PROGNOS
| PROGNOS: PREDICTING IN-LAKE RESPONSES TO CHANGE USING NEAR REAL TIME MODELS | |||||||||
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Project presentation: PROGNOS |
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Coordinator: Don Pierson - Department of Limnology, Institute of Ecology and Genetics, Uppsala University, Sweden | ||||||||
| Projects Partner and Institution:
Eleanor Jennings, Centre for Freshwater and environmental Studies, Dundalk Technology, Ireland |
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Key words: Water Management , Water Supply, Modelling, Water Monitoring, Algal Blooms, DOC |
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Abstract: 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. This will potentially provide a greater window of opportunity over which to make water quality management decisions, and will increase the value of HF monitoring data, ensuring that their potential to guide water quality management is fully realized. This project will promote innovative solutions for water-related challenges across Europe. It will develop, demonstrate and disseminate forecast based adaptive management solutions for two specific water quality threats: nuisance algal blooms and the production disinfection by-products from DOC. The technology demonstrated here has the potential to transform water management and foster the growth of European companies that specialize in adaptive water management and water quality forecasting systems. The project consortium includes expertise from European sites that have been involved in the forefront of HF monitoring systems since the late 1990s, expertise in modelling algal blooms and DOC levels, and expertise in assessing societal benefits from changes in water management. |
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Project structure: |
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Implementation: WP1 project management and reporting; WP2 Data harmonization, QA/QC, monitoring station maintenance; WP3 Identification of case studies, data archiving and reporting; WP4 Model development, model testing and calibration, development of model automation, testing of forecasting; WP5 Collection of data on costs of forecasting system and water treatment, cost benefit analysis; WP6 Web and social media dissemination, outreach to water management authorities. |
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Outcome/deliverables: Demonstrations of new modelling systems that allow NRT forecasting for the water resources sector; A cost benefit analysis on the use of these tools focused on specific case studies; Outreach to the water management sector i.e. policy briefs, workshops etc.; Peer reviewed publications and conference proceedings; Archived data from case studies; project developed computer code. |
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References coordinator and leaders of each WP: |
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Contact Point for Communication/Dissemination activities: Eleanor Jennings |
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Contact Point for Open Data/Open Access activities: Don Pierson |
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Results of the project: Data and resources
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