Decision Support System for Optimizing Reservoir Operations Using Ensemble Streamflow Predictions

Alemu, Eset T.; Palmer, Richard N.; Polebitski, Austin; Meaker, Bruce F.

doi:10.1061/(asce)wr.1943-5452.0000088

Cited by 130 publications

(75 citation statements)

References 18 publications

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“…An emerging field of research has begun to demonstrate the value of seasonal streamflow forecasts when applied to real-world water management problems, such as determining the appropriate water release from a reservoir -the focus of the present study. Water release decisions can be improved with seasonal forecasts across a variety of reservoir types, including hydropower dams (Kim and Palmer, 1997;Faber and Stedinger, 2001;Hamlet et al, 2002;Alemu et al, 2010;Block, 2011), water supply reservoirs (Anghileri et al, 2016;Zhao and Zhao, 2014;Li et al, 2014) and reservoir systems operated for multiple competing objectives (Graham and Georgakakos, 2010;Georgakakos et al, 2012). Operators considering whether to adopt a forecast-informed operating scheme should be encouraged by these outcomes.…”

Section: Introductionmentioning

confidence: 99%

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

Turner

Bennett

Robertson

et al. 2017

Hydrol. Earth Syst. Sci.

108

105

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Abstract. Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made -namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

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Section: Introductionmentioning

confidence: 99%

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

Turner

Bennett

Robertson

et al. 2017

Hydrol. Earth Syst. Sci.

108

105

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show abstract

“…The synthetic generation of inflow hydrographs and stochastic prediction of flood events (stochastic approach) allows the ensemble of inputs to be representative of heavy to extreme flood events and also permits consideration of the uncertainty associated with the input variables (Alemu et al, 2011;Faber and Stedinger, 2001). The stochastic approach is also of interest because it allows risk analysis, which is relevant for the reservoir flood control operation in connection with floodplain management (Jain et al, 1992;Lund, 2002;Apel et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Risk-based methodology for parameter calibration of a reservoir flood control model

Bianucci

Sordo‐Ward

Pérez

et al. 2013

Nat. Hazards Earth Syst. Sci.

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Flash floods are of major relevance in natural disaster management in the Mediterranean region. In many cases, the damaging effects of flash floods can be mitigated by adequate management of flood control reservoirs. This requires the development of suitable models for optimal operation of reservoirs. A probabilistic methodology for calibrating the parameters of a reservoir flood control model (RFCM) that takes into account the stochastic variability of flood events is presented. This study addresses the crucial problem of operating reservoirs during flood events, considering downstream river damages and dam failure risk as conflicting operation criteria. These two criteria are aggregated into a single objective of total expected damages from both the maximum released flows and stored volumes (overall risk index). For each selected parameter set the RFCM is run under a wide range of hydrologic loads (determined through Monte Carlo simulation). The optimal parameter set is obtained through the overall risk index (balanced solution) and then compared with other solutions of the Pareto front. The proposed methodology is implemented at three different reservoirs in the southeast of Spain. The results obtained show that the balanced solution offers a good compromise between the two main objectives of reservoir flood control management

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“…Streamflow forecasts play a significant role in the management of water resources [1][2][3][4]. Forecasts at different time scales can provide valuable information for decision-making in water regulation.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, few studies have investigated how pre-processor (bias-corrected ECMWF forecasts) method and post-processor (bias-corrected hydrological output directly generated by ECMWF) method contribute to the skill of hydrological ensemble system prediction. Based on a typical subtropical monsoon region, Upper Hanjiang River Basin, we compare three forecasting scenarios: (1) Original forecasts (without any bias correction); (2) QMprep forecasts (with bias-corrected precipitation but without bias-corrected streamflow); (3) LSdis forecasts (without bias-corrected precipitation but with bias-corrected streamflow). In this study, we aim to compare the effect of the pre-processor method and post-processor method on the improvement of streamflow forecast efficiency.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Precipitation and Streamflow Correcting for Ensemble Streamflow Forecasts

Jiang

Lei

et al. 2018

Water

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Meteorological centers constantly make efforts to provide more skillful seasonal climate forecast, which has the potential to improve streamflow forecasts. A common approach is to bias-correct the general circulation model (GCM) forecasts prior to generating the streamflow forecasts. Less attention has been paid to the issue of bias-corrected streamflow forecasts that were generated by GCM forecasts. This study compares the effect of bias-corrected GCM forecasts and bias-corrected streamflow outputs on the improvement of streamflow forecast efficiency. Based on the Upper Hanjiang River Basin (UHRB), the authors compare three forecasting scenarios: original forecasts, bias-corrected precipitation forecasts and bias-corrected streamflow forecasts. We apply the quantile mapping method to bias-correct precipitation forecasts and the linear scaling method to bias-correct the original streamflow forecasts. A semi-distributed hydrological model, namely the Tsinghua Representative Elementary Watershed (THREW) model, is employed to transform precipitation into streamflow. The effects of bias-corrected precipitation and bias-corrected streamflow are assessed in terms of accuracy, reliability, sharpness and overall performance. The results show that both bias-corrected precipitation and bias-corrected streamflow can considerably increase the overall forecast skill in comparison to the original streamflow forecasts. Bias-corrected precipitation contributes mainly to improving the forecast reliability and sharpness, while bias-corrected streamflow is successful in increasing the forecast accuracy and overall performance of the ensemble forecasts.

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Decision Support System for Optimizing Reservoir Operations Using Ensemble Streamflow Predictions

Cited by 130 publications

References 18 publications

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

Risk-based methodology for parameter calibration of a reservoir flood control model

Comparison of Precipitation and Streamflow Correcting for Ensemble Streamflow Forecasts

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