Non-stationary frequency analysis of extreme precipitation in South Korea using peaks-over-threshold and annual maxima

The conventional approach to the frequency analysis of extreme precipitation is complicated by non-stationarity resulting from climate variability and change. This study utilized a non-stationary frequency analysis to better understand the time-varying behavior of short-duration (1-, 6-, 12-, and 24-h) precipitation extremes at 65 weather stations scattered across South Korea. Trends in precipitation extremes were diagnosed with respect to both annual maximum precipitation (AMP) and peaks-over-threshold (POT) extremes. Non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with model parameters made a linear function of time were applied to AMP and POT respectively. Trends detected using the Mann–Kendall test revealed that the stations showing an increasing trend in AMP extremes were concentrated in the mountainous areas (the northeast and southwest regions) of South Korea. Trend tests on POT extremes provided fairly different results, with a significantly reduced number of stations showing an increasing trend and with some stations showing a decreasing trend. For most of stations showing a statistically significant trend, non-stationary GEV and GPD models significantly outperformed their stationary counterparts, particularly for precipitation extremes with shorter durations. Due to a significant-increasing trend in the POT frequency found at a considerable number of stations (about 10 stations for each rainfall duration), the performance of modeling POT extremes was further improved with a non-homogeneous Poisson model. The large differences in design storm estimates between stationary and non-stationary models (design storm estimates from stationary models were significantly lower than the estimates of non-stationary models) demonstrated the challenges in relying on the stationary assumption when planning the design and management of water facilities. This study also highlighted the need of caution when quantifying design storms from POT and AMP extremes by showing a large discrepancy between the estimates from those two approaches.     

On the variation of frequency-based rainfall amounts: a case study for evaluating recent extreme rainfalls in Korea

Extreme rainfall events recently occurring in Korea have been shown to change frequency-based rainfall amounts quite significantly. Regardless of the reason for these extremes, the general concern of most hydrologists is how to handle these events for practical applications in Hydrology. Our study aim is to evaluate these extremes with their effect on frequency-based rainfall amounts, especially if they can be assumed to be within normal levels. As there is no commonly accepted methodology to be applied to this kind of study, we follow simplified steps such as: (1) estimation of the climatological variance of frequency-based rainfall amounts, (2) estimation of confidence intervals of frequency-based rainfall amounts (lower and upper bounds for the 5 and 1% significance levels estimated using the climatological variance), and (3) evaluation of the effect of extra rainfall events on the frequency-based rainfall amounts. Twelve stations on the Korean peninsula are selected as they have relatively longer data length. The annual maximum rainfall data collected from 1954 to 1998 are used. From this study we concluded that (1) at least 30 years of data length should be used for the frequency analysis in order to assure the stability of the variance of frequency-based rainfall amounts, (2) the climatological variances estimated all range from 5 to 8% of the frequency-based rainfall amounts, and (3) even though the frequency-based rainfall amount seems to become extreme with seemingly abnormal events, it still remains under its upper bound for the 5 or 1% significance levels estimated using the climatological variance, as well as it decays exponentially to the normal level as extra events are added. Thus, we conclude that we do not need to panic over seemingly abnormal events occurring so far, but just need to consider the variability inherent in frequency-based rainfall amounts.     

Applicability of Wakeby distribution in flood frequency analysis: a case study for eastern Australia

Parametric method of flood frequency analysis (FFA) involves fitting of a probability distribution to the observed flood data at the site of interest. When record length at a given site is relatively longer and flood data exhibits skewness, a distribution having more than three parameters is often used in FFA such as log‐Pearson type 3 distribution. This paper examines the suitability of a five‐parameter Wakeby distribution for the annual maximum flood data in eastern Australia. We adopt a Monte Carlo simulation technique to select an appropriate plotting position formula and to derive a probability plot correlation coefficient (PPCC) test statistic for Wakeby distribution. The Weibull plotting position formula has been found to be the most appropriate for the Wakeby distribution. Regression equations for the PPCC tests statistics associated with the Wakeby distribution for different levels of significance have been derived. Furthermore, a power study to estimate the rejection rate associated with the derived PPCC test statistics has been undertaken. Finally, an application using annual maximum flood series data from 91 catchments in eastern Australia has been presented. Results show that the developed regression equations can be used with a high degree of confidence to test whether the Wakeby distribution fits the annual maximum flood series data at a given station. The methodology developed in this paper can be adapted to other probability distributions and to other study areas. Copyright © 2014 John Wiley & Sons, Ltd.     

Pooled flood frequency analysis: a comparison based on peaks-over-threshold and annual maximum series

Despite some theoretical advantages of peaks-over-threshold (POT) series over annual maximum (AMAX) series, some practical aspects of flood frequency analysis using AMAX or POT series are still subject to debate. Only minor attention has been given to the POT method in the context of pooled frequency analysis. The objective of this research is to develop a framework to promote the implementation of pooled frequency modelling based on POT series. The framework benefits from a semi-automated threshold selection method. This study introduces a formalized and effective approach to construct homogeneous pooling groups. The proposed framework also offers means to compare the performance of pooled flood estimation based on AMAX or POT series. An application of the framework is presented for a large collection of Canadian catchments. The proposed POT pooling technique generally improved flood quantile estimation in comparison to the AMAX pooling scheme, and achieved smaller uncertainty associated with the quantile estimates.     

Comparison between the peaks-over-threshold method and the annual maximum method for flood frequency analysis

Abstract

Flood frequency analysis can be made by using two types of flood peak series, i.e. the annual maximum (AM) and peaks-over-threshold (POT) series. This study presents a comparison of the results of both methods for data from the Litija 1 gauging station on the Sava River in Slovenia. Six commonly used distribution functions and three different parameter estimation techniques were considered in the AM analyses. The results showed a better performance for the method of L-moments (ML) when compared with the conventional moments and maximum likelihood estimation. The combination of the ML and the log-Pearson type 3 distribution gave the best results of all the considered AM cases. The POT method gave better results than the AM method. The binomial distribution did not offer any noticeable improvement over the Poisson distribution for modelling the annual number of exceedences above the threshold.

Editor D. Koutsoyiannis

Citation Bezak, N., Brilly, M., and ?raj, M., 2014. Comparison between the peaks-over-threshold method and the annual maximum method for flood frequency analysis. Hydrological Sciences Journal, 59 (5), 959–977.     

Application of artificial neural networks in regional flood frequency analysis: a case study for Australia

Regional flood frequency analysis (RFFA) is widely used in practice to estimate flood quantiles in ungauged catchments. Most commonly adopted RFFA methods such as quantile regression technique (QRT) assume a log-linear relationship between the dependent and a set of predictor variables. As non-linear models and universal approximators, artificial neural networks (ANN) have been widely adopted in rainfall runoff modeling and hydrologic forecasting, but there have been relatively few studies involving the application of ANN to RFFA for estimating flood quantiles in ungauged catchments. This paper thus focuses on the development and testing of an ANN-based RFFA model using an extensive Australian database consisting of 452 gauged catchments. Based on an independent testing, it has been found that ANN-based RFFA model with only two predictor variables can provide flood quantile estimates that are more accurate than the traditional QRT. Seven different regions have been compared with the ANN-based RFFA model and it has been shown that when the data from all the eastern Australian states are combined together to form a single region, the ANN presents the best performing RFFA model. This indicates that a relatively larger dataset is better suited for successful training and testing of the ANN-based RFFA models.     

Selection of the best fit flood frequency distribution and parameter estimation procedure: a case study for Tasmania in Australia

Selection of a flood frequency distribution and associated parameter estimation procedure is an important step in flood frequency analysis. This is however a difficult task due to problems in selecting the best fit distribution from a large number of candidate distributions and parameter estimation procedures available in the literature. This paper presents a case study with flood data from Tasmania in Australia, which examines four model selection criteria: Akaike Information Criterion (AIC), Akaike Information Criterion—second order variant (AIC_c), Bayesian Information Criterion (BIC) and a modified Anderson–Darling Criterion (ADC). It has been found from the Monte Carlo simulation that ADC is more successful in recognizing the parent distribution correctly than the AIC and BIC when the parent is a three-parameter distribution. On the other hand, AIC and BIC are better in recognizing the parent distribution correctly when the parent is a two-parameter distribution. From the seven different probability distributions examined for Tasmania, it has been found that two-parameter distributions are preferable to three-parameter ones for Tasmania, with Log Normal appears to be the best selection. The paper also evaluates three most widely used parameter estimation procedures for the Log Normal distribution: method of moments (MOM), method of maximum likelihood (MLE) and Bayesian Markov Chain Monte Carlo method (BAY). It has been found that the BAY procedure provides better parameter estimates for the Log Normal distribution, which results in flood quantile estimates with smaller bias and standard error as compared to the MOM and MLE. The findings from this study would be useful in flood frequency analyses in other Australian states and other countries in particular, when selecting an appropriate probability distribution from a number of alternatives.     

Rainfall frequency analysis using a mixed Gamma distribution: evaluation of the global warming effect on daily rainfall. Chulsang Yoo,Kwang‐Sik Jung and Tae‐Woong Kim,hyp.5985

The original article to which this Erratum refers was published in Hydrological Processes, hyp.5985. Copyright © 2006 John Wiley & Sons, Ltd.     

Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River

As an alternative to the commonly used univariate flood frequency analysis, copula frequency analysis can be used. In this study, 58 flood events at the Litija gauging station on the Sava River in Slovenia were analysed, selected based on annual maximum discharge values. Corresponding hydrograph volumes and durations were considered. Different bivariate copulas from three families were applied and compared using different statistical, graphical and upper tail dependence tests. The parameters of the copulas were estimated using the method of moments with the inversion of Kendall's tau. The Gumbel–Hougaard copula was selected as the most appropriate for the pair of peak discharge and hydrograph volume (Q‐V). The same copula was also selected for the pair hydrograph volume and duration (V‐D), and the Student‐t copula was selected for the pair of peak discharge and hydrograph duration (Q‐D). The differences among most of the applied copulas were not significant. Different primary, secondary and conditional return periods were calculated and compared, and some relationships among them were obtained. Copyright © 2014 John Wiley & Sons, Ltd.     

Statistical distribution of seismic performance criteria of retrofitted multi-column bridge bents using incremental dynamic analysis: a case study

Probabilistic performance assessment requires the development of probability distributions that can predict different performance levels of structures with reasonable accuracy. This study evaluates the performance of a non-seismically designed multi-column bridge bent retrofitted with four different alternatives, and based on their performance under an ensemble of earthquake records it proposes accurate prediction models and distribution fits for different performance criteria as a case study. Here, finite element methods have been implemented where each retrofitting technique has been modeled and numerically validated with the experimental results. Different statistical distributions are employed to represent the variation in the considered performance criteria for the retrofitted bridge bents. The Kolmogorov-Smirnov goodness-of-fit test was carried out to compare different distributions and find the suitable distribution for each performance criteria. An important conclusion drawn here is that the yield displacement of CFRP, steel, and ECC jacketed bridge bents are best described by a gamma distribution. The crushing displacement and crushing base shear of all four retrofitted bent follow a normal and Weibull distribution, respectively. A probabilistic model is developed to approximate the seismic performance of retrofitted bridge bents. These probabilistic models and response functions developed in this study allow for the performance prediction of retrofitted bridge bents.     

Bivariate drought frequency curves and confidence intervals: a case study using monthly rainfall generation

Although water resources management practices recently use bivariate distribution functions to assess drought severity and its frequency, the lack of systematic measurements is the major hindrance in achieving quantitative results. This study aims to suggest a statistical scheme for the bivariate drought frequency analysis to provide comprehensive and consistent drought severities using observed rainfalls and their uncertainty using synthesized rainfalls. First, this study developed a multi-variate regression model to generate synthetic monthly rainfalls using climate variables as causative variables. The causative variables were generated to preserve their correlations using copula functions. This study then focused on constructing bivariate drought frequency curves using bivariate kernel functions and estimating their confidence intervals from 1,000 likely replica sets of drought frequency curves. The confidence intervals achieved in this study may be useful for making a comprehensive drought management plan through providing feasible ranges of drought severity.     

An analysis of change in alpine annual maximum discharges: implications for the selection of design discharges

The paper presents an analysis of 17 long annual maximum series (AMS) of flood flows for Swiss Alpine basins, aimed at checking the presence of changes in the frequency regime of annual maxima. We apply Pettitt's change point test, the nonparametric sign test and Sen's test on trends. We also apply a parametric goodness‐of‐fit test for assessing the suitability of distributions estimated on the basis of annual maxima collected up to a certain year for describing the frequency regime of later observations. For a number of series the tests yield consistent indications for significant changes in the frequency regime of annual maxima and increasing trends in the intensity of annual maximum discharges. In most cases, these changes cannot be explained by anthropogenic causes only (e.g. streamflow regulation, construction of dams). Instead, we observe a statistically significant relationship between the year of change and the elevation of the catchment outlet. This evidence is consistent with the findings of recent studies that explain increasing discharges in alpine catchments with an increase in the temperature controlling the portion of mountain catchments above the freezing point. Finally, we analyse the differences in return periods (RPs) estimated for a given flood flow on the basis of recent and past observations. For a large number of the study AMS, we observe that, on average, the 100‐year flood for past observations corresponds to a RP of approximately 10 to 30 years on the basis of more recent observation. From a complementary perspective, we also notice that estimated RP‐year flood (i.e. flood quantile (FQ) associated with RP) increases on average by approximately 20% for the study area, irrespectively of the RP. Practical implications of the observed changes are illustrated and discussed in the paper. Copyright © 2011 John Wiley & Sons, Ltd.     

Slope movements in the Flysch Carpathians of Eastern Czech Republic triggered by extreme rainfalls in 1997: a case study

The severe rainfalls in Northern and Central Moravia and Eastern Bohemia in July 1997, triggered slope movements recorded particularly in the flysch-type rocks of Northern and Eastern Czech Republic. The basis of the diversified woodland and agricultural landscape of the Western Carpathians is the highland and mountain erosion–denudation and structure-denudation relief, which consists of flysch rock complexes. The geomorphological and geological setting is predisposed to extensive slope deformations––especially landslides. Slope movements play an important role in topography modelling and cause great direct and indirect damages in the countryside as well. Slope deformations of different intensity and aerial extent control the character of relief on the local or regional scale. Significant changes of land use may result from the slope movements, especially when the reclamation of the slope deformations are not undertaken. Landslides themselves have a great impact on the water regime of the landscape and consequently on the development of ecosystems. That is why they are often considered to be a significant biodiversity factor. It is also necessary to pay attention to the opposite effect since the land use (dispersed woody patches, landscape mosaic) is to a certain extent influencing the development and progress of the slope deformations. Based on the order we focused our geological investigations on the area in which the slope movements began to develop to such an extent that human lives and properties were considerably endangered. The total damages from activated landslides reached about 40 million USD. The landslide research differs in individual localities and their topical situations and is accompanied by a stability assessment of the locality and a proposal of rescue measures. An alternative solution should consist of an assessment of necessary changes in the area plans of affected villages, prospects of their future habitability, and displacements of engineering networks and roads.     

Composition of high frequency ambient noise from cross-correlation:A case study using a small aperture array

Long-time cross correlation of ambient noise has been proved as a powerful tool to extract Green's function between two receivers.The study of composition of ambient noise is important for a better understanding of this method.Previous studies confirm that ambient noise in the long period (3 s and longer) mostly consists of surface wave,and 0.25-2.5 s noise consists more of body waves.In this paper,we perform cross correlation processing at much higher frequency (30-70 Hz) using ambient noise recorded by a small aperture array.No surface waves emerge from noise correlation function (NCF),but weak P waves emerge.The absence of surface wave in NCF is not due to high attenuation since surface waves are strong from active source,therefore probably the high ambient noise mostly consists of body wave and lacks surface wave.Origin of such high frequency body waves in ambient noise remains to be studied.     

Regionalisation of the parameters of the log‐Pearson 3 distribution: a case study for New South Wales,Australia

The index flood method is widely used in regional flood frequency analysis (RFFA) but explicitly relies on the identification of ‘acceptable homogeneous regions’. This paper presents an alternative RFFA method, which is particularly useful when ‘acceptably homogeneous regions’ cannot be identified. The new RFFA method is based on the region of influence (ROI) approach where a ‘local region’ can be formed to estimate statistics at the site of interest. The new method is applied here to regionalize the parameters of the log‐Pearson 3 (LP3) flood probability model using Bayesian generalized least squares (GLS) regression. The ROI approach is used to reduce model error arising from the heterogeneity unaccounted for by the predictor variables in the traditional fixed‐region GLS analysis. A case study was undertaken for 55 catchments located in eastern New South Wales, Australia. The selection of predictor variables was guided by minimizing model error. Using an approach similar to stepwise regression, the best model for the LP3 mean was found to use catchment area and 50‐year, 12‐h rainfall intensity as explanatory variables, whereas the models for the LP3 standard deviation and skewness only had a constant term for the derived ROIs. Diagnostics based on leave‐one‐out cross validation show that the regression model assumptions were not inconsistent with the data and, importantly, no genuine outlier sites were identified. Significantly, the ROI GLS approach produced more accurate and consistent results than a fixed‐region GLS model, highlighting the superior ability of the ROI approach to deal with heterogeneity. This method is particularly applicable to regions that show a high degree of regional heterogeneity. Copyright © 2014 John Wiley & Sons, Ltd.     

分层扰动位能理论及其应用——以南海夏季风的年际变化为例

在扰动位能(PPE)理论的基础上,针对不同高度上局地环流能量转换问题,本文提出了分层扰动位能(LPPE)的概念.研究表明850hPa的分层扰动位能一阶矩(LPPE1)在热带地区为正,高纬度地区为负,200 hPa高度LPPE1在北美高纬度地区出现正值分布,100 hPa及以上LPPE1热带地区为负,高纬度为正.LPPE1冬季半球的分布与年平均相似,北半球夏季大陆上出现正的极大值.在局地,LPPE1在数值上远远大于分层扰动位能二阶矩(LPPE2)及更高阶矩,因此,LPPE的分布与LPPE1的分布相似.南海季风区低层动能的季节变化与LPPE呈现反向变化关系.相关分析表明,南海夏季风(SCSSM)与春季的LPPE1偶极型分布之间存在着显著的年际(正)相关关系,可以作为SCSSM强度的一个预报因子.春季赤道印度洋、西太平洋海表温度(SST)的负(正)异常对应春季、夏季LPPE1的南负北正(南正北负)偶极型分布,夏季(JJAS) LPPE1的偶极型分布与南海季风区动能的一致增大(减小)是两者耦合模态的主导模态,夏季南海季风区的西风增强(减弱), SCSSM增强(减弱),这是能量异常影响SCSSM的一个可能的机制.     

The two-component extreme value distribution for flood frequency analysis: Derivation of a new estimation method

The two component extreme value (TCEV) distribution has recently been shown to account for most of the characteristics of the real flood experience. A new method of parameter estimation for this distribution is derived using the principle of maximum entropy (POME). This method of parameter estimation is suitable for application in both the site-specific and regional cases and appears simpler than the maximum likelihood estimation method. Statistical properties of the regionalized estimation were evaluated using a Monte Carlo approach and compared with those of the maximum likelihood regional estimators.     

A bivariate gamma distribution for use in multivariate flood frequency analysis

A gamma distribution is one of the most frequently selected distribution types for hydrological frequency analysis. The bivariate gamma distribution with gamma marginals may be useful for analysing multivariate hydrological events. This study investigates the applicability of a bivariate gamma model with five parameters for describing the joint probability behavior of multivariate flood events. The parameters are proposed to be estimated from the marginal distributions by the method of moments. The joint distribution, the conditional distribution, and the associated return periods are derived from marginals. The usefulness of the model is demonstrated by representing the joint probabilistic behaviour between correlated flood peak and flood volume and between correlated flood volume and flood duration in the Madawask River basin in the province of Quebec, Canada. Copyright © 2001 John Wiley & Sons, Ltd.