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.