Regional growth curves and improved design value estimates of extreme precipitation events in the Czech Republic

Kyselý, Jan; Picek, Jan

doi:10.3354/cr033243

Cited by 84 publications

(69 citation statements)

References 36 publications

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“…Note that the set of climatological characteristics makes the latter variant of the ROI method consistent with the HW regional approach (Section 3.2), in which the delineation of fixed regions makes use of cluster analysis based on the same climatological characteristics (Kyselý and Picek, 2007). The site attributes are used to calculate the Euclidean distance between each pair of sites in the attribute space.…”

Section: Roi Methodsmentioning

confidence: 99%

“…The percentage of the missing daily records in the entire dataset was only 0.05%. Compared to the regional frequency analysis by Kyselý and Picek (2007), the current dataset involves a much larger number of sites (209 against 78), more evenly covers the area of the Czech Republic, and extends to the very recent past.…”

Section: Datamentioning

confidence: 99%

“…Basic features of the precipitation regime of the Czech Republic, with a focus on extremes, are described in Kyselý and Picek (2007) and Kyselý (2009).…”

Section: Datamentioning

confidence: 99%

“…The delineation of homogeneous regions for 1D and multi-day precipitation extremes in the Czech Republic has been updated and modified with respect to the original one presented in Kyselý and Picek (2007). The reason for revisiting the original results of the homogeneity tests was the extension of the dataset of daily precipitation amounts, which now consists of 209 stations (compared to 78 in Kyselý and Picek, 2007), includes more recent past (up to 2007 compared to 2000), and altogether covers 9573 station-years (compared to the original 3120). The two largest regions of the original regionalization became heterogeneous when the new data were included, and have been split into three and two smaller areas.…”

Section: Durationmentioning

confidence: 99%

“…The methodology has been widely applied into modelling probabilities of heavy precipitation in many European regions, including the United Kingdom (Fowler and Kilsby, 2003), Belgium (Gellens, 2002), Slovakia (Gaál, 2006), Italy (Norbiato et al, 2007) and the Czech Republic (Kyselý and Picek, 2007). In the latter study, the area of the Czech Republic was divided into four homogeneous regions; this regionalization is revised in the present paper (Section 3.2), after data from a dense network of rain-gauges became available, since some of the original regions were found heterogeneous with more data involved in the tests.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of regional and at‐site approaches to modelling probabilities of heavy precipitation

Kyselý¹,

Gaál²,

Picek³

2011

Intl Journal of Climatology

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Several approaches to estimating distributions of precipitation extremes are compared by means of simulation experiments, and their applications into observed data in the Czech Republic are evaluated. Regional frequency models, which take into account data in fixed or flexible 'regions' when fitting a distribution at any site, lead to estimates with much smaller errors compared to a single-site analysis, and efficiently reduce random and climatologically irrelevant variations in the estimates of the model parameters and high quantiles. The region-of-influence (ROI) methodology with a built-in regional homogeneity test is recognized as a useful approach, with the model based on proximity of sites outperforming the Hosking-Wallis regional frequency analysis. Comparison of estimates of the return period of a heavy precipitation event on 24 June 2009, which triggered a disastrous local flash flood, illustrates that the at-site analysis leads to unrealistic and extremely uncertain estimates that strongly depend on whether or not a single outlying observation is involved in the sample, while all regional methods yield return periods in the same order of several hundreds of years, notwithstanding whether the 2009 data are included in the sample. The ROI method may be found useful for modelling probabilities of other meteorological variables, extremes of which are strongly influenced by sampling variability, and may also represent an efficient tool for 'smoothing' random variations in the estimates of model parameters and high quantiles of precipitation in high-resolution regional climate model simulations.

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Section: Roi Methodsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

“…Basic features of the precipitation regime of the Czech Republic, with a focus on extremes, are described in Kyselý and Picek (2007) and Kyselý (2009).…”

Section: Datamentioning

confidence: 99%

Section: Durationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparison of regional and at‐site approaches to modelling probabilities of heavy precipitation

Kyselý¹,

Gaál²,

Picek³

2011

Intl Journal of Climatology

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Incorporating spatial dependence in regional frequency analysis

Wang

Yan

Zhang

2014

Water Resources Research

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The efficiency of regional frequency analysis (RFA) is undermined by intersite dependence, which is usually ignored in parameter estimation. We propose a spatial index flood model where marginal generalized extreme value distributions are joined by an extreme-value copula characterized by a max-stable process for the spatial dependence. The parameters are estimated with a pairwise likelihood constructed from bivariate marginal generalized extreme value distributions. The estimators of model parameters and return levels can be more efficient than those from the traditional index flood model when the max-stable process fits the intersite dependence well. Through simulation, we compared the pairwise likelihood method with an L-moment method and an independence likelihood method under various spatial dependence models and dependence levels. The pairwise likelihood method was found to be the most efficient in mean squared error if the dependence model was correctly specified. When the dependence model was misspecified within the max-stable models, the pairwise likelihood method was still competitive relative to the other two methods. When the dependence model was not a max-stable model, the pairwise likelihood method led to serious bias in estimating the shape parameter and return levels, especially when the dependence was strong. In an illustration with annual maximum precipitation data from Switzerland, the pairwise likelihood method yielded remarkable reduction in the standard errors of return level estimates in comparison to the L-moment method.

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The relationship between Monte Carlo estimators of heterogeneity and error for daily to monthly time steps in a small Minnesota precipitation gauge network

Wright

Ferreira

Houck

et al. 2015

Water Resources Research

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Precipitation quantile estimates are used in engineering, agriculture, and a variety of other disciplines. Index flood regional frequency methods pool normalized gauge data in the case of homogeneity among the constituent gauges of the region. Unitless regional quantile estimates are outputted and rescaled at each gauge. Because violation of the homogeneity hypothesis is a major component of quantile estimation error in regional frequency analysis, heterogeneity estimators should be ''reasonable proxies'' of the error of quantile estimation. In this study, three Monte Carlo heterogeneity statistics tested in Hosking and Wallis (1997) are plotted against Monte Carlo estimates of quantile error for all five-or-more-gauge regionalizations in a 12 gauge network in the Twin Cities region of Minnesota. Upper-tail quantiles with nonexceedance probabilities of 0.75 and above are examined at time steps ranging from daily to monthly. A linear relationship between heterogeneity and error estimates is found and quantified using Pearson's r score. Two of Hosking and Wallis's (1997) heterogeneity measures, incorporating the coefficient of variation in one case and additionally the skewness in the other, are found to be reasonable proxies for quantile error at the L-moment ratio values characterizing these data. This result, in addition to confirming the utility of a commonly used coefficient of variation-based heterogeneity statistic, provides evidence for the utility of a heterogeneity measure that incorporates skewness information.

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Regional growth curves and improved design value estimates of extreme precipitation events in the Czech Republic

Cited by 84 publications

References 36 publications

Comparison of regional and at‐site approaches to modelling probabilities of heavy precipitation

Comparison of regional and at‐site approaches to modelling probabilities of heavy precipitation

Incorporating spatial dependence in regional frequency analysis

The relationship between Monte Carlo estimators of heterogeneity and error for daily to monthly time steps in a small Minnesota precipitation gauge network

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