Inland waters are an important component of the global carbon budget, emitting CO2 to the atmosphere. However, our ability to predict carbon fluxes from stream systems remains uncertain as small scales of pCO2 variability within streams (100-102 m), which makes efforts relying on monitoring data uncertain. 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), and 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 pCO2 data collected across the East River Watershed in the Colorado Rocky Mountains (USA). The stream network model accurately captures topography-driven pCO2 variability and significantly outperforms multiple linear regressions for predicting pCO2. 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 CO2 fluxes are 4-12x times smaller than upscaling technique predictions, largely due to inverse correlations between stream pCO2 and atmosphere 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.

Rivers and streams are an important component of the global carbon budget, emitting CO2 to the atmosphere. However, our ability to accurately predict carbon fluxes from stream systems remains uncertain due to small scales of pCO2 variability within streams (100-102 m), which make monitoring intractable. Here 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 and benthic hyporheic zone (BZ) respiration, downstream advection, and atmospheric exchange. We evaluate this model against existing statistical methods including upscaling techniques and multiple linear regression models through comparisons to high-resolution stream pCO2 data collected across the East River Watershed in the Colorado Rocky Mountains. The stream network model accurately captures topography-driven pCO2 variability and significantly outperforms multiple linear regressions for predicting pCO2. Further, the model provides estimates of CO2 contributions from internal versus external sources and suggests that streams transition from GW- to BZ-dominated sources between 3rd and 4th Strahler orders, with GW and BZ accounting for 53 and 47% of CO2 fluxes from the watershed, respectively. Lastly, stream network model CO2 fluxes are 5-13x times smaller than upscaling technique predictions, largely due to inverse correlations between stream pCO2 and atmosphere exchange velocities. Taken together, the stream network model presented improves our ability to predict and monitor stream CO2 dynamics, and future applications to regional and global scales may result in a significant downward revision of global flux estimates.

Inland waters are recognized as a significant source of CO2 to the atmosphere; however, the global magnitude of this flux remains uncertain. In particular, CO2 concentrations and fluxes in stream systems are extremely variable at scales of 10’s to 100’s of meters, complicating monitoring and prediction efforts. Thus, models of pCO2 that capture these scales of spatial variability are necessary for the accurate prediction and monitoring of stream CO2 fluxes. Despite a strong conceptual framework for the hydrologic processes that control stream CO2, predictive models to date have been empirical, based on Strahler stream order and regressions between observed pCO2 and landscape variables. We hypothesize that models incorporating well-described hydrologic processes may lead to new insights into the magnitude of various CO2 sources and improve predictions. Here, we develop and apply a process-based stream network model of CO2 based on NHDplus flowlines and driven by groundwater inputs, hyporheic exchange, water-column metabolism, advective transport, and atmospheric exchange. Model output is compared with 151 measurements of pCO2 (424 - 9718 ppm) collected in August, 2019 across the upper East River watershed in Gothic, CO, a mountainous, high-elevation headwaters system within the Colorado River basin. We find that modeled pCO2 captures observed spatial patterns and predicts measured values with a RMSE of ~250 ppm and R2 of 0.47 (p<10-15). Additionally, our process-based model performs significantly better than a multiple linear regression model between observations and a geomorphic variables (r2=0.35, p<10-7). Estimates from an optimized stream network model give additional insight into CO2 sources, suggesting that groundwater accounts for 70-80% of evasion fluxes, hyporheic processes for 20-30%, and water-column metabolism for ~1% across the East River watershed. The ability of our model to predict pCO2 at the spatial scales of variability may provide an important next step in estimating global CO2 fluxes, and future research will test the predictive power of process-based models at regional and global scales.