Water Resources Research · 1999 · 468 citations · 23 references
Bayesian StatisticBayesian Decision TheoryHydrological PredictionEngineeringEconomic ValueUncertainty ModelingEarth ScienceWater Quality ForecastingRational Decision MakingBayesian Forecasting SystemProbabilistic ForecastingData ScienceUncertainty QuantificationUncertainty EstimationManagementBayesian MethodsHydrological ModelingHydroclimate ModelingBayesian TheoryPredictive AnalyticsGeographyFlood ForecastingForecastingHydrologyBayesian StatisticsWater ResourcesFlood Risk Management
Hydrologic decision making requires probabilistic forecasts of predictands such as river stage, which are typically derived from deterministic catchment models. The study introduces a Bayesian forecasting system that generates probabilistic forecasts from any deterministic catchment model. The system decomposes total uncertainty into input and hydrologic components, quantifies them separately, and integrates them into a Bayes distribution. The resulting distribution is well calibrated, economically valuable, and outperforms Monte Carlo and ensemble methods when used alone, though each can contribute to the system.
Rational decision making (for flood warning, navigation, or reservoir systems) requires that the total uncertainty about a hydrologic predictand (such as river stage, discharge, or runoff volume) be quantified in terms of a probability distribution, conditional on all available information and knowledge. Hydrologic knowledge is typically embodied in a deterministic catchment model. Fundamentals are presented of a Bayesian forecasting system (BFS) for producing a probabilistic forecast of a hydrologic predictand via any deterministic catchment model. The BFS decomposes the total uncertainty into input uncertainty and hydrologic uncertainty, which are quantified independently and then integrated into a predictive (Bayes) distribution. This distribution results from a revision of a prior (climatic) distribution, is well calibrated, and has a nonnegative ex ante economic value. The BFS is compared with Monte Carlo simulation and “ensemble forecasting” technique, none of which can alone produce a probabilistic forecast that meets requirements of rational decision making, but each can serve as a component of the BFS.
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Empirical Model Building and Response Surfaces
Anne E. Freeny, George E. P. Box, Norman R. Draper · Technometrics · 1988 · 5.1K citations
Simulation modeling and analysis
Journal of Manufacturing Systems · 1999 · 3.8K citations
Engineering, Process Simulation Model, Systems Engineering +6