IMIS

A new integrated search interface will become available in the next phase of marineinfo.org.
For the time being, please use IMIS to search available data

 

[ report an error in this record ]basket (0): add | show Print this page
Quantification of rainfall forecast uncertainty and its impact on flood forecasting
Van Steenbergen, N.; Willems, P. (2014). Quantification of rainfall forecast uncertainty and its impact on flood forecasting, in: 11th International Conference on Hydroinformatics - Informatics and the Environment: Data and Model Integration in a Heterogeneous Hydro World, New York, USA, August 17-21, 2014. pp. [1-8]
In: (2014). 11th International Conference on Hydroinformatics - Informatics and the Environment: Data and Model Integration in a Heterogeneous Hydro World, New York, USA, August 17-21, 2014. [S.n.]: [s.l.]. , more
Available in  Authors 
Document type: Conference paper
Authors  Top 
  • Van Steenbergen, N.
  • Willems, P., more
Abstract
    Rainfall forecast errors are considered to be the key source of uncertainty in flood forecasting. To quantify the rainfall forecast uncertainty itself and its impact on the total flood forecast uncertainty, a Monte-Carlo based statistical method has been developed. This method takes into account the dependency of the rainfall forecast error with the lead time and the rainfall amount. The forecasted rainfall errors are described by truncated normal distributions, allowing to quantify the full uncertainty distribution of the deterministic rainfall forecast. By means of Monte-Carlo sampling and taking the forecast error autocorrelation into account, the impact of the rainfall forecast uncertainty on a flood forecast was quantified. This was done for the Rivierbeek river in Belgium. In addition, comparison was made between the total flood forecast uncertainty and the uncertainty due to the forecasted rainfall. The total flood forecast uncertainty was quantified by a non-parametric data-based approach. It was concluded that the forecasted rainfall uncertainty contributes for about 30 percent to the total flood forecast uncertainty.
All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy Top | Authors