Reliable probabilistic forecasts from an ensemble reservoir inflow forecasting system

Bourdin, Dominique R.; Nipen, Thomas N.; Stull, Roland B.

doi:10.1002/2014wr015462

Cited by 29 publications

(19 citation statements)

References 67 publications

(129 reference statements)

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“…The Continuous Ranked Probability Score (CRPS) is a widely used tool for the probabilistic forecast verification [47]. The CRPS might be considered as the mean absolute error of the predicted ensemble, with a lower CRPS value indicating a higher forecasting accuracy.…”

Section: Prediction Evaluationmentioning

confidence: 99%

Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China

et al. 2020

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Droughts are one of the costliest natural disasters. Reliable drought monitoring and prediction are valuable for drought relief management. This study monitors and predicts droughts in Xinjiang, an arid area in China, based on the three drought indicators, i.e., the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSMI) and the Multivariate Standardized Drought Index (MSDI). Results indicate that although these three indicators could capture severe historical drought events in the study area, the spatial coverage, persistence and severity of the droughts would vary regarding different indicators. The MSDI could best describe the overall drought conditions by incorporating the characteristics of the SPI and SSMI. For the drought prediction, the predictive skill of all indicators gradually decayed with the increasing lead time. Specifically, the SPI only showed the predictive skill at a 1-month lead time, the MSDI performed best in capturing droughts at 1- to 2-month lead times and the SSMI was accurate up to a 3-month lead time owing to its high persistence. These findings might provide scientific support for the local drought management.

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Section: Prediction Evaluationmentioning

confidence: 99%

Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China

et al. 2020

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“…Despite the great progress and breakthroughs in research on water information telemetry, radar rainfall measurement technology and hydrological models, hydrological forecasting has always been accompanied by inherent uncertainty (Moges et al, 2020). In order to quantify the uncertainty of hydrological forecasts, a large amount of research work has been carried out by scholars worldwide in the last years, and many effective theoretical methods have been proposed (Ajami et al, 2007;Beven and Freer, 2001;Bourdin et al, 2014;Ghaith and Li, 2020). According to the form of forecast release, hydrological forecasts can be divided into three categories: deterministic forecasts, probabilistic forecasts and ensemble forecasts (Cloke and Pappenberger, 2009).…”

Section: Introductionmentioning

confidence: 99%

Tracking the Uncertainty Propagation Process Between Hydrological Forecasting and Reservoir Real-Time Optimal Operation

Zhu¹,

Zhong²,

Xu³

et al. 2021

Preprint

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The inherent uncertainty in hydrological forecasting poses a challenge for reservoir real-time optimal operation. In this paper, a stochastic framework is proposed to track the uncertainty propagation process between hydrological forecasting and reservoir operation. The framework simulates the comprehensive uncertainty of hydrological forecasts in the form of ensemble forecasts and scenario trees. Based on the derived analytic relationship between the performance metric Nash-Sutcliffe efficiency coefficient (NSE) and forecast uncertainty probability distribution, we use three methods (two are commonly used classical methods and one is the Gaussian copula method) simultaneously to generate inflow forecast ensembles. Compared with the two classical methods, the Gaussian copula method additionally takes into account the temporal correlation of reservoir inflows. Then, the neural gas method is employed to transform the generated ensembles into a scenario tree, which is further used as an input for reservoir stochastic optimization. To improve the adaptability to uncertainties in inflow forecasts, we establish a stochastic optimization model that optimizes the expectation of objective values over all scenarios. Meanwhile, we propose a parallel differential evolution (DE) algorithm based on parallel computing techniques for solving the stochastic optimization model efficiently. Risk assessment is performed to capture the uncertainty and corresponding risk associated with the reservoir optimal decision. The proposed framework is demonstrated in a flood control reservoir system in China. Furthermore, we conduct several numerical experiments to explore the effect of forecast uncertainty level and temporal correlation on reservoir real-time optimal operation. The results indicate that the temporal correlation of inflows must be considered in inflow stochastic simulation and reservoir stochastic optimization, otherwise the operational risk is likely to be overestimated or underestimated, thus leading to operation failures. Based on the risk simulation surface, reservoir operators can examine the robustness of operational decisions and thus make more reliable final decisions.

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“…Over the past two decades, the uncertainty in the FODM chain has attracted much attention from academia, and great progress has been made in hydrology and water resources management. The first advance was the development of ensemble flood forecasts (see the review by Cloke & Pappenberger, 2009), shown to be an effective way of quantifying forecast uncertainty (Beven & Freer, 2001;Bourdin et al, 2014;Krzysztofowicz, 1999). However, gaps still exist between theory and application, and more efforts should be directed toward incorporating this type of forecast into water resources operations (Cloke & Pappenberger, 2009).…”

Section: Introductionmentioning

confidence: 99%

Real‐Time Optimal Flood Control Decision Making and Risk Propagation Under Multiple Uncertainties

Zhu

Zhong

Sun

et al. 2017

Water Resources Research

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Multiple uncertainties exist in the optimal flood control decision‐making process, presenting risks involving flood control decisions. This paper defines the main steps in optimal flood control decision making that constitute the Forecast‐Optimization‐Decision Making (FODM) chain. We propose a framework for supporting optimal flood control decision making under multiple uncertainties and evaluate risk propagation along the FODM chain from a holistic perspective. To deal with uncertainties, we employ stochastic models at each link of the FODM chain. We generate synthetic ensemble flood forecasts via the martingale model of forecast evolution. We then establish a multiobjective stochastic programming with recourse model for optimal flood control operation. The Pareto front under uncertainty is derived via the constraint method coupled with a two‐step process. We propose a novel SMAA‐TOPSIS model for stochastic multicriteria decision making. Then we propose the risk assessment model, the risk of decision‐making errors and rank uncertainty degree to quantify the risk propagation process along the FODM chain. We conduct numerical experiments to investigate the effects of flood forecast uncertainty on optimal flood control decision making and risk propagation. We apply the proposed methodology to a flood control system in the Daduhe River basin in China. The results indicate that the proposed method can provide valuable risk information in each link of the FODM chain and enable risk‐informed decisions with higher reliability.

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Reliable probabilistic forecasts from an ensemble reservoir inflow forecasting system

Cited by 29 publications

References 67 publications

Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China

Monitoring and Predicting Drought Based on Multiple Indicators in an Arid Area, China

Tracking the Uncertainty Propagation Process Between Hydrological Forecasting and Reservoir Real-Time Optimal Operation

Real‐Time Optimal Flood Control Decision Making and Risk Propagation Under Multiple Uncertainties

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