The first part of this session will address issues of predictive probability and uncertainty in hydrological forecasting. It will deal with definitions, basic concepts and case studies of the role and use of predictive probability in hydrological forecasting. It will also include methodologies of short- and mid-term real-time forecasting with an emphasis on uncertainty analysis, probabilistic forecasting and communication to decision-makers. The second part of the session will address the related topic of data assimilation in hydrological forecasting. Methods that help update forecasts in real-time mode to reduce bias and increase accuracy will form the main focus both in hypothetical settings and in real-world case studies. The models involved in both parts may include catchment models, runoff routing models, groundwater models, coupled meteorological-hydrological models as well as combinations of the above.
Contributions are expected to address the following issues:
(i) Uncertainty propagation in meteorological-hydrological forecasting.
(ii) Assessment of predictive probability and uncertainty propagation for decision-making under uncertainty.
(iii) Methods that allow use of ground-based hydrological data and remotely sensed data in real-time flow forecasting.
(iv) Methods for preparing meteorological forecast data as input to real-time hydrological simulations.
(v) Case studies of the above.