Headwater gas exchange quantified from O₂ mass balances at the reach scale

Abstract

Headwater streams are important in the carbon cycle and there is a need to better parametrize and quantify exchange of carbon‐relevant gases. Thus, we characterized variability in the gas exchange coefficient (k 2) and dissolved oxygen (O2) gas transfer velocity (k) in two lowland headwaters of the River Avon (UK). The traditional one‐station open‐water method was complemented by in situ quantification of riverine sources and sinks of O2 (i.e., groundwater inflow, photosynthesis, and respiration in both the water column and benthic compartment) enabling direct hourly estimates of k 2 at the reach–scale (~ 150 m) without relying on the nighttime regression method. Obtained k 2 values ranged from 0.001 h−1 to 0.600 h−1. Average daytime k 2 were a factor two higher than values at night, likely due to diel changes in water temperature and wind. Temperature contributed up to 46% of the variability in k on an hourly scale, but clustering temperature incrementally strengthened the statistical relationship. Our analysis suggested that k variability is aligned with dominant temperature trends rather than with short‐term changes. Similarly, wind correlation with k increased when clustering wind speeds in increments correspondent with dominant variations (1 m s−1). Time scale is thus an important consideration when resolving physical drivers of gas exchange. Mean estimates of k 600 from recent parametrizations proposed for upscaling, when applied to the settings of this study, were found to be in agreement with our independent O2 budget assessment (within < 10%), adding further support to the validity of upscaling efforts aiming at quantifying large‐scale riverine gas emissions.

Gas exchange in streams and rivers

“…Given a benthic O 2 flux, it is then possible to create an oxygen budget from which gas exchange can be calculated (Rovelli et al, 2018). This method provides an indirect, but likely accurate measure of k, and enables estimating variation within a day, for example, from the effects of wind (Rovelli et al, 2018). These underwater eddy covariance methods can also directly estimate gas exchange (Berg & Pace, 2017).…”

“…Although predicting gas exchange for any one site using models is highly uncertain, the good news is that methods for estimating gas exchange within a site are becoming easier to conduct. New empirical methods with tracer gas additions (Hall & Madinger, 2018) underwater eddy covariance (Berg & Pace, 2017;Rovelli et al, 2018), and linkages to hydrodynamics (Lorke et al, 2019) will improve estimates at any one site. Modeling oxygen dynamics allows estimating k 600 for many streams with long time series of oxygen collected for water quality purposes.…”

“…By stationing a threedimensional (3D) velocimeter and a rapid response O 2 sensor, one can measure benthic O 2 fluxes directly (Koopmans & Berg, 2015). Given a benthic O 2 flux, it is then possible to create an oxygen budget from which gas exchange can be calculated (Rovelli et al, 2018). This method provides an indirect, but likely accurate measure of k, and enables estimating variation within a day, for example, from the effects of wind (Rovelli et al, 2018).…”

“…Modeling approaches (Appling, Hall, et al, 2018;Holtgrieve, Schindler, Branch, & A'mar, 2010) use the gas exchangeinduced shape in the curve to evaluate K 600 separately from metabolism Elosegi, 2009;Genzoli & Hall, 2016;Izagirre, Agirre, Bermejo, Pozo, & Elosegi, 2008;Izagirre, Bermejo, Pozo, & Elosegi, 2007;Ulseth et al, 2017) but is not often compared to other methods (except see Aristegi et al, 2009). Rovelli et al (2018) found that the nighttime regression method overestimated K relative to their oxygen budget approach.…”

Coupling Concentration‐ and Process‐Discharge Relationships Integrates Water Chemistry and Metabolism in Streams

“…We confirmed modeled K using nighttime linear regression of dissolved oxygen (Hall & Hotchkiss, 2017, supporting information Figures S5 and S6). We found that the relationship between K-ER is lowest and potential for equifinality reduced when constraining K according to a K~Q relationship, compared to using air-water gas exchange scaled to a temperature of 20°C , as in Rovelli et al (2018); supporting information Figures S7 and S8). Additionally, we removed 30 days with K values below the 1% (<3.38 d −1 ) and above the 99% (>27.21 d −1 ) quantiles to further decrease the chances of using biased metabolism estimates in our P-Q analyses (Figure S6).…”