Block bootstrap for Mann–Kendall trend test of serially dependent data

Önöz, Bihrat; Bayazit, M.

doi:10.1002/hyp.8438

Cited by 155 publications

(99 citation statements)

References 22 publications

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“…Thus, it may be assumed that using the parametric distributions to assess the probability of occurrence of the Pre-extrem data will result in loss of no significant information due to the presence of serial correlation (Maia et al, 2007). This lack of temporal persistence also allowed us to apply the MK test in its original form (Önöz & Bayazit, 2011, among many others).…”

Section: Resultsmentioning

confidence: 99%

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Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data

Blain

Meschiatti

2014

Rev. bras. eng. agríc. ambient.

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Soil erosion, soil saturation and floods are frequently associated with extreme rainfall events. Thus, the scientific literature agrees on the need to carry out studies that improve the assessment of the probability of occurrence of extreme rainfall values. The main goal of this study was to compare the performance of the multi-parameters distributions Wakeby, Kappa and Generalized Extreme Value in fitting the annual maximums of daily, 2-day and 3-day rainfall amounts obtained from the weather station of Campinas, located in the State of São Paulo, Brazil (1890-2012. As a secondary aim, the presence of climate trends and serial correlation in these series was also evaluated. The auto-correlation function and the Mann-Kendall tests have shown the presence of no serial correlation and climate trends in the above mentioned series. The results obtained from goodness-of-fit procedures allowed us to conclude that the Kappa and the Generalized Extreme Value distributions present the best performance in describing the probabilistic structure of the series under analysis.Distribuições multi-paramétricas para avaliar a probabilidade de ocorrência de eventos extremos de precipitação pluvial R E S U M O Erosão e saturação hídrica do solo estão frequentemente associadas a eventos extremos de precipitação pluvial, tal como as enchentes. Desta forma, a literatura científica indica a necessidade de se realizar estudos que aprimorem a determinação da probabilidade de ocorrência desses eventos. O objetivo principal do estudo foi comparar o desempenho das distribuições multiparamétricas Wakeby, Kappa e Generalizada dos Valores Extremos na estimativa dos máximos anuais de totais de precipitação (diários, dois e três dias) da localidade Campinas-SP (1890-2012. O objetivo secundário foi avaliar a presença de tendências climáticas e a correlação serial nessas variáveis. A função autocorrelação e o teste de Mann-Kendall não indicaram a presença de significativas tendências climáticas e correlações serias em nenhuma das séries. Com base nos resultados dos testes de aderência concluiu-se que as distribuições Kappa e Generalizada dos Valores Extremos apresentam os melhores desempenhos na descrição probabilística das séries do estudo.

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Section: Resultsmentioning

confidence: 99%

“…The MannKendall (MK) test (Kendall & Stuart, 1967) was used to evaluate the presence of climate trends in the Pre-extrem data. According to Önöz & Bayazit (2011) the MK test can be seen as one of the most used non parametric methods for trend detection.…”

Section: Methodsmentioning

confidence: 99%

Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data

Blain

Meschiatti

2014

Rev. bras. eng. agríc. ambient.

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“…However, if a positive serial correlation exists, the probability of detecting a false significant trend increases , in which case pre-whitening methods need to be applied to remove the serial correlation. To avoid eliminating part of the temporal trend itself (which would result in underestimating the probability of detecting a significant trend), the trend-free pre-whitening (TFPW) and modified TFPW methods have been developed Önöz and Bayazit, 2012) along with other methods such as bootstrap method or variance correlation (see Khaliq et al, 2009, for a review).…”

Section: (I) Change Analysis Of Individual Time Seriesmentioning

confidence: 99%

Understanding flood regime changes in Europe: a state-of-the-art assessment

Hall

Arheimer

Borga

et al. 2014

Hydrol. Earth Syst. Sci.

483

335

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Abstract. There is growing concern that flooding is becoming more frequent and severe in Europe. A better understanding of flood regime changes and their drivers is therefore needed. The paper reviews the current knowledge on flood regime changes in European rivers that has traditionally been obtained through two alternative research approaches. The first approach is the data-based detection of changes in observed flood events. Current methods are reviewed together with their challenges and opportunities. For example, observation biases, the merging of different data sources and accounting for nonlinear drivers and responses. The second approach consists of modelled scenarios of future floods. Challenges and opportunities associated with flood change scenarios are discussed such as fully accounting for uncertainties in the modelling cascade and feedbacks. To make progress in flood change research, we suggest that a synthesis of these two approaches is needed. This can be achieved by focusing on long duration records and flood-rich and flood-poor periods rather than on short duration flood trends only, by formally attributing causes of observed flood changes, by validating scenarios against observed flood regime dynamics, and by developing low-dimensional models of flood changes and feedbacks. The paper finishes with a call for a joint European flood change research network.

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“…From a statistical standpoint, there is a debate over the methods used to detect this long-term trend (e.g., [25]); 2. From a hydrological standpoint, the long-term trend contribution to the determination of factors that cause shifts in the mean of a hydrological series is very limited; 3.…”

Section: + = T Tmentioning

confidence: 99%

Temporal Variability of Monthly Daily Extreme Water Levels in the St. Lawrence River at the Sorel Station from 1912 to 2010

Assani

Landry

Labrèche

et al. 2014

Water

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Abstract:Although climate models predict that the impacts of climate change on the temporal variability of water levels in the St. Lawrence River will be seasonally-dependent, such a seasonal effect on the current variability of extreme water levels has never been analyzed. To address this, we analyzed the temporal variability of three hydrological variables (monthly daily maximums and minimums, as well as their ratio) of water levels in the St. Lawrence River measured at the Sorel station since 1912, as they relate to climate indices. As for stationarity, the shifts in the mean values of maximum and minimum water levels revealed by the Lombard method took place prior to 1970 for spring water levels, but after that year, for winter water levels. Changes in the winter stationarity are thought to mainly relate to the decreasing snowfall observed in the St. Lawrence River watershed after 1970. In contrast, for spring, these changes are likely primarily related to human activity (digging of the St. Lawrence Seaway and construction of dams). Two shifts in the mean values of fall minimum extreme water levels were highlighted. The first of these shifts, which occurred in the first half of the 1960s decade, can also be linked to human activity (digging of the St. Lawrence Seaway and construction of dams), whereas the second shift, observed after the 1970s for the months of November and December, can be linked to decreasing amounts of snow in winter. AMO (Atlantic Multidecadal Oscillation) is the climate index that is most frequently correlated negatively with the hydrologic variables, mainly in winter and spring.

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Block bootstrap for Mann–Kendall trend test of serially dependent data

Cited by 155 publications

References 22 publications

Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data

Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data

Understanding flood regime changes in Europe: a state-of-the-art assessment

Temporal Variability of Monthly Daily Extreme Water Levels in the St. Lawrence River at the Sorel Station from 1912 to 2010

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