Journal of Conference Abstracts

Predicting Freshwater Critical Loads of Acidification at the Catchment Scale: An Empirical Model

M. Kernan (mkernan@geog.ac.ucl.uk), T. E. H. Allott (tallott@geog.ac.ucl.uk) &
R. W. Battarbee (rbattarb@geog.ac.ucl.uk)

Environmental Change Research Centre, Dept. of Geography, University College London,
26 Bedford Way, London, WC2H0AP, U.K.

Current applications of the critical loads concept are geared primarily towards targeting emission control strategies at national and international levels. In the UK, maps of critical loads of acidification for freshwaters are available at 10 km² resolution based on water chemistry from a single representative site in each grid square (CLAG, 1995). These maps do not take variations of water chemistry within mapping units into account and are therefore of limited use for application to non-mapped sites. Previous attempts to predict freshwater critical loads using nationally mapped data have employed grid based maps of freshwater sensitivity, land cover and soil critical load as surrogates for catchment characteristics (Hall et al., 1995; Kernan, 1995) This paper describes the development of an empirical statistical model which uses high resolution, catchment specific data to predict freshwater critical loads for catchments lacking the appropriate water chemistry information.

A calibration exercise using data from 78 catchments throughout Scotland is described. Water chemistry for each catchment has been determined and each catchment is characterised according to a number of attributes using a GIS approach. These include the proportion of different soil, geology, and land cover types together with catchment weighted values for soil chemical parameters. Additionally, a number of sensitivity classifications (Edmunds and Kinniburgh, 1986; Hornung et al., 1995) are also employed as predictors. Multivariate statistical analysis of these data shows clear relationships between catchment attributes and water chemistry and between water chemistry and the diatom, or baseline critical load (Battarbee et al., 1996). The key variables which explain most of the variation in critical load relate to soil, geology and land use within the catchment. Using these variables (as predictors) in a regression analysis critical load was predicted across a broad gradient of acid sensitivity (R²adj = c.0.8). The predictive power of the model is maintained when different combinations of explanatory variables are used. This accords the model a degree of flexibility in that model parameterisation can be geared towards availability of secondary data.

All the predictor variables derived can be scaled up so that national data sets can be used in model parameterisation. Consequently, given it's ability to differentiate between sensitive and non-sensitive sites, the model offers considerable scope for environmental managers to undertake national inventories of catchment sensitivity and specific assessments of individual catchments.

References

Battarbee, R.W., Allott, T.E.H., Juggins, S., Kreiser, A.M., Curtis, C. & Harriman, R, Ambio 25,366 369 (1996).

CLAG Freshwaters, Critical Loads and Acid Deposition for UK Freshwaters. Critical Loads Advisory Group for freshwaters, Report to the DoE (1995).

Edmunds, W.M & Kinniburgh, D.G., J. Royal Geol. Soc. Lon. 143, 707-720 (1986).

Hall, J., Wright, S.M., Sparks, T.H., Ullyet, J., Allott, T.E.H. & Hornung, M., Water, Air and Soil Pollution, 85, 2443-2448 (1995).

Hornung, M., Bull, K., Cresser, M., Ullyet, J., Hall, J.R., Langan, S., Loveland, P.J., & Wilson, M.J., Env. Pollution 87, 204-217.

Kernan, M., Water, Air and Soil Pollution, 85, 2479-2484 (1995).