The PROGNOS project has six work packages (WPs). WP 1 deals with project management and is run by the consortium coordinator and a management committee that includes all WP coordinators. WPs 2, 3 and 4 deal with data analysis and modelling: WP 2 focuses on high frequency data collection and collation; WP 3 focuses on case study selection; and WP 4 focuses on model development, testing and forecast system development. WP 5 will produce an evaluation of the economic cost/benefit of the completed forecast system at selected demonstration sites, focusing on drinking water and recreational uses. Finally, WP 6 carries out dissemination of project goals, results and the final deliverables.
 

Leader: Elvira de Eyto (MI)

This work package will ensure that high frequency monitoring (HFM) data from five study sites are collected, collated, quality controlled and made available to the project consortium, via four tasks:
  • WT2.1 Archived data will be collated, cleaned and formatted appropriately for initial model development and testing to be carried out. This data will include HFM lake data as well as low frequency and HFM catchment and meteorological data.
  • WT2.2 The maintenance of monitoring stations along with associated calibrations and data cleaning will continue for the lifetime of the project.
  • WT2.3 Scripts to enable automated QA/QC of near real time data will be developed to address, for example, data gaps and removal of outliers.
  • WT2.4 an IT infrastructure to enable the project participants to access these data in near real time, subsequent to automated QA/QC will be developed

Leader: Eric Jeppesen (AU)

In this WP, the extensive data archives provided through WP2 from the lake high frequency monitoring systems in Ireland, Norway, Sweden, and Israel, together with the data from the experimental site in Denmark, will be mined to inform the modelling and cost benefit analysis work packages, and to produce high quality outputs describing the effects of both algal blooms, high dissolved organic matter (DOM), high nutrient and heatwave events on lake ecosystems for both the research and water resources management communities. The case studies will focus on:
  • DOM and climate change interactions, and
  • nutrient-climate change interactions and risk of algal (cyanobacterial) blooms.

On-going work at the highly controlled experimental site in Denmark manipulates nutrient-climate warming interactions including heatwaves, and also deals with effects of extreme runoff of DOM at contrasting nutrient levels and climate. These data are a unique resource to the project.

Leader: Gideon Gal (IOLR)

This WP will aim to integrate the near real time HFM data products from WP2 with available long-term data and ecosystem models in order to produce reliable short and long-term simulations. Organization and running of the models will be conducted within the state of the art Framework for Aquatic Biogeochemical Models (FABM). The benefit of this is that it allows the user to automate tasks that would otherwise render model comparison and coupling impractical. These include coupling different hydrodynamic models with user-selected ecological models, simplified reading of meteorological forcing, and physicochemical profiler data to be assimilated, and execution of the model with data assimilation during runtime. The project will use GOTM and MyLake as physical driver models coupled with FABM. The products will include short-term ecosystem simulations, with a focus on phytoplankton blooms and dissolved organic matter (DOM), in an attempt to simulate into the immediate future (week-10 days) given the available weather forcing data and forecasting uncertainty.


We will further attempt to assimilate the near real time data into the model runs in order to improve calibration and performance. In addition, we will endeavor to extend the simulations beyond the range of the available short-terms data forecasts, by providing an envelope of probable future conditions (weeks-months scale), based on historical data. A key product will be short-term (days) forecasting followed by diverging long-term (weeks-months) probable forecasts based on possible future conditions. Completion of long-term forecasting is dependent on the achievement of the short-term forecasting.

Leader: Isabel Seifert-Dähnn (NIVA)

This WP will use a cost-benefit approach to contrast the costs for the installation, maintenance and continuous usage of a HF lake monitoring and forecasting system with the benefits derived from it. Our focus is on the benefits for drinking water provision and recreational use. We will follow the best-practice approach suggested by the European Commission for cost-benefit assessment of investment projects. Drinking water treatment plants will be evaluated to assess their ability for further exploiting already existing monitoring data by processing it into lake water quality forecasts. The work is divided into two main tasks:
  • WT5.1 Cost data for HF monitoring and modelling will be gathered from ongoing monitoring activities at demonstration sites, reflecting the range of technologies used (buoys, fixed monitoring installations, data transmission and handling, forecasting software, dissemination, etc.). Even though not all case-study lakes are used for recreational purposes or as drinking water sources, it is possible to deduce cost estimates from them, and apply to similar relevant cases. If necessary, the work will be supplemented by available literature data.
  • WT5.2 To assess the benefits of HF monitoring and of water quality forecasting for recreational purposes, we propose to apply a benefit-transfer approach. This approach does not require new data; it instead uses data from existing surveys on recreation and water quality available at our respective institutes and in the literature. Benefits for drinking water provision will be determined together with the end-users involved in the project.