Selecting the best probability distribution for at-site flood frequency analysis; a study of Torne River

Hassan, Mahmood Ul; Hayat, Omar; Noreen, Zahra

doi:10.1007/s42452-019-1584-z

Cited by 36 publications

(45 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In lines with the findings in Hassan et al (2019) and Ahmad, Fawad, and Mahmood (2015), from the results of present analysis, a single distribution does not emerge as the best‐suited distribution for all gauging sites. The sample size is not an important factor in favour of any particular distribution or estimation method.…”

Section: Resultssupporting

confidence: 86%

“…The basic descriptive statistics of five gauging sites are provided in Table 3. The basic assumptions in the statistical flood frequency analysis and fitting of probability distributions are independence, randomness, stationarity and skewness of the data series (Afreen & Muhammad, 2012; Hassan, Hayat, & Noreen, 2019; Kite, 1977). Therefore, in this article Wald‐Wolfowitz (WW) and Augmented Dickey‐Fuller (ADF) tests are applied to test for randomness and stationarity, respectively, while independence and skewness are judged by correlation coefficient (r) at lag‐1.…”

Section: Resultsmentioning

confidence: 99%

“…The sample size is not an important factor in favour of any particular distribution or estimation method. Overall, the LM method is the best estimation method for the identification of a suitable distribution (also see, Ahmad et al, 2015; Hassan et al, 2019). The site having the highest average of annual maxima of flood and the catchment area is best modelled by GEV distribution while the site having the lowest average of annual maxima of flood and the catchment area is best modelled by LN3 distribution (see Tables 1, 3, and 6).…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Some best‐fit probability distributions for at‐site flood frequency analysis of the Ume River

Kousar

Khan

Hassan

et al. 2020

J Flood Risk Management

Self Cite

View full text Add to dashboard Cite

At-site flood frequency analysis is a direct method of flood estimation at a given site. The choice of an appropriate probability distribution and parameter estimation method plays a vital role in at-site frequency analysis. In the current article, flood frequency analysis is carried out at five gauging sites of the Ume River in Sweden. Generalised extreme value, three-parameter lognormal, generalised logistic and Gumbel distributions are fitted to the annual maximum peak flow data. The L-moment and the maximum likelihood methods are used to estimate the parameters of the distributions. Based on different goodness-of-fit tests and accuracy measures, the three-parameter lognormal distribution has been identified as the best-fitted distribution by using the L-moments method of estimation for gauging sites Harrsele Krv, Gardiken and Överuman Nedre. The generalised extreme value distribution with the Lmoments estimation provided the best fit to maximum annual streamflow at gauging sites Solberg and Stornorrfors Krv. Finally, the best-fitted distribution for each gauging site is used to predict the maximum flow of water for return

show abstract

Section: Resultssupporting

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Some best‐fit probability distributions for at‐site flood frequency analysis of the Ume River

Kousar

Khan

Hassan

et al. 2020

J Flood Risk Management

Self Cite

View full text Add to dashboard Cite

show abstract

“…The principal statistical assumptions of at-site and RFA include independence, stationarity and randomness of data series at each gauging site (see e.g., Ul Hassan et al 2019;Hussain et al 2017;Shahzadi et al 2013 andKousar et al 2020). To investigate the serial correlation of the observed data series at each gauging site, we use the lag-1 correlation coefficient (r).…”

Section: Resultsmentioning

confidence: 99%

Regional frequency analysis of annual daily rainfall maxima in Skåne, Sweden

Hassan

Noreen

Ahmed

2021

Intl Journal of Climatology

Self Cite

View full text Add to dashboard Cite

Extreme daily rainfall events are critical for the urban drainage system, human life, agriculture and small catchments. The information about extreme rainfall magnitudes and frequencies is immensely important for civil engineers, city planners, scientists related to water management, rescue operations and flood control works. This study illustrates the results of regional frequency analysis (RFA) of annual maximum daily rainfall (AMDR) of Skåne County, Sweden. L-moments based heterogeneity measure (H) reveals that the Skåne County is a homogeneous region. Based on the L-moment ratio diagram and Z Dist statistic results, the generalized normal (GNO) distribution is selected as the most suitable regional distribution. The accuracy measures used in K-fold cross validation indicate that support vector machine (SVM) model is an appropriate model to find the index rainfall at ungauged sites in the region. The sites characteristics, elevation and latitude are identified as the most important variables to explain the variation in mean annual maximum daily rainfall (MAMDR). Finally, spatial maps of predicted MAMDR for different return periods are constructed by using index rainfall combined with regional quantiles. Spatial maps offer an overall view of the expected MAMDR in the region that is helpful for multiple decision makers including infrastructure planners, city planners, emergency managers, engineers and many others. K E Y W O R D S K-fold cross validation, L-moments, Monte Carlo simulation, quantile estimates, regional frequency analysis, spatial map 1 | INTRODUCTION Daily extreme rainfall is one of the utmost environmental events which cause sizeable damages throughout the world. The rainfall recorded in the last century was the highest over the past 8,500 years (Hong et al., 2014). A reliable estimate of extreme rainfall magnitude is important for flood management, irrigation, rescue operations, water management, construction of urban drainage systems and many other purposes. Skåne is the tenth largest county of Sweden. It has the best agricultural land and is known as a breadbasket of Sweden. It stands for about half of Sweden's food production (Skåne Region, 2014). Rainfall is an important factor for agriculture in Skåne. Skåne agriculture and food industries are highly dependent on this hydrological

show abstract

“…But, no single distribution methods can be accepted as the universal distribution for describing the flood frequency for any gauging site. The selection of suitable distribution methods typically relies on the properties of data of a particular site [2].…”

Section: Introductionmentioning

confidence: 99%

Flood Frequency Analysis for Burhi Gandak River Basin

Hanwat

Pal

Panda

et al. 2020

IJECC

View full text Add to dashboard Cite

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.

show abstract

Selecting the best probability distribution for at-site flood frequency analysis; a study of Torne River

Cited by 36 publications

References 16 publications

Some best‐fit probability distributions for at‐site flood frequency analysis of the Ume River

Some best‐fit probability distributions for at‐site flood frequency analysis of the Ume River

Regional frequency analysis of annual daily rainfall maxima in Skåne, Sweden

Flood Frequency Analysis for Burhi Gandak River Basin

Contact Info

Product

Resources

About