Assessing the performance of parametric and non-parametric tests for trend detection in partial duration time series

Renato Amorim; Gabriele Villarini

doi:10.1111/jfr3.12957

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Assessing the performance of parametric and non-parametric tests for trend detection in partial duration time series

Journal article

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Assessing the performance of parametric and non-parametric tests for trend detection in partial duration time series

Renato Amorim and Gabriele Villarini

Journal of flood risk management, Vol.17(1), e12957

03/2024

DOI: 10.1111/jfr3.12957

Appears in UI Libraries Support Open Access

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Abstract

The detection of nonstationarities in partial duration time series (PDS) depends on several factors, including the length of the time series, the selected statistical test, and the heaviness of the tail of the distribution. Because of the more limited attention received in the literature when compared to the trend detection on block maxima variables, we perform a Monte Carlo simulation study to evaluate the performance of different approaches: Spearman's rho, Mann-Kendall, ordinary least squares (OLS), Sen's slope estimator (SEN), and the nonstationary generalized Pareto distribution fit to identify the presence of trends in PDS records characterized by different sample sizes (n), shape parameter (xi) and degrees of nonstationarity. The results point to a power gain for all tests by increasing n and the degree of nonstationarity and by reducing xi. The use of a nonparametric test is recommended in samples with a high positive skew. Furthermore, the use of sampling rates greater than one to increase the PDS sample size is encouraged, especially when dealing with small records. The use of SEN to estimate the magnitude of a trend is preferable over OLS due to its slightly smaller probability of occurrence of type S error when xi is positive.

generalized Pareto distribution

Mann–Kendall

nonstationary

ordinary least squares

partial duration series

peaks over threshold

Sen's slope

Spearman rho

trend detection

UIOWA OA Agreement

Details

Title: Subtitle: Assessing the performance of parametric and non-parametric tests for trend detection in partial duration time series
Creators: Renato Amorim - University of Iowa
Gabriele Villarini - University of Iowa
Resource Type: Journal article
Publication Details: Journal of flood risk management, Vol.17(1), e12957
DOI: 10.1111/jfr3.12957
ISSN: 1753-318X
eISSN: 1753-318X
Publisher: Wiley
Number of pages: 16
Grant note: Division of Earth Sciences. Grant Number: EAR-1840742 U.S. Army Corps of Engineers
Language: English
Electronic publication date: 10/20/2023
Date published: 03/2024
Academic Unit: Civil and Environmental Engineering; IIHR--Hydroscience and Engineering
Record Identifier: 9984507023602771

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