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 estimated to be of a similar magnitude to
anthropogenic emissions. To understand CH4 flux from biogenic sources
in the United States (US) of America, a multi-scale CH4 observation
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 chamber
measurements and airborne or satellite platforms that inform policy
decisions and global climate agreements. Using dissimilarity,
multidimensional scaling, and cluster analysis, the US was divided into 10
clusters distributed across temperature and precipitation gradients. We
evaluated dissimilarity 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 priority areas for research infrastructure
to provide a more complete 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 capture, are the most
poorly represented, and all clusters require a greater representation of
vegetation types.