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Title: Assessment and modelling of water chemistry in a large catchment, River Dee, NE Scotland
Author: Wade, Andrew John
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 1999
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This thesis describes the water chemistry of the River Dee and its tributaries, and the potential water chemistry changes that may occur under acid deposition and land use change scenarios. Historic water quality and flow records were collated and supplemented with new water chemistry data. These data were analysed in relation to catchment geography and river flow using both mathematical modelling and novel, GIS based techniques. This analysis established the importance of diffuse inputs and highlighted differences between upland and lowland regions in the catchment. In headwater streams, different geological types create hydrochemical source areas that strongly influence stream chemistry whilst in lowland tributaries, agricultural sources are particularly important. In the upland region most major ions were diluted as flows increased, further emphasizing the influence of deeper geological sources on baseflow chemistry, but showing soilwater controls on stormflow composition. The headwaters, which drain predominantly acid rocks, are presently oligotrophic but threatened by the impact of acid deposition and land use change (re-afforestation). In some of the lowland tributaries, increased NO3-N concentrations have resulted from more intensive land management. The potential impacts of acid deposition and land use change were simulated in both upland and lowland catchments by considering existing and new models within a Functional Unit Network. For upland regions this consisted of developing a new, two component hydrochemical mixing model to simulate the spatial and flow-related variations in streamwater acidity. The mixing model was based on End Member Mixing Analysis (EMMA), and site specific end members (alkalinity and Ca) could be predicted from emergent catchment characteristics (soil and land use) using linear regression.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Acidity; Re-afforestation