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