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HS4.2/NH1.12

Predictive uncertainty estimation and data assimilation for hydrological forecasting and decision making (co-organized)
Convener: Oldrich Rakovec  | Co-Conveners: Albrecht Weerts , Hamid Moradkhani , Marie-Amélie Boucher , Rodolfo Alvarado Montero , Joshua K. Roundy 
This session will address the understanding of sources of predictability, as well as the quantification and reduction of predictive uncertainty in hydrologic forecasting, including the case of hydrologic extreme events. Including uncertainty estimation in operational forecasting systems is becoming more and more a common practice. However, a significant research challenge, and central interest of this session, is to understand the sources of predictability and to develop approaches, methods and techniques to enhance predictability (e.g. accuracy, reliability, etc.) as well as to quantify and reduce predictive uncertainty in general.
Providing uncertainty estimates for integrated catchment models involving forecasting models, either as a cascade or as alternative models, can prove particularly challenging and are an issue of interest to this session. Data assimilation and pre-/post-processing in real-time can provide important ways of improving the quality and reducing the uncertainty of hydrological forecasts. Methods that help update forecasts in real-time to reduce bias and increase accuracy, and case study demonstrations of their use, are also of interest to this session.
The models involved with the methods for predictive uncertainty, data assimilation, pre-processing and post-processing may include catchment models, runoff routing models, groundwater models, coupled meteorological-hydrological models as well as combinations of these. Demonstrations of the sources of predictability and subsequent reduction in predictive uncertainty of hydrologic extremes at different scales through improved representation of model processes (physics, parameterization, numerical solution, data support and calibration) and of errors in forcing and initial state are of special interest.
Contributions are expected to address the following issues:
- Sources of predictability (model, forcing, initial conditions) of hydrologic extremes,
- Quantification and reduction of predictive uncertainty
- Real-time data assimilation
- Untangling sources of uncertainty in the meteorological-hydrological forecasting of extremes
- Effect of (improved) representation of model process on forecast quality and predictive uncertainty
- Methods for preparing meteorological predictions as input to real-time hydrological forecasts
- Case studies of the above.