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

Author:

Forbrich Inke1,Yazbeck Theresia2ORCID,Sulman Benjamin3ORCID,Morin Timothy H.4ORCID,Tang Angela Che Ing1ORCID,Bohrer Gil2ORCID

Affiliation:

1. Department of Environmental Sciences University of Toledo Toledo OH USA

2. Department of Civil Environmental & Geodetic Engineering The Ohio State University Columbus OH USA

3. Environmental Sciences Division and Climate Change Science Institute Oak Ridge National Laboratory Oakridge TN USA

4. Department of Environmental Resources Engineering State University of New York College of Environmental Science and Forestry Syracuse NY USA

Abstract

AbstractEarth 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.

Funder

Earth Sciences Division

Publisher

American Geophysical Union (AGU)

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