Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE database for large scale basins in Brazil

Fan, Fernando Mainardi; Schwanenberg, Dirk; Collischonn, Walter; Weerts, Albrecht

doi:10.1016/j.ejrh.2015.05.012

Cited by 48 publications

(36 citation statements)

References 92 publications

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“…Raso et al (2013) and Schwanenberg et al (2015a) applied this methodology to the multi-stage stochastic optimization of reservoirs. In this approach, the control adapts to the resolution of the forecast uncertainty.…”

Section: Methodsmentioning

confidence: 99%

“…In the research of Fan et al (2014), a first version of an ensemble forecasting system based on the MGB-IPH ) hydrological model and on GEFS v2 meteorological forecasts (Hamill et al 2013) was shown. Subsequently, Fan et al (2015a) presented a second version of the ensemble forecasting system coupled with Delft-FEWS software shell (Werner et al 2013) and tested it using multiple ensemble rainfall forecasts from the TIGGE database (Bougeault et al 2010) and in comparison to other Brazilian basins. Finally, Schwanenberg et al (2015a) developed a reservoir optimization system by coupling the MGB-IPH hydrological model, ECMWF rainfall forecasts (Buizza et al 2007) and the RTC-Tools (Real-Time Control Tools) software (Schwanenberg et al 2015b) to suggest optimum operations of the Três Marias HPP dam.…”

Section: Test Case Descriptionmentioning

confidence: 99%

“…In the tests presented by Schwanenberg et al (2015a) that developed system were applied to the reservoir's operation during a single flood event, comparing results from perfect forecasts (e.g. observations), deterministic forecasts, and ensemble forecasts (stochastic optimization).…”

Section: Test Case Descriptionmentioning

confidence: 99%

“…It is a follow-up of a previous study presented by Schwanenberg et al (2015a) which used a similar approach, but restricted for a single-event simulation. The novel components of the present paper are: (i) the closed loop experiment of the stochastic optimization in application to a real-world case study and (ii) a novel, mass conservative scenario tree generation with a detailed comparison of the forecast skill of the ensemble against the reduced scenario tree.…”

mentioning

confidence: 99%

“…Fernández Bou et al (2015) showed the development of a methodology for 1-month flood forecasting in the upper region of the Uruguay River basin, at the Itá Hydroelectric Power Plant (HPP) reservoir. Finally, Fan et al (2014Fan et al ( , 2015a showed the assessment of ESF within operational forecasting systems.…”

mentioning

confidence: 99%

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Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir

Fan

Schwanenberg

Alvarado

et al. 2016

Water Resour Manage

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Hydropower is the most important source of electricity in Brazil. It is subject to the natural variability of water yield. One building block of the proper management of hydropower assets is the short-term forecast of reservoir inflows as input for an online, event-based optimization of its release strategy. While deterministic forecasts and optimization schemes are the established techniques for short-term reservoir management, the use of probabilistic ensemble forecasts and multi-stage stochastic optimization techniques is receiving growing attention. The present work introduces a novel, mass conservative scenario tree reduction in combination with a detailed hindcasting and closed-loop control experiments for a multipurpose hydropower reservoir in a tropical region in Brazil. The case study is the hydropower project Três Marias, which is operated with two main objectives: (i) hydroelectricity generation and (ii) flood control downstream. In the experiments, precipitation forecasts based on observed data, deterministic and probabilistic forecasts are used to generate streamflow forecasts in a hydrological model over a period of 2 years. Results for a perfect forecast show the potential benefit of the online optimization and indicate a desired forecast lead time of 30 days. In comparison, the use of actual forecasts of up to 15 days shows the practical benefit of Water Resour Manage (2016) operational forecasts, where stochastic optimization (15 days lead time) outperforms the deterministic version (10 days lead time) significantly. The range of the energy production rate between the different approaches is relatively small, between 78% and 80%, suggesting that the use of stochastic optimization combined with ensemble forecasts leads to a significantly higher level of flood protection without compromising the energy production.

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Section: Methodsmentioning

confidence: 99%

Section: Test Case Descriptionmentioning

confidence: 99%

Section: Test Case Descriptionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir

Fan

Schwanenberg

Alvarado

et al. 2016

Water Resour Manage

Self Cite

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Continental and global scale flood forecasting systems

et al. 2016

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Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist. As numerical weather prediction models continue to improve, operational centers are increasingly using their meteorological output to drive hydrological models, creating hydrometeorological systems capable of forecasting river flow and flood events at much longer lead times than has previously been possible. Furthermore, developments in, for example, modelling capabilities, data, and resources in recent years have made it possible to produce global scale flood forecasting systems. In this paper, the current state of operational large-scale flood forecasting is discussed, including probabilistic forecasting of floods using ensemble prediction systems. Six state-of-the-art operational large-scale flood forecasting systems are reviewed, describing similarities and differences in their approaches to forecasting floods at the global and continental scale. Operational systems currently have the capability to produce coarse-scale discharge forecasts in the mediumrange and disseminate forecasts and, in some cases, early warning products in real time across the globe, in support of national forecasting capabilities. With improvements in seasonal weather forecasting, future advances may include more seamless hydrological forecasting at the global scale alongside a move towards multi-model forecasts and grand ensemble techniques, responding to the requirement of developing multi-hazard early warning systems for disaster risk reduction.

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