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Network theory is applied to an array of streamflow gauges located in theCoast Mountains of British Columbia (BC) and Yukon, Canada. The goal of theanalysis is to assess whether insights from this branch of mathematical graphtheory can be meaningfully applied to hydrometric data, and, morespecifically, whether it may help guide decisions concerning stream gaugeplacement so that the full complexity of the regional hydrology isefficiently captured. The streamflow data, when represented as a complexnetwork, have a global clustering coefficient and average shortest pathlength consistent with small-world networks, which are a class of stable andefficient networks common in nature, but the observed degree distribution didnot clearly indicate a scale-free network. Stability helps ensure that thenetwork is robust to the loss of nodes; in the context of a streamflownetwork, stability is interpreted as insensitivity to station removal atrandom. Community structure is also evident in the streamflow network. Anetwork theoretic community detection algorithm identified separatecommunities, each of which appears to be defined by the combination of itsmedian seasonal flow regime (pluvial, nival, hybrid, or glacial, which inthis region in turn mainly reflects basin elevation) and geographic proximityto other communities (reflecting shared or different daily meteorologicalforcing). Furthermore, betweenness analyses suggest a handful of key stationswhich serve as bridges between communities and might  be highly valued. Wepropose that an idealized sampling network should sample high-betweennessstations, small-membership communities which are by definition rare orundersampled relative to other communities, and index stations having largenumbers of intracommunity links, while retaining some degree of redundancy tomaintain network robustness

Complex network theory, streamflow, and hydrometric monitoring system design