Joint Probability Distribution of Streamflows and Tides in Estuaries

Loganathan, G. V.; Kuo, Chin Y.; Yannaccone, John A.

doi:10.2166/nh.1987.0017

Cited by 22 publications

(12 citation statements)

References 1 publication

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“…These three representations can account for asymptotically dependent variables, in which the statistical dependence between the variables is maintained as they become increasingly extreme. These models allow for each extreme being a different type of variable (e.g., ocean level and streamflow) and many examples are available for a lower number of dimensions . The best examples of highly dimensional extreme value techniques are spatial extreme models where the same variable is being represented at many locations (e.g., rainfall over a region) …”

Section: A Framework For Analyzing Compound Eventsmentioning

confidence: 99%

A compound event framework for understanding extreme impacts

Leonard

Westra

Phatak

et al. 2013

WIREs Climate Change

571

443

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Climate and weather variables such as rainfall, temperature, and pressure are indicators for hazards such as tropical cyclones, floods, and fires. The impact of these events can be due to a single variable being in an extreme state, but more often it is the result of a combination of variables not all of which are necessarily extreme. Here, the combination of variables or events that lead to an extreme impact is referred to as a compound event. Any given compound event will depend upon the nature and number of physical variables, the range of spatial and temporal scales, the strength of dependence between processes, and the perspective of the stakeholder who defines the impact. Modeling compound events is a large, complex, and interdisciplinary undertaking. To facilitate this task we propose the use of influence diagrams for defining, mapping, analyzing, modeling, and communicating the risk of the compound event. Ultimately, a greater appreciation of compound events will lead to further insight and a changed perspective on how impact risks are associated with climate‐related hazards. WIREs Clim Change 2014, 5:113–128. doi: 10.1002/wcc.252 This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models Assessing Impacts of Climate Change > Representing Uncertainty

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Section: A Framework For Analyzing Compound Eventsmentioning

confidence: 99%

A compound event framework for understanding extreme impacts

Leonard

Westra

Phatak

et al. 2013

WIREs Climate Change

571

443

View full text Add to dashboard Cite

show abstract

“…Correia (1987) obtained the joint distribution of flood peak and duration by using the partial duration series method on the basis of the assumptions that: (i) both flood peak and duration are exponentially distributed; and (ii) the conditional distribution of flood peak given flood duration is normal. Sackl and Bergmann (1987), Loganathan et al (1987), Chang et al (1994), Goel et al (1998), and Yue (1999Yue ( , 2000a used the bivariate normal distribution to represent the joint distributions of floods or storms. Yue (2000b) applied the bivariate lognormal distribution for describing the joint statistical properties of…”

Section: Introductionmentioning

confidence: 99%

Bivariate frequency analysis: discussion of some useful concepts in hydrological application

Yue

Rasmussen

2002

Hydrological Processes

175

119

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Abstract:Basic concepts such as conditional probability distributions, conditional return periods, and joint return periods are important to understand and to interpret multivariate hydrological events such as floods and storms. However, these concepts are not well documented in the open literature. This paper assembles and clarifies these concepts, and illustrates their practical utility. Relationships between joint return periods and univariate return periods are also derived. These concepts and relationships are demonstrated by applying a bivariate extreme value distribution to represent the joint distribution of flood peak and volume from an actual basin.

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“…Then one only needs to deal with joint distribution of the normalized variables using the multivariate normal distribution. A few efforts have been made on the bivariate normal distribution used in hydrological frequency analysis (see for example Sackl & Bergmann, 1987;Goel et al, 1998;Loganathan et al, 1987). Sackl & Bergmann (1987) and Goel et al (1998) used the bivariate normal distribution to represent the joint distribution of flood peaks and volumes.…”

Section: Open For Discussion Until 1 October 2000mentioning

confidence: 99%

“…Sackl & Bergmann (1987) and Goel et al (1998) used the bivariate normal distribution to represent the joint distribution of flood peaks and volumes. Loganathan et al (1987) applied the bivariate normal distribution to describe the joint occurrence probability of streamflows and tides in estuaries.…”

Section: Open For Discussion Until 1 October 2000mentioning

confidence: 99%

Joint probability distribution of annual maximum storm peaks and amounts as represented by daily rainfalls

Yue¹

2000

Hydrological Sciences Journal

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A procedure is presented for using the bivariate normal distribution to describe the joint distribution of storm peaks (maximum rainfall intensities) and amounts which are mutually correlated. The Box-Cox transformation method is used to normalize original marginal distributions of storm peaks and amounts regardless of the original forms of these distributions. The transformation parameter is estimated using the maximum likelihood method. The joint cumulative distribution function, the conditional cumulative distribution function, and the associated return periods can be readily obtained based on the bivariate normal distribution. The method is tested and validated using two rainfall data sets from two meteorological stations that are located in different climatic regions of Japan. The theoretical distributions show a good fit to observed ones.Distribution conjointe de la probabilité du maximum annuel de la quantité et du pic de la pluie à partir de données pluviométriques journalières Résumé Cette étude présente une méthode utilisant la distribution normale bivariée pour décrire la distribution conjointe de probabilité de la quantité et du pic de la pluviométrie. Les distributions marginales initiales sont normalisées par la transformation de Box-Cox, indépendamment de leur forme initiale. La méthode du maximum de vraisemblance permet d'estimer les paramètres de la transformation. Les fonctions de distribution cumulée (conditionnelle ou non) et les périodes de retour des événements associés sont obtenues par la méthode de la distribution normale bivariée. L'approche utilisée a été expérimentée et validée sur des données pluviométriques provenant de deux stations météorologiques du Japon, de caractéristiques climatiques différentes. On obtient une bonne concordance entre les distributions théoriques et les observations.

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Joint Probability Distribution of Streamflows and Tides in Estuaries

Cited by 22 publications

References 1 publication

A compound event framework for understanding extreme impacts

A compound event framework for understanding extreme impacts

Bivariate frequency analysis: discussion of some useful concepts in hydrological application

Joint probability distribution of annual maximum storm peaks and amounts as represented by daily rainfalls

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