scholarly journals Flood frequency analysis by an event-based rainfall-runoff model in selected catchments of southern Poland

2018 ◽  
Vol 13 (No. 3) ◽  
pp. 170-176 ◽  
Author(s):  
Młyński Dariusz ◽  
Petroselli Andrea ◽  
Walega Andrzej
Keyword(s):  
Statistical Method ◽  
Return Period ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Peak Flows ◽  
Southern Poland ◽  
Annual Peak ◽  
Pearson Type ◽  
Pearson Type Iii Distribution ◽  
Event Based

The study evaluated the applicability of Event-Based Approach for Small and Ungauged Basins (EBA4SUB) for calculating annual peak flows with a specific return period (Q<sub>T</sub>) in southern Poland. Data used in the calculations in a form of observation series of annual peak flows were derived from the Institute of Meteorology and Water Management in Warsaw and covered a multi-year period 1971–2015. The data were statistically verified for their homogeneity, significance of monotonic trends, outliers and equality of variances. Peak flows with a given return period were estimated by a statistical method of Pearson Type III distribution, and by EBA4SUB model. The analysis showed that Q<sub>T</sub> for the investigated catchments was the most accurately matching the values derived from the statistical method when EBA4SUB model was employed. This was evidenced by the values of average relative errors that reached 34% for EBA4SUB model (with beta hyetograph). The results of the study demonstrated usefulness of EBA4SUB model for the estimation of Q<sub>T</sub> quantiles in catchments of the upper Vistula water region.

Le risque d'inondation et son évolution sur la rivière Châteauguay

1999 ◽  
Vol 26 (2) ◽  
pp. 186-196 ◽  
Author(s):  
M -C Bouillon ◽  
F P Brissette ◽  
C Marche
Keyword(s):  
Return Period ◽  
Flood Risk ◽  
Flood Frequency ◽  
Water Levels ◽  
Flood Frequency Analysis ◽  
Pearson Type ◽  
First Results ◽  
Order Of Magnitude ◽  
Study Sites ◽  
Pearson Type Iii Distribution

This article presents the first results of a three-year study that aimed at studying, understanding, and characterizing the evolution of flood risk in Quebec. In this study, flood risk is defined as the product of the return period of an event and the damages caused by this event. It is therefore important that both these components of the flood risk be assessed historically. The two components have been evaluated for a 32 km reach of the Châteauguay River located between the Canadian-American border and Ormstown, Quebec. A flood frequency analysis was undertaken on historical flow data for two gauging stations on the river and the data fitted with a log-Pearson type III distribution. The flood risk was then established using a three-step methodology. The first step was to establish flood levels over a range of discharges using a hydraulic model. Then the computed water levels were processed to define the flooded area and determine the property damage. The last step established the global flood risk, taking into account the complete flood distribution function. The results show that over the last 60 years, the global flood risk has increased for all of the study sites along the reach of interest. When the global flood risk is standardized based on population, the evolution of the risk differs greatly between study sites. For one site, the standardized global flood risk has increased by one order of magnitude over the period studied. The results also demonstrate that 75% of the global flood risk is due to floods having a return period of 4 years or less.Key words: flood, risk, damages, numerical modelling, flood forecasting.[Journal translation]

scholarly journals Flood Frequency Analysis for Burhi Gandak River Basin

2020 ◽  
pp. 82-89
Author(s):  
Kuldeepak Pal ◽  
Kanhu Charan Panda ◽  
Gaurav Sharma ◽  
Suryansh Mandloi
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Stream Flow ◽  
Flood Frequency ◽  
Peak Discharge ◽  
Flood Frequency Analysis ◽  
Discharge Data ◽  
Type Iii ◽  
Pearson Type ◽  
Pearson Type Iii Distribution

The study is aimed at finding the best distribution to match the steam flow and calculation of magnitude and frequency of flow. In the current study, we have used several statistical distributions to find the best fit distribution for stream flow and used flood frequency analysis techniques to find the magnitude and frequency of stream flow and non-exceedance probability of peak discharge. The study has been performed at Sikandarpur and Rosera gauging sites of BurhiGandak River. Historical (50 years) maximum annual peak discharge data of each station are used for statistical analysis for estimating maximum peak discharge in 5, 10, 25, 50, 100 year return period. In this study, Lognormal distribution, Galton distribution, Gamma distribution, Log Pearson Type III distribution, Gumbell distribution, Generalised extreme values distribution have been considered to describe the annual maximum stream flow. Flood frequency analysis methods were used for estimating the magnitude of the extreme flow events and their associated return periods. For both Sikandarpur and Rosera stations, Log Pearson type III distributions showed the lowest value of K–S and Chi-square test statistic. The annual probable peak discharge for 5, 10, 25, 50, and 100 years return period is calculated for each distribution. The most suitable distribution for both the stations is found to be the log-Pearson type III distribution.

Flood Frequency Analysis of the Mahi Basin by Using Log Pearson Type III Probability Distribution

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Uttam Pawar ◽  
Pramodkumar Hire
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Return Period ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Series Data ◽  
Period Range ◽  
Type Iii ◽  
Pearson Type ◽  
Data Points

Flood frequency analysis is one of the techniques of examination of peak stream flow frequency and magnitude in the field of flood hydrology, flood geomorphology and hydraulic engineering. In the present study, Log Pearson Type III (LP-III) probability distribution has applied for flood series data of four sites on the Mahi River namely Mataji, Paderdi Badi, Wanakbori and Khanpur and of three sites on its tributaries such as Anas at Chakaliya, Som at Rangeli and Jakham at Dhariawad. The annual maximum series data for the record length of 26-51 years have been used for the present study. The time series plots of the data indicate that two largest ever recorded floods were observed in the year 1973 and 2006 on the Mahi River. The estimated discharges of 100 year return period range between 3676 m3/s and 47632 m3/s. The return period of the largest ever recorded flood on the Mahi River at Wankbori (40663 m3/s) is 127-yr. The recurrence interval of mean annual discharges (Qm) is between 2.73-yr and 3.95-yr, whereas, the return period of large floods (Qlf) range from 6.24-yr to 9.33-yr. The magnitude-frequency analysis curves represent the reliable estimates of the high floods. The fitted lines are fairly close to the most of the data points. Therefore, it can be reliably and conveniently used to read the recurrence intervals for a given magnitude and vice versa.

scholarly journals The Effect of Involving Exceptional Outlier Data on Design Flood Magnitude

2015 ◽  
Vol 10 (2) ◽  
pp. 698-706
Author(s):  
Bagher Heidarpour ◽  
Bahram Saghafian ◽  
Saeed Golian
Keyword(s):  
Return Period ◽  
Rare Events ◽  
Flood Frequency ◽  
The Other ◽  
Flood Frequency Analysis ◽  
Data Series ◽  
Design Flood ◽  
Type Iii ◽  
Pearson Type ◽  
Outlier Data

The term "outlier" is generally used to refer to single data points that appear to depart significantly from the trend of the other data. Outliers are classified into three types: incorrect observations, rare events resulting from essentially the same phenomena as the other maxima, and rare events resulting from a different phenomenon. Flood frequency analysis was first performed on complete data series (including the outlier) and then on the series with the outlier removed. Results revealed that omission of the outlier data didn’t affect the probability distribution function (Log-Pearson type III), but the design discharge reduced by 60 percent in 10000 year return period from 3320 (m3/s) to 1340 (m3/s). Furthermore, the method proposed by the U.S. Water Resources Council (WRC), and the HEC-SSP software were applied in order to compose outlier data with other systematic data and to modify the parameters of the statistical distribution. Using WRC method, the estimated 10000-year flood was equaled to 1907 (m3/s) by designating the outlier as the 200-year return period and revising the parameters of Log-Pearson type III distribution; that is about 43 percent decrease over the scenario involving the outlier.

scholarly journals Improved Mixed Distribution Model Considering Historical Extraordinary Floods under Changing Environment

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1016 ◽  
Author(s):  
Jianzhu Li ◽  
Yanchen Zheng ◽  
Yimin Wang ◽  
Ting Zhang ◽  
Ping Feng ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Distribution Model ◽  
Return Periods ◽  
Design Flood ◽  
Mixed Distribution ◽  
Pearson Type ◽  
The Difference ◽  
Pearson Type Iii Distribution

Historical extraordinary floods are an important factor in non-stationary flood frequency analysis and they may occur at any time, regardless of whether the environment is changing or not. Based on mixed distribution (MD) modeling, this paper proposed an improved mixed distribution (IMD) model to consider the discontinuity and non-stationarity of flood samples simultaneously, which adds historical extraordinary floods in both sub-series divided by a change point. As a case study, the annual maximum peak discharge and volume series of Ankang hydrological station, located in the upper Hanjiang River Basin of China, were selected to identify non-stationarity by using the variation diagnosis system. MD and IMD were used to fit the flood characteristic series and a genetic algorithm was employed to estimate the optimal parameters. Compared with the design flood values fitted by the stationary Pearson type-III distribution, the results computed by IMD decreased at low return periods and increased at high return periods, with the difference varying from −6.67% to 7.19%. The results highlighted that although the design flood values of IMD are slightly larger than those of MD with different return periods, IMD provided a better result than MD. IMD provides a new perspective for non-stationary flood frequency analysis.

scholarly journals Flood Frequency Analysis for the Annual Peak Flows Simulated by an Event-Based Rainfall-Runoff Model in an Urban Drainage Basin

Water ◽  
2014 ◽  
Vol 6 (12) ◽  
pp. 3841-3863 ◽  
Author(s):  
Jeonghwan Ahn ◽  
Woncheol Cho ◽  
Taereem Kim ◽  
Hongjoon Shin ◽  
Jun-Haeng Heo
Keyword(s):  
Frequency Analysis ◽  
Drainage Basin ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Urban Drainage ◽  
Rainfall Runoff ◽  
Annual Peak ◽  
Rainfall Runoff Model ◽  
Event Based ◽  
Runoff Model

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.

scholarly journals Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

2014 ◽  
Vol 18 (1) ◽  
pp. 353-365 ◽  
Author(s):  
U. Haberlandt ◽  
I. Radtke
Keyword(s):  
Time Series ◽  
Frequency Analysis ◽  
Model Calibration ◽  
Hydrological Model ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Rainfall Data ◽  
Flood Frequency Analysis ◽  
Peak Flows ◽  
Hourly Rainfall

Abstract. Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the application for derived flood frequency analysis.

Sign in / Sign up

Export Citation Format

Share Document