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

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.