PROGNOS: PREDICTING IN-LAKE RESPONSES TO CHANGE USING NEAR REAL TIME MODELS

Lakes and reservoirs are under continuous pressure from urbanization and agricultural intensification, and from changes in climate, including an increasing occurrence of extreme climatic events. These pressures can reduce water quality by promoting the occurrence of nuisance algal blooms and higher levels of dissolved organic carbon (DOC), two issues that can substantially increase the costs for water treatment. To monitor such changes in water quality, automated high frequency (HF) monitoring systems are increasingly being adopted for lake and reservoir management across Europe. These HF data are mostly used to provide near real time (NRT) information on the present lake state. An even more valuable tool for water management, however, would be to use HF data to run computer models that forecast the probability of a change in lake state in the coming weeks or months. In PROGNOS, we will develop an integrated approach that couples HF lake monitoring data to dynamic water quality models to forecast short-term changes in lake water quality. More

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Maintainer Pierson Donald
Last Updated August 27, 2019, 14:17 (UTC)
Created November 21, 2018, 10:00 (UTC)
Acronym PROGNOS
Project Coordinator Pierson Donald
Partner 1 Eleanor Jennings - Dundalk Institute of Technology - Ireland
Partner 2 Elvira de Eyto - Marine Institute - Ireland
Partner 3 Erik Jeppesen - Aarhus University - Denmark
Partner 4 Gideon Gal - Israel Oceanographic and Limnological Research - Israel