Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.936 IF 4.936
  • IF 5-year value: 5.615 IF 5-year
    5.615
  • CiteScore value: 4.94 CiteScore
    4.94
  • SNIP value: 1.612 SNIP 1.612
  • IPP value: 4.70 IPP 4.70
  • SJR value: 2.134 SJR 2.134
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 107 Scimago H
    index 107
  • h5-index value: 63 h5-index 63
Discussion papers
https://doi.org/10.5194/hess-2019-363
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-363
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 08 Aug 2019

Submitted as: research article | 08 Aug 2019

Review status
This discussion paper is a preprint. A revision of the manuscript was accepted for the journal Hydrology and Earth System Sciences (HESS).

Power of parametric and non-parametric tests for trend detection in annual maximum series

Vincenzo Totaro, Andrea Gioia, and Vito Iacobellis Vincenzo Totaro et al.
  • Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Bari, 70125, Italy

Abstract. The need of fitting time series characterized by the presence of trend or change points has generated in latest years an increased interest in the investigation of non-stationary probability distributions. Considering that the available hydrological time series can be recognized as the observable part of a stochastic process with a definite probability distribution, two main topics can be tackled in this context: the first one is related to the definition of an objective criterion for choosing whether the stationary hypothesis can be adopted, while the second one regards the effects of non-stationarity on the estimation of distribution parameters and quantiles for assigned return period and flood risk evaluation. Although the time series trend or change points can be recognized using classical tests available in literature (e.g. Mann–Kendal or CUSUM test), for design purpose it is still required the correct selection of the stationary or non-stationary probability distribution. By this light, the focus is shifted toward model selection criteria which implies the use of parametric methods with all related issues on parameters estimation. The aim of this study is to compare the performance of parametric and non-parametric methods for trend detection analysing their power and focusing on the use of traditional model selection tools (e.g. Akaike Information Criterion and Likelihood Ratio test) within this context. Power and efficiency of parameter estimation, including the trend coefficient, were investigated through Monte Carlo simulations using Generalized Extreme Value distribution as parent with selected parameter sets.

Vincenzo Totaro et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Vincenzo Totaro et al.
Vincenzo Totaro et al.
Viewed  
Total article views: 707 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
613 91 3 707 7 5
  • HTML: 613
  • PDF: 91
  • XML: 3
  • Total: 707
  • BibTeX: 7
  • EndNote: 5
Views and downloads (calculated since 08 Aug 2019)
Cumulative views and downloads (calculated since 08 Aug 2019)
Viewed (geographical distribution)  
Total article views: 434 (including HTML, PDF, and XML) Thereof 427 with geography defined and 7 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 07 Dec 2019
Publications Copernicus
Download
Short summary
Detection of trend in hydrological time series is still an open field of research. We highlight that classical tests, in the analysis of extreme events, are too keen to accept the hypothesis of stationarity (i.e. the absence of a climate change) and are not independent from the underlying distribution of data. We propose the use of a procedure, based on model selection criteria, that allows to check the test power and is suitable for implementation in the evaluation of design quantities.
Detection of trend in hydrological time series is still an open field of research. We highlight...
Citation