 0.6 ppb yr -1 ) 3-6 . Whether the renewed increase is caused by emissions from anthropogenic or natural sources, or by a decline in the oxidative capacity of the atmosphere, or a combination of all three factors remains unresolved [7][8][9] . Depending on the approach used, total Rivers (ice-corrected) 5.8 (1.8-21.0) 30.5 ± 17.1 This study Lakes (ice-cover, ice-melt corrected) < 0.001 km 2 21.2 (9.1-53.5) 54.5 ± 48.5 This study 0.001 -0.01 km 2 13.2 (5.6-33.1) 31.1 ± 23.7 This study 0.01 -0.1 km 2 4.4 (1.4-16.7) 22.4 ± 18.4 This study 0.1 -1 km 2 3.0 (1.1-8.0) 9.9 ± 7.0 This study > 1 km 2 14.0 (6.0-31.0) 33.0 ± 45.0 This study All lakes 55.8 (23.3-142.3) 150.9 ± 73.0 This study Reservoirs (ice-cover, ice-melt corrected) < 1 km 2 0.4 (0.1-1.3) 2.4 ± 4.7 This study > 1 km 2 14.7 (8.7-27.1) 22.0 ± 6.4 This study All reservoirs 15.1 (8.8-28.4) 24.3 ± 8.0 This study Freshwater wetlands 150.1 (138.3-164.6) 148.6 ± 15.2 Saunois et al. 11 (A) Freshwater aquaculture ponds 4.4 (0.4-7.9) 14.0 ± 18.8 This study Rice cultivation 29.9 (24.9-32.1) 29.8 ± 6.7 Saunois et al. 11 (B) Total inland waters 261.0 (197.5-396.2) 398.1 ± 79.4 This study Estuaries 0.23 (0.02-0.91) 0.90 ± 0.29 This study Coastal wetlands Saltmarshes 0.18 (0.02-0.89) 2.00 ± 1.51 This study Mangroves 0.21 (0.06-0.77) 1.46 ± 0.91 This study Seagrasses 0.13 (0.07-0.21) 0.18 ± 0.19 This study Tidal flats 0.17 (0.04...

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Section: Uncertainties In Aquatic Methane Sourcesmentioning

confidence: 99%

Half of global methane emissions come from highly variable aquatic ecosystem sources

et al. 2021

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“…Examination of the 19 different wetland models used as part of the two model ensembles indicates a large inter‐model variability in the magnitude and spatial distribution of CH 4 emissions (see Text S6 in Supporting Information S1). Simulated emissions were greater for the GCP models that used a diagnostic wetland area map (WAD2M (Zhang et al., 2021)), as opposed to a prognostic (internally calculated) map (Figure 8). Additionally, many of the land surface models simulated substantial emissions from model grid cells containing mostly open water, despite no clear evidence in the aircraft data for large CH 4 emissions from these areas (Figure 1): the lack of significant CH 4 enhancement over open water implied limited emissions, compared with the much stronger signals measured over surrounding vegetated wetland.…”

Section: Resultsmentioning

confidence: 99%

“…The GCP data set comprises 13 land surface models run under a common protocol (Saunois et al., 2020). Wetland spatial extent was prescribed using either a remote‐sensing based diagnostic wetland map (consistent between models; Zhang et al., 2021) or a prognostic wetland map (where models used their own internal approach for simulating wetland area).…”

Section: Methodsmentioning

confidence: 99%

Large Methane Emission Fluxes Observed From Tropical Wetlands in Zambia

Shaw

Allen

Barker

et al. 2022

Global Biogeochemical Cycles

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Methane (CH4) is a potent greenhouse gas with a warming potential 84 times that of carbon dioxide (CO2) over a 20‐year period. Atmospheric CH4 concentrations have been rising since the nineteenth century but the cause of large increases post‐2007 is disputed. Tropical wetlands are thought to account for ∼20% of global CH4 emissions, but African tropical wetlands are understudied and their contribution is uncertain. In this work, we use the first airborne measurements of CH4 sampled over three wetland areas in Zambia to derive emission fluxes. Three independent approaches to flux quantification from airborne measurements were used: Airborne mass balance, airborne eddy‐covariance, and an atmospheric inversion. Measured emissions (ranging from 5 to 28 mg m−2 hr−1) were found to be an order of magnitude greater than those simulated by land surface models (ranging from 0.6 to 3.9 mg m−2 hr−1), suggesting much greater emissions from tropical wetlands than currently accounted for. The prevalence of such underestimated CH4 sources may necessitate additional reductions in anthropogenic greenhouse gas emissions to keep global warming below a threshold of 2°C above preindustrial levels.

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“…We use the Wetland Area and Dynamics for Methane Modeling (WAD2M) wetland extent dataset (Zhang et al, 2021) which provides global 0.25 • x 0.25 • estimates of wetland fraction for inundated and non-inundated vegetated wetlands, derived from microwave remote sensing. In this study we use the updated version which spans 2000-2018.…”

Section: Wetland Extent Datasetsmentioning

confidence: 99%

Evaluation of Wetland CH₄ in the JULES Land Surface Model Using Satellite Observations

Parker

Wilson

Comyn‐Platt

et al. 2022

Preprint

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Abstract. Wetlands are the largest natural source of methane. The ability to model the emissions of methane from natural wetlands accurately is critical to our understanding of the global methane budget and how it may change under future climate scenarios. The simulation of wetland methane emissions involves a complicated system of meteorological drivers coupled to hydrological and biogeochemical processes. The Joint UK Land Environment Simulator (JULES) is a process-based land surface model that underpins the UK Earth System Model and is capable of generating estimates of wetland methane emissions. In this study we use GOSAT satellite observations of atmospheric methane along with the TOMCAT global 3-D chemistry transport model to evaluate the performance of JULES in reproducing the seasonal cycle of methane over a wide range of tropical wetlands. By using an ensemble of JULES simulations with differing input data and process configurations, we investigate the relative importance of the meteorological driving data, the vegetation, the temperature dependency of wetland methane production and the wetland extent. We find that JULES typically performs well in replicating the observed methane seasonal cycle. We calculate correlation coefficients to the observed seasonal cycle of between 0.58 to 0.88 for most regions, however the seasonal cycle amplitude is typically underestimated (by between 1.8 ppb and 19.5 ppb). This level of performance is comparable to that typically provided by state-of-the-art data-driven wetland CH4 emission inventories. The meteorological driving data is found to be the most significant factor in determining the ensemble performance, with temperature dependency and vegetation having moderate effects. We find that neither wetland extent configuration out-performs the other but this does lead to poor performance in some regions. We focus in detail on three African wetland regions (Sudd, Southern Africa and Congo) where we find the performance of JULES to be poor and explore the reasons for this in detail. We find that neither wetland extent configuration used is sufficient in representing the wetland distribution in these regions (underestimating the wetland seasonal cycle amplitude by 11.1 ppb, 19.5 ppb and 10.1 ppb respectively, with correlation coefficients of 0.23, 0.01 and 0.31). We employ the CaMa-Flood model to explicitly represent river and floodplain water dynamics and find these JULES-CaMa-Flood simulations are capable of providing wetland extent more consistent with observations in this regions, highlighting this as an important area for future model development.

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Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

Cited by 18 publications

References 38 publications

Half of global methane emissions come from highly variable aquatic ecosystem sources

Half of global methane emissions come from highly variable aquatic ecosystem sources

Large Methane Emission Fluxes Observed From Tropical Wetlands in Zambia

Evaluation of Wetland CH₄ in the JULES Land Surface Model Using Satellite Observations

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Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

Cited by 18 publications

References 38 publications

Half of global methane emissions come from highly variable aquatic ecosystem sources

Half of global methane emissions come from highly variable aquatic ecosystem sources

Large Methane Emission Fluxes Observed From Tropical Wetlands in Zambia

Evaluation of Wetland CH4 in the JULES Land Surface Model Using Satellite Observations

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Evaluation of Wetland CH₄ in the JULES Land Surface Model Using Satellite Observations