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Although time series in ecosystem metabolism are well characterized in small and medium rivers, patterns in the world's largest rivers are almost unknown. Large rivers present technical difficulties, including depth measurements, gas exchange (, ) estimates, and the presence of large dams, which can supersaturate gases. We estimated reach‐scale metabolism for the Hanford Reach of the Columbia River (Washington state, USA), a free‐flowing stretch with an average discharge of 3173 . We calculated from semi‐empirical models and directly estimated it from tracer measurements. We fixed at the median value from these calculations (0.5 ), and used maximum likelihood to estimate reach‐scale, open‐channel metabolism. Both gross primary production (GPP) and ecosystem respiration (ER) were high (GPP range: 0.3–30.8 g , ER range: 0.8–30.6 g ), with peak GPP and ER occurring in the late summer or early fall. GPP increased exponentially with temperature, consistent with metabolic theory, while light was seasonally saturating. Annual average GPP, estimated at 1500 g carbon , was in the top 2% of estimates for other rivers. GPP and ER were tightly coupled and 90% of GPP was immediately respired, resulting in net ecosystem production near 0. Patterns in the Hanford Reach contrast with those in small‐medium rivers, suggesting that metabolism magnitudes and patterns in large rivers may not be simply scaled from knowledge of smaller rivers.

Directly observing autotrophic biomass at ecologically relevant frequencies is difficult in many ecosystems, hampering our ability to predict productivity through time. Since disturbances can impart distinct reductions in river productivity through time by modifying underlying standing stocks of biomass, mechanistic models fit to productivity time series can infer underlying biomass dynamics. We incorporated biomass dynamics into a river ecosystem productivity model for six rivers to identify disturbance flow thresholds and understand the resilience of primary producers. The magnitude of flood necessary to disturb biomass and thereby reduce ecosystem productivity was consistently lower than the more commonly used disturbance flow threshold of the flood magnitude necessary to mobilize river bed sediment. The estimated daily maximum percent increase in biomass (a proxy for resilience) ranged from 5% to 42% across rivers. Our latent biomass model improves understanding of disturbance thresholds and recovery patterns of autotrophic biomass within river ecosystems.

Estimates of primary productivity in aquatic ecosystems are commonly based on variation in , rather than . The photosynthetic quotient (PQ) is used to convert primary production estimates from units of to C. However, there is a mismatch between the theory and application of the PQ. Aquatic ecologists use PQ = 1–1.4. Meanwhile, PQ estimates from the literature support PQ = 0.1–4.2. Here, we describe the theory on why PQ may vary in aquatic ecosystems. We synthesize the current understanding of how processes such as assimilation and photorespiration can affect the PQ. We test these ideas with a case study of the Clark Fork River, Montana, where theory predicts that PQ could vary in space and time due to variation in environmental conditions. Finally, we highlight research needs to improve our understanding of the PQ. We suggest departing from fixed PQ values and instead use literature‐based sensitivity analyses to infer C dynamics from primary production estimated using .

Gas exchange across the air–water boundary of streams and rivers is a globally large biogeochemical flux. Gas exchange depends on the solubility of the gas of interest, the gas concentrations of the air and water, and the gas exchange velocity (k), usually normalized to a Schmidt number of 600, referred to ask₆₀₀. Gas exchange velocity is of intense research interest because it is problematic to estimate, is highly spatially variable, and has high prediction error. Theory dictates that molecular diffusivity and turbulence drives variation ink₆₀₀in flowing waters. We measurek₆₀₀via several methods from direct measures, gas tracer experiments, to modeling of diel changes in dissolved gas concentrations. Many estimates ofk₆₀₀show that surface turbulence explains variation ink₆₀₀leading to predictive models based upon geomorphic and hydraulic variables. These variables include stream channel slope and stream flow velocity, the product of which, is proportional to the energy dissipation rate in streams and rivers. These empirical models provide understanding of the controls onk₆₀₀, yet high residual variation ink₆₀₀show that these simple models are insufficient for predicting individual locations. The most appropriate method to estimate gas exchange depends on the scientific question along with the characteristics of the study sites. We provide a decision tree for selecting the best method to estimatek₆₀₀for individual river reaches to scaling to river networks.

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Water and Life > Nature of Freshwater Ecosystems

Rivers denitrify a portion of their nitrate () load, but estimates are difficult using microcosm or reach‐scale measurements that require specific biogeochemical and hydrologic conditions. Measuring reach‐scale oxygen (O₂) respiration fluxes is easier than nitrogen (N₂) fluxes, thus we paired microcosm estimates of denitrification by N₂production with estimates of aerobic respiration. The median molar ratio of ΔN₂:−ΔO₂from 13 streams was 0.011 (95% credible interval 0.0002–0.027 mol:mol). We then measured diel O₂concentrations from 11 streams and converted to ecosystem respiration (ER) using a multiday oxygen model. Given reach‐scale ER of −160 mmol O₂m⁻²d⁻¹, the estimated median denitrification was 1.5 mmol N₂m⁻² d⁻¹(credible interval (CI): 0.18–4.21) across our streams. Our estimates of denitrification constituted 19% of grossuptake (CI: 0–51%). In streams, ΔN₂:−ΔO₂was lower than in estuarine and marine ecosystems. Despite multiple sources of error, this approach estimates reach‐scale denitrification and variation withconcentrations.

High‐resolution data are improving our ability to resolve temporal patterns and controls on river productivity, but we still know little about the emergent patterns of primary production at river‐network scales. Here, we estimate daily and annual river‐network gross primary production (GPP) by applying characteristic temporal patterns of GPP (i.e., regimes) representing distinct river functional types to simulated river networks. A defined envelope of possible productivity regimes emerges at the network‐scale, but the amount and timing of network GPP can vary widely within this range depending on watershed size, productivity in larger rivers, and reach‐scale variation in light within headwater streams. Larger rivers become more influential on network‐scale GPP as watershed size increases, but small streams with relatively low productivity disproportionately influence network GPP due to their large collective surface area. Our initial predictions of network‐scale productivity provide mechanistic understanding of the factors that shape aquatic ecosystem function at broad scales.