Hydrological forecasting: application, uncertainty estimation, data assimilation and decision-making
|Convener: Robert Moore | Co-Conveners: Günter Blöschl , Vadim Kuzmin , Henrik Madsen , Jan Szolgay , Albrecht Weerts|
The session intends building bridges and revealing gaps between practice and science in hydrological forecasting. It will first focus on presenting hydrological methods and models which have already found application in operational local, regional and national hydrological forecasting systems. Contributions are expected both on new flood forecast initiatives and on experience gained with pre-existing systems. Presentations of real-world case studies of system implementations - configured at local, regional and national scales - are encouraged, including trans-boundary issues.
The aim of the session is to bring together researchers in the field of forecasting and modelling, operational practioners, and those able to address specific user requirements and societal issues of hydrological forecasting. Questions related to the nature of hydrological forecasting under normal and flood conditions, together with issues of predictability and its limits are to be covered. Ways of presenting and communicating forecast results can play an important role and of relevance to the session.
A second focus will be on methods for estimating forecast uncertainty- including its reduction through data assimilation and improved model formulation - to aid decision-making in flood warning and water management. Encompassing uncertainty estimation within operational forecasting systems is becoming a more commonplace 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 for use in hydrological hazard mitigation and resource management. Providing uncertainty estimates for integrated catchment models involving a chain of forecasting models can prove particularly challenging, and an issue of particular interest.
Data assimilation in real-time can prove an important way 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 especial relevance to consider. 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 these. Demonstrations of the reduction in predictive uncertainty through improved representation of model process and error are encouraged for presentation.
Whilst the value of a measure of forecast reliability is recognised by practitioners, realising the full benefit of predictive uncertainty in decision-making schemes is an issue that is actively being investigated in conjunction with researchers. Flood warning can be greatly improved in terms of better probability of detection and reduced false alarm rates. Of interest to the session are new performance measures relevant to probabilistic forecasting and novel ways of communicating uncertainty through time-series and map displays in real-time. On-line reservoir management can also benefit from knowledge of uncertainty through 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 through case studies, how it can be applied in practice to support decision-making.
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) Assessment of predictive uncertainty for decision-making under uncertainty.
(iv) Improved ways of communicating forecast 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.