Catchment similarity for regional flood frequency analysis using seasonality measures

Burn, Donald H.

doi:10.1016/s0022-1694(97)00068-1

Cited by 283 publications

(251 citation statements)

References 9 publications

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“…One such source is the Dartmouth Flood Observatory (DFO), a large, publicly accessible repository of major flood events globally over 1985-2008, based on media and governmental reports and instrumental and remote-sensing sources. Delineations of affected areas are the best estimates (Brakenridge, 2011). The DFO records provide the start time, end time and duration of each flooding event, as defined by the report or source, and represented as the occurrence (start) month (Fig.…”

Section: Modeled High-flow Seasons Versus Actual Flood Recordsmentioning

confidence: 99%

Defining high-flow seasons using temporal streamflow patterns from a global model

Lee

Ward

Block

2015

Hydrol. Earth Syst. Sci.

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Abstract. Globally, flood catastrophes lead all natural hazards in terms of impacts on society, causing billions of dollars of damages annually. Here, a novel approach to defining high-flow seasons (3-month) globally is presented by identifying temporal patterns of streamflow. The main high-flow season is identified using a volume-based threshold technique and the PCR-GLOBWB model. In comparison with observations, 40 % (50 %) of locations at a station (subbasin) scale have identical peak months and 81 % (89 %) are within 1 month, indicating fair agreement between modeled and observed high-flow seasons. Minor high-flow seasons are also defined for bi-modal flow regimes. Identified major and minor high-flow seasons together are found to well represent actual flood records from the Dartmouth Flood Observatory, further substantiating the model's ability to reproduce the appropriate high-flow season. These high-spatial-resolution high-flow seasons and associated performance metrics allow for an improved understanding of temporal characterization of streamflow and flood potential, causation, and management. This is especially attractive for regions with limited observations and/or little capacity to develop early warning flood systems.

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Section: Modeled High-flow Seasons Versus Actual Flood Recordsmentioning

confidence: 99%

Defining high-flow seasons using temporal streamflow patterns from a global model

Lee

Ward

Block

2015

Hydrol. Earth Syst. Sci.

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“…The directional mean and variance measures can be represented on polar coordinates by a vector with a direction given by θ and a length r . Then, by means of equation (4), a distance (similarity) Dist i,j between two flood regimes i and j can be measured by (Burn, 1997):…”

Section: Determination Of Flood Seasonalitymentioning

confidence: 99%

“…In regional flood frequency analysis, regions are frequently delineated according to river basin similarity in flood seasonality Burn, 1997;Lecce, 2000;Cunderlik & Burn, 2002a). Also, in seasonal flood frequency models the information on flood seasonality is used for separating mixed-distribution floods generated by different atmospheric mechanisms (Hirschboeck, 1991, Ouarda et al, 2000.…”

Section: Introductionmentioning

confidence: 99%

“…Magilligan & Graber (1996) used multiple regression models for analysing relationships among the MDF and r seasonality measures and river basin physiographic controls. Burn (1997) grouped river basins according to similarity in the MDF and r measures using the region of influence (ROI) regionalization approach. Ouarda et al (1993) and Ashkar et al (1993) derived a graphical technique for determining flood seasonality from peaks-over-threshold data.…”

Section: Introductionmentioning

confidence: 99%

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Determination of flood seasonality from hydrological records / Détermination de la saisonnalité des crues à partir de séries hydrologiques

Cunderlik

Ouarda

Bobée

2004

Hydrological Sciences Journal

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The identification of flood seasonality is a procedure with many practical applications in hydrology and water resources management. Several statistical methods for capturing flood seasonality have emerged during the last decade. So far, however, little attention has been paid to the uncertainty involved in the use of these methods, as well as to the reliability of their estimates. This paper compares the performance of annual maximum (AM) and peaks-over-threshold (POT) sampling models in flood seasonality estimation. Flood seasonality is determined by two most frequently used methods, one based on directional statistics (DS) and the other on the distribution of monthly relative frequencies of flood occurrence (RF). The performance is evaluated for the AM and three common POT sampling models depending on the estimation method, flood seasonality type and sample record length. The results demonstrate that the POT models outperform the AM model in most analysed scenarios. The POT sampling provides significantly more information on flood seasonality than the AM sampling. For certain flood seasonality types, POT samples can lead to estimation uncertainty that is found in up to ten-times longer AM samples. The performance of the RF method does not depend on the flood seasonality type as much as that of the DS method, which performs poorly on samples generated from complex seasonality distributions.Key words flood seasonality; annual maximum; peaks-over-threshold; performance evaluation Détermination de la saisonnalité des crues à partir de séries hydrologiquesRésumé L'identification de la saisonnalité des crues est une procédure dont les applications sont nombreuses en hydrologie et en gestion des ressources en eau. Plusieurs méthodes statistiques pour caractériser la saisonnalité des crues ont émergé lors de la dernière décennie. Néanmoins, jusqu'à présent, l'attention a été faiblement portée sur l'incertitude impliquée par l'utilisation de ces méthodes, et sur la confiance de leurs estimations. Cet article compare les performances des modèles d'échantillonnage de maximum annuel (MA) et de dépassement (D) pour l'estimation de la saisonnalité des crues. La saisonnalité est déterminée par deux méthodes fréquemment utilisées, l'une basée sur les statistiques directionnelles (SD) et l'autre sur la distribution des fréquences relatives mensuelles des occurrences de crues (FR). La performance est évaluée pour le modèle MA et pour trois modèles D courants selon la méthode d'estimation, le type de saisonnalité et la longueur de l'échantillon enregistré. Les résultats montrent que les modèles D surpassent le modèle MA dans la plupart des scénarios analysés. L'échantillonnage D fournit une information sur la saisonnalité significativement plus importante que l'échantillonnage MA. Pour certains types de saisonnalité, les échantillons D peuvent engendrer une incertitude sur l'estimation jusqu'à dix fois plus longue que les échantillons MA. La performance de la méthode FR ne dépend pas autant de la saisonnalité que celle ...

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“…In recent years, similarity measures based on flood seasonality have become popular alternatives for identifying hydrologically homogeneous pooling groups Black and Werritty, 1997;Burn, 1997;Lecce, 2000;Castellarin et al, 2001;Cunderlik andBurn, 2002a, 2002b;Ouarda et al, 2001, Cunderlik et al, 2004a. The main advantage of this approach is that flood date data used for describing flood seasonality are practically error-free and are more robust than flood magnitude data.…”

Section: Introductionmentioning

confidence: 99%

Switching the pooling similarity distances: Mahalanobis for Euclidean

Cunderlik

Burn

2006

Water Resources Research

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[1] In recent years, catchment similarity measures based on flood seasonality have become popular alternatives for identifying hydrologically homogeneous pooling groups used in regional flood frequency analysis. Generally, flood seasonality pooling measures are less prone to errors and are more robust than measures based on flood magnitude data. However, they are also subject to estimation uncertainty resulting from sampling variability. Because of sampling variability, catchment similarity in flood seasonality can significantly deviate from the true similarity. Therefore sampling variability should be directly incorporated in the pooling algorithm to decrease the level of pooling uncertainty. This paper develops a new pooling approach that takes into consideration the sampling variability of flood seasonality measures used as pooling variables. A nonparametric resampling technique is used to estimate the sampling variability for the target site, as well as for every site that is a potential member of the pooling group for the target site. The variability is quantified by Mahalanobis distance ellipses. The similarity between the target site and the potential site is then assessed by finding the minimum confidence interval at which their Mahalanobis ellipses intersect. The confidence intervals can be related to regional homogeneity, which allows the target degree of regional homogeneity to be set in advance. The approach is applied to a large set of catchments from Great Britain, and its performance is compared with the performance of a previously used pooling technique based on the Euclidean distance. The results demonstrate that the proposed approach outperforms the previously used approach in terms of the overall homogeneity of delineated pooling groups in the study area.

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Catchment similarity for regional flood frequency analysis using seasonality measures

Cited by 283 publications

References 9 publications

Defining high-flow seasons using temporal streamflow patterns from a global model

Defining high-flow seasons using temporal streamflow patterns from a global model

Determination of flood seasonality from hydrological records / Détermination de la saisonnalité des crues à partir de séries hydrologiques

Switching the pooling similarity distances: Mahalanobis for Euclidean

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