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Intercomparison of homogenization techniques for precipitation data
Author(s) -
Beaulieu Claudie,
Seidou Ousmane,
Ouarda Taha B. M. J.,
Zhang Xuebin,
Boulet Gilles,
Yagouti Abderrahmane
Publication year - 2008
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2006wr005615
Subject(s) - homogeneity (statistics) , bivariate analysis , homogenization (climate) , homogeneous , precipitation , environmental science , regression , series (stratigraphy) , computer science , meteorology , statistics , climatology , geology , mathematics , geography , biodiversity , ecology , biology , paleontology , combinatorics
This paper presents an intercomparison of eight statistical tests to detect inhomogeneities in climatic data. The objective was to select those that are more suitable for precipitation data in the southern and central regions of the province of Quebec, Canada. The performances of these methods were evaluated by simulation on several thousands of homogeneous and inhomogeneous synthetic series. These series were generated to reproduce the statistical characteristics of typical precipitations observed in the southern and central parts of the province of Quebec and nearby areas, Canada. It was found that none of these methods was efficient for all types of inhomogeneities, but some of them performed substantially better than others: the bivariate test, the Jaruskova's method, and the standard normal homogeneity test. Techniques such as the Student sequential test and the two‐phase regression led to the worst performances. The analysis of the performances of each method in several situations allowed the design of an optimal procedure that takes advantage of the strengths of the best performing techniques.