A knowledge-based approach to modelling fast response catchments
This thesis describes research in to flood forecasting on rapid response catchments,
using knowledge based principles. Extensive use was made of high resolution single
site radar data from the radar site at Hameldon Hill in North West England.
Actual storm events and synthetic precipitation data were used in an attempt to identify
'knowledge' of the rainfall - runoff process. Modelling was carried out with the use
of transfer functions, and an analysis is presented of the problems in using this type of
model in hydrological forecasting. A physically realisable' transfer function model is
outlined, and storm characteristics were analysed to establish information about model
tuning. The knowledge gained was built into a knowledge based system (KBS) to
enable real-time optimisation of model parameters.
A rainfall movement forecasting program was used to provide input to the system.
Forecasts using the KBS tuned parameters proved better than those from a naive
transfer function model in most cases. In order to further improve flow forecasts a
simple catchment wetness procedure was developed and included in the system, based
on antecedent precipitation index, using radar rainfall input.
A new method of intensity - duration - frequency analysis was developed using
distributed radar data at a 2Km by 2Km resolution. This allowed a new application of
return periods in real time, in assessing storm severity as it occurs. A catchment
transposition procedure was developed allowing subjective catchment placement
infront of an approaching event, to assess rainfall `risk', in terms of catchment
history, before the event reaches it.
A knowledge based approach, to work in real time, was found to be successful. The
main drawback is the initial procurement of knowledge, or information about
thresholds, linkages and relationships.