Gaps in network infrastructure limit our understanding of biogenic methane emissions for the United States

Burba, George; Goodrich, Jordan P.; Loescher, Henry W.; Varner, Ruth K.; Contosta, Alexandra R.; Arndt, Kyle A.; Commane, Roisin; Malone, Sparkle L.; Oh, Youmi; Starr, Gregory

Citeable URL: https://scholar.colorado.edu/concern/articles/g158bj872

Abstract

Understanding the sources and sinks of methane (CH4) is critical to both predicting and mitigating future climate change. There are large uncertainties in the global budget of atmospheric CH4, but natural emissions are esti- mated to be of a similar magnitude to anthropogenic emis- sions. To understand CH4 flux from biogenic sources in the United States (US) of America, a multi-scale CH4 obser- vation network focused on CH4 flux rates, processes, and scaling methods is required. This can be achieved with a network of ground-based observations that are distributed based on climatic regions and land cover. To determine the gaps in physical infrastructure for developing this network, we need to understand the landscape representativeness of the current infrastructure. We focus here on eddy covariance (EC) flux towers because they are essential for a bottom-up framework that bridges the gap between point-based cham- ber measurements and airborne or satellite platforms that in- form policy decisions and global climate agreements. Using dissimilarity, multidimensional scaling, and cluster analysis, the US was divided into 10 clusters distributed across tem- perature and precipitation gradients. We evaluated dissimi- larity within each cluster for research sites with active CH4 EC towers to identify gaps in existing infrastructure that limit our ability to constrain the contribution of US biogenic CH4 emissions to the global budget. Through our analysis using climate, land cover, and location variables, we identified pri- ority areas for research infrastructure to provide a more com- plete understanding of the CH4 flux potential of ecosystem types across the US. Clusters corresponding to Alaska and the Rocky Mountains, which are inherently difficult to cap- ture, are the most poorly represented, and all clusters require a greater representation of vegetation types.

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