Enhancing the effectiveness of prewhitening in trend analysis of hydrologic data

Hamed, Khaled H.

doi:10.1016/j.jhydrol.2009.01.040

Cited by 195 publications

(142 citation statements)

References 36 publications

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“…Before performing these analyses, whether the lag-1 correlation is significantly different from 0 at the 5% level is checked. The presence of autocorrelation can affect the significance of the trend tests (Kulkarni and von Storch, 1995;Yue et al, 2003;Yue and Wang, 2004;Hamed, 2009), resulting in the detection of statistically significant trends even though no trend is present (Cox and Stuart, 1955;Cohn and Lins, 2005). Out of 27 stations, only one has a lag-1 value slightly outside of the 95% confidence intervals.…”

Section: Stationaritymentioning

confidence: 99%

Analyses of extreme flooding in Austria over the period 1951–2006

Villarini

Smith

Serinaldi

et al. 2011

Intl Journal of Climatology

102

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ABSTRACT:Analyses of extreme flooding in Austria is performed using daily discharge time series from 27 stations over the period . The main research questions revolve around: (1) temporal non-stationarities in the flood record, (2) upper tail and scaling properties of the flood peak records, and (3) relation between magnitude and frequency of flooding and the North Atlantic Oscillation (NAO). Two datasets are derived from the daily discharge time series: annual maximum daily discharge and peaks-over-threshold (POT) data. The validity of the stationarity assumption in the annual maximum discharge record is assessed by investigating the presence of abrupt and slowly varying changes using nonparametric tests. The time series are tested for abrupt changes both in the mean and variance of the flood peak distributions by means of the Pettitt test. The presence of monotonic trends is investigated by means of the Mann-Kendall and Spearman tests. Violations of the stationarity assumption are associated with abrupt rather than gradual changes. These step changes generally involve river regulation through construction of dams or other major engineering works. It is not possible to make conclusive statements about the presence of an anthropogenic climate change signal in the flood peak record. Similar conclusions are obtained when focussing on the frequency of POT floods. The Generalised Extreme Value distribution is used to study the upper tail and scaling properties of annual maximum daily discharge records. The location and scale parameters exhibit power-law behaviour as a function of drainage area. The shape parameters indicate that the flood peak distributions for Austria have a heavy tail. Non-stationary modelling of the annual maximum daily discharge and POT time series is used to explore the relation between flood magnitude and frequency and NAO. The results indicate that NAO is a significant covariate in explaining the magnitude and frequency of occurrence of flooding over a large part of Austria.

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Section: Stationaritymentioning

confidence: 99%

Analyses of extreme flooding in Austria over the period 1951–2006

Villarini

Smith

Serinaldi

et al. 2011

Intl Journal of Climatology

102

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“…To mitigate this impact, some efforts focus on adjusting the original test to the auto-correlated data, commonly a pre-whitening (PW) approach, trend-free pre-whitening (TFPW). However, these approaches may be inadequate to analyze real data because of the high variance of slope estimators [27]. These problems were solving by involving the correction of both the slope and serial variances in the original TFPW approach, Variance Correction Pre-Whitening Method.…”

Section: Variance Correction Pre-whitening Mann-kendall Testmentioning

confidence: 99%

Spatiotemporal Characteristics of Groundwater Drought and Its Response to Meteorological Drought in Jiangsu Province, China

Liu

Zhou

et al. 2016

Water

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Abstract:In this study, the temporal and spatial variations of groundwater drought using a Standardized Groundwater Level Index (SGI) were analyzed based on 40 monthly groundwater level observation wells from 1989 to 2012 in Jiangsu Province, China. Meteorological drought, calculated by the Standardized Precipitation Index (SPI), was also included to reveal its propagation and impact on the groundwater drought process. Results showed that the southern region of Jiangsu faced more frequent groundwater droughts and lower intensity, while the northern region faced less frequent groundwater drought with higher intensity. Furthermore, the cross-correlation between the spatial average of SGI and SPI for SPI accumulation periods of q = 1 to 12 was computed. The relationship between SGI and SPI varied in different regions. Detailed analysis of the characteristics of groundwater and meteorological drought for each region showed that meteorological droughts happened more frequently than groundwater drought in Jiangsu Province during the study period, while the mean duration and mean magnitude of groundwater droughts were longer and larger than those of meteorological droughts. It is expected that this study will provide useful information for drought monitoring and mitigation in Jiangsu and similar areas.

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“…Rejection Ratio " N rej {10000 " tType I error, β " 0; Power, β ‰ 0u (19) where N rej is the number of samples that the null hypothesis is rejected by the test. We set the pre-assigned significance level to 0.05.…”

Section: Simulation Designmentioning

confidence: 99%

“…Recognizing these disadvantages, Bayazit and Önöz [18] suggested that PW is not suitable in the case of large sample sizes and strong trends. Hamed [19] improved the PW approach by correcting the bias in the estimation of the lag-one autocorrelation coefficient and carefully selecting the autocorrelation model.…”

Section: Introductionmentioning

confidence: 99%

Linear Trend Detection in Serially Dependent Hydrometeorological Data Based on a Variance Correction Spearman Rho Method

Wang

Chen

Becker

et al. 2015

Water

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Hydrometeorological data are commonly serially dependent and thereby deviate from the assumption of independence that underlies the Spearman rho trend test. The presence of autocorrelation will influence the significance of observed trends. Specifically, the positive autocorrelation inflates Type I errors, while it deflates the power of trend detection in some cases. To address this issue, we derive a theoretical formula and recommend an appropriate empirical formula to calculate the rho variance of dependent series. The proposed procedure of the variance correction for the Spearman rho method is capable of mitigating the effect of autocorrelation on both, Type I error and power of the test. Similar to the Block Bootstrap method, it has the advantage that it does not require an initial knowledge of the autocorrelation structure or modification of the series. In comparison, the capability of the Pre-Whitening method is sensitive to model misspecification if the series are whitened by a parametric autocorrelation model. The Trend-Free Pre-Whitening method tends to lead to an overestimation of the statistical significance of trends, similar to the original Spearman rho test. The results of the study emphasize the importance of selecting a reliable method for trend detection in serially dependent data.

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Enhancing the effectiveness of prewhitening in trend analysis of hydrologic data

Cited by 195 publications

References 36 publications

Analyses of extreme flooding in Austria over the period 1951–2006

Analyses of extreme flooding in Austria over the period 1951–2006

Spatiotemporal Characteristics of Groundwater Drought and Its Response to Meteorological Drought in Jiangsu Province, China

Linear Trend Detection in Serially Dependent Hydrometeorological Data Based on a Variance Correction Spearman Rho Method

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