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Drivers of Methane Flux Differ Between Lakes and Reservoirs, Complicating Global Upscaling Efforts
Author(s) -
Deemer B. R.,
Holgerson M. A.
Publication year - 2021
Publication title -
journal of geophysical research: biogeosciences
Language(s) - English
Resource type - Journals
eISSN - 2169-8961
pISSN - 2169-8953
DOI - 10.1029/2019jg005600
Subject(s) - methane , environmental science , ecosystem , flux (metallurgy) , greenhouse gas , hydrology (agriculture) , productivity , atmospheric sciences , atmospheric methane , physical geography , ecology , geography , biology , geology , chemistry , geotechnical engineering , macroeconomics , organic chemistry , economics
Methane is an important greenhouse gas with growing atmospheric concentrations. Freshwater lakes and reservoirs contribute substantially to atmospheric methane concentrations, but the magnitude of this contribution is poorly constrained. Uncertainty stems partially from whether the sites currently sampled represent the global population as well as incomplete knowledge of which environmental variables predict methane flux. Thus, determining the main drivers of methane flux across diverse waterbody types will inform more accurate upscaling approaches. Here we use a new database of total, diffusive, and ebullitive areal methane emissions from 313 lakes and reservoirs (ranging in surface area from 6 m 2 to 5,400 km 2 ) to identify the best predictors of methane emission. We found that the best predictors of methane emission differed by waterbody type (lakes vs. reservoirs), and that ecosystem morphometric variables (e.g., surface area and maximum depth) were more important predictors in lakes whereas metrics of autochthonous production (e.g., chlorophyll a ) were more important in reservoirs. We also found that productivity strongly predicted methane ebullition, whereas ecosystem morphometry and waterbody type were more important predictors of diffusive methane flux. Finally, we identify several knowledge gaps that limit upscaling efforts. First, we need more methane emission measurements in small reservoirs, large lakes, and both natural and artificial ponds. Additionally, more accurate upscaling efforts require improved global information about waterbody surface area, waterbody type (lake vs. reservoir), ice phenology, and the distribution of productivity‐related predictor variables such as total phosphorus, DOC, and chlorophyll a .