Variable physical drivers of near-surface turbulence in a regulated river

Abstract

Inland waters, such as lakes, reservoirs and rivers, are important sources of climate forcing trace gases. A key parameter that regulates the gas exchange between water and the atmosphere is the gas transfer velocity, which itself is controlled by near-surface turbulence in the water. While in lakes and reservoirs, near-surface turbulence is mainly driven by atmospheric forcing, in shallow rivers and streams it is generated by bottom friction of gravity-forced flow. Large rivers represent a transition between these two cases. Near-surface turbulence has rarely been measured in rivers and the drivers of turbulence have not been quantified. We analyzed continuous measurements of flow velocity and quantified turbulence as the rate of dissipation of turbulent kinetic energy over the ice-free season in a large regulated river in Northern Finland. Measured dissipation rates agreed with predictions from bulk parameters, including mean flow velocity, wind speed, surface heat flux, and with a one-dimensional numerical turbulence model. Values ranged from ∼10 −10 m 2 s −3 to 10 −5 m 2 s −3 . Atmospheric forcing or gravity was the dominant driver of near-surface turbulence for similar fraction of the time. Large variability in near-surface dissipation rate occurred at diel time scales, when the flow velocity was strongly affected by downstream dam operation. By combining scaling relations for boundary-layer turbulence at the river bed and at the air-water interface, we derived a simple model for estimating the relative contributions of wind speed and bottom friction of river flow as a function of depth. Plain Language SummaryInland water bodies such as lakes, reservoirs and rivers are an important source of climate forcing trace gases to the atmosphere. Gas exchange between water and the atmosphere is regulated by the gas transfer velocity and the concentration difference between the water surface and the atmosphere. The gas transfer velocity depends on near-surface turbulence, but robust formulations have not been developed for river systems. Their surface area is sufficiently large for meteorological forcing to cause turbulence, as in lakes and reservoirs, but turbulence generated from bed and internal friction of gravity-driven flows is also expected to contribute. Here we quantify nearsurface turbulence using data from continuous air and water side measurements conducted over the ice-free season in a large subarctic regulated river in Finland. We find that turbulence, quantified as the dissipation rate of turbulent kinetic energy, is well described using equations for predicting turbulence from meteorological data for sufficiently high wind speeds whereas the contribution from bottom shear dominated at higher flow velocities. A one-dimensional river model successfully captured these processes.

Energy Flux Paths in Lakes and Reservoirs

“…The noise level was determined as the logarithmically averaged high-frequency end of the spectra at frequencies higher than 0.2 Hz. To find the lower frequency limit for inertial subrange fitting, we used the optimization procedure described in [54]. We used three criteria for quality assurance for calculated dissipation rates: validity of Taylor's frozen turbulence hypothesis, coefficient of determination of the fit (for both-see [47]) and the length of observed inertial subrange (set to a minimum of 10/8 of decade).…”

Physically Based Scaling Models to Predict Gas Transfer Velocity in Streams and Rivers

“…This finding is also supported by field measurements on a small stream with smooth and rippled flows, showing that estimated dissipation rate ε s agrees with locally measured ε and K L follows ε s 1/4 scaling (Lorke et al., 2019). However, most rivers and a few streams show low correlations between ε s 1/4 ( ε D 1/4 or U *) and K L 20 (Figure 5 and Figures ), suggesting that factors other than bottom friction increasingly influence near‐surface turbulence and K L 20 (Guseva et al., 2020).…”

“…Studies have shown multiple macroscale factors could influence near‐surface turbulence dissipation rate ( ε ), but it remains unclear how dominant macroscale factors vary across a range of streams and rivers (Alin et al., 2011; Guseva et al., 2020; Moog & Jirka, 1999b). Studies on K L prediction for streams and rivers often assume that near‐surface turbulence is dominated by bottom‐shear‐induced turbulence.…”

“…The estimation of site‐ and discharge‐specific K L followed three steps: (1) select the days with daily variations in DO amplitude >2 mg/L and daily mean wind speed <3 m/s from continuous DO time series. Only days with low wind speed (<3 m/s) were selected to minimize the effect of winds on K L (Chu & Jirka, 2003; Guseva et al., 2020; Wanninkhof et al., 2009); (2) group the selected days according to discharge. Each group has a within‐group daily discharge difference <1%, a minimum of 2 days, and a maximum of 5 days; and (3) estimate K L for each group by solving the DO mass balance equation and using maximum likelihood estimation (i.e., inverse modeling approach).…”

“…However, most rivers and a few streams show low correlations between ε s 1/4 (ε D 1/4 or U*) and K L20 ( Figure 5 and Figures S4-S6), suggesting that factors other than bottom friction increasingly influence near-surface turbulence and K L20 (Guseva et al, 2020).…”