Bivariate frequency analysis of floods using a diffusion based kernel density estimator

Santhosh, D and Srinivas, VV (2013) Bivariate frequency analysis of floods using a diffusion based kernel density estimator. In: WATER RESOURCES RESEARCH, 49 (12). pp. 8328-8343.

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Abstract

Recent focus of flood frequency analysis (FFA) studies has been on development of methods to model joint distributions of variables such as peak flow, volume, and duration that characterize a flood event, as comprehensive knowledge of flood event is often necessary in hydrological applications. Diffusion process based adaptive kernel (D-kernel) is suggested in this paper for this purpose. It is data driven, flexible and unlike most kernel density estimators, always yields a bona fide probability density function. It overcomes shortcomings associated with the use of conventional kernel density estimators in FFA, such as boundary leakage problem and normal reference rule. The potential of the D-kernel is demonstrated by application to synthetic samples of various sizes drawn from known unimodal and bimodal populations, and five typical peak flow records from different parts of the world. It is shown to be effective when compared to conventional Gaussian kernel and the best of seven commonly used copulas (Gumbel-Hougaard, Frank, Clayton, Joe, Normal, Plackett, and Student's T) in estimating joint distribution of peak flow characteristics and extrapolating beyond historical maxima. Selection of optimum number of bins is found to be critical in modeling with D-kernel.

Item Type:	Journal Article
Publication:	WATER RESOURCES RESEARCH
Publisher:	AMER GEOPHYSICAL UNION
Additional Information:	Copyright for this article belongs to the AMER GEOPHYSICAL UNION, USA
Keywords:	floods; bivariate frequency analysis; kernel density estimation; copulas
Department/Centre:	Division of Mechanical Sciences > Civil Engineering
Date Deposited:	26 Feb 2014 07:32
Last Modified:	26 Feb 2014 07:32
URI:	http://eprints.iisc.ac.in/id/eprint/48473

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