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\begin{document}

\title{A Process Based Stream Network Model for Predicting CO\textsubscript{2}
Concentrations and Fluxes at High Resolution}



\author[1]{Brian Saccardi}%
\author[1]{Matthew Winnick}%
\affil[1]{University of Massachusetts Amherst}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\begin{abstract}
Inland waters are an important component of the global carbon budget,
emitting CO\textsubscript{2} to the atmosphere. However, our ability to
predict carbon fluxes from stream systems remains uncertain as small
scales of pCO\textsubscript{2} variability within streams
(10\textsuperscript{0}-10\textsuperscript{2} m), which makes efforts
relying on monitoring data uncertain. We incorporate CO\textsubscript{2}
input and output fluxes into a stream network advection-reaction model,
representing the first process-based representation of stream
CO\textsubscript{2} dynamics at watershed scales. This model includes
groundwater (GW) CO\textsubscript{2} 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 pCO\textsubscript{2} data
collected across the East River Watershed in the Colorado Rocky
Mountains (USA). The stream network model accurately captures
topography-driven pCO\textsubscript{2} variability and significantly
outperforms multiple linear regressions for predicting
pCO\textsubscript{2}. Further, the model provides estimates of
CO\textsubscript{2} contributions from internal versus external sources
suggesting that streams transition from GW- to BHZ-dominated sources
between 3\textsuperscript{rd} and 4\textsuperscript{th} Strahler orders,
with GW, BHZ, and WC accounting for 49.3, 50.6, and 0.1\% of
CO\textsubscript{2} fluxes from the watershed, respectively. Lastly,
stream network model CO\textsubscript{2} fluxes are 4-12x times smaller
than upscaling technique predictions, largely due to inverse
correlations between stream pCO\textsubscript{2} and atmosphere exchange
velocities. Taken together, this stream network model improves our
ability to predict stream CO\textsubscript{2} dynamics and efflux.
Furthermore, future applications to regional and global scales may
result in a significant downward revision of global flux estimates.%
\end{abstract}%



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