Frequency analysis of a sequence of dependent and/or non-stationary hydro-meteorological observations: A review

Khaliq, M. N.; Ouarda, Taha B. M. J.; Ondo, J.-C.; Gachon, Philippe; Bobée, Bernard

doi:10.1016/j.jhydrol.2006.03.004

Cited by 394 publications

(279 citation statements)

References 111 publications

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“…According to Khaliq et al (2009) parametric tests that involve non-stationarity frequency analysis of hydrological variables are generally more powerful in modelling and investigating the trends in observations over the non-parametric tests. However, the trend analysis based on parametric process involves many assumptions about the trend behavioursee Khaliq et al (2006) for details about the parametric approaches. On the other hand, nonparametric methods (i.e., MK, Spearman's Rho) are more robust with respect to nonnormality, nonlinearity, missing values, serial dependence, sensitivity to outliers (extremes), and seasonality (Yue et al, 2002).…”

Section: Different Methods For Trend Detectionmentioning

confidence: 99%

“…A failure to take such change/variability into account can undermine the usefulness of the concept of return period, and can lead to underestimation/overestimation of design flood estimates (Khaliq et al, 2006), which in turn will have important implications on the design and operation of water infrastructure. Recent research carried out in some regions of the world has questioned the validity of the traditional flood risk assumptions of stationarity and homogeneity (Power et al,, 1999;Douglas et al, 2000;Strupczewski et al, 2001a;Franks and Kuczera, 2002;Cunderlik and Burn, 2003;Prudhomme et al, 2003;Micevski et al, 2006;Leclerc and Ouarda, 2007, among many others) especially with the recognition that climate naturally varies at all scales.…”

Section: Introductionmentioning

confidence: 99%

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Preliminary Analysis of Trends in Australian Flood Data

Ishak¹,

Rahman²,

Westra³

et al. 2010

World Environmental and Water Resources Congress 2010

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In recent years, the potential impacts of climate variability and change on the hydrologic regime have received a great deal of attention from researchers. Review of hydrological data recorded in different parts of the world has provided evidence of regime-like or quasiperiodic climate behaviour and of systematic trends in key climate variables due to climate change and/or climate variability. It has been established that a changing climate will have notable impacts on the rainfall runoff process, and thus hydrologic time series (e.g., flood data) can no longer be assumed to be stationary. A failure to take such change/variability into account can lead to underestimation/overestimation of the design flood estimate, which in turn will have important implications on the design and operation of water infrastructures. This paper presents preliminary results from a study aimed to identify the nature of time trends in flood data in the Australian continent with the final objective of assessing the impacts of climatic change on regional floods in Australia. This research is being carried out as a part of the on-going revision of Australian Rainfall and Runoff -the national guide of design flood estimation in Australia. For this study, 491 suitable stations with flood data in the range of 30 to 97 years have been selected across the Australian continent. Two trend tests are applied: Mann-Kendall test and Spearman's Rho test to the data set. Preliminary trend analysis results show that about 30% of the selected stations show trends in annual maxima flood series data, with downward trends in the southern part of Australia and upward trends in the northern part. Further investigation is needed before any firm conclusion can be made about the trends in Australian flood data. Future work aims to address the influence of spatial correlation and autocorrelation on the ability to detect trend in annual maximum flood series data in Australia and assess the relationship between the observed trends in annual maximum flood data and other meteorological variables.

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Section: Different Methods For Trend Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Preliminary Analysis of Trends in Australian Flood Data

Ishak¹,

Rahman²,

Westra³

et al. 2010

World Environmental and Water Resources Congress 2010

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show abstract

“…Em particular, a identificação de tendências em séries de variáveis hidrológicas, entre elas e precipitação, tornou-se notadamente importante dado o interesse crescente por modelos estocásticos focados na predição e preparação da sociedade para os impactos provocados por possíveis eventos extremos ou raros (Paiva e Clarke, 1995;Katz et al, 2002;Khaliq et al, 2006;Milly et al, 2008;Villarini et al, 2009;Gilroy e McCuen, 2012;Ishak et al, 2013). Entretanto, Vogel et al (2013) demonstram algumas preocupações que concernem aos testes para detecção de tendências em tais variáveis, principalmente pelo fato de serem formulados sob a perspectiva dos Testes de Significância da Hipótese Nula -NHST, tradução da terminologia de língua inglesa Null-Hypothesis Significance Testing.…”

Section: Introductionunclassified

Detecção de Tendências Monotônicas Temporais e Relação com Erros dos Tipos I e II: Estudo de Caso em Séries de Precipitações Diárias Máximas Anuais do Estado do Acre

Moreira

Naghettini

2016

Rev. bras. meteorol.

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ResumoAtualmente, é quase consenso que mudanças climáticas estão acontecendo e, provavelmente, se intensificarão no futuro. Com isso, os testes estatísticos para detecção de tendências em séries de observações de variáveis hidrológicas tornaram-se ferramentas importantes para a construção e melhoramento dos modelos de predição e de planos de preparação da sociedade para os possíveis impactos causados por eventos extremos. Sob este foco, o presente artigo busca discutir uma metodologia proposta recentemente para a detecção de tendências, à luz dos erros do tipo I e II associados a testes de significância estatística, e comparar com o teste não paramétrico de Mann-Kendall, complementado pelo estimador de declive de Sen. Contempla, ainda, um estudo de caso nas três principais séries de precipitações diárias máximas anuais do estado do Acre, Brasil. Os resultados evidenciaram tendência significativamente crescente apenas para a série de observações localizada em Tarauacá, um dos municípios do estado em questão, e que dispõe de uma estação meteorológica de referência. Palavras-chave: detecção de tendências, erros do tipo I e tipo II, séries de precipitações máximas. Detecting Monotonous Time Trends as Related to Type I and Type II Errors: Case Study in Annual Maximum Daily Precipitation Series Observed in the State of Acre AbstractClimate changes are taking place and will probably be reinforced in the future. In such a context, statistical tests for detecting trends in time series of hydrologic observations are certainly important tools for building and improving prediction models and societal preparedness plans to deal with possible impacts from extreme events. This paper focuses on discussing a recent method proposed for detecting monotonous trends in time series by accounting for both type I and type II errors, and comparing its results with the conventional Mann-Kendall nonparametric test, as complemented by Sen slope estimator, following application to the three main series of annual maximum daily rainfall observed in the Brazilian state of Acre. The results point out a significant increasing tendency for the series observed at the raingauge of Tarauacá.

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“…To this end, we present a fresh empirical statistical approach and case study from New England region. In answering this question, this work complements the emerging research literature that focuses on nonstationarity from the standpoint of extreme event magnitude and intensity [Khaliq et al, 2006;Mirhosseini et al, 2013;Salas and Obeysekera, 2013;Cheng and AghaKouchak, 2014].…”

Section: Introductionmentioning

confidence: 97%

Nonstationarity in seasonality of extreme precipitation: A nonparametric circular statistical approach and its application

Dhakal

Jain

Gray

et al. 2015

Water Resources Research

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Changes in seasonality of extreme storms have important implications for public safety, storm water infrastructure, and, in general, adaptation strategies in a changing climate. While past research on this topic offers some approaches to characterize seasonality, the methods are somewhat limited in their ability to discern the diversity of distributional types for extreme precipitation dates. Herein, we present a comprehensive approach for assessment of temporal changes in the calendar dates for extreme precipitation within a circular statistics framework which entails: (a) three measures to summarize circular random variables (traditional approach), (b) four nonparametric statistical tests, and (c) a new nonparametric circular density method to provide a robust assessment of the nature of probability distribution and changes. Two 30 year blocks (1951-1980 and 1981-2010) of annual maximum daily precipitation from 10 stations across the state of Maine were used for our analysis. Assessment of seasonality based on nonparametric approach indicated nonstationarity; some stations exhibited shifts in significant mode toward Spring season for the recent time period while some other stations exhibited multimodal seasonal pattern for both the time periods. Nonparametric circular density method, used in this study, allows for an adaptive estimation of seasonal density. Despite the limitation of being sensitive to the smoothing parameter, this method can accurately characterize one or more modes of seasonal peaks, as well as pave the way toward assessment of changes in seasonality over time.

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Frequency analysis of a sequence of dependent and/or non-stationary hydro-meteorological observations: A review

Cited by 394 publications

References 111 publications

Preliminary Analysis of Trends in Australian Flood Data

Preliminary Analysis of Trends in Australian Flood Data

Detecção de Tendências Monotônicas Temporais e Relação com Erros dos Tipos I e II: Estudo de Caso em Séries de Precipitações Diárias Máximas Anuais do Estado do Acre

Nonstationarity in seasonality of extreme precipitation: A nonparametric circular statistical approach and its application

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