Neal, J., Atkinson, P.M. and Hutton, C.W.
Flood inundation model updating using an ensemble
Kalman filter and spatially distributed measurements
Journal of Hydrology, 336, . (doi:10.1016/j.jhydrol.2007.01.012).
Full text not available from this repository.
This paper examines critically the application to a site along the River Crouch,
Essex of a river-flow forecasting approach based on a one-dimensional hydraulic flow simulation
model updated using real-time data within an ensemble Kalman filtering framework.
Given a specified validation location and forecast period the objective of the forecasting
model was to estimate water level more accurately with updating than without. The method
used to estimate both model state and state uncertainty was evaluated in terms of its forecast
accuracy and representation of forecast uncertainty. The ensemble Kalman filter lead
to an increase in forecast accuracy of between 50% and 70% depending on location. The
hyperparameters of the filter could be calibrated to make estimates of forecast uncertainty
at a specific location, where the most data were available. However, the presence of systematic
errors in the simulation model and especially measurement data meant that uncertainty
estimates were inaccurate at other locations. Although, the major source of
uncertainty in this model came from the boundary condition, additional uncertainty within
the model domain was required, particularly between channel and floodplain. Changing the
temporal sampling rate and spatial density of samples had little effect on the accuracy of
forecasts at this site. However, uncertainty was under-estimated when the temporal sampling
rate was decreased, indicating that the relative uncertainties prescribed to the simulation
model and measurement model were inadequate.
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