Like all natural hazards, flooding is a complex and inherently uncertain phenomenon.  Despite advances in developing flood forecasting models and techniques, the uncertainty in forecasts remains unavoidable.  This uncertainty needs to be acknowledged, and uncertainty estimation in flood forecasting provides a rational basis for risk-based criteria. This book presents the development and applications of various methods based on probablity and fuzzy set theories for modelling uncertainty in flood forecasting systems. In particular, it presents a methodology for uncertainty assessment using disaggregation of time series inputs in the framework of both the Monte Carlo method and the Fuzzy Extention Principle.    It reports an improvement in the First Order Second Moment method, using second degree reconstruction, and derives qualitative scales for the interpretation of qualitative uncertainty.  Application is to flood forecasting models for the Klodzko catchment in POland and the Loire River in France.   Prospects for the hybrid techniques of uncertainty modelling and probability-possibility transformations are also explored and reported.