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Forecasting river levels during flash floods using Data Based Mechanistic models, online data assimilation and meteorological forecasts.

Research output: Contribution to conferenceConference paper

Published

Publication date07/2010
Number of pages0
Original languageEnglish

Conference

ConferenceBritish Hydrological Society International symposium
CountryUnited Kingdom
CityNewcastle
Period1/07/10 → …

Abstract

The parsimonious time series models used within the Data-Based Mechanistic (DBM) modelling framework are readily transferred into a State-Space form allowing the implementation of data assimilation using the Kalman filter. Multiple case studies have demonstrated the effectiveness of this framework in providing probabilistic forecasts for many hydrological situations, such as flood events on large UK rivers. The recent work presented here has applied the DBM methodology to forecast floods in catchments with a rapid response to rainfall. The resulting DBM models which, amongst other adaptations, utilise meteorological predictions to increase the forecast horizon are demonstrated using a European case study.

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