Articles | Volume 24, issue 1
Hydrol. Earth Syst. Sci., 24, 473–488, 2020
Hydrol. Earth Syst. Sci., 24, 473–488, 2020

Research article 29 Jan 2020

Research article | 29 Jan 2020

Numerical investigation on the power of parametric and nonparametric tests for trend detection in annual maximum series

Vincenzo Totaro et al.

Related subject area

Subject: Engineering Hydrology | Techniques and Approaches: Stochastic approaches
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Hydrol. Earth Syst. Sci., 24, 3967–3982,,, 2020
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Hossein Foroozand and Steven V. Weijs
Hydrol. Earth Syst. Sci. Discuss.,,, 2020
Revised manuscript accepted for HESS
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Cited articles

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Beven, K.: Facets of uncertainty: Epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication, Hydrolog. Sci. J., 61, 1652–1665,, 2016. 
Burnham, K. P. and Anderson, D. R.: Model selection and multimodel inference, Springer, New York, 2004. 
Cheng, L., AghaKouchak, A., Gilleland, E., and Katz, R. W.: Non-stationary extreme value analysis in a changing climate, Climatic Change, 127, 353–369,, 2014. 
Chow, V. T. (Ed.): Statistical and probability analysis of hydrologic data, in: Handbook of applied hydrology, McGraw-Hill, New York, 8.1–8.97, 1964. 
Short summary
We highlight the need for power evaluation in the application of null hypothesis significance tests for trend detection in extreme event analysis. In a wide range of conditions, depending on the underlying distribution of data, the test power may reach unacceptably low values. We propose the use of a parametric approach, based on model selection criteria, that allows one to choose the null hypothesis, to select the level of significance, and to check the test power using Monte Carlo experiments.