Uncertainty, data assimilation and decision-making in hydrological forecasting (including Outstanding Young Scientist Lecture)
Convener: Robert Moore  | Co-Conveners: Henrik Madsen , Ezio Todini 
Oral Programme
 / Thu, 06 May, 15:30–17:30  / Room 39
Poster Programme
 / Attendance Thu, 06 May, 17:30–19:00  / Hall A

This session will address methods for estimating the uncertainty of hydrological forecasts, for reducing this uncertainty through data assimilation, and for using predictive uncertainty in decision-making to support flood warning and water management. Predictive uncertainty estimates are becoming more common in hydrological forecasting systems but improving their specification presents a research challenge and a central interest of this session.

Whilst operational end-users recognise the merit of providing a measure of forecast reliability, there is not always a full appreciation of the benefits that can arise from the use of predictive uncertainty in their decision-making schemes. Flood warning can be greatly improved in terms of reduction of false alarm and missed alarm rates. Similarly, on-line reservoir management can greatly benefit in terms of increase of expected economical gain and reduction of expected losses. To raise awareness of the beneficial effects of operationally using predictive uncertainty, the session invites scientists and decision-makers to demonstrate, possibly with practical examples, how it can be applied in practice to support decision-making.

An important way of reducing the predictive uncertainty of hydrological forecasts is via data assimilation in real-time. Methods that help update forecasts in real-time to reduce bias and increase accuracy will be of interest to this session along with demonstrations of their use in both hypothetical settings and real-world case studies. The models involved with the methods for predictive uncertainty, data assimilation and decision-making 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 uncertainty for decision-making under uncertainty.
(iii) Methods that allow use of ground-based hydrological data and remotely-sensed data in real-time forecasting.
(iv) Methods for preparing meteorological forecast data as input to real-time hydrological simulations.
(v) Case studies of the above.