A novel index based on the cusp catastrophe theory for predicting harmful algae blooms

Abstract

Harmful algae blooms (HABs) may occur in lakes and reservoirs, causing serious water quality problems. The occurrence of HABs is related to many water environmental indices. Hence, it is of great importance to develop new indices to predict HABs. In this study, a novel HABs prediction index named DCCPI (Discriminant of Cusp Catastrophe as the Prediction Index) was proposed based on the catastrophe theory. The DCCPI was calibrated and validated using a three-year dataset of observations of algae biomass, nutrients and environmental variables from the WS1 Reservoir in Northern China. The index was trained using the first two years and verified using the third year of observations. The index could accurately predict a HAB every year. The main advantage of this index is its ability to yield accurate predictions without the need for large training datasets. Thus, it can be especially suitable for areas with limited monitoring data. The DCCPI could serve as an effective tool for water managers to ensure the quality of water in lakes and reservoirs.