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A Bayesian Decision Approach to Rainfall Thresholds Based Flood Warning : Volume 10, Issue 3 (07/06/2006)

By Martina, M. L. V.

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Book Id: WPLBN0004010084
Format Type: PDF Article :
File Size: Pages 14
Reproduction Date: 2015

Title: A Bayesian Decision Approach to Rainfall Thresholds Based Flood Warning : Volume 10, Issue 3 (07/06/2006)  
Author: Martina, M. L. V.
Volume: Vol. 10, Issue 3
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Libralon, A., Todini, E., & V. Martin, M. L. (2006). A Bayesian Decision Approach to Rainfall Thresholds Based Flood Warning : Volume 10, Issue 3 (07/06/2006). Retrieved from

Description: Dept. of Earth and Geo-Environmental Sciences, Univ. of Bologna, Piazza di Porta San Donato, 1, Bologna, 40126, Italy. Operational real time flood forecasting systems generally require a hydrological model to run in real time as well as a series of hydro-informatics tools to transform the flood forecast into relatively simple and clear messages to the decision makers involved in flood defense. The scope of this paper is to set forth the possibility of providing flood warnings at given river sections based on the direct comparison of the quantitative precipitation forecast with critical rainfall threshold values, without the need of an on-line real time forecasting system. This approach leads to an extremely simplified alert system to be used by non technical stakeholders and could also be used to supplement the traditional flood forecasting systems in case of system failures. The critical rainfall threshold values, incorporating the soil moisture initial conditions, result from statistical analyses using long hydrological time series combined with a Bayesian utility function minimization. In the paper, results of an application of the proposed methodology to the Sieve river, a tributary of the Arno river in Italy, are given to exemplify its practical applicability.

A Bayesian decision approach to rainfall thresholds based flood warning


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