Improving Predictions of Stream CO2 Concentrations and Fluxes Using a Stream Network Model: A Case Study in the East River Watershed, CO, USA

Inland waters are an important component of the global carbon budget. However, our ability to predict carbon fluxes from stream systems remains uncertain, as pCO(2) varies within streams at scales of 1-100 m. This makes direct monitoring of large-scale CO2 fluxes impractical. We incorporate CO2 input and output fluxes into a stream network advection-reaction model, representing the first process-based representation of stream CO2 dynamics at watershed scales. This model includes groundwater (GW) CO2 inputs, water column (WC), benthic hyporheic zone (BHZ) respiration, downstream advection, and atmospheric exchange. We evaluate this model against existing statistical methods including upscaling and multiple linear regressions through comparisons to high-resolution stream pCO(2) data collected across the East River Watershed in the Colorado Rocky Mountains (USA). The stream network model accurately captures GW, evasion, and respiration-driven pCO(2) variability and significantly outperforms multiple linear regressions for predicting pCO(2). Further, the model provides estimates of CO2 contributions from internal versus external sources suggesting that streams transition from GW- to BHZ-dominated sources between 3rd and 4th Strahler orders, with GW, BHZ, and WC accounting for 49.3%, 50.6%, and 0.1% of CO2 fluxes from the watershed, respectively. Lastly, stream network model atmospheric CO2 fluxes are 4-12x times smaller than upscaling technique predictions, largely due to relationships between stream pCO(2) and gas exchange velocities. Taken together, this stream network model improves our ability to predict stream CO2 dynamics and efflux. Furthermore, future applications to regional and global scales may result in a significant downward revision of global flux estimates.

Automated summary

Inland waters are crucial for the global carbon budget, but predicting carbon fluxes in streams has been challenging due to variability in pCO(2) at different scales. A new stream network model incorporating CO2 input and output fluxes provides a process-based representation of stream CO2 dynamics, outperforming traditional statistical methods. This model reveals the transition of CO2 sources in streams and shows that atmospheric CO2 fluxes may be significantly underestimated on regional and global scales.