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River networks modify material transfer from land to ocean. Understanding the factors regulating this function for different gaseous, dissolved, and particulate constituents is critical to quantify the local and global effects of climate and land use change. We propose the River Network Saturation (RNS) concept as a generalization of how river network regulation of material fluxes declines with increasing flows due to imbalances between supply and demand at network scales. River networks have a tendency to become saturated (supply) demand) under higher flow conditions because supplies increase faster than sink processes. However, the flow thresholds under which saturation occurs depends on a variety of factors, including the inherent process rate for a given constituent and the abundance of lentic waters such as lakes, ponds, reservoirs, and fluvial wetlands within the river network. As supply increases, saturation at network scales is initially limited by previously unmet demand in downstream aquatic ecosystems. The RNS concept describes a general tendency of river network function that can be

We present a method for estimating nitrogen (N) removal based on high-resolution longitudinal profiling, which facilitates repeated measurement in larger rivers. The Lagrangian reference frame allows removal to be spatially disaggregated, enabling identification of removal ''hot spots,'' and potentially passive assessment of reaction kinetics using ambient longitudinal concentration gradients. Applying the method in six spring-fed rivers in North Florida, we tested the hypothesis that removal is controlled by spatial variation in channel hydraulics and vegetation. Removal estimates obtained using this method were statistically robust, spatially consistent across replicate profiles, and comparable to measurements made in these and other systems of similar size using alternative methods. We observed significantly increased removal in reaches with lower specific discharge and in more heavily vegetated reaches. Assessing uptake kinetics directly from longitudinal profiles assumes negligible sampling effects from temporally variable removal, non-instantaneous sampling velocities, and the mixing of water along the flowpath. Results from a reactive transport model suggest these effects are significant, producing complex profile geometries. Relatively small differences between alternative kinetic models substantially limit inferences about reaction order when utilizing the small concentration gradients observed longitudinally within rivers. Across rivers, N removal follows either efficiency-loss (exponent 5 0.39) or Michaelis-Menten kinetics, but not zero-or first-order kinetics.

Biota imprint their stoichiometry on relative rates of elemental cycling in the environment.Despite this coupling, producer-driven diel solute variation in rivers and streams is more apparent for some solutes (e.g., dissolved oxygen-DO) than others (e.g., nitrate-NO 2 3 ). We hypothesized that these differences arise from atmospheric equilibration, with signals emerging and evolving differently for gaseous and nongaseous solutes. Measurements of DO and NO 3 in a spring-fed river, where constant inputs isolate in-stream processing, support this hypothesis, as do results from reactive transport modeling of river solute dynamics. Atmospheric equilibration dramatically shortens the benthic footprint over which signals integrate, facilitating emergence of diel DO signals in response to in-stream metabolism. In contrast, upstream influences persist much further downstream for nongaseous solutes, confounding and potentially obscuring the diel signals from in-stream assimilatory processing. Isolating diel NO 3 signals from in-stream processing requires a two-station approach wherein metabolic impacts on solute variation are measured by difference between upstream and downstream sensors. Notably, two-station inference improves markedly when hydraulic controls on signal propagation such as dispersion and storage are explicitly considered. We conclude that the absence of diel signals at a single station for nongaseous solutes such as NO 2 3 cannot be interpreted as lack of autotroph demand or element coupling. As advances in sensors enable the acquisition of an increasingly rich array of solute signals, controlling for differences in the emergence and downstream evolution of gaseous versus nongaseous solutes will dramatically improve inferences regarding the timing and magnitude of coupled elemental processing. Key Points:Diel metabolic signal emergence varies between gaseous and nongaseous solutes Because of reaeration, gaseous solute signals integrate over shorter distance Extracting reach-scale metabolism for nongaseous solutes requires two stations

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