Over het archief
Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.
Dit archief wordt uitgebouwd en beheerd volgens de principes van de Open Access Movement, en het daaruit ontstane Open Archives Initiative.
Basisinformatie over ‘Open Access to scholarly information'.
Identification of uncertainty in sources in flood forecasting
Van Steenbergen, N.; Willems, P.; Deschamps, M. (2013). Identification of uncertainty in sources in flood forecasting, in: Proceedings of the international conference on flood resilience: experiences in Asia and Europe, 5-7 September 2013, Exeter, United Kingdom. pp. [1-8]
In: (2013). Proceedings of the international conference on flood resilience: experiences in Asia and Europe, 5-7 September 2013, Exeter, United Kingdom. University of Exeter: Exeter. ISBN 978-0-9926529-0-6, meer
|
| Trefwoord |
Prediction > Flood forecasting
|
| Author keywords |
Hydrological modelling; Uncertainty decomposition |
| Auteurs | | Top |
- Van Steenbergen, N.
- Willems, P., meer
- Deschamps, M., meer
|
|
|
| Abstract |
An essential factor to be taken into account in river flood forecasting is uncertainty. Each flood forecast is subject to different sources of uncertainty. Understanding the relative importance of the different sources of uncertainty could significantly increase the effectiveness of improvement actions in the flood forecasting system. This research considers three key uncertainty sources for hydrological flood forecasting: model uncertainty, forecasted rainfall uncertainty and uncertainty in the initial conditions of the modelrun. A non-parametric data-based approach is applied to quantify the total flood forecast uncertainty. By resimulation of the historical forecasts with optimal initial conditions and rainfall observations, the importance of these uncertainty sources is identified. The methodology is applied to the catchment of the Rivierbeek, Belgium. It was found that the most important source of uncertainty for the considered case study was the model uncertainty. Therefore model recalibration was suggested as a first action to be taken in order to reduce the forecast uncertainty. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
