Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.560684
Title: Improved rainfall downscaling for real-time urban pluvial flood forecasting
Author: Wang, Li-Pen
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2012
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Abstract:
Traditionally, hydrologists had a relatively minor role in rainfall data processing; they usually simply took data from meteorologists. However, meteorological organisations usually provide weather service over a larger area and scale (i.e. country level); the applicability of this large-scale information to urban hydrological applications is therefore questionable. This work tries to provide a local view on rainfall processing, aiming to improve the suitability (in terms of accuracy and resolution) of operational rainfall data for urban hydrological uses. This work explores advanced downscaling and adjustment techniques to address the identified issues in urban hydrology: accuracy and resolution. On the basis of a a review and the testing of state of the art techniques, the Bayesian-based adjustment technique and the newly-developed cascade-based downscaling techniques are found to be suitable tools to improve respectively the accuracy, and the resolution of operational radar (and raingauge) rainfall estimates. In addition, a combined application of these two techniques is tested; the results suggested that, although extra uncertainty may appear, this combination demonstrates a clear potential for providing accurate and high-resolution (street-scale and 5-min) rainfall estimates.
Supervisor: Maksimovic, Cedo ; Onof, Christian Sponsor: Ministry of Education Republic of China (Taiwan)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.560684  DOI: Not available
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