The formation of groups for regional flood frequency analysis

Burn, Donald H.; Goel, N. K.

doi:10.1080/02626660009492308

Cited by 124 publications

(78 citation statements)

References 13 publications

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“…They then dispensed with an a priori definition of region and proposed a dynamic construction of a region based upon the similarity of the characteristics of the gauged catchments to those of the ungauged site. This method, described by Burn (1990a,b) as the region of influence (ROI) for an ungauged site, has been applied to flood estimation by Burn and Zrinji (1994), Robson and Reed (1999) and Burn and Goel (2000).…”

Section: Approaches To the Estimation Of Natural Low Flow Statisticsmentioning

confidence: 99%

A region of influence approach to predicting flow duration curves within ungauged catchments

Holmes

Young

Gustard

et al. 2002

Hydrol. Earth Syst. Sci.

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The development of regionalised hydrological models or procedures for estimating flow duration statistics has been the subject of international research since the 1970s. Historically these models have been based on multivariate statistical models that relate flow statistics to the physical and climatic characteristics of a catchment. The a priori classification of catchments has often been a component of this analysis. This paper discusses the background to the development of such models, with particular emphasis on the United Kingdom; it describes a new region of influence approach to estimating flow duration statistics and compares the performance of this method with current multivariate regression based methods for estimating flow duration statistics within the United Kingdom.

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Section: Approaches To the Estimation Of Natural Low Flow Statisticsmentioning

confidence: 99%

A region of influence approach to predicting flow duration curves within ungauged catchments

Holmes

Young

Gustard

et al. 2002

Hydrol. Earth Syst. Sci.

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“…Traditionally hard clusters were formed for RFFA by hydrologists using algorithms such as partitional [e.g., Wiltshire, 1986;Burn, 1989;Bhaskar and O'Connor, 1989;Burn and Goel, 2000], hierarchical [e.g., Mosley, 1981;Tasker, 1982;Nathan and McMahon, 1990;Burn et al, 1997], hybrid of partitional and hierarchical clustering [Hosking and Wallis, 1997;Rao and Srinivas, 2006a], and self-organizing feature maps (SOFMs) [e.g., Hall and Minns, 1999;Hall et al, 2002;Jingyi and Hall, 2004]. In a few studies [e.g., Bargaoui et al, 1998;Hall and Minns, 1999;Jingyi and Hall, 2004;Rao and Srinivas, 2006b], fuzzy clusters were formed.…”

Section: Introductionmentioning

confidence: 99%

Regional flood frequency analysis using kernel‐based fuzzy clustering approach

Basu

Srinivas

2014

Water Resources Research

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Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites.

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“…To this end, a regression model on the basis of a set of site descriptors (such as the mean elevation) can be used [Laio et al, 2011]. Note that this clustering approach was considered in a number of RFA studies [e.g., Burn and Goel, 2000;Ouarda et al, 2008]. In order to obtain normality, a required condition for clustering, nonlinear transformations were applied to some of the clustering variables: a logarithmic transformation to AREA, MBS, and MAP and a square root transformation to ABE.…”

Section: Resultsmentioning

confidence: 99%

Depth-based regional index-flood model

Wazneh

Chebana

Ouarda³

2013

Water Resour. Res.

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[1] Regional flood frequency analysis aims to estimate flood risk at sites where little or no hydrological data are available. The index-flood model is one of the commonly employed models for this purpose. In this model, the predicted value depends on the growth curve and its regional parameters. The latter are estimated as weighted averages of the at-site parameters. Traditional approaches are mainly based on site record lengths or region size to define these weights. Hence, they are not representative of the hydrological similarity between sites within a region. In addition, they are not defined to reach optimality in terms of model performance. To overcome these limitations, the present paper aims to propose a new optimal iterative weighting scheme to the index-flood model. The proposed approach is based on a number of elements : a statistical depth function to introduce similarity between sites, a weight function to amplify and control the depth values, an iterative procedure to improve estimation accuracy, and an optimization algorithm to objectively automate the choice of the weight function. A data set from the Island of Sicily (Italy) is used to compare the proposed approach with traditional ones. On the basis of the L-moments and using cluster analysis techniques, the studied region is subdivided into three homogeneous subregions. The results indicate that the proposed approach performs significantly better than traditional ones both in terms of relative bias and relative root mean squares error. The proposed approach allows identification of cross correlation in the region and provides a significant performance improvement.

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The formation of groups for regional flood frequency analysis

Cited by 124 publications

References 13 publications

A region of influence approach to predicting flow duration curves within ungauged catchments

A region of influence approach to predicting flow duration curves within ungauged catchments

Regional flood frequency analysis using kernel‐based fuzzy clustering approach

Depth-based regional index-flood model

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