Project Page Views: [ 725 ]
| Project Metadata Element | Details |
| Project Title | Predicting ecological status of unmonitored lakes based on relationships between status, hydrogeomorphological and landuse characteristics |
| Research Area | Water |
| Project Acronym | |
| Principal Investigator or Lead Irish Partner | Caroline Wynne |
| Lead Institution or Organisation | University of Dublin, Trinity College (TCD) |
| Lead Country | Ireland |
| Latitude, Longitude (of Lead Institution) | 53.34449, -6.25867 |
| Lead Funding Entity | Environmental Protection Agency |
| Approximate Project Start Date | 09/01/2012 |
| Approximate Project Finishing Date | 08/01/2014 |
| Project Website (if any) | |
| Links to other Web-based resources | |
| Project Keywords | Surface water; Nutrient loading; Eutrophication; Lake Ecological Status |
| Project Abstract | Surface waters in Ireland, and indeed across much of the world, are at risk from the effects of anthropogenic activities (Lucey 2009). Industry, agriculture and domestic waste and wastewater contribute to the eutrophication, acidification and physical degradation of rivers and lakes (Monteith et al. 2005; Schindler 2006; Boon et al. 2010). Critical to the management of such risks to surface waters is an increased understanding of and ability to predict the effects of anthropogenic disturbance on ecosystems and an acknowledgement of the importance of scale. The Water Framework Directive (WFD; 2000/60/EC) requires that both monitored and unmonitored water bodies be assigned ecological status. The goal of the proposed study is to predict the likely ecological status of unmonitored lakes based on relationships between ecological status, catchment landuse, population densities and hydromorphological characteristics. The current project will add to our understanding and ability to predict status by quantification of the relationships between hydrogeomorphological and landuse factors and ecological status and development of risk thresholds for significant landuse and chemical parameters, thus enabling the prediction of status in unmonitored lakes. |