Improving representation of tropical wetland methane emissions with CYGNSS inundation maps

Gerlein‐Safdi, Cynthia; Bloom, A. Anthony; Plant, Genevieve; Kort, E. A.; Ruf, Christopher S.

doi:10.1002/essoar.10504845.1

Cited by 4 publications

(3 citation statements)

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“…The model ensemble highlights WSL and HBL as CH4 hotspots in the high latitudes, with good agreements of annual magnitudes with atmospheric inversions and in situ observations (Bohn et al, 2015;Glagolev et al, 2011;Pickett-Heaps et al, 2011), while the models have lower estimates for Alaska compared to the inversions (Chang et al, 2014;. However, for the two hotspots of the Pantanal and Sudd wetlands, the models tended to underestimate the annual eCH4 compared to a few recent satellite-based estimates (Gerlein-Safdi et al, 2021;Gloor et al, 2021;Lunt et al, 2021;Pandey et al, 2021), with a large uncertainty range of up to two orders of magnitude across the model ensemble (Fig. S5).…”

Section: Spatial Distribution Of Ech4mentioning

confidence: 68%

“…In addition to the regions where eCH4 are being underestimated, recent studies (France et al, 2022;Shaw et al, 2022) based on aircraft measurements suggest that the bottom-up models likely underestimate high eCH4 fluxes in some little-studied wetlands, such as those in Zambia and Bolivia. The underestimations by process-based wetland models can be attributed to: 1) the challenge in accurately capturing the areal dynamics of wetlands under varying hydrological conditions, such as in flat terrains that receives lateral transport of water from upper streams (Li et al, 2024;Lunt et al, 2021;Gerlein-Safdi et al, 2021); 2) existing knowledge gaps in mapping wetlands in remote areas, which affect the parameterization of inundation modeling; 3) the limited representation of water table regulation (Chen et al, 2021) and wetland PFTs (Bastviken et al, 2023) on eCH4 in biogeochemical models. Eur: Europe; NAm: North America; NAs: North Asia; Oz: Oceania; SAm: South America; SAs: South Asia; SEAs: Southeast Asia.…”

Section: Spatial Distribution Of Ech4mentioning

confidence: 99%

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Ensemble estimates of global wetland methane emissions over 2000–2020

Zhang,

Poulter,

Melton

et al. 2024

Preprint

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Abstract. Due to ongoing climate change, methane (CH4) emissions from vegetated wetlands are projected to increase during the 21st century, challenging climate mitigation efforts aimed at limiting global warming. However, despite reports of rising emission trends, a comprehensive evaluation and attribution of recent changes is still lacking. Here we assessed global wetland CH4 emissions from 2000 to 2020 based on an ensemble of sixteen process-based wetland models. Our results estimated global average wetland CH4 emissions at 158±24 (mean ± 1σ) Tg CH4 yr-1 for the period 2010–2020, with an average decadal increase of 6–7 Tg CH4 yr-1 compared to the decade of 2000–2009. The increases in the four latitudinal bands of 90° S–30° S, 30° S–30° N, 30° N–60° N, and 60° N–90° N were 0.1–0.2 Tg CH4 yr-1, 3.6–3.7 Tg CH4 yr-1, 1.8–2.4 Tg CH4 yr-1, and 0.6–0.8 Tg CH4 yr-1, respectively, over the two decades. The modeled CH4 sensitivities to temperature show reasonable consistency with eddy covariance-based measurements from 34 sites. Rising temperature was the primary driver of the increase, while precipitation and rising atmospheric CO2 concentrations played secondary roles with high levels of uncertainty. These modeled results suggest climate change is driving increased wetland CH4 emissions and that direct and sustained measurements are needed to monitor developments.

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Section: Spatial Distribution Of Ech4mentioning

confidence: 68%

Section: Spatial Distribution Of Ech4mentioning

confidence: 99%

Ensemble estimates of global wetland methane emissions over 2000–2020

Zhang,

Poulter,

Melton

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Here, we use WetCHARTs version 1.3.1 and we introduce the CYGNSS‐based inundation maps as a direct source of information for monthly inundation extent. For this purpose, the CYGNSS maps are downscaled to match WetCHARTs coarser resolution: the maps generated give a fractional water percentage that corresponds to the percentage of the 0.01° × 0.01° pixels within a 0.5° × 0.5° that are marked as flooded in the CYGNSS watermasks (Gerlein‐Safdi & Ruf, 2021). These inundation maps are available for download on Zenodo (https://doi.org/10.5281/zenodo.5621107).…”

Section: Methodsmentioning

confidence: 99%

Improving Representation of Tropical Wetland Methane Emissions With CYGNSS Inundation Maps

Gerlein‐Safdi

Bloom

Plant

et al. 2021

Global Biogeochemical Cycles

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Wetlands are the single largest source of methane to the atmosphere and their emissions are expected to respond to a changing climate. Inaccuracy and uncertainty in inundation extent drives differences in modeled wetland emissions and impacts representation of wetland emissions on inter‐annual and seasonal time frames. Existing wetland maps are based on optical or NIR satellite data obscured by clouds and vegetation, often leading to underestimates in wetlands extent, especially in the Tropics. Here, we present new inundation maps based on the Cyclone Global Navigation Satellite System (CYGNSS) satellite constellation, operating in L‐band that is not impacted by clouds or vegetation, providing reliable observations through canopy and cloudy periods. We map the temporal and spatial dynamics of the Pantanal and Sudd wetlands, two of the largest wetlands in the world, using CYGNSS data and a computer vision algorithm. We link these inundation maps to methane fluxes via WetCHARTs, a global wetland methane emissions model ensemble. We contrast CYGNSS‐modeled methane emissions with WetCHARTs standard runs that use monthly rainfall data from ECMWF re‐analysis (ERA5), as well as the commonly used SWAMPS wetland maps. We find that the CYGNSS‐based inundation maps modify the methane emissions in multiple ways. The seasonality of inundation and methane emissions is shifted by two months because of the lag in wetland recharge following peak rainfall. Both inundation and methane emissions also respond non‐linearly to wet‐season precipitation totals, leading to large interannual variability in emissions. Finally, the annual magnitude of emissions is found to be greater than previously estimated.

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