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.