DATA ASSIMILATION TO IMPROVE FORECAST QUALITY OF ...

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XVI International Conference on Computational Methods in Water Resources (CMWR-XVI) Ingeniørhuset

DATA ASSIMILATION TO IMPROVE FORECAST QUALITY OF RIVER BASIN MODELS
Paper
Author:Anne Katrine Falk <akf@dhi.dk> (DHI - Water & Environment)
Michael B. Butts <mib@dhi.dk> (DHI - Water & Environment)
Henrik Madsen <hem@dhi.dk> (DHI - Water & Environment)
Johan Hartnack <jnh@dhi.dk> (DHI - Water & Environment)
Presenter:Anne Katrine Falk <akf@dhi.dk> (DHI - Water & Environment)
Date: 2006-06-18     Track: Special Sessions     Session: Data assimilation in water resources modelling
DOI:10.4122/1.1000000624
DOI:10.4122/1.1000000625

Ideally, real-time flood management decisions must be based on an understanding of the uncertainties and associated risks. It is therefore central 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 (e.g. 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. Within the EU 5th framework project FLOODRELIEF, an ensemble-based approach has been developed to address the issue of handling and quantifying forecasting and modelling uncertainties. A general stochastic framework for flood forecast modelling is presented based on the Ensemble Kalman Filter (Evensen, 1994). 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 several case studies including the US NWS study catchment, the Blue river basin and the Welland and Glen River Basin in the UK. Two methods for introducing uncertainties into the model are compared: 1. Stochastic errors are added to the runoff calculated by the catchment model. Only states in the river channel model are updated 2. Stochastic errors are added to the input to the catchment model (e.g. precipitation and evaporation). States in both the catchment model and in the river channel model are updated. In particular, an investigation of the value of these two approaches for rapidly responding river basins versus more slowly responding systems is presented. As expected it is observed that updating in both the catchment model and the river channel model has a longer lasting effect on the forecast than updating in the river channel alone. Finally the results of this evaluation highlight the fact that one of the major outstanding problems in estimating the forecast uncertainty is the characterisation of the sources of uncertainty. References: Evensen, G. (1994), Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophysical Research, vol. 99, no. C5, pp. 10143-10162.