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A data based mechanistic approach to nonlinear flood routing and adaptive flood level forecasting
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
2008 (English)In: Advances in Water Resources, ISSN 0309-1708, E-ISSN 1872-9657, Vol. 31, no 8, 1048-1056 p.Article in journal (Refereed) Published
Abstract [en]

Operational flood forecasting requires accurate forecasts with a suitable lead time, in order to be able to issue appropriate warnings and take appropriate emergency actions. Recent improvements in both flood plain characterization and computational capabilities have made the use of distributed flood inundation models more common. However, problems remain with the application of such models. There are still uncertainties associated with the identifiability of parameters, with the computational burden of calculating distributed estimates of predictive uncertainty, and with the adaptive use of such models for operational, real-time flood inundation forecasting. Moreover, the application of distributed models is complex, costly and requires high degrees of skill. This paper presents an alternative to distributed inundation models for real-time flood forecasting that provides fast and accurate, medium to short-term forecasts. The Data Based Mechanistic (DBM) methodology exploits a State Dependent Parameter (SDP) modelling approach to derive a nonlinear dependence between the water levels measured at gauging stations along the river. The transformation of water levels depends on the relative geometry of the channel cross-sections, without the need to apply rating curve transformations to the discharge. The relationship obtained is used to transform water levels as an input to a linear, on-line, real-time and adaptive stochastic DBM model. The approach provides an estimate of the prediction uncertainties, including allowing for heterescadasticity of the multi-step-ahead forecasting errors. The approach is illustrated using an 80 km reach of the River Severn, in the UK.

Place, publisher, year, edition, pages
2008. Vol. 31, no 8, 1048-1056 p.
Keyword [en]
flood forecasting, state dependent parameter, on-line data assimilation, uncertainty
National Category
Earth and Related Environmental Sciences
URN: urn:nbn:se:uu:diva-109153DOI: 10.1016/j.advwatres.2008.04.015ISI: 000258390300003OAI: oai:DiVA.org:uu-109153DiVA: diva2:249008
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2011-01-10Bibliographically approved

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