Observation impact, domain length and parameter estimation in data assimilation for flood forecasting

Cooper, E. S., Dance, S. L. ORCID: https://orcid.org/0000-0003-1690-3338, Garcia-Pintado, J., Nichols, N. K. ORCID: https://orcid.org/0000-0003-1133-5220 and Smith, P. J. ORCID: https://orcid.org/0000-0003-4570-4127 (2018) Observation impact, domain length and parameter estimation in data assimilation for flood forecasting. Environmental Modelling and Software, 104. pp. 199-214. ISSN 1364-8152 doi: 10.1016/j.envsoft.2018.03.013

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/76153
Identification Number/DOI	10.1016/j.envsoft.2018.03.013
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Elsevier
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