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

Fiorentino, Mauro; Arora, Kishore; Singh, Vijay P.

doi:10.1007/bf01543891

Cited by 19 publications

(8 citation statements)

References 9 publications

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“…The parameters of TTMD in Equation need to be estimated jointly from the AMFS. Many parameter estimation approaches have been proposed to estimate the statistical parameters of mixture distributions (Evin et al ., ; Fiorentino et al ., ; Leytham, ; Rossi et al ., ). Among them, the expectation–maximization (EM) algorithm is the most popular approach despite its weakness of local maximization and divergence with small samples.…”

Section: Methodsmentioning

confidence: 99%

Frequency analysis of nonstationary annual maximum flood series using the time‐varying two‐component mixture distributions

Yan

Xiong

Liu

et al. 2016

Hydrological Processes

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The most popular practice for analysing nonstationarity of flood series is to use a fixed single-type probability distribution incorporated with the time-varying moments. However, the type of probability distribution could be both complex because of distinct flood populations and time-varying under changing environments. To allow the investigation of this complex nature, the time-varying two-component mixture distributions (TTMD) method is proposed in this study by considering the time variations of not only the moments of its component distributions but also the weighting coefficients. Having identified the existence of mixed flood populations based on circular statistics, the proposed TTMD was applied to model the annual maximum flood series of two stations in the Weihe River basin, with the model parameters calibrated by the meta-heuristic maximum likelihood method. The performance of TTMD was evaluated by different diagnostic plots and indexes and compared with stationary single-type distributions, stationary mixture distributions and time-varying single-type distributions. The results highlighted the advantages of TTMD with physically-based covariates for both stations. Besides, the optimal TTMD models were considered to be capable of settling the issue of nonstationarity and capturing the mixed flood populations satisfactorily. The most optimal model with time or physically-based covariates is highlighted in bold. Stationarity in the last column means the situation where distribution parameters do not vary with explanatory variables. 78 L. YAN ET AL.

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Section: Methodsmentioning

confidence: 99%

Frequency analysis of nonstationary annual maximum flood series using the time‐varying two‐component mixture distributions

Yan

Xiong

Liu

et al. 2016

Hydrological Processes

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“…Its parameters were estimated by the maximum likelihood (ML) method. Fiorentino et al (1987) estimated the parameters by using the principle of maximum entropy (POME). The POME method was found simpler than the ML method, and was applicable in both the site-specific and regional cases.…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

Frequency analysis of nonidentically distributed hydrologic flood data

Singh

Wang²,

Zhang

2005

Journal of Hydrology

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“…Several approaches for estimating TCEV regional parameters were developed. We can mention: a maximum likelihood procedure (TCEV-ML, [22,32]); a probability-weighted moments procedure (TCEV-PWM, [11]); a methodology using the Principle of Maximum Entropy (TCEV-POME, [33]).…”

Section: The Tcev Distributionmentioning

confidence: 99%

Stationary and Non-Stationary Frameworks for Extreme Rainfall Time Series in Southern Italy

Luca

Galasso

2018

Water

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This study tests stationary and non-stationary approaches for modelling data series of hydro-meteorological variables. Specifically, the authors considered annual maximum rainfall accumulations observed in the Calabria region (southern Italy), and attention was focused on time series characterized by heavy rainfall events which occurred from 1 January 2000 in the study area. This choice is justified by the need to check if the recent rainfall events in the new century can be considered as very different or not from the events occurred in the past. In detail, the whole data set of each considered time series (characterized by a sample size N > 40 data) was analyzed, in order to compare recent and past rainfall accumulations, which occurred in a specific site. All the proposed models were based on the Two-Component Extreme Value (TCEV) probability distribution, which is frequently applied for annual maximum time series in Calabria. The authors discussed the possible sources of uncertainty related to each framework and remarked on the crucial role played by ergodicity. In fact, if the process is assumed to be non-stationary, then ergodicity cannot hold, and thus possible trends should be derived from external sources, different from the time series of interest: in this work, Regional Climate Models’ (RCMs) outputs were considered in order to assess possible trends of TCEV parameters. From the obtained results, it does not seem essential to adopt non-stationary models, as significant trends do not appear from the observed data, due to a relevant number of heavy events which also occurred in the central part of the last century.

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

Cited by 19 publications

References 9 publications

Frequency analysis of nonstationary annual maximum flood series using the time‐varying two‐component mixture distributions

Frequency analysis of nonstationary annual maximum flood series using the time‐varying two‐component mixture distributions

Frequency analysis of nonidentically distributed hydrologic flood data

Stationary and Non-Stationary Frameworks for Extreme Rainfall Time Series in Southern Italy

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