Abstract
High concentrations of chlorophyll-a (chl-a) during summer are by definition a common problem in eutrophicated lakes. Several models have been designed to predict chl-a concentrations but are unable to estimate the probability of predicted concentrations or concentration spans during subsequent months. Two different methods were developed to compute the probabilities of obtaining a certain chl-a concentration. One method is built on discrete Markov chains and the other method on a direct relationship between median chl-a concentrations from two months. Lake managers may use these methods to detect and counteract the risk of high chl-a concentrations and algal blooms during coming months. Both methods were evaluated and applied along different scenarios to detect the probability to exceed chl-a concentration in different coming months. The procedure of computing probabilities is strictly based on general statistics which means that neither method is constrained for chl-a but can also be used for other variables. A user-friendly software application was developed to facilitate and extend the use of these two methods.
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