Disaggregation models in hydrology revisited

Mej­ía, José; Rousselle, Jean

doi:10.1029/wr012i002p00185

Cited by 134 publications

(60 citation statements)

References 2 publications

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“…The disaggregation model proposed by Valencia and Schaake (1973) and extended by Mejia and Rousselle (1976) and Tao and Delleur (1976) allows for the generation of synthetic flows that reproduce statistics both at the annual level and at the seasonal level. Subsequent improvements and variations are described by Stedinger and Vogel (1984), Maheepala and Perera (1996), Koutsoyiannis and Manetas (1996) and Tarboton et al (1998).…”

Section: Disaggregation Modelmentioning

confidence: 99%

An Introduction to Probability, Statistics, and Uncertainty

Loucks

Beek

2017

Water Resource Systems Planning and Management

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Section: Disaggregation Modelmentioning

confidence: 99%

An Introduction to Probability, Statistics, and Uncertainty

Loucks

Beek

2017

Water Resource Systems Planning and Management

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“…Since both the cyclical and seasonal statistics of the generated time series should be similar to historic series, applications of Box-Jenkins models in hydrology were combined with the so-called disaggregation models. In this approach, annual flow series are generated first, making sure the annual statistics are on target, and then they are broken into seasonal (typically monthly) time steps using various disaggregation algorithms (Valencia and Schaake 1973, Mejia and Rousselle 1976, Koutsoyiannis 2001. A comprehensive review of the history of previous efforts is provided by Srinivas and Srinivasan (2005).…”

Section: Introductionmentioning

confidence: 99%

An effective three-step algorithm for multi-site generation of stochastic weekly hydrological time series

Ilich¹

2013

Hydrological Sciences Journal

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A new method is presented to generate stationary multi-site hydrological time series. The proposed method can handle flexible time-step length, and it can be applied to both continuous and intermittent input series. The algorithm is a departure from standard decomposition models and the Box-Jenkins approach. It relies instead on the recent advances in statistical science that deal with generation of correlated random variables with arbitrary statistical distribution functions. The proposed method has been tested on 11 historic weekly input series, of which the first seven contain flow data and the last four have precipitation data. The article contains an extensive review of the results.Key words stochastic hydrology; correlated random variables; time series; empirical distribution Un algorithme efficace en trois étapes pour la génération stochastique multi-sites de séries chronologiques hydrologiques hebdomadaires Résumé Nous présentons une nouvelle méthode de génération de séries chronologiques hydrologiques multi-sites stationnaires. La méthode proposée peut gérer différentes durées du pas de temps et elle peut être appliquée à des séries d'entrée continues ou intermittentes. L'algorithme est différent des modéles de décomposition standard et de l'approche de Box-Jenkins. Il s'appuie au contraire sur les derniéres avancées statistiques traitant de la génération de variables aléatoires corrélées de fonctions de distribution statistique arbitraires. La méthode proposée a été testée sur 11 séries d'entrée historiques hebdomadaires, sept étant des données de débits, et quatre des données de précipitations. L'article présente une analyse approfondie des résultats.

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“…In the context of stochastic modeling of streamflows, a major limitation of the widely used periodic parametric models is their inability to simultaneously reproduce summary statistics and dependence structure at different temporal levels. To circumvent this, linear disaggregation models were developed [Harms and Campbell, 1967;Valencia and Schaake, 1973;Mejia and Rousselle, 1976;Lane, 1979;Stedinger, 1988, 1990;Santos and Salas, 1992]. However, these models are not parsimonious, and in addition they require empirical adjustments in order to restore summability of the disaggregated flows to the aggregate flows, in the event of normalizing transformations being applied.…”

Section: Introductionmentioning

confidence: 99%

A hybrid stochastic model for multiseason streamflow simulation

Srinivas¹,

Srinivasan²

2001

Water Resources Research

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Abstract. A hybrid model is presented for stochastic simulation of multiseason streamflows. This involves partial prewhitening of the streamflows using a parsimonious linear periodic parametric model, followed by resampling the resulting residuals using moving block bootstrap to obtain innovations and subsequently postblackening these innovations to generate synthetic replicates. This model is simple and is efficient in reproducing both linear and nonlinear dependence inherent in the observed streamflows. The first part of this paper demonstrates the hybrid character of the model through stochastic simulations performed using monthly streamflows of Weber River (Utah) that exhibit a complex dependence structure. In the latter part of the paper the hybrid model is shown to be efficient in simulating multiseason streamflows, through an example of the San Juan River (New Mexico). This model ensures annual-to-monthly consistency without the need for any adjustment procedures. Furthermore, the hybrid model is able to preserve both within-year and cross-year monthly serial correlations for multiple lags. Also, it is seen to be consistent in predicting the reservoir storage (validation) statistic at low as well as high demand levels.

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Disaggregation models in hydrology revisited

Abstract: The disaggregation model proposed by Schaake et al. (1972) is revised to include linkages with the past at the different levels of aggregation. This modification produces a more realistic hydrologic model.

Cited by 134 publications

References 2 publications

An Introduction to Probability, Statistics, and Uncertainty

An Introduction to Probability, Statistics, and Uncertainty

An effective three-step algorithm for multi-site generation of stochastic weekly hydrological time series

A hybrid stochastic model for multiseason streamflow simulation

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