Hydro-economic assessment of hydrological forecasting systems

Boucher, Marie‐Amélie; Tremblay, Denis; Delorme, L. D.; Perreault, Luc; Anctil, François

doi:10.1016/j.jhydrol.2011.11.042

Cited by 91 publications

(66 citation statements)

References 24 publications

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“…Roulin (2007) shows the improvement of a flood early warning system by using ensemble forecasts instead of deterministic ones in a simple static cost-loss decision model for two test catchments. Boucher et al (2011) compare the use of deterministic and ensemble hydrological forecasts in a two-stage decision tree for the operation of a hydropower system, and show that the two types of forecasts products bring 5 similar good performances. Recently, Raso et al (2014) presented a novel technique to handle ensemble forecasts in MPC optimization, named Tree-Based MPC (TB-MPC).…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts

Ficchì

Raso

Dorchies

et al. 2016

J. Water Resour. Plann. Manage.

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms Correspondence to: andrea.ficchi@irstea.fr 2 AbstractWe investigate the improvement of the operation of a four-reservoir system in the Seine River basin, France, by use of deterministic and ensemble weather forecasts and real-time control. In the current management, each reservoir is operated independently from the others and following prescribed rule-curves, designed to reduce floods and sustain low-flows under the historical hydrological conditions. However, this management system is inefficient when inflows are significantly different from their seasonal average and may become even more inadequate to cope with the predicted increase in extreme events induced by climate change.In this work, we develop and test a centralized real-time control system to improve reservoirs operation by exploiting numerical weather forecasts that are becoming increasingly available.The proposed management system implements a well-established optimization technique, Model Predictive Control (MPC) and its recently modified version that can incorporate uncertainties, Tree-Based Model Predictive Control (TB-MPC), to account for deterministic and ensemble forecasts respectively. The management system is assessed by simulation over historical events and compared to the "no-forecasts" strategy based on rule-curves.Simulation results show that the proposed real-time control system largely outperforms the "no-forecasts" management strategy, and that explicitly considering forecasts uncertainty through ensembles can compensate for the loss in performance due to forecasts inaccuracy.

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Section: Introductionmentioning

confidence: 99%

Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts

Ficchì

Raso

Dorchies

et al. 2016

J. Water Resour. Plann. Manage.

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“…The use of probabilistic forecasts to achieve an increased potential economic value has been investigated in many disciplines such as meteorology (Zhu et al, 2002;Buizza, 2008), irrigation (Cai et al, 2011) or wind energy (Tina and Brunetto, 2010;Tsikalakis et al, 2009). In hydrology, recent studies have proposed methods based on cost/loss ratio analysis (McCollor and Stull, 2008;Muluye, 2011;Verkade and Werner, 2011) or stochastic optimisation models (Boucher et al, 2012). Based on simple optimal decision-making and different target applications (continuous flow forecasts, flood forecasts and warnings, inflows to hydroelectric production reservoirs), they all basically show that probabilistic or ensemble-based forecasts have the potential to generate higher benefits than climatology or deterministic forecasts.…”

Section: H Ramos Et Al: Do Probabilistic Forecasts Lead To Bettementioning

confidence: 99%

Do probabilistic forecasts lead to better decisions?

Ramos¹,

Andel

Pappenberger

2013

Hydrol. Earth Syst. Sci.

151

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Abstract. The last decade has seen growing research in producing probabilistic hydro-meteorological forecasts and increasing their reliability. This followed the promise that, supplied with information about uncertainty, people would take better risk-based decisions. In recent years, therefore, research and operational developments have also started focusing attention on ways of communicating the probabilistic forecasts to decision-makers. Communicating probabilistic forecasts includes preparing tools and products for visualisation, but also requires understanding how decision-makers perceive and use uncertainty information in real time. At the EGU General Assembly 2012, we conducted a laboratorystyle experiment in which several cases of flood forecasts and a choice of actions to take were presented as part of a game to participants, who acted as decision-makers. Answers were collected and analysed. In this paper, we present the results of this exercise and discuss if we indeed make better decisions on the basis of probabilistic forecasts.

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“…Although meteorological ensembles do not sample all uncertainties in the forecasting system (for example, uncertainties in the hydrological model are not considered), the meteorological forecast are usually the ones which introduce the highest amount of uncertainty to the system. This method for generating hydrological ensemble forecasts is actually broadly applied and has been showing better performance than deterministic forecasts in terms of performance metrics (Renner et al 2009;Velázquez et al 2009;Jaun and Ahrens 2009), decision making assessments (Boucher et al 2011;Ramos et al 2013), and also more recently to reservoirs operation assessments (Mccollor and Stull 2008;Zhao et al 2011;Boucher et al 2012;Raso et al 2013). …”

Section: Introductionmentioning

confidence: 99%

“…On reservoir operation using ensembles, Boucher et al (2012) reports the possibility to achieve better results for hydropower reservoirs operation by the use of ensemble forecasts in comparison to a high-resolution deterministic one. The authors as well show that results depend on the forecast skill (ensemble forecasts are not always better than the deterministic ones), and that the lead-time performance metrics not necessarily match with the best reservoir operation.…”

Section: Introductionmentioning

confidence: 99%

“…Another contribution of our work is the novel application of multi-stage stochastic optimization for flood control by a multi-purpose reservoir. Whereas existing work considers only single-stage setups (Boucher et al 2012), the introduction of multiple stages at branching points of the scenario tree makes the management more adaptive to the resolution of uncertainty and should lead to a better performance.…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Reservoir Optimization for Flood Mitigation under Meteorological and Hydrological Forecast Uncertainty

Schwanenberg

Fan

Naumann

et al. 2015

Water Resour Manage

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State-of-the-art applications of short-term reservoir management integrate several advanced components, namely hydrological modelling and data assimilation techniques for predicting streamflow, optimization-based techniques for decision-making on the reservoir operation and the technical framework for integrating these components with data feeds from gauging networks, remote sensing data and meteorological weather predictions. In this paper, we present such a framework for the short-term management of reservoirs operated by the a drainage area of approximately 55,000 km and its operation for flood mitigation. Basis for the anticipatory short-term management of the reservoir over a forecast horizon of up to 15 days are streamflow predictions of the MGB hydrological model. The semi-distributed model is well suited to represent the watershed and shows a Nash-Sutcliffe model performance in the order of 0.83-0.90 for most streamflow gauges of the data-sparse basin. A lead time performance assessment of the deterministic and probabilistic ECMWF forecasts as model forcing indicate the superiority of the probabilistic model. The novel short-term optimization approach consists of the reduction of the ensemble forecasts into scenario trees as an input of a multi-stage stochastic optimization. We show that this approach has several advantages over commonly used deterministic methods which neglect forecast uncertainty in the short-term decision-making. First, the probabilistic forecasts have longer forecast horizons that allow an earlier and therefore better anticipation of critical flood events. Second, the stochastic optimization leads to more robust decisions than deterministic procedures which consider only a single future trajectory. Third, the stochastic optimization permits to introduce advanced chance constraints for refining the system operation.

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Hydro-economic assessment of hydrological forecasting systems

Cited by 91 publications

References 24 publications

Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts

Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts

Do probabilistic forecasts lead to better decisions?

Short-Term Reservoir Optimization for Flood Mitigation under Meteorological and Hydrological Forecast Uncertainty

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