Homogeneity testing for skewed and cross-correlated data in regional flood frequency analysis
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Date
2016
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Abstract
In regional frequency analysis the homogeneity of a group of multiple
stations is an essential pre-assumption. A standard procedure in hydrology to
evaluate this condition is the test based on the homogeneity measure of Hosking and
Wallis, which applies L-moments. Disadvantages of it are the lack of power when
analysing highly skewed data and the implicit assumption of spatial independence.
To face these issues we generalize this procedure in two ways. Copulas are utilised to
model intersite dependence and trimmed L-moments as a more robust alternative to
ordinary L-moments. The results of simulation studies are presented to discuss the
influence of different copula models and different trimming parameters. It turns out
that the usage of asymmetrically trimmed L-moments improves the heterogeneity
detection in skewed data, while maintaining a reasonable error rate. Simple copula
models are sufficient to incorporate the dependence structure of the data in the
procedure. Additionally, a more robust behaviour against extreme events at single
stations is achieved with the use of trimmed L-moments. Strong intersite dependence
and skewed data reveal the need of a modified procedure in a case study with data
from Saxony, Germany.
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Keywords
homogeneity test, copulas, TL-moments, regional flood frequency analysis