Project Page Views: [ 679 ]
| Project Metadata Element | Details |
| Project Title | Predicting in-lake responses to change using real time models |
| Research Area | Water |
| Project Acronym | PROGNOS |
| Principal Investigator or Lead Irish Partner | Eleanor Jennings |
| Lead Institution or Organisation | Dundalk Institute of Technology |
| Lead Country | Ireland |
| Latitude, Longitude (of Lead Institution) | 53.9835294,-6.3975772 |
| Lead Funding Entity | Environmental Protection Agency |
| Approximate Project Start Date | 16/05/2016 |
| Approximate Project Finishing Date | 14/05/2019 |
| Project Website (if any) | http://prognoswater.org/ |
| Links to other Web-based resources | Not Applicable |
| Project Keywords | Lakes; cultural eutrophication; climate change; algal blooms; dissolved inorganic matter; water treatment; high frequency monitoring; Burrishoole; forecasting; water management; |
| Project Abstract | Lakes in Ireland and across Europe are under pressures from cultural eutrophication, and changes in climate, including increases in the occurrence of extreme events. These can reduce water quality through, for example, promoting nuisance algal blooms, or higher levels of dissolved organic matter (DOM), and increase costs of water treatment. Increasingly, automated high frequency monitoring (HFM) systems are being adopted for water management across Europe. In Ireland, the Burrishoole catchment in Mayo includes two in-situ HFM systems on Loughs Feeagh and Furnace, and three river systems, and now has a unique >10 year data archive on key parameters. The PROGNOS project will develop an integrated approach that couples HFM data to dynamic models to forecast short-term changes in lake state, and inform management decisions to safeguard lake ecosystem services. The Irish partners and project consortium includes expertise from sites at the forefront of HFM monitoring systems since the late 1990s, expertise in modelling, and expertise in assessing societal benefits. They will use these resources to develop short-term water quality forecasts that can be based on weather forecast input, and long-term probability forecasts based forecasts from climatology inputs. |