Adaptive forecasting of phytoplankton communities

The global proliferation of harmful algal blooms poses an increasing threat to water resources, recreation
and ecosystems. Predicting the occurrence of these blooms is therefore needed to assist water managers
in making management decisions to mitigate their impact. Evaluation of the potential for forecasting of
algal blooms using the phytoplankton community model PROTECH was undertaken in pseudo-real-time.
This was achieved within a data assimilation scheme using the Ensemble Kalman Filter to allow uncertainties
and model nonlinearities to be propagated to forecast outputs. Tests were made on two
mesotrophic lakes in the English Lake District, which differ in depth and nutrient regime. Some forecasting
success was shown for chlorophyll a, but not all forecasts were able to perform better than a
persistence forecast. There was a general reduction in forecast skill with increasing forecasting period but
forecasts for up to four or five days showed noticeably greater promise than those for longer periods.
Associated forecasts of phytoplankton community structure were broadly consistent with observations
but their translation to cyanobacteria forecasts was challenging owing to the interchangeability of
simulated functional species.