Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298825
Title: Predicting surface water critical loads at the catchment scale
Author: Kernan, Martin Richard
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 1998
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Abstract:
Current applications of the critical loads concept are geared primarily towards targeting emission control strategies at a national and international level. In the UK maps of critical loads for freshwaters are available at 10km2 resolution based on a single representative site in each grid square. 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. This thesis describes the development of an empirical statistical model, which uses nationally available secondary 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. Multivariate statistical analysis of these data shows clear relationships between catchment attributes and water chemistry and between water chemistry and diatom critical load. 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 diatom critical load could be predicted across a broad gradient of sensitivity (R2adj = c. 0.8). The predictive power of the model was maintained when different combinations of explanatory variables were used. This accords the model a degree of flexibility in that model paramaterisation can be geared towards availability of secondary data. There are limitations with the model. These relate to the nature of the predictor variables and the ability of the model to predict critical loads for more sensitive sites. Nevertheless the ability of the model to differentiate between sensitive and non-sensitive sites offers considerable scope for environmental managers to undertake national inventories of catchment sensitivity and specific assessments of individual catchments.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.298825  DOI: Not available
Keywords: Water chemistry; Freshwater Hydrology Water Pollution Water Pollution Sewage
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