Predictive uncertainty assessment in real time flood forecasting

Scope of the present paper is to provide an assessment of the state of the art of predictive uncertainty in flood forecasting. After defining what is meant by predictive uncertainty, the role and the importance of estimating predictive uncertainty within the context of flood management and in particular flood emergency management, is here discussed. Furthermore, the role of model and parameter uncertainty is presented together with alternative approaches aimed at taking them into account in the estimation of predictive uncertainty. In terms of operational tools, the paper also describes three of the recently developed Hydrological Uncertainty Processors. Finally, given the increased interest in meteorological ensemble precipitation forecasts, the paper discusses possible approaches aimed at incorporating input forecasting uncertainty in predictive uncertainty. Keyword: forecasting.

[1]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[2]  David Draper,et al.  Assessment and Propagation of Model Uncertainty , 2011 .

[3]  S. Sorooshian,et al.  A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters , 2002 .

[4]  Mario L. V. Martina,et al.  Flood forecasting using a fully distributed model: application of the TOPKAPI model to the Upper Xixian Catchment , 2005 .

[5]  Ezio Todini Using phase-state modelling for inferring forecasting uncertainty in nonlinear stochastic decision schemes , 1999 .

[6]  Adrian E. Raftery,et al.  Bayesian Model Selection in Structural Equation Models , 1992 .

[7]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .

[8]  J. S. Long,et al.  Testing Structural Equation Models , 1993 .

[9]  Roman Krzysztofowicz,et al.  Hydrologic uncertainty processor for probabilistic river stage forecasting , 2000 .

[10]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[11]  Keith Beven,et al.  Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology , 2001 .

[12]  Mario L. V. Martina,et al.  Reply to comment by Keith Beven, Paul Smith and Jim Freer on “Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology” , 2007 .

[13]  Jonathan Rougier,et al.  Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations , 2007 .

[14]  Mark E. Borsuk,et al.  On Monte Carlo methods for Bayesian inference , 2003 .

[15]  Howard Raiffa,et al.  Applied Statistical Decision Theory. , 1961 .

[16]  D. Lindley The Choice of Variables in Multiple Regression , 1968 .

[17]  Lotfi A. Zadeh,et al.  General System Theory , 1962 .

[18]  Bruce A. Robinson,et al.  Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging , 2007 .

[19]  H. Raiffa,et al.  Applied Statistical Decision Theory. , 1961 .

[20]  P. Mantovan,et al.  Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology , 2006 .

[21]  T. Palmer,et al.  Stochastic representation of model uncertainties in the ECMWF ensemble prediction system , 2007 .

[22]  Roman Krzysztofowicz,et al.  Bayesian theory of probabilistic forecasting via deterministic hydrologic model , 1999 .

[23]  Ezio Todini,et al.  A model conditional processor to assess predictive uncertainty in flood forecasting , 2008 .

[24]  Mario L. V. Martina,et al.  A Bayesian decision approach to rainfall thresholds based flood warning , 2005 .

[25]  B. M. Hill,et al.  Theory of Probability , 1990 .

[26]  E. Todini Hydrological catchment modelling: past, present and future , 2007 .

[27]  David Lindley,et al.  Optimal Statistical Decisions , 1971 .

[28]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .