Linking catchment characteristics and water chemistry with the ecological status of Irish rivers.

Requirements of the EU Water Framework Directive for the introduction of ecological quality objectives for surface waters and the stipulation that all surface waters in the EU must be of 'good' ecological status by 2015 necessitate a quantitative understanding of the linkages among catchment attributes, water chemistry and the ecological status of aquatic ecosystems. Analysis of lotic ecological status, as indicated by an established biotic index based primarily on benthic macroinvertebrate community structure, of 797 hydrologically independent river sites located throughout Ireland showed highly significant inverse associations between the ecological status of rivers and measures of catchment urbanisation and agricultural intensity, densities of humans and cattle and chemical indicators of water quality. Stepwise logistic regression suggested that urbanisation, arable farming and extent of pasturelands are the principal factors impacting on the ecological status of streams and rivers in Ireland and that the likelihood of a river site complying with the demands of the EU Water Framework Directive, and be of 'good' ecological status, can be predicted with reasonable accuracy using simple models that utilise either widely available landcover data or chemical monitoring data. Non-linear landcover and chemical 'thresholds' derived from these models provide a useful tool in the management of risk in catchments, and suggest strongly that more careful planning of land use in Ireland is essential in order to restore and maintain water quality as required by the Directive.

Developing an optimal river typology for biological elements within the Water Framework Directive.

The Water Framework Directive requires a river classification based on environmental variables (a typology) to be created as a structure for reporting ecological status. A single permutation procedure, utilising the same variables repeatedly but with different categorical divisions, enabled both the choice of variables and the boundary divisions for these variables to be optimised simultaneously in the development of the typology. This, in addition to a data set which appropriately combined different biological elements, enabled a typology to be developed which was far more effective than a System A, CCA-derived or expert opinion-based typology in segregating communities. This optimal typology could be used to improve the performance of ecological quality assessment methods.

Modeling phosphorus concentrations in Irish rivers using land use, soil type, and soil phosphorus data.

Modeling diffuse phosphorus (P) loss may indicate management strategies to minimize P loss from agricultural sources. An empirical model predicting flow-weighted phosphorus concentrations (MRP) was derived using data collected from 35 Irish river catchments. Monitoring records of riverine P and stream flow data were used to calculate MRP values averaged for the years 1991-1994. These data were modeled using land use, soil type, and soil P data. Soil type in catchments was described using soil survey classifications weighted according to their P desorption properties from laboratory results. Soil test P concentrations for the studied watersheds were obtained from a national database. Soil P levels were weighted based on the results of field experiments measuring P losses in overland flow from fields at different soil test P levels. The 35 catchments were statistically clustered into two populations (A and B) based on differences in soil type, specifically, soil hydrology. Catchments in Cluster A had predominantly poorly drained soils and comparatively higher MRP concentrations (0.03-0.17 mg L(-1)) than Cluster B areas (0.01-0.7 mg L(-1)) with mostly well-drained soils. Regression equations derived for A and B type catchments predicted MRP values with 68 and 62% of the variation explained in the models, respectively. Data extracted for the rest of the country were applied to the models to delineate areas at risk on a national scale. While the models were only moderately accurate they highlighted the influence of land management, specifically, high production grassland receiving high P inputs, in conjunction with the effect of soil type and soil hydrology on the transport of P to surface waters.