Control of dissolved oxygen in northern temperate lakes over scales ranging from minutes to days

Abstract

Dissolved oxygen (DO) observations from in situ sensors show complex temporal patterns, suggesting that the balance of control by underlying processes changes across scales. At scales ranging from minutes to days, a number of physical and biological processes, such as internal waves, mixing, and ecosystem metabolism, may impart pattern on observed DO. In discriminating the control over DO variability by scale, this helps us to reduce uncertainty in estimates of important ecosystem rates, such as gross primary production and respiration. In this study, we examined DO variability over scales ranging from minutes to days and assessed the relative contributions from several physical and biotic drivers. High frequency measurements of DO, wind, temperature, and photosynthetically available radiation (PAR) were obtained over periods of approximately 4 d from 25 lakes in northern Wisconsin. Patterns in data were isolated by time scale through wavelet transforms. A suite of predictors were related to DO across time scales using artificial neural networks. At the diel scale, PAR explained most of the variability in DO signals. At sub-diel scales, temperature and wind largely explained variability in DO. However, the nature, strength, and time scale of the relationships between drivers and DO may be a function of lake size.

Estimating primary production from oxygen time series: A novel approach in the frequency domain

“…A full treatment of how other nondeterministic drivers of ‐dynamics affect diel oxygen methods is out of the scope of this manuscript, but we believe that further analysis will contribute to a increased understanding of how the different scales of the deterministic and nondeterministic parts of ‐dynamics affect estimates of metabolism. This is a topic that has not received a lot of attention [ see , however, Langman et al (); Coloso et al ()].…”

“…Kester et al () already demonstrated that the spectral analysis of oxygen time series data can provide relevant information on the different processes that affect the oxygen dynamics in natural waters. More recently, Coloso et al () and Langman et al () applied wavelet techniques to separate the ‐dynamics at different time scales. Here, we elaborate this idea by combining the diel oxygen method with spectral analysis.…”

Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty

“…Mixing driven by wind or heat loss can alter observed DO concentrations by transporting water masses with different DO concentrations past a sensor. In a recent study exploring short-term variability in high frequency DO measurements, wind was consistently related to DO variability over short time periods in scales ranging from minutes to hours (Langman et al 2010).…”

Free‐water lake metabolism: addressing noisy time series with a Kalman filter

“…The Linear Model (LM) is similar to the simple model described in Hanson et al (2008) in that it fits parameters to the DO time series and includes a term accounting for error in the modeled process. Models such as LM use fitted parameters and are common in metabolism studies (Staehr and Sand-Jensen 2006;Hanson et al 2006;Van de Bogert et al 2007;Langman et al 2010), and contrast with the bookkeeping approach in that they include an error term, instead of subsuming the error in the estimates of the metabolism parameters. The third model makes use of a Kalman filter, an approach that is well established in statistics (Kalman 1960;Harvey 1989).…”