Comparison of Two Nonstationary Flood Frequency Analysis Methods within the Context of the Variable Regime in the Representative Polish Rivers

Strupczewski, Witold G.; Kochanek, Krzysztof; Bogdanowicz, Ewa; Markiewicz, Iwona; Feluch, W.

doi:10.1515/acgeo-2015-0070

Cited by 22 publications

(22 citation statements)

References 32 publications

(26 reference statements)

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“…It can be done by assuming trends in explicit distribution parameters or in distribution moments, more precisely in the mean and the standard deviation. The second method is recommended (Strupczewski and Kaczmarek 2001;Kochanek et al 2013;Strupczewski et al 2016). It enables mutual comparisons of the results obtained by different models and the classical climatological approach based on analysis of the variation of the mean and the standard deviation of climatic data over time.…”

Section: Methodology Seasonal Approachmentioning

confidence: 99%

“…The method used for the assessment of the seasonal time dependent upper quantile is an adaptation of the so called two-stage (TS) method presented by, e.g., Strupczewski et al (2016). The TS method consists of two stages:…”

Section: Two-stage Methodsmentioning

confidence: 99%

“…• An aggregate estimation of time-dependent mean and standard deviation performed by the weighted least squares (WLS) method (Strupczewski and Kaczmarek 2001;Strupczewski et al 2016;Kochanek et al 2013), standardization of the time series using the timedependent moments, then estimation of constant shape parameters of the candidate distributions by means of the method of moments or (originally) L-moments and the calculation of the standardized quantiles.…”

Section: Two-stage Methodsmentioning

confidence: 99%

“…The studies by Markiewicz et al (2010); Strupczewski et al (2016) involved with comparison of the accuracy of the ML and TS methods for various distributions and Monte Carlo simulated sample sizes. Here, we present only the results of the first two configurations mentioned above.…”

Section: Simulation Studymentioning

confidence: 99%

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A comparison of three approaches to non-stationary flood frequency analysis

2017

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Non-stationary flood frequency analysis (FFA) is applied to statistical analysis of seasonal flow maxima from Polish and Norwegian catchments. Three non-stationary estimation methods, namely, maximum likelihood (ML), two stage (WLS/TS) and GAMLSS (generalized additive model for location, scale and shape parameters), are compared in the context of capturing the effect of non-stationarity on the estimation of time-dependent moments and design quantiles. The use of a multimodel approach is recommended, to reduce the errors due to the model misspecification in the magnitude of quantiles. The results of calculations based on observed seasonal daily flow maxima and computer simulation experiments showed that GAMLSS gave the best results with respect to the relative bias and root mean square error in the estimates of trend in the standard deviation and the constant shape parameter, while WLS/TS provided better accuracy in the estimates of trend in the mean value. Within three compared methods the WLS/TS method is recommended to deal with nonstationarity in short time series. Some practical aspects of the GAMLSS package application are also presented. The detailed discussion of general issues related to consequences of climate change in the FFA is presented in the second part of the article entitled ''Around and about an application of the GAMLSS package in non-stationary flood frequency analysis''.Keywords Non-stationary flood frequency analysis Á Maximum likelihood Á Two-stage methodology Á GAMLSS Á Multimodel approach Á Monte Carlo simulations

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Section: Methodology Seasonal Approachmentioning

confidence: 99%

Section: Two-stage Methodsmentioning

confidence: 99%

Section: Two-stage Methodsmentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

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A comparison of three approaches to non-stationary flood frequency analysis

2017

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“…The RB and the RRMSE were calculated for 10,000 simulations with a sample size of n = 50, 100 and 200 generated by considered parent distribution. Compared to the weighted least squares (WLS), described in Strupczewski and Kaczmarek (2001), Kochanek et al (2013) and Strupczewski et al (2015), the algorithm GAMLSS showed better efficiency in the estimation of the trend in the standard deviation and worse in the mean value. This finding also applies to the real flood data when sometimes the algorithm GAMLSS identified the trend in the mean as the opposite to that observed.…”

Section: Hydrological Applications and Assessment Of The Gamlss Softwarementioning

confidence: 99%

Around and about an application of the GAMLSS package to non-stationary flood frequency analysis

2017

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The non-stationarity of hydrologic processes due to climate change or human activities is challenging for the researchers and practitioners. However, the practical requirements for taking into account non-stationarity as a support in decision-making procedures exceed the up-todate development of the theory and the of software. Currently, the most popular and freely available software package that allows for non-stationary statistical analysis is the GAMLSS (generalized additive models for location, scale and shape) package. GAMLSS has been used in a variety of fields. There are also several papers recommending GAMLSS in hydrological problems; however, there are still important issues which have not previously been discussed concerning mainly GAMLSS applicability not only for research and academic purposes, but also in a design practice. In this paper, we present a summary of our experiences in the implementation of GAMLSS to nonstationary flood frequency analysis, highlighting its advantages and pointing out weaknesses with regard to methodological and practical topics.

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Identification of flood seasonality and drivers across Canada

Singh

Ghosh

Simonović́

et al. 2021

Hydrological Processes

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Floods are the most frequently occurring natural hazard in Canada. An in-depth understanding of flood seasonality and its drivers at a national scale is essential. Here, a circular, statistics-based approach is implemented to understand the seasonality of annual-maximum floods (streamflow) and to identify their responsible drivers across Canada. Nearly 80% and 70% of flood events were found to occur during spring and summer in eastern and western watersheds across Canada, respectively. Flooding in

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Comparison of Two Nonstationary Flood Frequency Analysis Methods within the Context of the Variable Regime in the Representative Polish Rivers

Cited by 22 publications

References 32 publications

A comparison of three approaches to non-stationary flood frequency analysis

A comparison of three approaches to non-stationary flood frequency analysis

Around and about an application of the GAMLSS package to non-stationary flood frequency analysis

Identification of flood seasonality and drivers across Canada

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