Comparison of two methods of regional flood frequency analysis by using L-moments

Mosaffaie, Jamal

doi:10.1134/s0097807815030112

Cited by 20 publications

(9 citation statements)

References 19 publications

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“…Several physiographical and meteorological variables were estimated and employed in our analysis. The first seven variables (7Var) included in the database have been used generally in previous studies (Ouarda et al 2001;Shu and Ouarda 2007;Mosaffaie 2015). These include drainage area (AREA), length of the main channel (LMC), slope of the main channel (SMC), mean basin slope (MBS), curve number (CN), annual mean precipitation (AMP), and annual mean temperature (AMT).…”

Section: A Datasetmentioning

confidence: 99%

On the Value of River Network Information in Regional Frequency Analysis

Jung

Ouarda²,

Marpu

2021

Journal of Hydrometeorology

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Regional frequency analysis (RFA) is widely used in the design of hydraulic structures at locations where streamflow records are not available. RFA estimates depend on the precise delineation of homogenous regions for accurate information transfer. This study proposes new physiographical variables based on river network features and tests their potential to improve the accuracy of hydrological feature estimates. Information about river network types is used both in the definition of homogenous regions and in the estimation process. Data from 105 river basins in arid and semi-arid regions of the USA were used in our analysis. Artificial neural network ensemble models and canonical correlation analysis were used to produce flood quantile estimates, which were validated through tenfold cross- and jackknife validations. We conducted analysis for model performance based on statistical indices, such as the Nash–Sutcliffe Efficiency, root mean square error, relative root mean square error, mean absolute error, and relative mean bias. Among various combinations of variables, a model with 10 variables produced the best performance. Further, 49, 36, and 20 river networks in the 105 basins were classified as dendritic, pinnate, and trellis networks, respectively. The model with river network classification for the homogenous regions appeared to provide a superior performance compared with a model without such classification. The results indicated that including our proposed combination of variables could improve the accuracy of RFA flood estimates with the classification of the network types. This finding has considerable implications for hydraulic structure design.

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Section: A Datasetmentioning

confidence: 99%

On the Value of River Network Information in Regional Frequency Analysis

Jung

Ouarda²,

Marpu

2021

Journal of Hydrometeorology

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“…There are many RFFA techniques that have been proposed in different countries, such as Probabilistic Rational Method (Pilgrim et al 1987;Rahman et al 2010), Index Flood Method (IFM) (Dalrymple 1960;Formetta et al 2018;Kalai et al 2020;Mosaffaie 2015;Stedinger and Lu 1995;Strnad et al 2020;Yang 2016), Quantile Regression Techniques (QRT) (Ahn and Palmer 2016;Formetta et al 2021;Haddad et al 2015;Mosaffaie 2015;Rahman 2005;, Parameter Regression Technique (PRT) (Ahn and Palmer 2016;Haddad and Rahman 2012;Haddad et al 2015;Perez et al 2019), artificial intelligence based methods (Aziz et al 2017;Janizadeh and Vafakhah 2021;Khan et al 2021;Rahman et al 2019b;Sharifi Garmdareh et al 2018;Zalnezhad et al 2022a, b, c) and some mixed and combined methods (Ahn and Palmer 2016;Allahbakhshian-Farsani et al 2020;Aziz et al 2013;Brodie 2013;Formetta et al 2021;Janizadeh and Vafakhah 2021;Mosavi et al 2018;Vafakhah et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Design flood estimation at ungauged catchments using index flood method and quantile regression technique: a case study for South East Australia

Zalnezhad,

Rahman,

Ahamed

et al. 2023

Nat Hazards

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Flood is one of the worst natural disasters, which causes the damage of billions of dollars each year globally. To reduce the flood damage, we need to estimate design floods accurately, which are used in the design and operation of water infrastructure. For gauged catchments, flood frequency analysis can be used to estimate design floods; however, for ungauged catchments, regional flood frequency analysis (RFFA) is used. This paper compares two popular RFFA techniques, namely the quantile regression technique (QRT) and the index flood method (IFM). A total of 181 catchments are selected for this study from south-east Australia. Eight predictor variables are used to develop prediction equations. It has been found that IFM outperforms QRT in general. For higher annual exceedance probabilities (AEPs), IFM generally demonstrates a smaller estimation error than QRT; however, for smaller AEPs (e.g. 1 in 100), QRT provides more accurate quantile estimates. The IFM provides comparable design flood estimates with the Australian Rainfall and Runoff—the national guide for design flood estimation in Australia.

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“…The method of L-moments has been widely used in the regionalization of hydrologic data, although there are different approaches for regionalization. Due to their several advantages over conventional moments (Sankarasubramanian and Srinivasan, 1999;Gubareva and Garstman, 2010), the procedure of L-moments has been adopted progressively in hydrologic studies by many researchers since the introduction of the approach (Abolverdi and Khalili, 2010;Modarres, 2010;Zakaria and Shabri, 2013;Anilan et al, 2015;Mosaffaie, 2015;Sarmadi and Shokoohi, 2015;Yin et al, 2015;Serra et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Regional extreme rainfall estimation in the Middle Black Sea Region, Turkey

Yürekli

Enginsu²,

Erdoğan

2022

Atm.

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The occurrence probabilities of heavy rainfalls brought about flood events have an essential role in designing water-related structures and water resource management. In many cases, data for analysis are either not available or are insufficient for reliable design of water-related structures. Regional frequency analysis is frequently preferred to provide design information at sites with especially inadequate data available. Our study implemented L-moment procedures to annual maximum rainfall series from 70 gauging stations in the Middle Black Sea Region (MBSR) in Turkey to estimate regional rainfall quantiles. The first attempt for regionalization was to evaluate the entire area as an only homogeneous region. The sub-regions were initially defined with the ward’s clustering algorithm due to the presence of discordant sites under a presumption of a single homogeneous region. In compliance with the results of the discordancy and heterogeneity measures, the most promising classification was achieved with 6 clusters (sub-regions). The sub-regions have satisfied the homogeneity condition as “acceptably homogeneous.” The GEV and GLO distributions in five sub-regions, GNO distribution in four sub-regions, and PE3 distribution in three sub-regions were decided to be acceptable as a regional frequency distribution. In comparison, GPA is a candidate distribution in none of six sub-regions.

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Comparison of two methods of regional flood frequency analysis by using L-moments

Cited by 20 publications

References 19 publications

On the Value of River Network Information in Regional Frequency Analysis

On the Value of River Network Information in Regional Frequency Analysis

Design flood estimation at ungauged catchments using index flood method and quantile regression technique: a case study for South East Australia

Regional extreme rainfall estimation in the Middle Black Sea Region, Turkey

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