Reducing predictive uncertainty and data assimilation techniques for hydrological forecasting
This session will address the understanding of sources of predictability and quantification and reduction of predictive uncertainty of hydrological extremes in operational hydrologic forecasting. Including uncertainty estimation in operational forecasting systems is becoming a more common practice. However, a significant research challenge and central interest of this session is to understand the sources of predictability and development of approaches, methods and techniques to enhance predictability (e.g. accuracy, reliability etc.) and 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 the session. Data assimilation or pre-/post-processing in real-time can provide important ways of improving the quality (e.g. accuracy, reliability) 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 of interest to this session.
The models involved with the methods for predictive uncertainty, data assimilation, post-processing and decision-making 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 at different scales through improved representation of model process (physics, parameterization, numerical solution, data support and calibration) and error, forcing and initial state are of special interest to the session.
Contributions are expected to address the following issues:
(i) Sources of predictability (model, forcing, initial conditions)
(ii) Quantification and reduction of predictive uncertainty
(iii) Real-time data assimilation
(iii) Untangling sources of uncertainty in the meteorological-hydrological forecasting chain
(iv) Effect of (improved) representation of model process on forecast quality and predictive uncertainty
(v) Methods for preparing meteorological predictions as input to real-time hydrological probability forecasts
(vi) Verification (methods) of hydrologic forecasts
(vii) Case studies of the above
Solicited speaker is Maurizio Mazzoleni (from Uppsala University) who will give a talk about Real-time assimilation of crowdsourced observations in hydrological and hydraulic models.