Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows

Srinivas, V. V.; Srinivasan, K.

doi:10.1016/j.jhydrol.2004.07.011

Cited by 70 publications

(45 citation statements)

References 42 publications

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“…Among them it might be mentioned the k-nearest neighborhood resampling [13] [15] or the hybrid approach of [16]. The best and simplest one for the problem considered here is the moving block bootstrap used in [12].…”

Section: Wind Power Series Synthesis Using the Movingmentioning

confidence: 99%

“…The other proposed techniques were developed mainly for hydrological series, which have different features from the hourly wind production series considered here. Besides, the k-nearest neighborhood raises considerable problems for the selection of the optimal dimensioning of the kernel functions needed in it [16], and the residual resampling schemes issued in the hybrid approach does not have great advantages over the simpler moving block bootstrap method [12]. In the method proposed here, a variant of the k-nearest neighborhood will be applied for improving the selection of the subsequent block, aiming at improving the similarity between the original and synthetized series.…”

Section: Wind Power Series Synthesis Using the Movingmentioning

confidence: 99%

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Synthesis of hourly wind power series using the Moving Block Bootstrap method

Usaola

2014

2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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Abstract-Reliability studies of power systems will have to include different renewable resources, because of their increasing importance in the generation mix. Wind is now the most developed intermittent renewable source of energy, and wind power series are needed to include it in Monte Carlo based studies. However, to generate correct wind power series for a given power system using ARIMA/GARCH methods is complex because of their nonlinear character and seasonality. In this paper a simpler approach based on the Moving Block Bootstrap method is proposed. This method is easy to implement, and effective if there is enough representative data. The paper describes the features of wind power production in peninsular Spain, proposes a bootstrap method to generate wind power production scenarios and shows the applicability of the method by means of an adequacy study of the IEEE-RTS system adding wind and concentrating solar power.

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Section: Wind Power Series Synthesis Using the Movingmentioning

confidence: 99%

Section: Wind Power Series Synthesis Using the Movingmentioning

confidence: 99%

Synthesis of hourly wind power series using the Moving Block Bootstrap method

Usaola

2014

2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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“…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%

“…As documented by Bras and Rodriguez-Iturbe (1985), classical time series models involve significant effort and knowledge to identify the appropriate model and estimate its parameters, as well as to assess the shape of the multivariate probabilities and their transformations from normal to skewed distributions. Srinivas and Srinivasan (2005) point out that, in spite of the numerous reports on models in stochastic hydrology, none has gained universal acceptance. In fact, the US Bureau of Reclamation (USBR) uses a simple approach of recycling subsets of historical flow series, an approach known as the index sequential sampling (ISM) method (Kendall and Dracup 1991), rather than rely on any of the complex models which have been the subject of so much research in the past few decades.…”

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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“…More recently, hybrid methods have been introduced that combine the strength of both parametric and nonparametric approaches [Srinivas and Srinivasan, 2005].…”

Section: Introductionmentioning

confidence: 99%

Reliable conjunctive use rules for sustainable irrigated agriculture and reservoir spill control

Schoups

Addams

Minjares³

et al. 2006

Water Resources Research

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[1] We develop optimal conjunctive use water management strategies that balance two potentially conflicting objectives: sustaining irrigated agriculture during droughts and minimizing unnecessary spills and resulting water losses from the reservoir during wet periods. Conjunctive use is specified by a linear operating rule, which determines the maximum surface water release as a function of initial reservoir storage. Optimal strategies are identified using multiobjective interannual optimization for sustainability and spill control, combined with gradient-based annual profit maximization. Application to historical conditions in the irrigated system of the Yaqui Valley, Mexico, yields a Pareto curve of solutions illustrating the trade-off between sustaining agriculture and minimizing spills and water losses. Minimal water losses are obtained by maximizing surface water use and limiting groundwater pumping, such that reservoir levels are kept sufficiently low. Maximum agricultural sustainability, on the other hand, results from increased groundwater use and keeping surface water reservoir levels high during wet periods. Selected optimal operating rules from the multiobjective optimization are tested over a large number of equally probable streamflow time series, generated with a stochastic time series model. In this manner, statistical properties, such as the mean sustainability and sustainability percentiles, are determined for each optimal rule. These statistical properties can be used to select rules for water management that are reliable over a wide range of streamflow conditions.

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Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows

Cited by 70 publications

References 42 publications

Synthesis of hourly wind power series using the Moving Block Bootstrap method

Synthesis of hourly wind power series using the Moving Block Bootstrap method

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

Reliable conjunctive use rules for sustainable irrigated agriculture and reservoir spill control

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