Quantifying pCO₂ Evasion at River Steps: Hydraulic Controls Under Baseline and Alkalinity-Dosed Conditions

Anthropogenic climate change is amplifying carbon-cycle perturbations across aquatic and terrestrial systems, increasing the need for accurate greenhouse-gas accounting. Rivers, though limited in global surface area, exert outsized influence on the carbon and alkalinity balance through coupled weathering and gas-exchange processes and are estimated to emit ~1.8 Pg C yr^-1 as CO₂. A key unresolved challenge is the absence of a systematic, scalable approach to quantify CO₂ evasion from short, high-energy lotic segments –  hydraulic “hotspots” where dissolved CO₂ and exchange rates change sharply over space and time. Discrete features such as steps, cascades, and waterfalls can dominate reach-scale CO₂ evasion despite occupying negligible surface area, yet prevailing monitoring approaches rarely resolve step-specific contributions, often miss local dynamics occurring over seconds to minutes, and do not yield low-cost proxies suitable for widespread deployment. This knowledge gap is especially consequential for River Alkalinity Enhancement (RAE), a carbon dioxide removal (CDR) strategy in which alkaline minerals, such as calcite, are added to raise alkalinity and dissolved inorganic carbon, lower aqueous pCO2, and promote conversion of atmospheric CO₂ to bicarbonate for long-term ocean storage. If dosed waters traverse high-energy steps during equilibration, turbulence-driven gas exchange may provide a mechanism for improving removal efficiency and CDR credibility by reversing the gradient of CO₂ invasion. This research presents the ongoing development of an approach to quantify step-resolved CO₂ evasion by measuring pCO₂ “damping” across discrete hydraulic steps, with avenues to examine other factors influencing simulated reach evasion. Controlled mesocosm experiments systematically vary hydraulic conditions while collecting high-frequency dissolved CO₂ observations under baseline and calcite-dosed scenarios, enabling empirical constraints that support scalable hotspot accounting, improved RAE siting and design.