Uncertainties in wetland methane‐flux estimates

Yazbeck, Theresia; Bohrer, Gil

doi:10.1111/gcb.16754

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…In addition, the lack of representation of lateral flow between grid-cells, which limits the ability of LSMs in resolving reactive transport, which in turn will result in biases with modeling methane oxidation and inundation dynamics. Many efforts are underway to overcome these limitations and have a better representation of methane dynamics in wetlands [49], and improved patch-type observations will serve these efforts well.…”

Section: Percent Of Pixels (%)mentioning

confidence: 99%

Integrating NDVI-Based Within-Wetland Vegetation Classification in a Land Surface Model Improves Methane Emission Estimations

Yazbeck,

Bohrer,

Shchehlov

et al. 2024

Remote Sensing

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Earth system models (ESMs) are a common tool for estimating local and global greenhouse gas emissions under current and projected future conditions. Efforts are underway to expand the representation of wetlands in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) by resolving the simultaneous contributions to greenhouse gas fluxes from multiple, different, sub-grid-scale patch-types, representing different eco-hydrological patches within a wetland. However, for this effort to be effective, it should be coupled with the detection and mapping of within-wetland eco-hydrological patches in real-world wetlands, providing models with corresponding information about vegetation cover. In this short communication, we describe the application of a recently developed NDVI-based method for within-wetland vegetation classification on a coastal wetland in Louisiana and the use of the resulting yearly vegetation cover as input for ELM simulations. Processed Harmonized Landsat and Sentinel-2 (HLS) datasets were used to drive the sub-grid composition of simulated wetland vegetation each year, thus tracking the spatial heterogeneity of wetlands at sufficient spatial and temporal resolutions and providing necessary input for improving the estimation of methane emissions from wetlands. Our results show that including NDVI-based classification in an ELM reduced the uncertainty in predicted methane flux by decreasing the model’s RMSE when compared to Eddy Covariance measurements, while a minimal bias was introduced due to the resampling technique involved in processing HLS data. Our study shows promising results in integrating the remote sensing-based classification of within-wetland vegetation cover into earth system models, while improving their performances toward more accurate predictions of important greenhouse gas emissions.

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Section: Percent Of Pixels (%)mentioning

confidence: 99%

Integrating NDVI-Based Within-Wetland Vegetation Classification in a Land Surface Model Improves Methane Emission Estimations

Yazbeck,

Bohrer,

Shchehlov

et al. 2024

Remote Sensing

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“…See a review of top-down modeling approaches in Houweling et al (2017). There is a well reported discrepancy between top-down and bottom-up methane emission estimates and an active research effort to understand its causes and reduce global methane flux estimates (Chang et al, 2023;Yazbeck & Bohrer, 2023). Some of the challenges in incorporating top-down estimates as data sources for parameterization/validation of bottom-up models are related to reconciling very different sources of error between uncertainties in the top-down satellite retrievals, and the bottom-up model representation errors (Ma et al, 2021).…”

Section: Overview Of Regional/global Methane Flux Observation Data Setsmentioning

confidence: 99%

“…Wetland‐specific functional types, which parallel the model representation of dryland vegetation variability through plant functional types (PFTs), have been developed for northern wetlands, where there is a need to characterize moss function as well as flood‐tolerant graminoids (Ricciuto et al., 2021; Wania et al., 2009a). However, to simulate wetland vegetation in other climate zones, it seems likely that the number of functional types will have to increase (Yazbeck & Bohrer, 2023), including woody vegetation (Pangala et al., 2017). As for the terrestrial biosphere in general, it is difficult to include human activities and their impact on biogeochemical processes.…”

Section: Modeling Methane Dynamics In Land Surface Modelsmentioning

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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“…However, the global bottom‐up wetland CH 4 budget and its future predictions remain highly uncertain (Saunois et al., 2020). These uncertainties are in part due to an incomplete understanding of fine‐scale CH 4 ‐flux processes (Yazbeck & Bohrer, 2023) and their contribution to spatiotemporal variation in net CH 4 emissions. Wetland plant properties (hereafter, traits) may help predict this variation because plants contribute to wetland spatial heterogeneity (Arsenault et al., 2019) and fine‐scale CH 4 variability (e.g., Bastviken et al., 2023; Riutta et al., 2007).…”

Section: Introductionmentioning

confidence: 99%

The hidden roots of wetland methane emissions

Määttä,

Malhotra

2024

Global Change Biology

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Wetlands are the largest natural source of methane (CH4) globally. Climate and land use change are expected to alter CH4 emissions but current and future wetland CH4 budgets remain uncertain. One important predictor of wetland CH4 flux, plants, play an important role in providing substrates for CH4‐producing microbes, increasing CH4 consumption by oxygenating the rhizosphere, and transporting CH4 from soils to the atmosphere. Yet, there remain various mechanistic knowledge gaps regarding the extent to which plant root systems and their traits influence wetland CH4 emissions. Here, we present a novel conceptual framework of the relationships between a range of root traits and CH4 processes in wetlands. Based on a literature review, we propose four main CH4‐relevant categories of root function: gas transport, carbon substrate provision, physicochemical influences and root system architecture. Within these categories, we discuss how individual root traits influence CH4 production, consumption, and transport (PCT). Our findings reveal knowledge gaps concerning trait functions in physicochemical influences, and the role of mycorrhizae and temporal root dynamics in PCT. We also identify priority research needs such as integrating trait measurements from different root function categories, measuring root‐CH4 linkages along environmental gradients, and following standardized root ecology protocols and vocabularies. Thus, our conceptual framework identifies relevant belowground plant traits that will help improve wetland CH4 predictions and reduce uncertainties in current and future wetland CH4 budgets.

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Uncertainties in wetland methane‐flux estimates

Cited by 6 publications

References 22 publications

Integrating NDVI-Based Within-Wetland Vegetation Classification in a Land Surface Model Improves Methane Emission Estimations

Integrating NDVI-Based Within-Wetland Vegetation Classification in a Land Surface Model Improves Methane Emission Estimations

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

The hidden roots of wetland methane emissions

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