Nonparametric Flood‐Frequency Analysis With Historical Information

Adamowski, Kaz; Feluch, W.

doi:10.1061/(asce)0733-9429(1990)116:8(1035)

Cited by 40 publications

(22 citation statements)

References 11 publications

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“…Previous research has shown that the choice of the kernel function, such as the Gaussian or Epanechnikov kernel, only has a minor impact on the ®nal PDF (Adamowski and Feluch, 1990). That study's asymmetric Epanechnikov kernel function, K, is used with an optimal smoothing factor, h, obtained by the least-squares cross-validation method.…”

Section: Non-parametric Methodsmentioning

confidence: 99%

Regional flood frequency analysis using GIS, L-moment and geostatistical methods

Daviau¹,

Adamowski

Patry

2000

Hydrol. Process.

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Section: Non-parametric Methodsmentioning

confidence: 99%

Regional flood frequency analysis using GIS, L-moment and geostatistical methods

Daviau¹,

Adamowski

Patry

2000

Hydrol. Process.

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“…The parametric estimation approach is commonly used in hydrologic FA. In the univariate context some nonparametric approaches have been employed in hydrologic FA [see, e.g., Adamowski and Feluch, 1990;Ouarda et al, 2001] but these methods are of limited use for hydraulic design of major structures as indicated by Singh and Strupczewski [2002].…”

Section: Multivariate Quantilesmentioning

confidence: 99%

Index flood–based multivariate regional frequency analysis

Chebana

Ouarda

2009

Water Resources Research

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[1] Because of their multivariate nature, several hydrological phenomena can be described by more than one correlated characteristic. These characteristics are generally not independent and should be jointly considered. Consequently, univariate regional frequency analysis (FA) cannot provide complete assessment of true probabilities of occurrence. The objective of the present paper is to propose a procedure for regional flood FA in a multivariate framework. In the present paper, the focus is on the estimation step of regional FA. The proposed procedure represents a multivariate version of the index flood model and is based on copulas and a multivariate quantile version with a focus on the bivariate case. The model offers increased flexibility to designers by leading to several scenarios associated with the same risk. The univariate quantiles represent special cases corresponding to the extreme scenarios. A simulation study is carried out to evaluate the performance of the model in a bivariate framework. Simulation results show that bivariate FA provides the univariate quantiles with equivalent accuracy. Similarity is observed between results of the bivariate model and those of the univariate one in terms of the behavior of the corresponding performance criteria. The procedure performs better when the regional homogeneity is high. Furthermore, the impacts of small variations in the record length at gauged sites and the region size on the performance of the proposed procedure are not significant.

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“…The nonparametric method can solve the linear limitation problem for the reason that the process of parent distribution assumption is avoided. Therefore, it was employed and emphasized in this field gradually [38][39][40][41][42][43][44]. Nevertheless, the defect of the nonparametric method is also evident that there is no specific criteria about how to select a better kernel function and window width in different situations.…”

Section: Introductionmentioning

confidence: 99%

The Probability Density Evolution Method for Flood Frequency Analysis: A Case Study of the Nen River in China

Wang

Zhou

Zhang

2015

Water

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Abstract:A new approach for flood frequency analysis based on the probability density evolution method (PDEM) is proposed. It can avoid the problem of linear limitation for flood frequency analysis in a parametric method and avoid the complex process for choosing the kernel function and window width in the nonparametric method. Based on the annual maximum peak discharge (AMPD) in 54 years from the Dalai hydrologic station which is located on the downstream of Nen River in Heilongjiang Province of China, a joint probability density function (PDF) model about AMPD is built by the PDEM. Then, the numerical simulation results of the joint PDF model are given by adopting the one-sided difference scheme which has the property of direction self-adaptive. After that, according to the relationship between the marginal function and joint PDF, the PDF of AMPD can be obtained. Finally, the PDF is integrated and the frequency curve could be achieved. The results indicate that the flood frequency curve obtained by the PDEM has a better agreement with the empirical frequency than that of the parametric method widely used at present. The method based on PDEM is an effective way for hydrologic frequency analysis. OPEN ACCESSWater 2015, 7 5135 Keywords: probability density evolution method (PDEM); annual maximum peak discharge (AMPD); flood frequency analysis

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Nonparametric Flood‐Frequency Analysis With Historical Information

Cited by 40 publications

References 11 publications

Regional flood frequency analysis using GIS, L-moment and geostatistical methods

Regional flood frequency analysis using GIS, L-moment and geostatistical methods

Index flood–based multivariate regional frequency analysis

The Probability Density Evolution Method for Flood Frequency Analysis: A Case Study of the Nen River in China

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