case study: a real-time flood forecasting system with predictive uncertainty estimation for the godavari river, india

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ID: 214164
2016
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Abstract
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.
Reference Key
barbetta2016watercase Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Silvia Barbetta;Gabriele Coccia;Tommaso Moramarco;Ezio Todini
Journal Journal of food biochemistry
Year 2016
DOI
10.3390/w8100463
URL
http://www.mdpi.com/2073-4441/8/10/463
Keywords Keywords not found

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