Markov‐switching model for nonstationary runoff conditioned on El Niño information

Gelati, Emiliano; Madsen, Henrik; Rosbjerg, Dan

doi:10.1029/2009wr007736

Cited by 21 publications

(23 citation statements)

References 31 publications

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“…We also considered the Trans-Niño Index (TNI), which is the difference between Niño 1 + 2 and Niño 4 SST anomalies (Trenberth and Stepaniak 2001 (-0.19 and -0.08). This suggests that El Niño is well correlated with positive inflow anomalies, while La Niña does not have a significant impact on inflow, as also found by Gelati et al (2010b) for Daule Peripa. El Niño and (Gelati et al 2010b).…”

Section: Inflow and Enso Datasupporting

confidence: 62%

“…This suggests that El Niño is well correlated with positive inflow anomalies, while La Niña does not have a significant impact on inflow, as also found by Gelati et al (2010b) for Daule Peripa. El Niño and (Gelati et al 2010b).…”

Section: Inflow and Enso Datasupporting

confidence: 62%

“…A thorough illustration of model performance for univariate inflow can be found in Gelati et al (2010b). Model performance for univariate and bivariate inflow is comparable.…”

Section: Stochastic Inflow Forecastsmentioning

confidence: 98%

“…The developed method extends the model presented by Gelati et al (2010b), which applies to univariate inflow.…”

Section: A Multivariate Inflow Model Using Enso Informationmentioning

confidence: 99%

“…To account for the influence of ENSO, we extended the stochastic model by Gelati et al (2010b) to generate multivariate inflow forecast scenarios. The model is a mixture of autoregressive models with exogenous input (ARX) using ENSO indices as regressors.…”

Section: Introductionmentioning

confidence: 99%

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Reservoir operation using El Niño forecasts—case study of Daule Peripa and Baba, Ecuador

Gelati

Madsen

Rosbjerg

2014

Hydrological Sciences Journal

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Reservoir operation is studied for the Daule Peripa and Baba system in Ecuador, where El Niño events cause anomalously heavy precipitation. Reservoir inflow is modelled by a Markov-switching model using El Niño-Southern Oscillation (ENSO) indices as input. Inflow is forecast using 9-month lead time ENSO forecasts. Monthly reservoir releases are optimized with a genetic algorithm, maximizing hydropower production during the forecast period and minimizing deviations from storage targets. The method is applied to the existing Daule Peripa Reservoir and to a planned system including the Baba Reservoir. Optimized operation is compared to historical management of Daule Peripa. Hypothetical management scenarios are used as the benchmark for the planned system, for which no operation policy is known. Upper bounds for operational performance are found via dynamic programming by assuming perfect knowledge of future inflow. The results highlight the advantages of combining inflow forecasts and storage targets in reservoir operation.Key words reservoir operation; optimization; inflow forecast; genetic algorithm; El Niño; Ecuador Gestion de réservoir utilisant des prévisions d'El Niño-étude de cas de Daule Peripa et de Baba, Equateur Résumé On a étudié la gestion d'un réservoir pour le système de Daule Peripa et de Baba en Equateur, où les événements El Niño causent des précipitations anormalement importantes. Les apports au réservoir ont été modélisés par un modèle à changement de régime markovien dont l'indice ENSO constitue l'entrée. Les prévisions d'apports utilisent les prévisions 9 mois à l'avance de l'indice ENSO. Les lâchures mensuelles du réservoir ont été optimisées grâce à un algorithme génétique, en maximisant la production hydroélectrique au cours de la période de prévision et en minimisant les écarts aux consignes de remplissage. La méthode a été appliquée au réservoir existant de Daule Peripa et à un système projeté comprenant le réservoir de Baba. La gestion optimisée a été comparée à l'historique de la gestion de Daule Peripa. Des scénarios de gestion hypothétiques ont été utilisés comme références pour le système projeté, pour lequel aucune politique de gestion n'est connue. Les limites supérieures de la performance opérationnelle ont été déterminées par programmation dynamique en supposant une connaissance parfaite des apports futurs. Les résultats mettent en évidence l'avantage pour la gestion des réservoirs de combiner les prévisions d'apports et les consignes de remplissage.

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Section: Inflow and Enso Datasupporting

confidence: 62%

Section: Inflow and Enso Datasupporting

confidence: 62%

“…A thorough illustration of model performance for univariate inflow can be found in Gelati et al (2010b). Model performance for univariate and bivariate inflow is comparable.…”

Section: Stochastic Inflow Forecastsmentioning

confidence: 98%

“…The developed method extends the model presented by Gelati et al (2010b), which applies to univariate inflow.…”

Section: A Multivariate Inflow Model Using Enso Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reservoir operation using El Niño forecasts—case study of Daule Peripa and Baba, Ecuador

Gelati

Madsen

Rosbjerg

2014

Hydrological Sciences Journal

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A hiddenMarkov model combined with climate indices for multidecadal streamflow simulation

Bracken

Rajagopalan

Zagona

2014

Water Resources Research

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Hydroclimate time series often exhibit very low year-to-year autocorrelation while showing prolonged wet and dry epochs reminiscent of regime-shifting behavior. Traditional stochastic time series models cannot capture the regime-shifting features thereby misrepresenting the risk of prolonged wet and dry periods, consequently impacting management and planning efforts. Upper Colorado River Basin (UCRB) annual flow series highlights this clearly. To address this, a simulation framework is developed using a hidden Markov (HM) model in combination with large-scale climate indices that drive multidecadal variability. We demonstrate this on the UCRB flows and show that the simulations are able to capture the regime features by reproducing the multidecadal spectral features present in the data where a basic HM model without climate information cannot.

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Regime‐shifting streamflow processes: Implications for water supply reservoir operations

Turner

Galelli

2016

Water Resources Research

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This paper examines the extent to which regime-like behavior in streamflow time series impacts reservoir operating policy performance. We begin by incorporating a regime state variable into a well-established stochastic dynamic programming model. We then simulate and compare optimized release policies-with and without the regime state variable-to understand how regime shifts affect operating performance in terms of meeting water delivery targets. Our optimization approach uses a Hidden Markov Model to partition the streamflow time series into a small number of separate regime states. The streamflow persistence structures associated with each state define separate month-to-month streamflow transition probability matrices for computing penalty cost expectations within the optimization procedure. The algorithm generates a four-dimensional array of release decisions conditioned on the within-year time period, reservoir storage state, inflow class, and underlying regime state. Our computational experiment is executed on 99 distinct, hypothetical water supply reservoirs fashioned from the Australian Bureau of Meteorology's Hydrologic Reference Stations. Results show that regime-like behavior is a major cause of suboptimal operations in water supply reservoirs; conventional techniques for optimal policy design may misguide the operator, particularly in regions susceptible to multiyear drought. Stationary streamflow models that allow for regime-like behavior can be incorporated into traditional stochastic optimization models to enhance the flexibility of operations. Key Points:Regime shifts in streamflow impact reservoir operating performance A regime state can be incorporated into stochastic reservoir optimization Allowing retrospectively for regime shifts improves simulated operating performance Vogel, 2015]. In this study, we examine the limitations of short-memory streamflow models when applied to reservoir operating problems. We focus on the use of such models for informing water release decisions through a classical stochastic dynamic programming (SDP) method, and seek to quantify the potential gains in operating performance that could be proffered by allowing for regime-like behavior in reservoir inflow. PUBLICATIONS The Role of Hydrological Information in Reservoir OperationThe question of how to best incorporate hydrological information into reservoir operations has been the focus of many studies. When constructing an optimization model, one normally aims to draw on useful hydrological information to train the policy, whist also acknowledging that this information will need to be available to the operator when deciding how much water to release. The seminal studies of Gal [1979] and Maidment and Chow [1981] employed the previous period's inflow, which is useful if the inflow time series has significant autocorrelation. Alternatively, if one assumes a reservoir operator can adjust the release as the decision period unfolds-which may be appropriate for monthly operations-the current period inflow can be used as th...

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Markov‐switching model for nonstationary runoff conditioned on El Niño information

Cited by 21 publications

References 31 publications

Reservoir operation using El Niño forecasts—case study of Daule Peripa and Baba, Ecuador

Reservoir operation using El Niño forecasts—case study of Daule Peripa and Baba, Ecuador

A hiddenMarkov model combined with climate indices for multidecadal streamflow simulation

Regime‐shifting streamflow processes: Implications for water supply reservoir operations

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