Ensemble Methods for Flood Forecasting Developed in the Floodrelief Project.
Abstract
To have real value, real-time flood management decisions must be based on an understanding of the uncertainties and associated risks. It is therefore critical for effective flood management tools to provide reliable estimates of the forecast uncertainty. Only by quantifying the inherent uncertainties involved in flood forecasting can effective real-time flood management and warning be carried out. Forecast uncertainty requires the estimation of the uncertainties associated with both the hydrological model inputs (precipitation observations and forecasts), model structure, parameterisation and calibration, and methodologies that predict how the uncertainties from different sources propagate through the hydrological and hydraulic system. Ensemble-based approaches are attractive because they allow effects of a wide range of uncertainties to be incorporated. Within the EU 5th framework project FLOODRELIEF, two complementary ensemble-based approaches are being developed to address the issue of handling and quantifying forecasting and modelling uncertainties. A general stochastic framework based on the Ensemble Kalman Filter (Evensen, 1994) has been developed for flood forecast modelling. The Kalman filter provides a natural framework for determining how the different sources of uncertainty propagate through the hydrological and hydraulic models and to reduce forecast uncertainty via data assimilation of real-time observations. An evaluation of this framework is presented for two case studies, US NWS study catchment, the Blue river basin and the UK FLOODRELIEF study catchment, the Welland and Glen. The results of this evaluation highlight the fact that one of the major outstanding problems in uncertainty estimation is the characterisation of the sources of uncertainty. The second approach is the development of an internet-based decision support system to provide highly accessible real-time flood management tools. This decision support system has been designed together with forecasting end-users to support ensemble forecasting. Ensemble forecasting using different forecasting inputs provides an alternative method of estimating uncertainty. For example rainfall forecasts using meso-scale meteorological forecasts, downscaled meteorological forecasts, radar forecasts, best case and worst case forecasts can be used by operational forecasters to models to estimate an uncertainty range. In this manner a direct and intuitive estimate of forecast uncertainties can be achieved to address the issue of how ensemble results can be communicated to flood managers and decision-makers.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2004
- Bibcode:
- 2004AGUFM.H21H..07B
- Keywords:
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- 6309 Decision making under uncertainty;
- 3337 Numerical modeling and data assimilation;
- 3220 Nonlinear dynamics;
- 1854 Precipitation (3354);
- 1860 Runoff and streamflow