Optimizing Reservoir Operations to Mitigate Nutrient and Phytoplankton Exports From a Eutrophic Lake

Abstract

Harmful algal blooms have large impacts on aquatic ecosystem and human health. Nutrient enrichment, in combination with warm water temperatures, high sunlight availability, and low water turbulence, have been proven to be major factors driving algal blooms. In this study, lake eutrophication processes, including phytoplankton production and nutrient cycling, were simulated and coupled with a reservoir operations model to optimize multi‐criteria lake operation goals. The main objective of this study was thus to design reservoir operations that would minimize phosphorus (P), nitrate‐nitrogen (NOx), and phytoplankton loads to downstream water bodies, while meeting other societal water resource demands in eutrophic lakes. We used an open‐source, multi‐objective evolutionary algorithm framework with four optimization objectives (minimizing P, NOx, and phytoplankton loads and water demand deficits), assessing each constituent separately and in combination. In addition, different optimization scenarios associated with each objective were investigated. To effectively demonstrate our findings, we implemented our approach in Lake Okeechobee, the largest subtropical lake in the US. We identified multiple opportunities to reduce downstream loads while minimizing impacts on water demand deficits. Notably, considering combined load objectives yielded substantial reductions in summertime P, NOx, and phytoplankton exports by up to 73%, 82%, and 73%, respectively, with minimal increases in water demand deficits. This supports the idea that alternative operational strategies could provide an effective and economical reservoir management strategy for balancing downstream water quality and societal water resource needs.