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JPIWATER-Water challenges for a changing world

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

Awarded: NOK 3.7 mill.

Project Manager:

Project Number:

258142

Application Type:

Project Period:

2016 - 2020

Location:

Partner countries:

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. In PROGNOS, we are developing an integrated approach that couples high frequency (HF) lake monitoring data to dynamic water quality models to forecast short-term changes in these two specific water quality threats. 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 realised. 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. After 1 year in the project we have prepared lake models able to predict lake temperature every hour as a function of weather and inflowing water quality. We have adapted an automated-calibration routine at each site. We are now working on modeling the biology and chemistry, namely DOC and phytoplankton blooms. After 2 years in the project we have automated data gathering form online sources to be used as drivers for the models and have gone a way into establishing the computational architecture to allow real time forecasting of biogeochemical processes. At the end of the project, we have developed a carbon model as well as a lake-ice model and performed and economic analysis of the benefits of high-frequency monitoring.

-Collection and quality control of high-frequency monitoring data was achieved. -A Dissolved Organic Carbon Module for the FABM framework was developed and tested -Collection and quality control of meteorological forcing data was automated -A methodology for cost-benefit analysis was developed and tested -A framework for the automated calibration and real-time forecast of lake carbon processes was developed. All code is open source.

In order to achieve the objectives of the project, the following objectives will be met: -> Demonstration sites have been identified in each partner country. Data from the sensor networks already operating at these sites will be placed in a harmonized format and made available in near-real-time. -> Commonly used water quality algorithms used to predict algal and dissolved organic matter concentrations will be incorporated into the Framework for Aquatic Biogeochemical Models (FABM) so these can be consistently run within a common model framework at all sites. Site specific calibrations will be made for all models. -> Methods will be developed to assimilate the recent records of sensor based water quality, short term weather forecast and longer term climatology data into FABM. -> Short-term water quality forecasts will be produced based on weather forecast input, and long-term probability based forecasts from climatology inputs. -> An economic cost benefit analysis comparing the costs of installing, maintaining, and operating the forecasting system with the benefits derived from it will be prepared. -> We will disseminate information about the technologies developed, and the benefits that can be derived from the forecast system. Target audiences will include the local government agencies involved in water-quality regulation, drinking-water utilities and the general public.

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Funding scheme:

JPIWATER-Water challenges for a changing world