Seminar - Predictive uncertainty in real time flood forecasting
This seminar aims at discussing the use of “predictive uncertainty” in flood forecasting and water resources management, particularly when meteorological ensemble forecasts are available. Using data from actual operational flood forecasting systems, one can show the improved expected benefits that can be obtained by fully incorporating predictive uncertainty into the decision making process, instead of using deterministic forecasts (as presently done) or by simply delivering to the end user uncertain and hardly understood, forecasts (as commonly planned to be done). The presently available continuous (Hydrologic Uncertainty Processor, Bayesian Model Averaging, Model Conditional Processor, etc.) and binary ( Logistic Regression, Binary Multivariate Bayesian Processor, etc.) uncertainty processors, will also be introduced, showing their performances on the basis of actual data derived from operational flood forecasting systems. Finally, the problem of incorporating meteorological ensembles into hydrological predictive uncertainty will be discussed and a number of possible alternatives will be presented setting into evidence the problems that currently limit their use. The main problems for proficiently use meteorological ensembles relate to (1) the lack of long forecasting meteorological runs for which precipitation forecasts have been saved as opposed to the presently available re-analyses; (2) the continuous improvements in the meteorological models that modify in time their performances combined to the lack of willingness of the meteorological centres of re-running the new versions on past data; and (3) the difficulty at tagging the different members of the ensembles .
Ezio Todini is a Professor of Hydrology at the University of Bologna, a position he has held since 1980. Prior to this he combined his role as a Research Scientist at the IBM Pisa Scientific Centre (1970-79) with that of Professor of Applied Hydromechanics at the University of Pisa (1973-80) and of Water Resources Planning at the University of Florence (1979-81). His background and experience includes Hydrology, Hydraulics, Statistics, Numerical Methods and Operations Research. Professor Todini is currently a member of the High Risks Commission of the Emilia Romagna Region; of the Scientific Committee of WWF Italy; and of the Italian Statistical Society, the European Geophysical Union and the American Geophysical Union. In the past he has been Vice-President of the International Water Resources Management Committee of IAHS as well as Vice-President of IAHS and he is presently a member of the Willis Research Network. Professor Todini has also been the co-ordinator for the writing of a report on "Understanding and Reducing Uncertainty in Flood Forecasting", within the frame of the Concerted Action ACTIF; the author of "Hydrological Models for Flood Forecasting", Chapter HSA131 of the Encyclopedia of Hydrological Sciences (J. Wiley & Sons) and is currently a member of the of WMO OPACHE group for the writing of the WMO Manual on Real Time Flood Forecasting. Professor Todini's research activity, documented by more than 200 articles, has always been directed towards both theory as well as practical application. His original studies in synthetic hydrology have produced a stochastic generation model, which preserves both long and short-term correlation for the statistical assessment of the Aswan reservoir operating policy. His research in rainfall-runoff modelling has produced several models, the CLS (1976), the ARNO (1989) (which was also included as the soil moisture balance component of the ECHAM GCM model) and the TOPKAPI (1998) models, that have been and still are extensively applied all over the world. His approach to flood routing has produced models such as the PAB and the PABL, particularly suited for real-time flood forecasting. These models are now in operation on many rivers, together with a 1D/2D package based upon Control Volume Finite Elements. More recently Professor Todini has found the reason for the non conservation of mass in the Muskingum-Cunge method and has produced a corrected algorithm, which compares favourably with the results of full de Saint Venant equations based commercial packages such as Mike11, Hec and Sobek. His current research includes the clarification of the concept of predictive uncertainty in Hydrological Forecasting and the development of uncertainty post processors. Professor Todini also deals with the development of Decision Support Systems aimed at Sustainable Water Resources Planning and Management.
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