Observation impact, domain length and parameter estimation in data assimilation for flood forecasting. (June 2018) - Cooper, E.S.; Dance, S.L.; Garcia-Pintado, J.; Nichols, N.K.; Smith, P.J.
Abstract: Accurate inundation forecasting provides vital information about the behaviour of fluvial flood water. Using data assimilation with an Ensemble Transform Kalman Filter we combine forecasts from a numerical hydrodynamic model with synthetic observations of water levels. We show that reinitialising the model with corrected water levels can cause an initialisation shock and demonstrate a simple novel solution. In agreement with others, we find that although assimilation can accurately correct water levels at observation times, the corrected forecast quickly relaxes to the open loop forecast. Our new work shows that the time taken for the forecast to relax to the open loop case depends on domain length; observation impact is longer-lived in a longer domain. We demonstrate that jointly correcting the channel friction parameter as well as water levels greatly improves the forecast. We also show that updating the value of the channel friction parameter can compensate for bias in inflow. Highlights: Data assimilation is applied to simulated flood forecasts and SAR-like observations. Reinitialisation shock due to water level correction is removed using a novel method. Observation impact is linked to domain length when updating only water levels. Updating the channel friction parameter leads to marked improvement in forecast skill. Updating the channel friction parameter can compensate for biased inflow.