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A nonparametric trend test for seasonal data with serial dependence.

Water Resources Research

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ORCID iD and
https://doi.org/10.1029/WR020i006p00727

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Abstract

Statistical tests for monotonic trend in seasonal (e.g., monthly) hydrologic time series are commonly confounded by some of the following problems: nonnormal data, missing values, seasonality, censoring (detection limits), and serial dependence. An extension of the Mann-Kendall test for trend (designed for such data) is presented here. Because the test is based entirely on ranks, it is robust against nonnormality and censoring. Seasonality and missing values present no theoretical or computational obstacles to its application. Monte Carlo experiments show that, in terms of type I error, it is robust against serial correlation except when the data have strong long-term persistence (e.g., ARMA (1, 1) monthly processes with ϕ > 0.6) or short records (∼ 5 years). When there is no serial correlation, it is less powerful than a related simpler test which is not robust against serial correlation.

Additional publication details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
A nonparametric trend test for seasonal data with serial dependence.
Series title:
Water Resources Research
DOI:
10.1029/WR020i006p00727
Volume:
20
Issue:
6
Year Published:
1984
Language:
English
Publisher:
American Geophysical Union
Description:
6 p.
First page:
727
Last page:
732