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Inverse modelling of diffuse pollution sources in the River Eden catchment

By PATRICK CHRISTOPHER BUCKLEY

Abstract

The UK has an obligation for its waters to meet the minimum standards as set out in the Water Framework Directive legislation by 2015. In recent years tighter controls on pollutants from point sources has led to diuse sources (i.e. agricultural) having a greater contribution to degradation in water quality.\ud \ud The Catchment Sensitive Farming program has been set up to attempt to advise and support landowners and farmers with various land mangement techniques which can be applied to rural areas to mitigate against some of the contributions that agricultural activities have to poor water quality.\ud \ud In order for any such measures to be either cost-eective or successful at improving water quality they must be applied in suitable areas of a catchment. This research takes the River Eden catchment in Cumbria as a case study and uses mathematical modelling of measured low resolution field nutrient data together with high-resolution quasi-continuous discharge data to drive a reduced complexity \ud diuse pollution modelling framework (SCIMAP) to identify the areas most likely to be causing water quality\ud problems.\ud \ud Results of inverse modelling showed arable land was a particular risky land use within the Eden catchment. Several areas (mainly surrounding the River Eden in the lower reaches) within the catchment were identied as being the most likely to be causing water quality problems. As a form of control the SCIMAP model was run with logical\ud risk values assigned to diferent landuses as well as those derived from inverse modelling of nutrient data. The model outputs driven by the statistically improved data were very\ud similar to those which were driven by a priori judgment.\ud Several conclusions were drawn; (1) the SCIMAP model run driven by a very simple dataset based on nationally available data produced similar results to an identical model run driven by a large nutrient and discharge dataset, suggesting that the process of identifying risky areas to further examine within a catchment can be completed relatively easily (in terms of data availability), and (2) even low infrequent nutrient data can capture enough information when combined with continuous discharge data to be used in the SCIMAP model

Topics: SCIMAP, inverse modelling, diffuse agricultural pollution, CSA, CSF, DEFRA, water quality, hydrology.
Year: 2010
OAI identifier: oai:etheses.dur.ac.uk:278
Provided by: Durham e-Theses

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