Development of a structurally dynamic model for forecasting the effects of restoration of Lake Fure, Denmark

A structurally dynamic model based on the UNEP software PAMOLARE I [Jørgensen, S.E., Tsuno, H., Hidaka, T., Mahler, H., Santiago V., 2003. PAMOLARE I Training Package, Planning and Management of Lakes and Reservoirs: Models for Eutrophication Management. UNEP-DTIE-IETC and ILEC, 1091 Oroshimo-cho, Kusatsu, Shiga 525-0001, Japan], was developed for Lake Fure situated 12-17 km north of Copenhagen, Denmark. The model was calibrated and validated against two years data from 2001 and 2002, respectively. Furthermore, the model was applied for examining the effects of an ongoing restoration project in the lake by testing three different prognosis scenarios: (1) aeration of the hypolimnion, (2) biomanipulation by removing the trash fish from the lake, (3) two processes described in the two scenarios were applied simultaneously. The calibration yielded acceptable results with a standard deviation (S.D.) of less than 45% for the phytoplankton abundance considered as the most important state variable in the model. The validation results were acceptable for the key state variable, phytoplankton abundance, compared to the observations. Thus, the model was applied to set up prognoses and the results from the prognoses showed that a major improvement in the water quality of the lake might be expected with a significant reduction in the phytoplankton abundance and in the concentration of total phosphorus (TP) in 10 years as expected from the restoration project in the lake. Moreover, the model suggested that the combined application of biomanipulation and aeration practices described in Scenario 3 was the most effective among the three restoration scenarios tested, in obtaining a significant reduction in both phytoplankton abundance and the concentration of TP in the lake. The validation of prognoses is possible when data are available on the development of the lake's status at later stages of the restoration project and after. The lower confidence in data possibly resulted in the high discrepancy from the observations in the validation simulations for zooplankton, the peak values of cyanobacteria, and the timing of phytoplankton, zooplankton and cyanobacteria in the calibration and validation simulations. These have to be taken into account during evaluating the results from the model for the management of the lake. It is recommended that an improved predictive model based on the one presented in this paper is developed due to the explained limitations in the current model developed in this study. It is also suggested that such a model is incorporated into monitoring of the lake's status for a better-informed decision-making in the management of Lake Fure in the future.