An explosion in high frequency dissolved oxygen (DO) observations at river network scales is creating new opportunities to understand dynamic signals in streams and rivers. Among the most informative metrics obtained from DO time series is stream metabolism—comprising gross primary production (GPP) and ecosystem respiration (ER)—but its estimation is non-trivial. There is thus interest in simpler metrics that can capture spatiotemporal patterns in stream metabolism and their consequences for critical ecosystem processes. Using hourly DO time series from 43 agricultural headwater streams reaches (Strahler order 1–5) across five watersheds and two years, we tested the hypothesis that simple DO metrics are useful proxies of stream metabolism, capturing key features of its spatiotemporal variation, and predicting attendant patterns in dissolved organic matter quality and catchment nitrogen processing via denitrification. Our results suggest the diel DO range scaled by stream depth is an excellent proxy for GPP throughout the network, accurately describing its spatial and temporal patterns. In contrast, we found that DO metrics were less successful as proxies for ER, with the maximum daily DO deficit scaled by depth being a good proxy for ER only in higher order streams. We also observed that DO metrics were strongly related to variation in dissolved organic matter quality and denitrification far better than GPP or ER. Finally, we found that DO metrics, GPP, and to a lesser extent ER, had power-law relationships with watershed area (scaling exponents, β = 0.2–0.5), implying increasing downstream metabolic activity. However, because lower order streams occupy ~75% of network benthic area, total network GPP and ER (g O2 d 1) were disproportionately provided by lower order streams, consistent with recent theoretical modeling. These findings reveal the rich inference space that simple DO metrics can provide, and support their use as proxies for stream metabolism and for inferring network patterns of biogeochemical function.