Using stochastic state-space modelling to specify and reduce hydrograph error

Collier, CG and Robbins, GL 2007, 'Using stochastic state-space modelling to specify and reduce hydrograph error' , Hydrological Processes, 22 (8) , pp. 1173-1179.

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A Bayesian post-processor is used to generate a representation of the likely hydrograph forecast flow error distribution using raingauge and radar input to a stochastic catchment model and its deterministic equivalent. A hydrograph ensemble is so constructed. Experiments are analysed using the model applied to the River Croal in north-west England. It is found that for rainfall input to the model having errors less than 3mm h-1, corresponding to about a 15% error in peak flow, the stochastic model outperforms the deterministic model. The range of hydrographs associated with the different model simulations and the measured hydrographs are compared. The significant improvement possible using a stochastic approach is demonstrated for a specific case study, although the mean hydrograph derived using the stochastic model has an error range associated with it.

Item Type: Article
Themes: Subjects / Themes > T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication (Inc. www, lidar, radio, radar, phone, TV)
Subjects / Themes > Q Science > QC Physics > QC851-999 Meteorology - Climatology
Subjects / Themes > G Geography. Anthropology. Recreation > GE Environmental Sciences
Subjects / Themes > Q Science > QC Physics > QC221-246 Acoustics - Sound
Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science > QA076 Computer software
Subjects outside of the University Themes
Schools: Schools > School of the Built Environment
Journal or Publication Title: Hydrological Processes
Publisher: John Wiley & Sons
Refereed: Yes
ISSN: 1099-1085
Depositing User: H Kenna
Date Deposited: 24 Sep 2007 10:06
Last Modified: 27 Aug 2021 22:03

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