Trend detection in hydrological series: when series are negatively correlated

Rivard, Christine; Vigneault, Harold

doi:10.1002/hyp.7370

Cited by 26 publications

(17 citation statements)

References 4 publications

(20 reference statements)

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“…However, the MK statistic is highly sensitive to the serial dependence of a time series (Yue and Wang, 2002;Yue et al, , 2003. For instance, the variance of MK statistic S increases (decreases) with the magnitude of significant positive (negative) autocorrelation of a time series, which leads to an overestimation (underestimation) of the trend detection probability (Douglas et al, 2000;Wu et al, 2008;Rivard and Vigneault, 2009). To eliminate such affect, Von and Kulkarni and von Storch (1995) proposed a pre-whitening procedure that removes the lag-1 autocorrelation prior to applying the MK test, as employed by Río et al (2013) amid the above-cited studies.…”

Section: Introductionmentioning

confidence: 99%

“…To eliminate such affect, Von and Kulkarni and von Storch (1995) proposed a pre-whitening procedure that removes the lag-1 autocorrelation prior to applying the MK test, as employed by Río et al (2013) amid the above-cited studies. However, such a procedure is particularly inefficient when a time series either features a trend or is serially dependent negatively (Rivard and Vigneault, 2009). In fact, the presence of a trend can lead to false detection of significant positive (negative) autocorrelation in a time series (Rivard and Vigneault, 2009), and removing this through a pre-whitening may remove (inflate) the portion of a trend, leading to the underestimation (overestimation) of trend detection probability and trend magnitude (Yue and Wang, 2002;Yue et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…However, such a procedure is particularly inefficient when a time series either features a trend or is serially dependent negatively (Rivard and Vigneault, 2009). In fact, the presence of a trend can lead to false detection of significant positive (negative) autocorrelation in a time series (Rivard and Vigneault, 2009), and removing this through a pre-whitening may remove (inflate) the portion of a trend, leading to the underestimation (overestimation) of trend detection probability and trend magnitude (Yue and Wang, 2002;Yue et al, 2003). To avoid this, proposed a trend-free pre-whitening (TFPW) in which the trend component of a time series is separated prior to pre-whitening and then blended back to the resultant time series, as adopted by Khattak et al (2011).…”

Section: Introductionmentioning

confidence: 99%

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Prevailing climatic trends and runoff response from Hindukush–Karakoram–Himalaya, upper Indus Basin

Böhner²,

2017

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Abstract. Largely depending on the meltwater from the Hindukush-Karakoram-Himalaya, withdrawals from the upper Indus Basin (UIB) contribute half of the surface water availability in Pakistan, indispensable for agricultural production systems, industrial and domestic use, and hydropower generation. Despite such importance, a comprehensive assessment of prevailing state of relevant climatic variables determining the water availability is largely missing. Against this background, this study assesses the trends in maximum, minimum and mean temperatures, diurnal temperature range and precipitation from 18 stations (1250-4500 m a.s.l.) for their overlapping period of record (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) and, separately, from six stations of their long-term record . For this, a Mann-Kendall test on serially independent time series is applied to detect the existence of a trend, while its true slope is estimated using the Sen's slope method. Further, locally identified climatic trends are statistically assessed for their spatial-scale significance within 10 identified subregions of the UIB, and the spatially (field-) significant climatic trends are then qualitatively compared with the trends in discharge out of corresponding subregions. Over the recent period (1995-2012), we find warming and drying of spring (field-significant in March) and increasing early melt season discharge from most of the subregions, likely due to a rapid snowmelt. In stark contrast, most of the subregions feature a field-significant cooling within the monsoon period (particularly in July and September), which coincides well with the main glacier melt season. Hence, a decreasing or weakly increasing discharge is observed from the corresponding subregions during mid-to late melt season (particularly in July). Such tendencies, being largely consistent with the long-term trends , most likely indicate dominance of the nival but suppression of the glacial melt regime, altering overall hydrology of the UIB in future. These findings, though constrained by sparse and short observations, largely contribute in understanding the UIB melt runoff dynamics and address the hydroclimatic explanation of the "Karakoram Anomaly".

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prevailing climatic trends and runoff response from Hindukush–Karakoram–Himalaya, upper Indus Basin

Böhner²,

2017

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“…The basic idea of this approach is to preserve the true slope of linear trends by removing trend components prior to PW before recombining trend components and trend-free pre-whitened series. Rivard and Vigneault [22] and Blain [23] suggested that TFPW is suitable for trend detection in negative and positive serially dependent data if slopes of trends are estimated properly. However, recent studies provided empirical and theoretical proof that the TFPW is unsuitable to preserve the correct Type I error, which is the only indicator that can be controlled to prevent a false identification of numerous spurious trends.…”

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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“…Observed climatic series usually have short lengths and good correlations, so the MK results would be inaccurate and must be viewed with caution. To overcome the defects of the MK test, various approaches were suggested to remove the affects of serial correlation, such as pre-whitening, trend-free pre-whitening, variance correction and block resampling techniques [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, China

Sang

Wang

2012

Entropy

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Detection of abrupt change is a key issue for understanding the facts and trends of climate change, but it is also a difficult task in practice. The Mann-Kendall (MK) test is commonly used for treating the issue, while the results are usually affected by the correlation and seasonal characters and sample size of series. This paper proposes a discrete wavelet entropy-aided approach for abrupt change detection, with the temperature analyses in the Haihe River Basin (HRB) as an example. The results show some obviously abrupt temperature changes in the study area in the 1960s-1990s. The MK test results do not reflect those abrupt temperature changes after the 1980s. Comparatively, the proposed approach can detect all main abrupt temperature changes in HRB, so it is more effective than the MK test. Differing from the MK test which only considers series' value order or the conventional entropy which mainly considers series' statistical random characters, the proposed approach is to describe the complexity and disorderliness of series using wavelet entropy theories, and it can fairly consider series' composition and characteristics under different scales, so the results can more accurately reflect not only the abrupt changes, but also the complexity variation of a series over time. However, since it is based on the entropy theories, the series analyzed must have big sample size enough and the sampling rates being smaller than the concerned scale for the accurate computation of entropy values.

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Trend detection in hydrological series: when series are negatively correlated

Cited by 26 publications

References 4 publications

Prevailing climatic trends and runoff response from Hindukush–Karakoram–Himalaya, upper Indus Basin

Prevailing climatic trends and runoff response from Hindukush–Karakoram–Himalaya, upper Indus Basin

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

Discrete Wavelet Entropy Aided Detection of Abrupt Change: A Case Study in the Haihe River Basin, China

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