The case for probabilistic forecasting in hydrology

That forecasts should be stated in probabilistic, rather than deterministic, terms has been argued from common sense and decision-theoretic perspectives for almost a century. Yet most operational hydrological forecasting systems produce deterministic forecasts and most research in operational hydrology has been devoted to finding the ‘best’ estimates rather than quantifying the predictive uncertainty. This essay presents a compendium of reasons for probabilistic forecasting of hydrological variates. Probabilistic forecasts are scientifically more honest, enable risk-based warnings of floods, enable rational decision making, and offer additional economic benefits. The growing demand for information about risk and the rising capability to quantify predictive uncertainties create an unparalleled opportunity for the hydrological profession to dramatically enhance the forecasting paradigm.

[1]  Mark S Antolik,et al.  An overview of the National Weather Service's centralized statistical quantitative precipitation forecasts , 2000 .

[2]  Charles Obled,et al.  Uncertainty in flood forecasting: a French case study , 1994 .

[3]  B. Golding.  Quantitative precipitation forecasting in the UK , 2000 .

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

[5]  Peter K. Kitanidis,et al.  Real‐time forecasting with a conceptual hydrologic model: 1. Analysis of uncertainty , 1980 .

[6]  Ernest Cooke,et al.  FORECASTS AND VERIFICATIONS IN WESTERN AUSTRALIA , 2022 .

[7]  M. Degroot,et al.  Probability and Statistics , 2021, Examining an Operational Approach to Teaching Probability.

[8]  Gerald N. Day,et al.  Extended Streamflow Forecasting Using NWSRFS , 1985 .

[9]  K. T Smith,et al.  Nowcasting precipitation — a proposal for a way forward , 2000 .

[10]  A. H. Murphy,et al.  Reliability of Subjective Probability Forecasts of Precipitation and Temperature , 1977 .

[11]  Roman Krzysztofowicz,et al.  Reply [to “Comment on ‘Bayesian theory of probabilistic forecasting via deterministic hydrologic model‘ by Roman Krzysztofowicz”] , 2001 .

[12]  Nilgun B. Harmancioglu,et al.  Coping with floods , 1994 .

[13]  Susan F. Zevin,et al.  Steps toward an Integrated Approach to Hydrometeorological Forecasting Services , 1994 .

[14]  J. C. Thompson,et al.  On the Operational Deficiences in Categorical Weather Forecasts , 1952 .

[15]  Charles Obled,et al.  Real-time flood forecasting using a stochastic rainfall generator , 1994 .

[16]  Konstantine P. Georgakakos.  Covariance propagation and updating in the context of real-time radar data assimilation by quantitative precipitation forecast models , 2000 .

[17]  A. H. Murphy,et al.  The Use of Probabilities in Subjective Quantitative Precipitation Forecasts: Some Experimental Results , 1985 .

[18]  G. Doms,et al.  Operational quantitative precipitation forecasting at the German Weather Service , 2000 .

[19]  F. Ramsey.  The Foundations of Mathematics and Other Logical Essays , 2001 .

[20]  A. H. Murphy,et al.  Probabilistic temperature forecasts: The case for an operational program , 1979 .

[21]  Chris G. Collier,et al.  Quantitative precipitation forecasting , 2000 .

[22]  Konstantine P. Georgakakos,et al.  Real‐Time Flash Flood Prediction , 1987 .

[23]  Konstantine P. Georgakakos,et al.  On improved hydrologic forecasting — Results from a WMO real-time forecasting experiment , 1990 .

[24]  Dou Long,et al.  Forecast sufficiency characteristic: Construction and application , 1991 .

[25]  L. J. Savage,et al.  Probability and the weighing of evidence , 1951 .

[26]  A. H. Murphy,et al.  Probabilities, Odds, and Forecasts of Rare Events , 1991 .

[27]  Witold F. Krajewski,et al.  Simulation study of the effects of model uncertainty in variational assimilation of radar data on rainfall forecasting , 2000 .

[28]  A. Szöllősi-Nagy,et al.  Comparative analysis of three recursive real-time river flow forecasting models: deterministic, stochastic, and coupled deterministic-stochastic , 1988 .

[29]  B. D. Finetti,et al.  Foresight: Its Logical Laws, Its Subjective Sources , 1992 .

[30]  Roman Krzysztofowicz.  Why should a forecaster and a decision maker use Bayes Theorem , 1983 .

[31]  G. A. Oberholzer,et al.  THE TORNADO OF JUNE 6, 1906, NEAR LA CROSSE, WIS. , 1906 .

[32]  Dong-Jun Seo,et al.  Simulation of precipitation fields from probabilistic quantitative precipitation forecast , 2000 .

[33]  Ashish Sharma,et al.  Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 3 — A nonparametric probabilistic forecast model , 2000 .

[34]  Roman Krzysztofowicz,et al.  Probabilistic Quantitative Precipitation Forecasts for River Basins , 1993, Weather and Forecasting.

[35]  Roman Krzysztofowicz,et al.  Probabilistic Hydrometeorological Forecasts: Toward a New Era in Operational Forecasting , 1998 .

[36]  Henry E. Kyburg,et al.  Studies in Subjective Probability , 1965 .