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A Process-based Stream Network Model for Predicting CO2 Concentrations and Fluxes
  • Brian Saccardi,
  • Matthew Winnick
Brian Saccardi
University of Massachusetts Amherst

Corresponding Author:[email protected]

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Matthew Winnick
University of Massachusetts Amherst
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Abstract

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.