Evaluation of metabolism models for free‐water dissolved oxygen methods in lakes

Abstract

Free-water measurements of dissolved oxygen (DO) in lakes are becoming common and provide opportunities for estimating ecosystem processes, such as gross primary production (GPP) and ecosystem respiration (R). The models used to estimate metabolism often subsume biological processes into one parameter each for GPP and R. However, high-frequency DO observations made over days show diverse patterns at multiple time scales, suggesting a complex suite of processes controls DO dynamics. Can we improve metabolism estimates and predictive ability for DO at diel scales by adding complexity to the models? In this study, we use data from two north temperate lakes to test a variety of metabolism models representing a suite of non-linear metabolic processes. To test whether alternative models can be discriminated, we simulated DO with assumed parameter values and auto-regressive noise, and fit the models to the simulated DO. The most complex model could be discriminated from simpler models and provided the most accurate and precise predictions. However, when models were fit to observed DO data from the sensor network, the simplest model predicted DO as well as the most complex one. The added complexity did not improve model performance. An analysis of the model residuals indicates that physics may explain some of the DO pattern not predicted, especially high-frequency oscillations and anomalies that appear to coincide with weather patterns. Under reasonably stable weather conditions and at scales of a few days, simple metabolism models explain the bulk of diel DO variability.

Intra- and inter-annual variability in metabolism in an oligotrophic lake

“…2) with a maximum likelihood method (Van de Bogert et al 2007;Hanson et al 2008;Solomon et al 2013). We acknowledge the potential for autocorrelated residuals because a smoothed model often cannot closely follow the irregular variability in a time series (see ''Results'' below for examples).…”

“…All analyses were conducted in the R statistical environment (v. 3.0.2, R Development Core Team 2013). GPP was modeled as a linear function of the above-lake irradiance (Hanson et al 2008). If the mixed layer depth was above the DO sensor at 1 m, then no atmospheric gaseous exchange (i.e., D t = 0) was considered for that time step (Solomon et al 2013).…”

“…Traditionally, metabolism has been estimated directly from bottle incubations or diel oxygen fluctuations using a 'bookkeeping' calculation (Odum 1956;Cole et al 2000;Staehr et al 2010). Advances in high-frequency dissolved oxygen sensor technology (e.g., Staehr et al 2012;Weathers et al 2013), increased metabolism model complexity, and use of parametric statistical techniques (van de Bogert et al 2007;Hanson et al 2008) have improved the spatial and temporal resolution of metabolism estimates. These advances have enabled new discoveries about the role of lakes in the global C cycle (e.g., Solomon et al 2013), controls on metabolism (Hoellein et al 2013), and spatial variability of metabolism within lakes (Van de Bogert et al 2012).…”

“…These advances have enabled new discoveries about the role of lakes in the global C cycle (e.g., Solomon et al 2013), controls on metabolism (Hoellein et al 2013), and spatial variability of metabolism within lakes (Van de Bogert et al 2012). Furthermore, these rapidly-developing techniques have facilitated investigation of the variability of metabolism at finely-resolved spatial (Klug et al 2012; Van de Bogert et al 2012) and temporal (Hanson et al 2008;Solomon et al 2013) scales.…”

Challenges and opportunities for integrating lake ecosystem modelling approaches

“…For example, assimilation of observations from real-time lake sensors to reduce error in model parameterizations is emerging as a promising method to manage the uncertainty of complex models. For water quality or ecological applications, such methodologies are in their infancy; however, signal processing techniques that estimate lake metabolism from real-time oxygen and temperature measurements (Hanson et al 2008) and similar approaches will ultimately support the validation of complex hydrodynamic-ecological models.…”