scholarly journals A Bivariate Extreme Value Distribution Applied to Flood Frequency Analysis

2001 ◽  
Vol 32 (1) ◽  
pp. 49-64 ◽  
Author(s):  
Sheng Yue
Keyword(s):  
Method Of Moments ◽  
Logistic Model ◽  
Joint Distribution ◽  
Extreme Value Distribution ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Return Periods ◽  
Conditional Distributions ◽  
Flood Events ◽  
Bivariate Extreme Value Distribution

This article presents a procedure for use of the Gumbel logistic model to represent the joint distribution of two correlated extreme events. Parameters of the distribution are estimated using the method of moments. On the basis of marginal distributions, the joint distribution, the conditional distributions, and the associated return periods can be deduced. The applicability of the model is demonstrated by using multiple episodic flood events of the Harricana River basin in the province of Quebec, Canada. It is concluded that the model is useful for describing joint probabilistic behavior of multivariate flood events.

Analysis of Magnitude and Frequency of Floods in the Damanganga Basin: Western India

2021 ◽  
Vol 5 (1) ◽  
pp. 1-11
Author(s):  
Vitthal Anwat ◽  
Pramodkumar Hire ◽  
Uttam Pawar ◽  
Rajendra Gunjal
Keyword(s):  
Probability Distribution ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Western India ◽  
Type I ◽  
Return Periods ◽  
Pearson Type ◽  
Kolmogorov Smirnov ◽  
Anderson Darling

Flood Frequency Analysis (FFA) method was introduced by Fuller in 1914 to understand the magnitude and frequency of floods. The present study is carried out using the two most widely accepted probability distributions for FFA in the world namely, Gumbel Extreme Value type I (GEVI) and Log Pearson type III (LP-III). The Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) methods were used to select the most suitable probability distribution at sites in the Damanganga Basin. Moreover, discharges were estimated for various return periods using GEVI and LP-III. The recurrence interval of the largest peak flood on record (Qmax) is 107 years (at Nanipalsan) and 146 years (at Ozarkhed) as per LP-III. Flood Frequency Curves (FFC) specifies that LP-III is the best-fitted probability distribution for FFA of the Damanganga Basin. Therefore, estimated discharges and return periods by LP-III probability distribution are more reliable and can be used for designing hydraulic structures.

Performance of L-moments and nonparametric flood frequency analysis

1994 ◽  
Vol 21 (5) ◽  
pp. 856-862 ◽  
Author(s):  
Denis Gingras ◽  
Kaz Adamowski
Keyword(s):  
Frequency Analysis ◽  
Extreme Value Distribution ◽  
Nonparametric Methods ◽  
Extreme Value ◽  
Flood Frequency ◽  
Value Distribution ◽  
Generalized Extreme Value Distribution ◽  
Flood Frequency Analysis ◽  
Generalized Extreme Value ◽  
L Moments

A simulation study was undertaken to compare parametric L-moments and nonparametric approaches in flood frequency analysis. Data of various sample lengths were generated from a given generalized extreme value distribution and the quantiles estimated using the fixed-kernel nonparametric method and from a generalized extreme value distribution fitted by L-moments. From the resulting root-mean-square errors for various quantiles, it was concluded for unimodal distributions that nonparametric methods are preferable for large return period floods estimated from short (<30 years) samples while parametric methods are preferable in other circumstances. It was also pointed out that nonparametric methods are more suitable for mixed distributions. Key words: frequency analysis, L-moments, nonparametric methods, simulation.

scholarly journals Historical changes in frequency of extreme floods in Prague

2015 ◽  
Vol 19 (10) ◽  
pp. 4307-4315 ◽  
Author(s):  
L. Elleder
Keyword(s):  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Flood Events ◽  
Central European ◽  
Extreme Floods ◽  
Flood Peak ◽  
The Past ◽  
Documentary Sources ◽  
Instrumental Period ◽  
Peak Discharges

Abstract. This study presents a flood frequency analysis for the Vltava River catchment using a major profile in Prague. The estimates of peak discharges for the pre-instrumental period of 1118–1824 based on documentary sources were carried out using different approaches. 187 flood peak discharges derived for the pre-instrumental period augmented 150 records for the instrumental period of 1825–2013. Flood selection was based on Q10 criteria. Six flood-rich periods in total were identified for 1118–2013. Results of this study correspond with similar studies published earlier for some central European catchments, except for the period around 1750. Presented results indicate that the territory of the present Czech Republic might have experienced extreme floods in the past, comparable – with regard to peak discharge (higher than or equal to Q10) and frequency – to the flood events recorded recently.

scholarly journals Use of historical data in flood frequency analysis: a case study for four catchments in Norway

2017 ◽  
Vol 49 (2) ◽  
pp. 466-486 ◽  
Author(s):  
Kolbjørn Engeland ◽  
Donna Wilson ◽  
Péter Borsányi ◽  
Lars Roald ◽  
Erik Holmqvist
Keyword(s):  
Flood Frequency ◽  
Water Levels ◽  
Added Value ◽  
Flood Frequency Analysis ◽  
Return Periods ◽  
Design Flood ◽  
Historical Information ◽  
Improve Design ◽  
Estimation Uncertainty ◽  
Flood Estimation

Abstract There is a need to estimate design floods for areal planning and the design of important infrastructure. A major challenge is the mismatch between the length of the flood records and needed return periods. A majority of flood time series are shorter than 50 years, and the required return periods might be 200, 500, or 1,000 years. Consequently, the estimation uncertainty is large. In this paper, we investigated how the use of historical information might improve design flood estimation. We used annual maximum data from four selected Norwegian catchments, and historical flood information to provide an indication of water levels for the largest floods in the last two to three hundred years. We assessed the added value of using historical information and demonstrated that both reliability and stability improves, especially for short record lengths and long return periods. In this study, we used information on water levels, which showed the stability of river profiles to be a major challenge.

scholarly journals Reassessing flood frequency For the Sussex Ouse, Lewes: the Inclusion of historical Flood Information since AD 1650

2013 ◽  
Vol 1 (6) ◽  
pp. 7615-7646 ◽  
Author(s):  
N. Macdonald ◽  
T. R. Kjeldsen ◽  
I. Prosdocimi ◽  
H. Sangster
Keyword(s):  
Sensitivity Analysis ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Flood Events ◽  
Combined Analysis ◽  
Historical Information ◽  
Site Analysis ◽  
Flood Level ◽  
Severe Flooding

Abstract. The application of historical flood information as a tool for augmenting instrumental flood data is increasingly recognised as a valuable tool; most previous studies have focused on large catchments with historic settlements, this paper applies the approach to the smaller lowland system of the Sussex Ouse in Southeast England. The reassessment of flood risk on the Sussex Ouse is pertinent in light of severe flooding in October 2000 and heightened concerns of a perceived increase in flooding nationally. Systematic flood level readings from 1960 and accounts detailing past flood events within the catchment are compiled back to c.1750. This extended flood record provides an opportunity to reassess estimates of flood frequency over a timescale not normally possible within flood frequency analysis. This paper re-evaluates flood frequency at Lewes on the Sussex Ouse downstream of the confluence of the Sussex Ouse and River Uck. The paper considers the strengths and weaknesses in estimates resulting from contrasting methods of analysis and their corresponding data: (i) single site analysis of gauged annual maxima; (ii) combined analysis of systematic annual maxima augmented with historical peaks of estimated magnitude; (iii) combined analysis of systematic annual maxima augmented with historical peaks of estimated magnitude exceeding a known threshold, and (iv) sensitivity analysis including only the very largest historical flood events. Use of the historical information was found to yield much tighter confidence intervals of risk estimates, with uncertainty reduced by up to 40% for the 100 yr return frequency event when historical information was added to the gauged data.

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