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Recent advances in remote sensing and the upcoming launch of the joint NASA/CNES/CSA/UKSA Surface Water and Ocean Topography (SWOT) satellite point toward improved river discharge estimates in ungauged basins. Existing discharge methods rely on “prior river knowledge” to infer parameters not directly measured from space. Here, we show that discharge estimation is improved by classifying and parameterizing rivers based on their unique geomorphology and hydraulics. Using over 370,000 in situ hydraulic observations as training data, we test unsupervised learning and an “expert” method to assign these hydraulics and geomorphology to rivers via remote sensing. This intervention, along with updates to model physics, constitutes a new method we term “geoBAM,” an update of the Bayesian At‐many‐stations hydraulic geometry‐Manning's (BAM) algorithm. We tested geoBAM on Landsat imagery over more than 7,500 rivers (108 are gauged) in Canada's Mackenzie River basin and on simulated hydraulic data for 19 rivers that mimic SWOT observations without measurement error. geoBAM yielded considerable improvement over BAM, improving the median Nash‐Sutcliffe efficiency (NSE) for the Mackenzie River from −0.05 to 0.26 and from 0.16 to 0.46 for the SWOT rivers. Further, NSE improved by at least 0.10 in 78/108 gauged Mackenzie rivers and 8/19 SWOT rivers. We attribute geoBAM improvement to parameterizing rivers by type rather than globally, but prediction accuracy worsens if parameters are misassigned. This method is easily mapped to rivers at the global scale and paves the way for improving future discharge estimates, especially when coupled with hydrologic models.

Lakes are central components of the inland water system distinct from, yet inextricably connected to, river networks. Currently, existing network‐scale biogeochemistry research, although robust, typically treats each of these components separately or reductively. Here, we incorporate lake morphometry into a fully connected stream/lake network for the Connecticut River watershed and model potential evasion of terrestrially sourced headwater CO₂as transported through the network, ignoring in‐stream production. We found that approximately 25%–30% of total potential soil CO₂evasion occurs in lakes, and percent evasion is inversely related to streamflow. A lake's ability to evade CO₂is controlled by residence time and size: most lakes with residence time over 7 days or surface area greater than 0.004 km²evade functionally all terrestrial CO₂entering from upstream, precluding further downstream transport. We conclude that lakes are important for soil CO₂degassing and that this coupled river/lake approach is promising for CO₂studies henceforth.

At‐many‐stations hydraulic geometry (AMHG), while useful for estimating river discharge from satellite data, remains empirical and has yet to be reconciled with the at‐a‐station hydraulic geometry (AHG) from which it was originally derived. Here we present evidence, using United States Geological Survey field measurements of channel hydraulics for 155 rivers, that AMHG can be hydraulically and geomorphically reconciled with AHG. Our results indicate that AMHG is rightly understood as an expression of a river‐wide model of hydraulics driven by changes in slope imposed upon AHG physics. The explanatory power of AHG and this river‐wide model combine to determine whether AMHG exists: if both AHG and the river‐wide model adequately describe hydraulics, then we show that AMHG is a necessary mathematical consequence of these two phenomena. We also orient these findings in the context of river discharge estimation and other applications.