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Case Study: A Real-Time Flood Forecasting System with Predictive Uncertainty Estimation for the Godavari River, India

National Research Council, IRPI, Via Madonna Alta 126, 06128 Perugia, Italy
BiGeA—University of Bologna, Via Zamboni 67, 40126 Bologna, Italy
Author to whom correspondence should be addressed.
Academic Editor: Paolo Reggiani
Water 2016, 8(10), 463;
Received: 27 June 2016 / Revised: 6 October 2016 / Accepted: 12 October 2016 / Published: 18 October 2016
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)
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This work presents the application of the multi-temporal approach of the Model Conditional Processor (MCP-MT) for predictive uncertainty (PU) estimation in the Godavari River basin, India. MCP-MT is developed for making probabilistic Bayesian decision. It is the most appropriate approach if the uncertainty of future outcomes is to be considered. It yields the best predictive density of future events and allows determining the probability that a critical warning threshold may be exceeded within a given forecast time. In Bayesian decision-making, the predictive density represents the best available knowledge on a future event to address a rational decision-making process. MCP-MT has already been tested for case studies selected in Italian river basins, showing evidence of improvement of the effectiveness of operative real-time flood forecasting systems. The application of MCP-MT for two river reaches selected in the Godavari River basin, India, is here presented and discussed by considering the stage forecasts provided by a deterministic model, STAFOM-RCM, and hourly dataset based on seven monsoon seasons in the period 2001–2010. The results show that the PU estimate is useful for finding the exceedance probability for a given hydrometric threshold as function of the forecast time up to 24 h, demonstrating the potential usefulness for supporting real-time decision-making. Moreover, the expected value provided by MCP-MT yields better results than the deterministic model predictions, with higher Nash–Sutcliffe coefficients and lower error on stage forecasts, both in term of mean error and standard deviation and root mean square error. View Full-Text
Keywords: flood forecasting; predictive uncertainty; hydrometric thresholds; flooding probability flood forecasting; predictive uncertainty; hydrometric thresholds; flooding probability

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Barbetta, S.; Coccia, G.; Moramarco, T.; Todini, E. Case Study: A Real-Time Flood Forecasting System with Predictive Uncertainty Estimation for the Godavari River, India. Water 2016, 8, 463.

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