uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Data assimilation and adaptive forecasting of water levels in the river Severn catchment, United Kingdom
Institute of Environmental and Natural Sciences, Lancaster University, Lancaster, UK.
2006 (English)In: Water resources research, ISSN 0043-1397, E-ISSN 1944-7973, Vol. 42, no 6, W06407- p.Article in journal (Refereed) Published
Abstract [en]

[ 1] This paper describes data assimilation (DA) and adaptive forecasting techniques for flood forecasting and their application to forecasting water levels at various locations along a 120 km reach of the river Severn, United Kingdom. The methodology exploits the top-down, data-based mechanistic (DBM) approach to the modeling of environmental processes, concentrating on the identification and estimation of those "dominant modes'' of dynamic behavior that are most important for flood prediction. In particular, hydrological processes active in the catchment are modeled using the state-dependent parameter ( SDP) method of estimating a nonlinear, effective rainfall transformation together with a linear stochastic transfer function (STF) method for characterizing both the effective rainfall - river level behavior and the river level routing processes. The complete model consists of these lumped parameter, linear and nonlinear stochastic, dynamic elements connected in a quasi-distributed manner that represents the physical structure of the catchment. The adaptive forecasting system then utilizes a state-space form of the complete catchment model, including allowance for heteroscedasticity in the errors, as the basis for data assimilation and forecasting using a Kalman filter forecasting engine. Here the predicted model states ( water levels) and adaptive parameters are updated recursively in response to input data received in real time from sensors in the catchment. Direct water level forecasting is considered, rather than flow, because this removes the need to transform the level measurement through the rating curve and tends to decrease the forecasting errors.

Place, publisher, year, edition, pages
2006. Vol. 42, no 6, W06407- p.
Keyword [en]
data-based mechanistic, flood forecasting, Kalman filter, recursive estimation
National Category
Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:uu:diva-140445DOI: 10.1029/2005WR004373ISI: 000238574700003OAI: oai:DiVA.org:uu-140445DiVA: diva2:383651
Available from: 2011-01-05 Created: 2011-01-05 Last updated: 2017-12-11Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Authority records BETA

Beven, Keith J

Search in DiVA

By author/editor
Beven, Keith J
In the same journal
Water resources research
Earth and Related Environmental Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 402 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf