Characterizing performance of freshwater wetland methane models across time scales at FLUXNET-CH4 sites using wavelet analyses

Zhang, Zhen; Bansal, Sheel; Chang, Kuang-Yu; Fluet‐Chouinard, Etienne; Delwiche, Kyle; Goeckede, Mathias; Gustafson, A. F.; Knox, Sara; Leppänen, Antti; Liu, Licheng; Liu, Jinxun; Malhotra, Avni; Markkanen, Tiina; McNicol, Gavin; Melton, Joe R.; Miller, Paul A.; Peng, Changhui; Raivonen, Maarit; Riley, William J.; Sonnentag, Oliver; Aalto, Tuula; Vargas, Rodrigo; Zhang, Wenxing; Zhu, Qing; Zhu, Qiuan; Zhuang, Qianlai; Windham‐Myers, Lisamarie; Jackson, Robert B.; Poulter, Benjamin

doi:10.1002/essoar.10512704.1

Cited by 2 publications

(4 citation statements)

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“…In the absence of multi‐decadal FCH 4 measurement time series, our ML predictions can help unearth the pathways in which flux seasonality and extreme events may have contributed to annual increases over the last four decades. The daily ML predictions underpinning this analysis can investigate these detailed temporal trends and evolutions that cannot be detected with global scale models with coarser timesteps (Chang et al., 2023; McNicol et al., 2023). Days of extreme FCH 4 may play a more vital role under climate change, and their characterization allowed us to detect changes in contributions to total seasonal fluxes over a period of 40 years.…”

Section: Discussionmentioning

confidence: 99%

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Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems

Feron,

Malhotra,

Bansal

et al. 2024

Global Change Biology

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Climate warming is expected to increase global methane (CH4) emissions from wetland ecosystems. Although in situ eddy covariance (EC) measurements at ecosystem scales can potentially detect CH4 flux changes, most EC systems have only a few years of data collected, so temporal trends in CH4 remain uncertain. Here, we use established drivers to hindcast changes in CH4 fluxes (FCH4) since the early 1980s. We trained a machine learning (ML) model on CH4 flux measurements from 22 [methane‐producing sites] in wetland, upland, and lake sites of the FLUXNET‐CH4 database with at least two full years of measurements across temperate and boreal biomes. The gradient boosting decision tree ML model then hindcasted daily FCH4 over 1981–2018 using meteorological reanalysis data. We found that, mainly driven by rising temperature, half of the sites (n = 11) showed significant increases in annual, seasonal, and extreme FCH4, with increases in FCH4 of ca. 10% or higher found in the fall from 1981–1989 to 2010–2018. The annual trends were driven by increases during summer and fall, particularly at high‐CH4‐emitting fen sites dominated by aerenchymatous plants. We also found that the distribution of days of extremely high FCH4 (defined according to the 95th percentile of the daily FCH4 values over a reference period) have become more frequent during the last four decades and currently account for 10–40% of the total seasonal fluxes. The share of extreme FCH4 days in the total seasonal fluxes was greatest in winter for boreal/taiga sites and in spring for temperate sites, which highlights the increasing importance of the non‐growing seasons in annual budgets. Our results shed light on the effects of climate warming on wetlands, which appears to be extending the CH4 emission seasons and boosting extreme emissions.

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Section: Discussionmentioning

confidence: 99%

“…In particular, in the winter season (DJF), gap‐filled data could make up to 56%. Although they have been used in previous studies (e.g., Chang et al., 2023; McNicol et al., 2023; Ouyang et al., 2023), the use of gap‐filled data is an additional source of uncertainty in our predictions.…”

Section: Methodsmentioning

confidence: 99%

Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems

Feron,

Malhotra,

Bansal

et al. 2024

Global Change Biology

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“…The Wetland and Wetland CH 4 Inter‐comparison of Models Project (WETCHIMP) yielded four major conclusions (Melton et al., 2013): (a) Models showed disagreement in predictions of wetland extent and emissions in space and time, (b) all models showed positive responses to climate change factors like atmospheric CO 2 concentrations, but differed in response to changes in temperature and precipitation, (c) Model validation was severely hampered by lack of flux observations and information on wetland extent, (d) a large range in flux predictions indicated substantial parameter and structural uncertainties in large scale wetland methane models. These findings have spurred increased research on dynamically mapping wetland extent (e.g., A. C. Zhang et al., 2022) and flux data syntheses (Knox et al., 2019; Turetsky et al., 2014) and we now see first systematic model evaluations against these data sets (Z. Zhang et al., 2023b).…”

Section: Modeling Methane Dynamics In Land Surface Modelsmentioning

confidence: 98%

“…For example, Ricciuto et al (2021) was designed specifically for northern peatlands (albite, without permafrost). Most models can realistically simulate monthly and annual flux behavior in northern wetlands, while emissions are less predictable in temperate and tropical wetlands (Z. Zhang et al, 2023b). However, model parametrization based on northern wetlands does not work well in tropical settings and for different plant functional types (Yuan et al, 2023).…”

Section: Current Developmentsmentioning

confidence: 99%

Three Decades of Wetland Methane Surface Flux Modeling by Earth System Models‐Advances, Applications, and Challenges

Forbrich,

Yazbeck,

Sulman

et al. 2024

JGR Biogeosciences

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Earth System Models (ESMs) simulate the exchange of mass and energy between the land surface and the atmosphere, with a key focus on modeling natural greenhouse gas feedbacks. Methane is the second most important greenhouse gas after carbon dioxide. There are growing concerns over the rapidly increasing methane concentration in the atmosphere, underscoring the need for accurate global modeling of its emissions using ESMs. Of the multitude of sources of methane globally, wetlands are the largest natural emitters for methane, leading to significant efforts targeting their representation in ESMs with a special focus on their methane emissions. In this review, we first provide a historical overview of including wetland‐methane components in ESMs and how methane modeling approaches have evolved over time. Second, we discuss recent modeling advancements that show promise for improvements in methane emissions predictions, namely the coupling of surface and atmospheric modules of ESMs, the representation of microtopography and transport mechanisms, the resolution of microbial processes at different spatial‐temporal scales, and the improved mapping of wetland area extent across the different wetland types. Third, we shed light on the different challenges hindering accurate estimations of wetland‐methane emissions, as shown by the consistent discrepancy between bottom‐up and top‐down models' predictions. Finally, we emphasize that more detailed representation of biogeochemistry and dynamic hydrology while resolving the within‐wetland vegetation heterogeneity should improve model predictions, especially when coupled with expanding ground‐based measurement networks and high‐resolution remote sensing mapping of methane‐relevant variables, such as water elevation, water table depth, and methane concentration.

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Characterizing performance of freshwater wetland methane models across time scales at FLUXNET-CH4 sites using wavelet analyses

Cited by 2 publications

References 4 publications

Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems

Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems

Three Decades of Wetland Methane Surface Flux Modeling by Earth System Models‐Advances, Applications, and Challenges

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