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Abstract. Air–water gas exchange is essential to understanding and quantifying many biogeochemical processes in streams and rivers, including greenhouse gas emissions and metabolism. Gas exchange depends on two factors, which are often quantified separately: (1) the air–water concentration gradient of the gas and (2) the gas exchange velocity.  There are fewer measurements of gas exchange velocity compared to concentrations in streams and rivers, which limits accurate characterization of air–water gas exchange (i.e., flux rates). The National Ecological Observatory Network (NEON) conducts SF6 gas-loss experiments in 22 of their 24 wadeable streams using standardized methods across all experiments and sites, and publishes raw concentration data from these experiments on the NEON data portal. NEON also conducts NaCl injections that can be used to characterize hydraulic geometry at all 24 wadeable streams. These NaCl injections are conducted both as part of the gas-loss experiments and separately. Here, we use these data to estimate gas exchange and water velocity using the reaRate R package. The dataset presented includes estimates of hydraulic parameters, cleaned raw concentration SF6 tracer-gas data (including removing outliers and failed experiments), estimated SF6 gas-loss rates, normalized gas exchange velocities (k600; m d−1) and normalized depth-dependent gas exchange rates (K600; d−1). This dataset provides one of the largest compilations of gas-loss experiments (n=339) in streams to date. This dataset is unique in that it contains gas exchange estimates from repeated experiments in geographically diverse streams across a range of discharges. In addition, this dataset contains information on the hydraulic geometry of all 24 NEON wadeable streams, which will support future research using NEON aquatic data. This dataset is a valuable resource that can be used to explore both within- and across-reach variability in the hydraulic geometry and gas exchange velocity in streams. The data are available at https://doi.org/10.6073/pasta/18dcc1871ee71cf0b69f2ee4082839d0 (Aho et al., 2024), and the reaRate R package code is available at https://doi.org/10.5281/zenodo.12786089 (Cawley et al., 2024). 

ABSTRACT Intermittent streams are characterized by significant periods of low to no flow, yet are also frequently subjected to flashy, high floods. Floods alter ecosystem function and result in variable successional patterns across the stream network. Yet, the timing of restored function after floods in intermittent stream networks is relatively unexplored. We measured recovery of stream ecosystem function using rates of gross primary production (GPP), ecosystem respiration (ER), net ecosystem production (NEP), and the primary production to respiration ratio (P/R) across eight locations in the Kings Creek drainage basin with differing preflood conditions (previously dry [intermittent] or flowing [perennial]) over a 30‐d period following a 2‐yr return interval flood. We found that all metabolic rates (GPP, ER, NEP, P/R) varied primarily by time (days since flood) and antecedent flow, but not spatial network position (i.e., drainage area). Intermittent sites exhibited high rates of ER (0.17–3.33 g dissolved oxygen [DO] m⁻²d⁻¹) following rewetting compared to perennial sites (0.03–1.17 g DO m⁻²d⁻¹), while GPP, NEP, and P/R were slower to recover and varied less between sites of differing preflood conditions. Metabolic rates were not strongly influenced by other environmental conditions. A large proportion of variation was explained by the random effect of location. Our results suggest that metabolism is temporally asynchronous and highly heterogenous across intermittent watersheds and that antecedent hydrology (drying prior to rewetting) stimulates heterotrophic activity, likely dependent on terrestrially derived organic matter and nutrient subsidies. 

Abstract River metabolism and, thus, carbon cycling are governed by gross primary production and ecosystem respiration. Traditionally river metabolism is derived from diel dissolved oxygen concentrations, which cannot resolve diel changes in ecosystem respiration. Here, we compare river metabolism derived from oxygen concentrations with estimates from stable oxygen isotope signatures (δ¹⁸O₂) from 14 sites in rivers across three biomes using Bayesian inverse modeling. We find isotopically derived ecosystem respiration was greater in the day than night for all rivers (maximum change of 113 g O₂ m⁻² d⁻¹, minimum of 1 g O₂ m⁻² d⁻¹). Temperature (20 °C) normalized rates of ecosystem respiration and gross primary production were 1.1 to 87 and 1.5 to 22-fold higher when derived from oxygen isotope data compared to concentration data. Through accounting for diel variation in ecosystem respiration, our isotopically-derived rates suggest that ecosystem respiration and microbial carbon cycling in rivers is more rapid than predicted by traditional methods. 

Abstract For over a century, ecologists have used the concept of trophic state (TS) to characterize an aquatic ecosystem's biological productivity. However, multiple TS classification schemes, each relying on a variety of measurable parameters as proxies for productivity, have emerged to meet use‐specific needs. Frequently, chlorophyll a, phosphorus, and Secchi depth are used to classify TS based on autotrophic production, whereas phosphorus, dissolved organic carbon, and true color are used to classify TS based on both autotrophic and heterotrophic production. Both classification approaches aim to characterize an ecosystem's function broadly, but with varying degrees of autotrophic and heterotrophic processes considered in those characterizations. Moreover, differing classification schemes can create inconsistent interpretations of ecosystem integrity. For example, the US Clean Water Act focuses exclusively on algal threats to water quality, framed in terms of eutrophication in response to nutrient loading. This usage lacks information about non‐algal threats to water quality, such as dystrophication in response to dissolved organic carbon loading. Consequently, the TS classification schemes used to identify eutrophication and dystrophication may refer to ecosystems similarly (e.g., oligotrophic and eutrophic), yet these categories are derived from different proxies. These inconsistencies in TS classification schemes may be compounded when interdisciplinary projects employ varied TS frameworks. Even with these shortcomings, TS can still be used to distill information on complex aquatic ecosystem function into a set of generalizable expectations. The usefulness of distilling complex information into a TS index is substantial such that usage inconsistencies should be explicitly addressed and resolved. To emphasize the consequences of diverging TS classification schemes, we present three case studies for which an improved understanding of the TS concept advances freshwater research, management efforts, and interdisciplinary collaboration. To increase clarity in TS, the aquatic sciences could benefit from including information about the proxy variables, ecosystem type, as well as the spatiotemporal domains used to classify TS. As the field of aquatic sciences expands and climatic irregularity increases, we highlight the importance of re‐evaluating fundamental concepts, such as TS, to ensure their compatibility with evolving science. 

Abstract Rivers that do not flow year-round are the predominant type of running waters on Earth. Despite a burgeoning literature on natural flow intermittence (NFI), knowledge about the hydrological causes and ecological effects of human-induced, anthropogenic flow intermittence (AFI) remains limited. NFI and AFI could generate contrasting hydrological and biological responses in rivers because of distinct underlying causes of drying and evolutionary adaptations of their biota. We first review the causes of AFI and show how different anthropogenic drivers alter the timing, frequency and duration of drying, compared with NFI. Second, we evaluate the possible differences in biodiversity responses, ecological functions, and ecosystem services between NFI and AFI. Last, we outline knowledge gaps and management needs related to AFI. Because of the distinct hydrologic characteristics and ecological impacts of AFI, ignoring the distinction between NFI and AFI could undermine management of intermittent rivers and ephemeral streams and exacerbate risks to the ecosystems and societies downstream.