Frequency analysis of nonidentically distributed hydrologic flood data

Singh, Vijay P.; Wang, S.X.; Zhang, L.

doi:10.1016/j.jhydrol.2004.10.029

Cited by 86 publications

(57 citation statements)

References 16 publications

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“…It lies in the subtropical region of warm and humid climate. Floods, mainly caused by rainfall, occur frequently in summer from June to early August when the monsoon fronts advance from south to north, or in the autumn from late August to early October, when the fronts withdraw from north to south (Singh et al 2005). The Geheyan Reservoir, which is a key control and multipurpose water resources engineering project, is located in the Qing River basin.…”

Section: Observed Data From River Basinsmentioning

confidence: 99%

“…Proper identification of flood seasonality is important for many practical applications in hydrology and water resources management (Cunderlik et al 2004a(Cunderlik et al , 2004b. Numerous hydrological designs require flood seasonality estimates (McCuen and Beighley 2003), such as seasonal flood frequency analysis (McCuen and Beighley 2003, Singh et al 2005, Fang et al 2007, Chen et al 2010; identification of relatively homogeneous regions in regional flood frequency analysis (Burn 1997, Castellarin et al 2001, Cunderlik and Burn 2002; seasonal inflow forecasting (Chowdhury and Ward 2007); and seasonal reservoir operation (Guo et al 2004).…”

Section: Introductionmentioning

confidence: 99%

“…One method is based on different meteorological and climate phenomena that generate floods, but can be very time-consuming (Black and Werrity 1997, Singh et al 2005, Liu et al 2010. Other methods are based on statistical analysis of flood data, such as the directional statistics (DS) method, the relative frequency (RF) method, and so on.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new method for identification of flood seasons using directional statistics

Chen

Singh

Guo

et al. 2013

Hydrological Sciences Journal

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Information on flood seasonality is required in many practical applications, such as estimation of seasonal design floods, flood forecasting and protection, flood-plain management and operation of water resources systems. Directional statistics (DS) and relative frequency (RF) are two of the popular methods for identifying flood seasonality. A new method, which is an improvement over DS, the IDS method, is proposed to identify flood seasonality and partition the flood season into sub-seasons. The performance of the IDS method was evaluated using synthetic data as well as observed data. The RF, DS and IDS methods were applied to the Geheyan and Baishan reservoir basins in China, and their results were compared and analysed. The results of flood seasonality identified by the IDS method were more objective than those of the RF and DS methods.Key words flood seasonality identification; directional statistics; relative frequency Une nouvelle méthode utilisant des statistiques directionnelles pour l'identification des saisons de crues Résumé Des informations sur la saisonnalité des crues sont nécessaires dans de nombreuses applications pratiques, telles que l'estimation des crues de projet saisonnières, la prévision et la protection contre les crues, la gestion des plaines inondables et l'exploitation des systèmes de ressources en eau. Les statistiques directionnelles (SD) et la fréquence relative (FR) sont deux des méthodes usuelles d'identification de la saisonnalité des crues. Nous proposons une nouvelle méthode, la méthode SDA (statistiques directionelles améliorées, en anglais IDS pour Improved Directional Statistics), qui est une amélioration de la méthode SD, pour identifier la saisonnalité des crues et découper la saison des crues en sous-saisons. La performance de la méthode SDA a été évaluée à l'aide de données synthétiques et de données observées. Les méthodes FR, SD et SDA ont été appliquées aux bassins des réservoirs Geheyan et Baishan, en Chine, et leurs résultats ont été comparés et analysés. Les résultats concernant la saisonnalité des crues identifiée par la méthode SDA sont plus objectifs que ceux des méthodes FR et SD.

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Section: Observed Data From River Basinsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new method for identification of flood seasons using directional statistics

Chen

Singh

Guo

et al. 2013

Hydrological Sciences Journal

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“…Karma dağılımlı yöntemleri öneren araştırmacılar, ilk olarak yıllık taşkın veri serisini oluşma mekanizmalarına göre alt gruplara ayırmakta, daha sonra alt grupların her birine uygun dağılım belirledikten sonra tüm veri setine uydurulmak üzere dağılımları biraraya getirmektedirler. Ancak önerilen bu yaklaşımın gözlem veri setine uygulanmasında iki önemli güçlük vardır: 1-uygun taşkın oluşma mekanizması veya alt grup sayılarının ve 2-her bir alt grup için olasılık dağılım fonksiyonlarının belirlenememesidir [15][16][17][18]. Bu kısıtlamaların yanı sıra eldeki örnek büyüklüğü karma toplumları tespit etmek için genelde yeterli olamadığından bu tip yöntemlerin uygulama alanları kısıtlı kalmıştır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Noktasal Taşkin Frekans Anali̇zi̇: Göksu Nehri̇ Ve Kayraktepe Baraji Vaka Anali̇zi̇

Şarlak¹,

Tiğrek²

2016

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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“…The two methods are very difficult to be used for engineering design because the designed value with a given probability are different at different time. Singh et al (2005) used conditional distribution function for hydrological frequency analysis considering that floods are generated by different mechanisms. Singh and Sinclair (1972) employed the mixed distribution function for frequency analysis.…”

Section: Introductionmentioning

confidence: 99%

Non-stationary hydrological frequency analysis based on the reconstruction of extreme hydrological series

Liang

Jiang

et al. 2015

Proc. IAHS

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Abstract. In this paper, a novel approach for non-stationary hydrological frequency analysis is proposed. The approach is due to the following consideration that, at present the data series used to detect mutation characteristic is very short, which may only reflect the partial characteristic of the population. That is to say, the mutation characteristic of short series may not fully represent the mutation characteristic of population, such as the difference of mutation degree between short sample and population. In this proposed method, an assumption is done that the variation hydrological series in a big time window owns an expected vibration center (EVC), which is a linear combination of the two mean values of the two subsample series obtained through separating the original hydrological series by a novel optimal segmentation technique (change rate of slope method). Then using the EVC to reconstruct non-stationary series to meet the requirement of stationary, and further ensure the conventional frequency analysis methods is valid.

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Frequency analysis of nonidentically distributed hydrologic flood data

Cited by 86 publications

References 16 publications

A new method for identification of flood seasons using directional statistics

A new method for identification of flood seasons using directional statistics

Noktasal Taşkin Frekans Anali̇zi̇: Göksu Nehri̇ Ve Kayraktepe Baraji Vaka Anali̇zi̇

Non-stationary hydrological frequency analysis based on the reconstruction of extreme hydrological series

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