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Eutrophication increases hypoxia in lakes and reservoirs, causing deleterious effects on biological communities. Quantitative models would help managers develop effective strategies to address hypoxia issues, but most existing models are limited in their applicability to lakes with temporally resolved dissolved oxygen data. We describe a hierarchical Bayesian model that predicts dissolved oxygen in lakes based on a mechanistic understanding of the factors that influence the development of hypoxia during summer stratification. These factors include the days elapsed since stratification, dissolved organic carbon concentration, lake depth, and chlorophyll concentration. We demonstrate that the model can be fit to two datasets: one in which temporally resolved dissolved oxygen profiles were collected from 20 lakes in a single state and one in which single profiles were collected from 381 lakes across the United States. Analyses of these two datasets yielded similar relationships between volumetric oxygen demand and chlorophyll concentration, suggesting that the model structure appropriately represented the effects of eutrophication on oxygen depletion. Combining both datasets in a single model further improved the precision of predictions.

Modeling hypolimnetic dissolved oxygen depletion using monitoring data