Onderzoeksoutput

Welkom op het expertplatform!

Dit platform verschaft informatie en kennis omtrent de WL expertisedomeinen 'hydraulica en sediment', 'havens en waterwegen', 'waterbouwkundige constructies', 'waterbeheer' en 'kustbescherming' - gaande van WL medewerkers met hun expertise, het curriculum van deze instelling, tot publicaties, projecten, data (op termijn) en evenementen waarin het WL betrokken is.

Het WL onderschrijft het belang van "open access" voor de ontsluiting van haar onderzoeksresultaten. Lees er meer over in ons openaccessbeleid.

log in

• 
  People
• 
  Curriculum
• 
  Projects
• 
  Data sets
• 
  Publications
• 
  Events

[ meld een fout in dit record ]

mandje (0): toevoegen | toon

---

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

---

Trefwoord

Prediction > Flood forecasting

Author keywords

Hydrological modelling; Uncertainty decomposition

---

Auteurs		Top
Van Steenbergen, N. Willems, P. 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.

---

Alle informatie in het Integrated Marine Information System (IMIS) valt onder het VLIZ Privacy beleid

Top | Auteurs

IMIS is ontwikkeld en wordt gehost door het VLIZ.