Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443746
Title: The use of geostatistics for hydromorphological assessment in rivers
Author: Rivas Casado, Monica
Awarding Body: Cranfield University
Current Institution: Cranfield University
Date of Award: 2006
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Abstract:
Assessment of river rehabilitation and restoration projects, as well as the monitoring of morphological changes in rivers requires collection of hydromorphological parameter data (i.e. depth, velocity and substrate). Field data collection is highly time and cost consuming and thus, effective and efficient monitoring programmes need to be designed. Interpolation techniques are often used to predict values of the variables under study at non measured locations. In this way, it is not necessary to collect detailed data sets of information. The accuracy of these predictions depends upon (i)the method used for the interpolation and/or extrapolation procedure and (ii) the sampling strategy applied for the collection of data. Even though the design of effective sampling strategies are of crucial importance when applying interpolation techniques, little work has been developed to determine the most effective way to collect hydromorphological data for this purpose. This project aimed to define a set of guidelines for effective and efficient hydromorphological data collection in rivers and relate this to the type of river site that is being sampled and to the objective for which the data are being collected. The project is structured in three main sections: spatial problem, the scaling problem and the temporal problem. Spatial problem refers to the location and number of points that need to be collected. Scaling problems focus on the study of the river length that needs to be sampled to characterise the spatial variability of a river site, whilst temporal problems determine how often a river site needs to be sampled to characterise the temporal variability associated with changes in discharge. Intensive depth data sets have been collected at a total of 20 river sites. These data sets have been used to investigate the spatial, temporal and scaling problems through geostatistical theory.
Supervisor: White, Sue Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.443746  DOI: Not available
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