More reliable forecasts regarding algal bloom in lakes

17 April 2008

One method of reducing algal blooms in eutrophied lakes is to build purification plants or manure pits nearby. In order to estimate whether this is worthwhile you need reliable forecasting models that will tell you how the intensity of the algal blooms would be affected. Andreas Bryhn has studied how the forecasts have improved in recent years.

Eutrophication of lakes is chiefly caused by sewage emissions and run-off from agricultural areas. In many places eutrophication has gradually decreased, thanks to improved purification technology and the fact that it is now increasingly possible to determine what is causing the eutrophication and why lakes are reacting differently to environmental disturbances. In this context algal bloom forecasting models have played a major role, as they have facilitated assessment of how the ecosystem will react to various planned measures against eutrophication.

In his thesis Andreas Bryhn of the Department of Earth Sciences has developed a forecasting model that can be applied to a large number of lakes with varying appearances and environmental conditions, from northern Lapland to central Florida.

"The only thing you need to change for each lake is easily available data such as the area of the lake, maximum depth, average depth, precipitation and latitude. At approx. 17 per cent, the unreliability of the model is relatively low, regardless of the type of lake it is applied to," he says.

The forecasting model is intended for use in practical environmental work. Many lakes have warmed up as a result of climate changes, and this development will probably continue.

"We largely know how temperature increases affect algal blooms, which means it is also possible to forecast how future climate changes will affect eutrophication," says Andreas Bryhn.

Another area of use is the EU's water directive, which requires 'good' water quality, i.e. limited human influence, in lakes. In this context forecasting models can be used as tools, partly to determine what 'good' water quality entails regarding algal content, and partly to determine what measures are the most cost-effective for attainment of such water quality.

Read the thesis at