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Abstract River networks play a crucial role in the global carbon cycle, as relevant sources of carbon dioxide (CO₂) to the atmosphere. Advancements in high‐frequency monitoring in aquatic environments have enabled measurement of dissolved CO₂concentration at temporal resolutions essential for studying carbon variability and evasion from these dynamic ecosystems. Here, we describe the adaptation, deployment, and validation of an open‐source and relatively low‐cost in situpCO₂sensor system for lotic ecosystems, the lotic‐SIPCO2. We tested the lotic‐SIPCO2 in 10 streams that spanned a range of land cover and basin size. Key system adaptations for lotic environments included prevention of biofouling, configuration for variable stage height, and reduction of headspace equilibration time. We then examined which input parameters contribute the most to uncertainty in estimating CO₂emission rates and found scaling factors related to the gas exchange velocity were the most influential when CO₂concentration was significantly above saturation. Near saturation, sensor measurement ofpCO₂contributed most to uncertainty in estimating CO₂emissions. We also found high‐frequency measurements ofpCO₂were not necessary to accurately estimate median emission rates given the CO₂regimes of our streams, but daily to weekly sampling was sufficient. High‐frequency measurements ofpCO₂remain valuable for exploring in‐stream metabolic variability, source partitioning, and storm event dynamics. Our adaptations to the SIPCO2 offer a relatively affordable and robust means of monitoring dissolved CO₂in lotic ecosystems. Our findings demonstrate priorities and related considerations in the design of monitoring projects of dissolved CO₂and CO₂evasion dynamics more broadly. 

Abstract River networks regulate carbon and nutrient exchange between continents, atmosphere, and oceans. However, contributions of riverine processing are poorly constrained at continental scales. Scaling relationships of cumulative biogeochemical function with watershed size (allometric scaling) provide an approach for quantifying the contributions of fluvial networks in the Earth system. Here we show that allometric scaling of cumulative riverine function with watershed area ranges from linear to superlinear, with scaling exponents constrained by network shape, hydrological conditions, and biogeochemical process rates. Allometric scaling is superlinear for processes that are largely independent of substrate concentration (e.g., gross primary production) due to superlinear scaling of river network surface area with watershed area. Allometric scaling for typically substrate-limited processes (e.g., denitrification) is linear in river networks with high biogeochemical activity or low river discharge but becomes increasingly superlinear under lower biogeochemical activity or high discharge, conditions that are widely prevalent in river networks. The frequent occurrence of superlinear scaling indicates that biogeochemical activity in large rivers contributes disproportionately to the function of river networks in the Earth system. 

Mean annual temperature and mean annual precipitation drive much of the variation in productivity across Earth's terrestrial ecosystems but do not explain variation in gross primary productivity (GPP) or ecosystem respiration (ER) in flowing waters. We document substantial variation in the magnitude and seasonality of GPP and ER across 222 US rivers. In contrast to their terrestrial counterparts, most river ecosystems respire far more carbon than they fix and have less pronounced and consistent seasonality in their metabolic rates. We find that variation in annual solar energy inputs and stability of flows are the primary drivers of GPP and ER across rivers. A classification schema based on these drivers advances river science and informs management. 

To assess the distribution, frequency, and global extent of riverine hypoxia, we compiled 118 million paired dissolved oxygen (DO) and water temperature measurements from 125,158 unique locations in rivers in 93 countries and territories across the globe. The dataset also includes site characteristics derived from StreamCat, the National Hydrography and HydroAtlas datasets and proximal land cover derived from MODIS-based IGBP land cover types compiled using Google Earth Engine (GEE).