Global climate change is a controversial issue facing researchers and climatologists today. In order to obtain the most reliable results when studying climate change, the data being analysed must be as homogeneous as possible. A homogeneous time series is one in which trends and variations are cause
Multivariate homogeneity testing in a northern case study in the province of Quebec, Canada
β Scribed by Fateh Chebana; Taha B. M. J. Ouarda; Pierre Bruneau; Marc Barbet; Salaheddine El Adlouni; Marco Latraverse
- Publisher
- John Wiley and Sons
- Year
- 2009
- Tongue
- English
- Weight
- 490 KB
- Volume
- 23
- Category
- Article
- ISSN
- 0885-6087
- DOI
- 10.1002/hyp.7304
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β¦ Synopsis
Abstract
In regional frequency analysis, the examination of the regional homogeneity represents an important step of the procedure. Flood events possess multivariate characteristics which can not be handled by classical univariate regional procedures. For instance, classical procedures do not allow to assess regional homogeneity while taking into consideration flood peak, volume and duration. Chebana and Ouarda proposed multivariate discordancy and homogeneity tests. They carried out a simulation study to evaluate the performance of these tests. In the present paper, practical aspects are investigated jointly on flood peak and flood volume of a data set from the CΓ΄teβNord region in the province of Quebec, Canada. It is shown that, after removing the discordant sites, the remaining ones constitute a homogeneous region for the volumes and heterogeneous region for the peaks. However, if both variables are jointly considered, the obtained region is possibly homogeneous. Furthermore, the results demonstrate the usefulness of the bivariate test to take into account the dependence structure between the variables representing the event, and to take advantage of more information from the hydrograph. Copyright Β© 2009 John Wiley & Sons, Ltd.
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