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Journal ArticleDOI

The influence of autocorrelation on the ability to detect trend in hydrological series

Sheng Yue, +3 more
- 30 Jun 2002 - 
- Vol. 16, Iss: 9, pp 1807-1829
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TLDR
In this article, the authors investigated the effect of serial correlation on the performance of the Mann-Kendall (MK) statistic and showed that the presence of a trend alters the estimate of the magnitude of serial correlations.
Abstract
This study investigated using Monte Carlo simulation the interaction between a linear trend and a lag-one autoregressive (AR(1)) process when both exist in a time series. Simulation experiments demonstrated that the existence of serial correlation alters the variance of the estimate of the Mann–Kendall (MK) statistic; and the presence of a trend alters the estimate of the magnitude of serial correlation. Furthermore, it was shown that removal of a positive serial correlation component from time series by pre-whitening resulted in a reduction in the magnitude of the existing trend; and the removal of a trend component from a time series as a first step prior to pre-whitening eliminates the influence of the trend on the serial correlation and does not seriously affect the estimate of the true AR(1). These results indicate that the commonly used pre-whitening procedure for eliminating the effect of serial correlation on the MK test leads to potentially inaccurate assessments of the significance of a trend; and certain procedures will be more appropriate for eliminating the impact of serial correlation on the MK test. In essence, it was advocated that a trend first be removed in a series prior to ascertaining the magnitude of serial correlation. This alternative approach and the previously existing approaches were employed to assess the significance of a trend in serially correlated annual mean and annual minimum streamflow data of some pristine river basins in Ontario, Canada. Results indicate that, with the previously existing procedures, researchers and practitioners may have incorrectly identified the possibility of significant trends. Copyright  2002 Environment Canada. Published by John Wiley & Sons, Ltd.

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Citations
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Journal ArticleDOI

Climate-induced variations in global wildfire danger from 1979 to 2013

TL;DR: This article used three daily global climate data sets and three fire danger indices to develop a simple annual metric of fire weather season length, and map spatio-temporal trends from 1979 to 2013.
Journal ArticleDOI

Analysis of changes in meteorological variables using Mann-Kendall and Sen's slope estimator statistical tests in Serbia

TL;DR: In this article, the authors analyzed the annual and seasonal trends of seven meteorological variables for twelve weather stations in Serbia during 1980-2010 and used the nonparametric Mann-Kendall and Sen's methods to determine whether there was a positive or negative trend in weather data with their statistical significance.
Journal ArticleDOI

Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis

TL;DR: In this article, the Mann-Kendall test is modified to account for the effect of scaling in hydrologic data, and the results show a considerable reduction in the number of stations with significant trends when the effects of scaling are taken into account.
Journal ArticleDOI

The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series

TL;DR: In this paper, effective sample size (ESS) has been proposed to modify the nonparametric Mann-Kendall (MK) statistical test to assess the significance of trend in hydrological time series.

Climate-induced variations in global wildfire danger from 1979-2013

TL;DR: It is shown that fire weather seasons have lengthened across 29.6 million km2 (25.3%) of the Earth's vegetated surface, resulting in an 18.7% increase in global mean fire weather season length.
References
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Journal ArticleDOI

Nonparametric tests against trend

Henry B. Mann
- 01 Jul 1945 - 
Journal ArticleDOI

Estimates of the Regression Coefficient Based on Kendall's Tau

TL;DR: In this article, a simple and robust estimator of regression coefficient β based on Kendall's rank correlation tau is studied, where the point estimator is the median of the set of slopes (Yj - Yi )/(tj-ti ) joining pairs of points with ti ≠ ti.
Book

Rank correlation methods

TL;DR: The measurement of rank correlation was introduced in this paper, and rank correlation tied ranks tests of significance were applied to the problem of m ranking, and variate values were used to measure rank correlation.
Journal ArticleDOI

Techniques of trend analysis for monthly water quality data

TL;DR: The seasonal Kendall test as discussed by the authors is a nonparametric test for trend applicable to data sets with seasonality, missing values, or values reported as "less than" or values below the limit of detection.