Algal blooms are disturbances to aquatic ecosystems that can impact water quality and ecosystem services. It is often difficult to detect algal bloom disturbance magnitudes and recovery times, making it challenging to compare disturbances within and among aquatic systems. Using high frequency phycocyanin and chlorophyll-a data and a disturbance-recovery algorithm, we quantified the magnitude and duration of algal bloom disturbances. We first applied the algorithm to nutrient enriched experimental lakes with detailed algal data. Next, we applied the algorithm to non-experimental lakes with long pigment sensor time series. The algorithm accurately detected algal bloom onsets, magnitudes, and recoveries in the experimental lakes, facilitating within and cross-system comparisons. In non-experimental lakes, we identified the most severe/intense disturbances and those that occurred at unusual times. Recovery time and peak disturbance magnitude differed among non-experimental lakes. Lakes with phycocyanin and chlorophyll-a time series rarely had concurrent disturbances in both variables. Algal bloom disturbances and recoveries can be accurately detected with appropriate reference data, but the problem is more difficult in non-experimental lakes due to the complexities of reference data selection, missing values, and pigment sensor errors. Overall the approach shows promise in quantifying algal bloom dynamics where long-term high frequency data are available.

Primary Presenter: Dat Ha, University of Virginia (dh3dv@virginia.edu)

Authors:

Dat Ha, Department of Environmental Sciences, University of Virginia   ()

Cal Buelo, U.S. Environmental Protection Agency  ()

Spencer Tassone, Department of Environmental Sciences, University of Virginia   ()

Jonathan Walter, Department of Environmental Sciences, University of Virginia and Center for Watershed Sciences, UC Davis  ()

Michael Pace, Department of Environmental Sciences, University of Virginia  ()