Predicting Phaeocystis globosa bloom in Dutch coastal waters by decision trees and nonlinear piecewise regression

Under European Commission project harmful algal bloom expert system (HABES), a rule-based model has been developed in the Dutch pilot study for predicting Phaeocystis globosa blooms in the Dutch coastal waters (Noordwijk 10) of the North Sea. The model uses decision trees to qualitatively predict bloom timing (bloom or not bloom in a certain day) and uses nonlinear piecewise regression to quantitatively predict bloom intensity (cell concentrations). A multi-variable regression model was also set up to predict bloom duration if bloom is forecasted to take place. The constructed model clearly indicates that the joint effects of mean water column irradiance of photic depth (Im), temperature (T) and dissolved inorganic phosphorus (DIP) determine bloom timing and intensity. The bloom duration depends on the bloom timing (starting day), starting intensity and temperature. Irradiance is seen to act just as one of the triggers to P. globosa bloom. As long as it is higher than the threshold, extra irradiance plays little role in bloom intensity or duration. River discharge from the Rhine does not have instant effect on P. globosa bloom. The threshold values of Im, T and DIP independently found by the model are in accordance with those discovered by other researchers through laboratory experiments. The model was tested by an independent dataset from the same area, and the model results agree well with the real observations both qualitatively and quantitatively. The developed rule-based model is sensible to be interpreted from ecological point of view and is applicable in practice. Due to splits of parameters’ space in decision trees and piecewise regression, the model has great advantages to deal with the common problem in algal blooms that limiting factor is changing. The research demonstrates that decision trees and nonlinear piecewise regression are quite promising alternative techniques in modelling harmful algal blooms.