HS4.2Hydrological forecasting: Untangling and reducing predictive uncertainty through improved model process description, data assimilation and post-processing
|Convener: Robert Moore | Co-Conveners: Henrik Madsen , Albrecht Weerts|
This session will address methods for untangling and estimating predictive uncertainty along with its reduction through data assimilation, improved model formulation and post-processing.
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 the need to improve the specification of methods to estimate and reduce predictive uncertainty as well as understanding the source of the uncertainty. Providing uncertainty estimates for integrated catchment models involving forecasting models, either as a cascade or as alternative models, can prove particularly challenging and an issue of interest to the session.
Data assimilation or post-processing in real-time can provide important ways of 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 reduction in predictive uncertainty at different scales through improved representation of model process (physics, parameterization, numerical solution, data support and calibration) and error are of special interest to the session.
Contributions are expected to address the following issues:
(i) Uncertainty propagation in meteorological-hydrological forecasting
(ii) Measures of performance of probability-based hydrological forecasts
(iii) Untangling of uncertainties in the meteorological-hydrological forecasting chain
(iv) Effect of (improved) representation of model process on forecast quality and predictive uncertainty
(v) Methods that allow use of ground-based hydrological data and remotely-sensed data in real-time forecasting, and account for their uncertainty
(vi) Methods for preparing meteorological predictions as input to real-time hydrological probability forecasts
(vii) Case studies of the above