FORECAST UNCERTAINTY IN WATER SUPPLY RESERVOIR OPERATION<sup>1</sup>

Mishalani, Nabeel; Palmer, Richard N.

doi:10.1111/j.1752-1688.1988.tb03043.x

Cited by 17 publications

(3 citation statements)

References 24 publications

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“…Inflow forecast is of great importance for real‐time reservoir operations (Mishalani & Palmer, ; Zhao, Cai, & Yang, ; Zhao et al, ) to determine water allocation between the current use and the carryover storage for mitigating the risk of extraordinary water shortage in the future (i.e., hedging rules; Draper & Lund, ; You & Cai, ). While the forecast lead time should be long enough to provide sufficient information for a certain decision purpose, forecast uncertainty and error that usually grow with the forecast lead time might reduce the usefulness of the forecast information (Maurer & Lettenmaier, , ; Mishalani & Palmer, ; Philbrick & Kitanidis, ; Zhao et al, ). Thus, in order to make the best use of forecast information, it is crucial to determine a forecast horizon that provides appropriate inflow information for reliable release decisions.…”

Section: Introductionmentioning

confidence: 99%

Determining Inflow Forecast Horizon for Reservoir Operation

Zhao

Cai

2019

Water Resources Research

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A critical study issue to incorporate imperfect forecast in real‐time reservoir operation is determining the forecast horizon. In this study, properties for the longest forecast horizon (LFH) and the effective forecast horizon (EFH) are derived from a multistage, deterministic optimization model for the operation of a single water supply reservoir with a concave benefit function. The LFH addresses the question of how long a forecast is sufficient to make an optimal reservoir release decision for the current stage if the effect of forecast uncertainty is not considered. The EFH represents a forecast horizon with the information for decision making as much as allowed by uncertainty effect control, which is set as prescribed decision reliability quantified by the error bound (i.e., the largest difference between the optimal release decisions made under any two inflow scenarios). The properties of LFH and EFH are used to specify the criteria and design the procedures for determining EFH and LFH. A hypothetical but typical case study is used to demonstrate the criteria and procedures. Both theoretical analysis and the case study results show that LFH and EFH are affected by multiple factors such as the reservoir capacity, inflow variability, forecast uncertainty, maximum allowable error bound, and ending storage estimate accuracy. LFH is longer with a larger capacity, smaller inflow variability, and smaller forecast uncertainty, and EFH is longer with smaller forecast uncertainty, larger error bound, and more accurate ending storage estimates.

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Section: Introductionmentioning

confidence: 99%

Determining Inflow Forecast Horizon for Reservoir Operation

Zhao

Cai

2019

Water Resources Research

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“…Several authors acknowledged that the use of forecasts in reservoir operation yields better results than reactive control, which does not consider forecasts (Labadie et al 1981, Mishalani and Palmer 1988, Georgakakos 1989. In several studies, short-term reservoir operation was implemented using inflow forecasts without considering any long-term information (Simonovic and Burn 1989, Mujumdar and Ramesh 1997, Mujumdar and Teegavarapu 1998.…”

Section: Introductionmentioning

confidence: 99%

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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“…Forecasts have a high economic value in planning hydropower production, irrigation water allocations, and flood control reservoir operations, among other management objectives (McCuen et ale 1979;Gordon and Lamb 1980;Mishalani and Palmer 1988). In addition, competing uses for water in the West are great and growing; for example, increasing demand for hydropower production conflicts with the use of water for irrigation, and new management objectives, such a~fisheries protection, are receiving high priorities (High Country News 1987;Long 1990;Northwest Power Planning Council 1991a,b;Palmer 1991).…”

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confidence: 99%

Modeling Techniques for Water Supply Forecasting in the Western United States

Garen¹

2000

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Water supply forecasting in the western United States is the prediction of the volume of water passing a given point on a stream during the primary snowmelt runoff season. Most water supply forecasts are produced from multiple linear regression models using snowpack, 2 precipitation, and streamflow measurements as independent variables. In recent years, conceptual watershed simulation models, typically using a time step of one day, have alsl~been used to produce these forecasts. This study examines model usage for: water supply forecasting in the West and has three specific pUlrposes. The first is to examine the traditional usage of mUltiple linear regression and develop improved regression techniques to overcome several recognized weaknesses in traditional practice. Four tec:hniques have been used in this study to improve water supply forecasts based on regression. They are: (1) basing the regression model onJly on data: known at forecast time (no future data); (2) principal cODlponents regression; (3) cross-validation; and (4) systematic searching for optimal I or near-optimal combinations of variables.The second purpose of the study is to develop a monthly streamflow simulation model suitable for use in water supply forecasting. Such a model has not previously been used in this application, and it provides a forecasting tool midway in cClmplexity hetween regression procedures and conceptual watershed simulation models.The third purpose of the .'tudy is to compare the accuracy of forecasts from regression, the monthly model developed here, and two conceptual watershed simulatio models. lIt has generally been assumed, but not tested, that complex s'mulation models will give more accurate forecasts than simpler models. This studly attempts to begin determining if this is true. Conceptual m deling results from previous studies on three basins in Idaho and Mont na were obtained to represent current practice in the usage of this type of model. This study has contributed to the practice of water supply forecasting by providing improvements to regression techniques, providing a new monthly model, developing a mean areal precipitation and temperature procedure based on kriging, and giving some initial direction for further investigations in the use of conceptual watershed models.The inability of the two simulation approaches to surpass regression in forecast accuracy brings up several issues with respect to modeling. These issues are in the areas of model calibration, model conceptualization, spatial and temporal aggregation, and areal averaging of input data. Further investigation is required to elucidate these issues before clear conclusions can be made about the relative forecasting abilities of simple and complex models. Further investigation is also required to study water management decision making and the kinds and accuracies of forecast information required to optimize these decisions.

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FORECAST UNCERTAINTY IN WATER SUPPLY RESERVOIR OPERATION¹

Cited by 17 publications

References 24 publications

Determining Inflow Forecast Horizon for Reservoir Operation

Determining Inflow Forecast Horizon for Reservoir Operation

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

Modeling Techniques for Water Supply Forecasting in the Western United States

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FORECAST UNCERTAINTY IN WATER SUPPLY RESERVOIR OPERATION1

Cited by 17 publications

References 24 publications

Determining Inflow Forecast Horizon for Reservoir Operation

Determining Inflow Forecast Horizon for Reservoir Operation

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

Modeling Techniques for Water Supply Forecasting in the Western United States

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FORECAST UNCERTAINTY IN WATER SUPPLY RESERVOIR OPERATION¹