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Title: Methods for rainfall-runoff continuous simulation and flood frequency estimation on an ungauged river catchment with uncertainty
Author: Wood, Andrew Charles
ISNI:       0000 0004 2713 2708
Awarding Body: Lancaster University
Current Institution: Lancaster University
Date of Award: 2010
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Historic methods for time series predictions on ungauged sites in the UK have tended to focus on the regionalisation and regression of model parameters against catchment characteristics. Owing to wide variations in catchment characteristics and the (often) poor identification of model parameters, this has resulted in highly uncertain predictions on the ungauged site. However, only very few studies have sought to assess uncertainties in the predicted hydrograph. Methods from the UK Flood Estimation Handbook, that are normally applied for an event design hydrograph, are adopted to choose a pooling group of hydrologically similar gauged catchments to an ungauged application site on the River Tyne. Model simulations are derived for each pooling group catchment with a BETA rainfall-runoff model structure conditioned for the catchment. The BETA rainfall-runoff model simulations are developed using a Monte Carlo approach. For the estimation of uncertainty a modification of the GLUE methodology is applied. Gauging station errors are used to develop limits of acceptability for selecting behavioural model simulations and the final uncertainty limits are obtained with a set of performance thresholds. Prediction limits are derived from a set of calibration and validation simulations for each catchment. Methods are investigated for the carry over of data from the pooled group of models to the ungauged site to develop a weighted model set prediction with pooled prediction limits. Further development of this methodology may offer some interesting approaches for cross-validation of models and further improvements in uncertainty estimation in hydrological regionalisation.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available