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

Limnology and Oceanography: Methods
By: , and 



Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400-700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8-3 m s–1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.
Publication type Article
Publication Subtype Journal Article
Title Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty
Series title Limnology and Oceanography: Methods
DOI 10.4319/lom.2014.12.303
Volume 12
Issue 5
Year Published 2014
Language English
Publisher American Society of Limnology and Oceanography
Contributing office(s) Center for Integrated Data Analytics
Description 10 p.
First page 303
Last page 312
Google Analytic Metrics Metrics page
Additional publication details