Articles | Volume 24, issue 1
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, Andrea Gioia, and Vito Iacobellis

Related subject area

Subject: Engineering Hydrology | Techniques and Approaches: Stochastic approaches
FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390,,, 2022
Short summary
Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system
Andrew J. Newman, Amanda G. Stone, Manabendra Saharia, Kathleen D. Holman, Nans Addor, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 5603–5621,,, 2021
Short summary
Characteristics and process controls of statistical flood moments in Europe – a data-based analysis
David Lun, Alberto Viglione, Miriam Bertola, Jürgen Komma, Juraj Parajka, Peter Valent, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 5535–5560,,, 2021
Short summary
Objective functions for information-theoretical monitoring network design: what is “optimal”?
Hossein Foroozand and Steven V. Weijs
Hydrol. Earth Syst. Sci., 25, 831–850,,, 2021
Short summary
Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach
Manuela I. Brunner and Eric Gilleland
Hydrol. Earth Syst. Sci., 24, 3967–3982,,, 2020
Short summary

Cited articles

Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Control, 19, 716–723,, 1974. 
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.