Pour savoir comment effectuer et gérer un dépôt de document, consultez le « Guide abrégé – Dépôt de documents » sur le site Web de la Bibliothèque. Pour toute question, écrivez à corpus@ulaval.ca.
 

Publication :
How does the quantification of uncertainties affects the quality and value of flood early warning systems?

En cours de chargement...
Vignette d'image

Date

2017-05-15

Auteurs

Direction de publication

Direction de recherche

Titre de la revue

ISSN de la revue

Titre du volume

Éditeur

Elsevier

Projets de recherche

Structures organisationnelles

Numéro de revue

Résumé

In an operational context, efficient decision-making is usually the ultimate objective of hydrometeorological forecasts. Because of the uncertainties that lay within the forecasting process, decisions are subject to uncertainty. A better quantification of uncertainties should provide better decisions, which often translate into optimal use and economic value of the forecasts. Six Early Warning Systems (EWS) based on contrasted forecasting systems are constructed to investigate how the quantification of uncertainties affects the quality of a decision. These systems differ by the location of the sources of uncertainty, and the total amount of uncertainty they take into account in the forecasting process. They are assessed with the Relative Economic Value (REV), which is a flexible measure to quantify the potential economic benefits of an EWS. The results show that all systems provide a gain over the case where no EWS is used. The most complex systems, i.e. those that consider more sources of uncertainty in the forecasting process, are those that showed the most reduced expected damages. Systems with better accuracy and reliability are generally the ones with higher REV, even though our analysis did not show a clear-cut relationship between overall forecast quality and REV in the context investigated.

Description

Revue

Journal of Hydrology, Vol. 551, 365–373 (2017)

DOI

10.1016/j.jhydrol.2017.05.014

URL vers la version publiée

Mots-clés

Forecast quality, Relative economic value, Uncertainty estimation, Ensemble prediction, Data assimilation, Ensemble Kalman Filter, Multimodel

Citation

Licence CC

Type de document