@article{Nerantzaki-2022-Assessing,
title = "Assessing extremes in hydroclimatology: A review on probabilistic methods",
author = "Nerantzaki, Sofia D. and
Papalexiou, S.",
journal = "Journal of Hydrology, Volume 605",
volume = "605",
year = "2022",
publisher = "Elsevier BV",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G22-29002",
doi = "10.1016/j.jhydrol.2021.127302",
pages = "127302",
abstract = "{\mbox{$\bullet$}} Comprehensive and extended review on probabilistic methods for hydroclimatic extremes. {\mbox{$\bullet$}} Synthesis of methods used in analyses of extremes in precipitation, streamflow and temperature. {\mbox{$\bullet$}} Over 20 probability distribution estimation methods in 25 comparative studies reviewed. {\mbox{$\bullet$}} Identification of most promising contemporary probabilistic methods. Here we review methods used for probabilistic analysis of extreme events in Hydroclimatology. We focus on streamflow, precipitation, and temperature extremes at regional and global scales. The review has four thematic sections: (1) probability distributions used to describe hydroclimatic extremes, (2) comparative studies of parameter estimation methods, (3) non-stationarity approaches, and (4) model selection tools. Synthesis of the literature shows that: (1) recent studies, in general, agree that precipitation and streamflow extremes should be described by heavy-tailed distributions, (2) the Method of Moments (MOM) is typically the first choice in estimating distribution parameters but it is outperformed by methods such as L-Moments (LM), Maximum Likelihood (ML), Least Squares (LS), and Bayesian Markov Chain Monte Carlo (BMCMC), (3) there are less popular parameter estimation techniques such as the Maximum Product of Spacings (MPS), the Elemental Percentile (EP), and the Minimum Density Power Divergence Estimator (MDPDE) that have shown competitive performance in fitting extreme value distributions, and (4) non-stationary analyses of extreme events are gaining popularity; the ML is the typically used method, yet literature suggests that the Generalized Maximum Likelihood (GML) and the Weighted Least Squares (WLS) may be better alternatives. The review offers a synthesis of past and contemporary methods used in the analysis of hydroclimatic extremes, aiming to highlight their strengths and weaknesses. Finally, the comparative studies summary helps the reader identify the most suitable modeling framework for their analyses, based on the extreme hydroclimatic variables, sample sizes, locations, and evaluation metrics reviewed.",
}
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<abstract>\bullet Comprehensive and extended review on probabilistic methods for hydroclimatic extremes. \bullet Synthesis of methods used in analyses of extremes in precipitation, streamflow and temperature. \bullet Over 20 probability distribution estimation methods in 25 comparative studies reviewed. \bullet Identification of most promising contemporary probabilistic methods. Here we review methods used for probabilistic analysis of extreme events in Hydroclimatology. We focus on streamflow, precipitation, and temperature extremes at regional and global scales. The review has four thematic sections: (1) probability distributions used to describe hydroclimatic extremes, (2) comparative studies of parameter estimation methods, (3) non-stationarity approaches, and (4) model selection tools. Synthesis of the literature shows that: (1) recent studies, in general, agree that precipitation and streamflow extremes should be described by heavy-tailed distributions, (2) the Method of Moments (MOM) is typically the first choice in estimating distribution parameters but it is outperformed by methods such as L-Moments (LM), Maximum Likelihood (ML), Least Squares (LS), and Bayesian Markov Chain Monte Carlo (BMCMC), (3) there are less popular parameter estimation techniques such as the Maximum Product of Spacings (MPS), the Elemental Percentile (EP), and the Minimum Density Power Divergence Estimator (MDPDE) that have shown competitive performance in fitting extreme value distributions, and (4) non-stationary analyses of extreme events are gaining popularity; the ML is the typically used method, yet literature suggests that the Generalized Maximum Likelihood (GML) and the Weighted Least Squares (WLS) may be better alternatives. The review offers a synthesis of past and contemporary methods used in the analysis of hydroclimatic extremes, aiming to highlight their strengths and weaknesses. Finally, the comparative studies summary helps the reader identify the most suitable modeling framework for their analyses, based on the extreme hydroclimatic variables, sample sizes, locations, and evaluation metrics reviewed.</abstract>
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%0 Journal Article
%T Assessing extremes in hydroclimatology: A review on probabilistic methods
%A Nerantzaki, Sofia D.
%A Papalexiou, S.
%J Journal of Hydrology, Volume 605
%D 2022
%V 605
%I Elsevier BV
%F Nerantzaki-2022-Assessing
%X \bullet Comprehensive and extended review on probabilistic methods for hydroclimatic extremes. \bullet Synthesis of methods used in analyses of extremes in precipitation, streamflow and temperature. \bullet Over 20 probability distribution estimation methods in 25 comparative studies reviewed. \bullet Identification of most promising contemporary probabilistic methods. Here we review methods used for probabilistic analysis of extreme events in Hydroclimatology. We focus on streamflow, precipitation, and temperature extremes at regional and global scales. The review has four thematic sections: (1) probability distributions used to describe hydroclimatic extremes, (2) comparative studies of parameter estimation methods, (3) non-stationarity approaches, and (4) model selection tools. Synthesis of the literature shows that: (1) recent studies, in general, agree that precipitation and streamflow extremes should be described by heavy-tailed distributions, (2) the Method of Moments (MOM) is typically the first choice in estimating distribution parameters but it is outperformed by methods such as L-Moments (LM), Maximum Likelihood (ML), Least Squares (LS), and Bayesian Markov Chain Monte Carlo (BMCMC), (3) there are less popular parameter estimation techniques such as the Maximum Product of Spacings (MPS), the Elemental Percentile (EP), and the Minimum Density Power Divergence Estimator (MDPDE) that have shown competitive performance in fitting extreme value distributions, and (4) non-stationary analyses of extreme events are gaining popularity; the ML is the typically used method, yet literature suggests that the Generalized Maximum Likelihood (GML) and the Weighted Least Squares (WLS) may be better alternatives. The review offers a synthesis of past and contemporary methods used in the analysis of hydroclimatic extremes, aiming to highlight their strengths and weaknesses. Finally, the comparative studies summary helps the reader identify the most suitable modeling framework for their analyses, based on the extreme hydroclimatic variables, sample sizes, locations, and evaluation metrics reviewed.
%R 10.1016/j.jhydrol.2021.127302
%U https://gwf-uwaterloo.github.io/gwf-publications/G22-29002
%U https://doi.org/10.1016/j.jhydrol.2021.127302
%P 127302
Markdown (Informal)
[Assessing extremes in hydroclimatology: A review on probabilistic methods](https://gwf-uwaterloo.github.io/gwf-publications/G22-29002) (Nerantzaki & Papalexiou, GWF 2022)
ACL
- Sofia D. Nerantzaki and S. Papalexiou. 2022. Assessing extremes in hydroclimatology: A review on probabilistic methods. Journal of Hydrology, Volume 605, 605:127302.
