Predicting Blue-Green Algal Blooms in Lakes using Long-Term Data

Excessive inputs of phosphorus (P) have long been known to cause excessive blue-green algal blooms and other eutrophication symptoms in lakes.  To predict how an individual lake would respond to changes in P inputs, scientists frequently have relied on models that link P inputs to in-lake P concentrations, which can be linked to summer algal densities or chlorophyll (Chl) concentrations.  These models were derived from cross-sectional analyses of many lakes and predict average concentrations of P or Chl at steady state conditions of P inputs - a condition that rarely occurs due to variability in runoff and other drivers.  Large prediction uncertainties exist when the models are applied to any one lake thus making model predictions difficult to interpret.  In addition, summer blue-green algal blooms are extreme and highly stochastic events whose occurrence can be masked in average conditions.  Unfortunately, few lakes have the necessary long-term data available to accurately predict algal bloom responses to stochastic watershed P input rates under a different set of land management practices.

To illustrate the value of long-term data for lake diagnostic studies, the probabilities of summer blue-green algae exceeding bloom concentrations of >2 and >5 mg L^-1 were predicted for P input loading rates for Lake Mendota, one of the North Temperate Lakes LTER study lakes (modified from Fig. 5, Lathrop et al. 1998).  These analyses were possible because of a 21-year record for P input loadings, in-lake P concentrations, and blue-green algal concentrations in the lake.  These analyses were conducted by a collaboration of LTER and state agency researchers and were used to justify the recommended 50% P input reduction as a target for the Lake Mendota Priority Watershed Project, which will commit over $16 million of state and local monies to improve water quality in the lake.  The long-term P loading data were also used by Carpenter et al. (1999) to demonstrate that to maximize the economic benefits of improving lake water quality, P input targets should be reduced below levels derived from traditional deterministic lake models because of the uncertainties in model predictions.

Carpenter, S.R., D. Ludwig, and W.A. Brock.  1999.  Management of eutrophication for lakes subject to potentially irreversible change.  Ecol. Appl. 9:751-771.

Lathrop, R.C., S.R. Carpenter, C.A. Stow, P.A. Soranno, and J.C. Panuska.  1998.  Phosphorus loading reductions needed to control blue-green algal blooms in Lake Mendota.  Can. J. Fish. Aquat. Sci. 55:1169-1178.