Methane emissions from wetlands: biogeochemical, microbial, and modeling perspectives from local to global scales

Abstract

Understanding the dynamics of methane (CH4) emissions is of paramount importance because CH4 has 25 times the global warming potential of carbon dioxide (CO2) and is currently the second most important anthropogenic greenhouse gas. Wetlands are the single largest natural CH4 source with median emissions from published studies of 164 Tg yr−1, which is about a third of total global emissions. We provide a perspective on important new frontiers in obtaining a better understanding of CH4 dynamics in natural systems, with a focus on wetlands. One of the most exciting recent developments in this field is the attempt to integrate the different methodologies and spatial scales of biogeochemistry, molecular microbiology, and modeling, and thus this is a major focus of this review. Our specific objectives are to provide an up‐to‐date synthesis of estimates of global CH4 emissions from wetlands and other freshwater aquatic ecosystems, briefly summarize major biogeophysical controls over CH4 emissions from wetlands, suggest new frontiers in CH4 biogeochemistry, examine relationships between methanogen community structure and CH4 dynamics in situ, and to review the current generation of CH4 models. We highlight throughout some of the most pressing issues concerning global change and feedbacks on CH4 emissions from natural ecosystems. Major uncertainties in estimating current and future CH4 emissions from natural ecosystems include the following: (i) A number of important controls over CH4 production, consumption, and transport have not been, or are inadequately, incorporated into existing CH4 biogeochemistry models. (ii) Significant errors in regional and global emission estimates are derived from large spatial‐scale extrapolations from highly heterogeneous and often poorly mapped wetland complexes. (iii) The limited number of observations of CH4 fluxes and their associated environmental variables loosely constrains the parameterization of process‐based biogeochemistry models.