The 14th Conference on Hydrology

1A.11
BAYESIAN THEORY OF PROBABILISTIC RIVER FORECASTING

Roman Krzysztofowicz, Univ. of Virginia, Charlottesville, VA

Rational decision making (for flood warning, navigation, or reservoir systems) requires that the total uncertainty about a hydrological predictand (such as river stage, discharge, or runoff volume) be quantified in terms of a probability distribution, conditional on all available information and knowledge. Hydrological knowledge is typically embodied in a deterministic catchment model (lumped, semi-distributed, or distributed). Fundamentals are presented of a Bayesian Forecasting System (BFS) for producing a probabilistic forecast of a hydrological predictand via any deterministic catchment model. The BFS decomposes the total uncertainty into precipitation uncertainty and hydrologic uncertainty, which are modeled independently and then are integrated into a predictive (Bayes) distribution. The BFS is compared with Monte Carlo simulation and "ensemble forecasting" technique, none of which can alone produce a probabilistic forecast that meets requirements of rational decision making.



The 14th Conference on Hydrology