Postupanje Sa Izuzetnim Vrednostima U Statističkoj Analizi Velikih Voda

Blagojević, Borislava; Mihailović, Vladislava; Plavšić, Jasna

doi:10.14415/konferencijagfs2014.081

Cited by 3 publications

(3 citation statements)

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“…GEV has been confirmed as the best distribution function in a large number of European basins [48,49] and beyond [50,51]. As a positive feature, the consistent estimation of the confidence interval is found [52], as well as minor sensitivity of the right distribution tail to low outliers, which is of considerable practical importance in flood analysis [53].…”

Section: At-site Flood Quantile Estimationmentioning

confidence: 84%

A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions

et al. 2023

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Flood quantile estimation in ungauged basins is often performed using regional analysis. A regionalization procedure consists of two phases: the definition of homogeneous regions among gauged basins, i.e., clusters of stations, and information transfer to the ungauged sites. Due to its simplicity and widespread use, a combination of hierarchical clustering by Ward’s algorithm and the index-flood method is applied in this research. While hierarchical clustering is very efficient, its shortcomings are the lack of flexibility in the definition of clusters/regions and the inability to transfer objects/stations from one cluster center to another. To overcome this, using silhouette width for induced clustering of stations in flood studies is proposed in this paper. A regionalization procedure is conducted on 53 gauging stations under a continental climate in the West Balkans. In the induced clustering, a negative silhouette width is used as an indicator for the relocation of station(s) to another cluster. The estimates of mean annual flood and 100-year flood quantiles assessed by the original and induced clustering are compared. A jackknife procedure is applied for mean annual flood estimation and 100-year flood quantiles. Both the Hosking–Wallis and Anderson–Darling bootstrap tests provide better results regarding the homogeneity of the defined regions for the induced clustering compared to the original one. The goodness-of-fit measures indicate improved clustering results by the proposed intervention, reflecting flood quantile estimation at the stations with significant overestimation by the original clustering.

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Section: At-site Flood Quantile Estimationmentioning

confidence: 84%

A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions

et al. 2023

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“…A new Multiple Grubbs-Beck outlier statistical test in B17C is a replacement for the simple Grubbs-Beck test of B17B, and as such, it is incorporated in the HEC-SSP. It is used in the outlier detection procedure, and in the outlier treatment, censoring for low outliers is applied [7]. Also, a multiple-threshold plotting positions according to Hirsch and Stedinger [8] is a default option in the HEC-SSP Bulletin 17C analysis.…”

Section: Methodsmentioning

confidence: 99%

Missing data representation by perception thresholds in flood flow frequency assessment

Đokić¹,

Blagojević²,

Mihailović³

2021

J Appl Eng Science

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Flood flow frequency analysis (FFA) plays one of the key roles in many fields of hydraulic engineering and water resources management. The output of the FFA are sets of flood quantiles which are the base for the next step of the flood related analyses. The reliability of these results depends of many factors, and the first one is the reliability of the input data - datasets of the annual peak flow. In practice, however, engineers often encounter the problem of incomplete datasets (missing data, data gaps and/or broken records). In this paper, we perform at-site focused analysis, and a complete dataset of annual peak flows from 1931 to 2016 at the hydrologic station Senta of the Tisa river we use as the reference dataset. From this original dataset we remove some data and thus we obtain 15 new series that have gaps of different lengths and locations. Each dataset we further subject to flood frequency assessment using USACE HEC-SSP Bulletin 17C analysis, which introduces the concept of „perception threshold“ that can be used for missing data representation. For the data representation in HEC-SSP we use infinity for perception threshold upper bound and different lower bounds for all missing flows in one dataset, so that we create 56 variants of input HEC-SSP datasets. The flood flow quantiles assessed from the datsets with missing data and different perception thresholds we evaluate through percentage error relative to the reference dataset and confidence interval width as uncertainty measure. The results for datasets with one gap up to 23% of the observation period, indicate acceptable flood quantile estimates are obtained even for larger return periods, by setting a lower perception threshold bound at the value of the highest observed flow in the available series of annual maxima.

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“…Blagojević et al [23] used the GB test on 68 streamflow gauging stations in Serbia to investigate the presence of high and low flow outliers in the AM flood series and concluded that outlier detection is important in design flood estimation. Lamontagne et al [24] proposed a MGB test to identify PILFs in AM flood data from California and they found that censoring and identifying PILFs improved FFA results to be more robust and precise.…”

Section: Introductionmentioning

confidence: 99%

Low-Flow Identification in Flood Frequency Analysis: A Case Study for Eastern Australia

Rima,

Haddad,

Rahman

2024

Water

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Design flood estimation is an essential step in many water engineering design tasks such as the planning and design of infrastructure to reduce flood damage. Flood frequency analysis (FFA) is widely used in estimating design floods when the at-site flood data length is adequate. One of the problems in FFA with an annual maxima (AM) modeling approach is deciding how to handle smaller discharge values (outliers) in the selected AM flood series at a given station. The objective of this paper is to explore how the practice of censoring (which involves adjusting for smaller discharge values in FFA) affects flood quantile estimates in FFA. In this regard, two commonly used probability distributions, log-Pearson type 3 (LP3) and generalized extreme value distribution (GEV), are used. The multiple Grubbs and Beck (MGB) test is used to identify low-flow outliers in the selected AM flood series at 582 Australian stream gauging stations. It is found that censoring is required for 71% of the selected stations in using the MGB test with the LP3 distribution. The differences in flood quantile estimates between LP3 (with MGB test and censoring) and GEV distribution (without censoring) increase as the return period reduces. A modest correlation is found (for South Australian catchments) between censoring and the selected catchment characteristics (correlation coefficient: 0.43), with statistically significant associations for the mean annual rainfall and catchment shape factor. The findings of this study will be useful to practicing hydrologists in Australia and other countries to estimate design floods using AM flood data by FFA. Moreover, it may assist in updating Australian Rainfall and Runoff (national guide).

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Postupanje Sa Izuzetnim Vrednostima U Statističkoj Analizi Velikih Voda

Cited by 3 publications

References 0 publications

A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions

A Silhouette-Width-Induced Hierarchical Clustering for Defining Flood Estimation Regions

Missing data representation by perception thresholds in flood flow frequency assessment

Low-Flow Identification in Flood Frequency Analysis: A Case Study for Eastern Australia

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