• DOI:10.1029/WR020I006P00727
• Corpus ID: 121572032

A Nonparametric Trend Test for Seasonal Data With Serial Dependence

@article{Hirsch1984ANT,
  title={A Nonparametric Trend Test for Seasonal Data With Serial Dependence},
  author={Robert M. Hirsch and James Richard Slack},
  journal={Water Resources Research},
  year={1984},
  volume={20},
  pages={727-732}
}

• R. Hirsch, J. R. Slack
• Published 1 June 1984
• Mathematics
• Water Resources Research

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.