Improving predictions of stream CO2 concentrations and fluxes using a stream network model: A case study in the East River watershed, CO, USA
Abstract
Abstract Inland waters are an important component of the global carbon budget. However, our ability to predict carbon fluxes from stream systems remains uncertain, as p CO 2 varies within streams at scales of 1–100 m. This makes direct monitoring of large‐scale CO 2 fluxes impractical. We incorporate CO 2 input and output fluxes into a stream network advection‐reaction model, representing the first process‐based representation of stream CO 2 dynamics at watershed scales. This model includes groundwater (GW) CO 2 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 p CO 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 p CO 2 variability and significantly outperforms multiple linear regressions for predicting p CO 2 . Further, the model provides estimates of CO 2 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 CO 2 fluxes from the watershed, respectively. Lastly, stream network model atmospheric CO 2 fluxes are 4‐12x times smaller than upscaling technique predictions, largely due to relationships between stream p CO 2 and gas exchange velocities. Taken together, this stream network model improves our ability to predict stream CO 2 dynamics and efflux. Furthermore, future applications to regional and global scales may result in a significant downward revision of global flux estimates.
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Cited By (32 times, 1 in Knowledge Hub)
References (73)
3 in Knowledge Hub, 70 external