scholarly journals Hierarchical Bayesian clustering for nonstationary flood frequency analysis: Application to trends of annual maximum flow in Germany

2015 ◽  
Vol 51 (8) ◽  
pp. 6586-6601 ◽  
Author(s):  
Xun Sun ◽  
Upmanu Lall ◽  
Bruno Merz ◽  
Nguyen Viet Dung
Keyword(s):  
Frequency Analysis ◽  
Maximum Flow ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Bayesian Clustering ◽  
Hierarchical Bayesian ◽  
Annual Maximum ◽  
Analysis Application

scholarly journals Flood Frequency Analysis at the Downstream of Sg. Perak River Basin using Annual Maximum Flow Discharge Data

2018 ◽  
Vol 7 (4.35) ◽  
pp. 709 ◽  
Author(s):  
Munir Snu ◽  
Sidek L.M ◽  
Haron Sh ◽  
Noh Ns.M ◽  
Basri H ◽  
...  
Keyword(s):  
Probability Distribution ◽  
River Basin ◽  
Frequency Analysis ◽  
Return Period ◽  
Maximum Flow ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
Discharge Data ◽  
Flow Discharge

The recent flood event occurred in 2014 had caused disaster in Perak and Sungai Perak is the main river of Perak which is a major natural drainage system within the state. The aim of this paper is to determine the expected discharge to return period downstream for Sg. Perak River Basin in Perak by using annual maximum flow data. Flood frequency analysis is a technique to assume the flow values corresponding to specific return periods or probabilities along the river at a different site. The method involves the observed annual maximum flow discharge data to calculate statistical information such as standard deviations, mean, sum, skewness and recurrence intervals. The flood frequency analysis for Sg. Perak River Basin was used Log Pearson Type-III probability distribution method. The annual maximum peak flow series data varying over period 1961 to 2016. The probability distribution function was applied to return periods (T) where T values are 2years, 5years, 10years, 25years, 50years, and 100years generally used in flood forecasting. Flood frequency curves are plotted after the choosing the best fits probability distribution for annual peak maximum data. The results for flood frequency analysis shows that Sg. Perak at Jambatan Iskandar much higher inflow discharge  which is 3714.45m3/s at the 100years return period compare to Sg. Plus at Kg Lintang and Sg. Kinta at Weir G. With this, the 100years peak flow at Sg Perak river mouth is estimated to be in the range of 4,000 m3/s. Overall, the analysis relates the expected flow discharge to return period for all tributaries of Sg. Perak River Basin.

Application of Higher Probability Weighted Moments in Flood Frequency Analysis

2012 ◽  
Vol 518-523 ◽  
pp. 4139-4143
Author(s):  
Yang Li ◽  
Song Bai Song
Keyword(s):  
Frequency Analysis ◽  
Maximum Flow ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
Gev Distribution ◽  
Probability Weighted Moments ◽  
Northern Shaanxi ◽  
Flood Analysis ◽  
Better Than

This paper aims to study the use of higher probability moments (PWMs) for flood frequency analysis. By estimating the parameters of GEV distribution and matching higher PWMs to annual maximum flow series in northern Shaanxi. The results show that higher PWMs describe the data reasonably better than lower PWMs in flood analysis. This method involves no more complication than lower PWMs that be commonly used, and is suitable for flood frequency analysis.

Comparison of Annual Maximum and Peaks Over Threshold Methods with Automated Threshold Selection in Flood Frequency Analysis: A Case Study for Australia

2021 ◽  
Author(s):  
Xiao Pan ◽  
Ataur Rahman
Keyword(s):  
Frequency Analysis ◽  
Flood Frequency ◽  
Distribution Functions ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
Threshold Selection ◽  
Peaks Over Threshold ◽  
Design Flood ◽  
Threshold Detection ◽  
Using Data

Abstract Flood frequency analysis (FFA) enables fitting of distribution functions to observed flow data for estimation of flood quantiles. Two main approaches, Annual Maximum (AM) and peaks-over-threshold (POT) are adopted for FFA. POT approach is under-employed due to its complexity and uncertainty associated with the threshold selection and independence criteria for selecting peak flows. This study evaluates the POT and AM approaches using data from 188 gauged stations in south-east Australia. POT approach adopted in this study applies a different average numbers of events per year fitted with Generalised Pareto (GP) distribution with an automated threshold detection method. The POT model extends its parametric approach to Maximum Likelihood Estimator (MLE) and Point Moment Weighted Unbiased (PMWU) method. Generalised Extreme Value (GEV) distribution using L-moment estimator is used for AM approach. It has been found that there is a large difference in design flood estimates between the AM and POT approaches for smaller average recurrence intervals (ARI), with a median difference of 25% for 1.01 year ARI and 5% for 50 and 100 years ARIs.

scholarly journals Flood frequency analysis for annual maximum streamflow using a non-stationary GEV model

2019 ◽  
Vol 79 ◽  
pp. 03022
Author(s):  
Shangwen Jiang ◽  
Ling Kang
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Yangtze River ◽  
Flood Frequency ◽  
Additive Models ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
The Yangtze River ◽  
Stationary Model ◽  
Risk Of Failure

Under changing environment, the streamflow series in the Yangtze River have undergone great changes and it has raised widespread concerns. In this study, the annual maximum flow (AMF) series at the Yichang station were used for flood frequency analysis, in which a time varying model was constructed to account for non-stationarity. The generalized extreme value (GEV) distribution was adopted to fit the AMF series, and the Generalized Additive Models for Location, Scale and Shape (GAMLSS) framework was applied for parameter estimation. The non-stationary return period and risk of failure were calculated and compared for flood risk assessment between stationary and non-stationary models. The results demonstrated that the flow regime at the Yichang station has changed over time and a decreasing trend was detected in the AMF series. The design flood peak given a return period decreased in the non-stationary model, and the risk of failure is also smaller given a design life, which indicated a safer flood condition in the future compared with the stationary model. The conclusions in this study may contribute to long-term decision making in the Yangtze River basin under non-stationary conditions.

scholarly journals A grey-based method for evaluating the effects of rating curve uncertainty on frequency analysis of annual maxima

2012 ◽  
Vol 15 (1) ◽  
pp. 194-210 ◽  
Author(s):  
S. Alvisi ◽  
M. Franchini
Keyword(s):  
Frequency Analysis ◽  
Measurement Errors ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
Rating Curve ◽  
Upper Limits ◽  
Using Data ◽  
River Po ◽  
Discharge Measurements

A grey-based technique for characterizing the rating curve uncertainty due to discharge measurement errors and its effect on flood frequency analysis is here presented. On the basis of river stage and discharge measurements, the grey parameters of the rating curve are estimated by using a grey non-linear regression. Commencing with this grey rating curve and a set of annual maximum stages, we show how the probability distribution (here assumed of EV1 type) of the grey annual maximum discharges can be estimated. The grey EV1 distribution can be estimated through two approaches, the first of which directly exploits the grey discharges corresponding to the annual maximum stages, whereas with the second approach two different sets of extreme (crisp) discharges, and therefore two EV1 distributions of extreme (crisp) values which delimit the grey discharges of a given return period, are obtained by considering the lower and upper limits of the grey parameters of the rating curve. The methodology is illustrated using data pertaining to a gauged section of the River Po (Italy). The results show that the first approach yields a wider grey EV1 distribution with respect to that resulting from the second approach: physical justification of this is given.

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