Publications des scientifiques de l'IRD

Renard B., Kochanek K., Lang M., Garavaglia F., Paquet E., Neppel L., Najib K., Carreau Julie, Arnaud P., Aubert Y., Borchi F., Soubeyroux J. M., Jourdain S., Veysseire J. M., Sauquet E., Cipriani T., Auffray A. (2013). Data-based comparison of frequency analysis methods : a general framework. Water Resources Research, 49 (2), p. 825-843. ISSN 0043-1397.

Titre du document
Data-based comparison of frequency analysis methods : a general framework
Année de publication
2013
Type de document
Article référencé dans le Web of Science WOS:000317828600011
Auteurs
Renard B., Kochanek K., Lang M., Garavaglia F., Paquet E., Neppel L., Najib K., Carreau Julie, Arnaud P., Aubert Y., Borchi F., Soubeyroux J. M., Jourdain S., Veysseire J. M., Sauquet E., Cipriani T., Auffray A.
Source
Water Resources Research, 2013, 49 (2), p. 825-843 ISSN 0043-1397
An abundance of methods have been developed over the years to perform the frequency analysis (FA) of extreme environmental variables. Although numerous comparisons between these methods have been implemented, no general comparison framework has been agreed upon so far. The objective of this paper is to build the foundation of a data-based comparison framework, which aims at complementing more standard comparison schemes based on Monte Carlo simulations or statistical testing. This framework is based on the following general principles: (i) emphasis is put on the predictive ability of competing FA implementations, rather than their sole descriptive ability measured by some goodness-of-fit criterion; (ii) predictive ability is quantified by means of reliability indices, describing the consistency between validation data (not used for calibration) and FA predictions; (iii) stability is also quantified, i. e., the ability of a FA implementation to yield similar estimates when calibration data change; and (iv) the necessity to subject uncertainty estimates to the same scrutiny as point estimates is recognized, and a practical approach based on the use of the predictive distribution is proposed for this purpose. This framework is then applied to a case study involving 364 gauging stations in France, where 10 FA implementations are compared. These implementations correspond to the local, regional, and local-regional estimation of Gumbel and generalized extreme value distributions. Results show that reliability and stability indices are able to reveal marked differences between FA implementations. Moreover, the case study also confirms that using the predictive distribution to indirectly scrutinize uncertainty estimates is a viable approach, with distinct FA implementations showing marked differences in the reliability of their uncertainty estimates. The proposed comparison framework therefore constitutes a valuable tool to compare the predictive reliability of competing FA implementations, along with the reliability of their uncertainty estimates.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020] ; Hydrologie [062]
Description Géographique
FRANCE
Localisation
Fonds IRD [F B010060846]
Identifiant IRD
fdi:010060846
Contact