The major benefit of geo-referenced exposure modelling tools is the provision of spatially distributed information on expected environmental concentrations. This allows for identifying local and regional concentration differences in the environment which facilitates the development of efficient mitigation strategies. Predicted substance concentrations in the environment are governed by emission rates and representation of the substances' transport and transformation processes on the one hand and by the description of the spatial environmental heterogeneity and temporal variability on the other hand.
The shape of river basins and streamflow variability within them is a product of physiographic and climatic factors like e. g. topography, land use, precipitation, or evapotranspiration. These factors are very variable in space and time. This heterogeneity in river basins may have an impact on surface water concentrations of various substances.
In this work a hydrological framework for geo-referenced exposure assessment in river networks has been developed which predominantly addresses spatial heterogeneity of river basins.
The theoretical background for parameterising a river network for the application of GREAT-ER (Geo-referenced Regional Exposure Assessment Tool for European Rivers) is elaborated and implemented.
Quantity of discharge, flow velocity of river water and depth of river bed have to be determined at any location in a river network for the representation of substance dilution, transport and degradation. Temporal variability is handled by a probabilistic approach which demands choice and parameterisation of probability distribution functions to describe the river network characteristics.
It is substantiated that discharge and its variation can be described by a lognormal probability distribution. This distribution can be parameterised by spatially distributed information on effective precipitation and specific low flow discharge from the German Hydrological Atlas. Geoprocessing methods are applied to couple information from these maps and the river network. Evaluation of discharge probability distributions by means of gauging data demonstrates good agreement.
River depth and flow velocity are estimated on the basis of spatially distributed river structure data and therefore account for actual river morphology more than former approaches do. A comparison with hitherto used flow velocity and depth estimation shows significant differences which trigger perceivable differences in surface water concentration estimates.
Identification of the sensitivity of hydrological parameters in terms of chemical fate estimation attaches importance to spatial explicit consideration of river networks. The main benefit of the presented methods is comprehensive incorporation of geo-referenced river basin characteristics into the data basis for the GREAT-ER model because this provides the basis for successful prediction of surface water concentrations by GREAT-ER.
| Identifer | oai:union.ndltd.org:uni-osnabrueck.de/oai:repositorium.ub.uni-osnabrueck.de:urn:nbn:de:gbv:700-201009306566 |
| Date | 30 September 2010 |
| Creators | Wissing, Jutta |
| Contributors | Prof. Dr. Michael Matthies, PD Dr. Michael Rode |
| Source Sets | Universität Osnabrück |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis |
| Format | application/pdf, application/zip |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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