Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling

JOURNAL OF HYDROLOGY

卷:495

Article

Garcia-Pintado, Javier^1,2  Neal, Jeff C.³  Mason, David C.^1,2  Dance, Sarah L.^1,2  Bates, Paul D.³ 

[1] Univ Reading, Sch Math & Phys Sci, Reading RG6 6AH, Berks, England

[2] Univ Reading, Natl Ctr Earth Observat, Reading RG6 6AH, Berks, England

[3] Univ Bristol, Sch Geog Sci, Bristol, Avon, England

关键词: Data assimilation;    Remote sensing;    Synthetic Aperture Radar;    Flood forecasting;    Urban flood;    Parameter estimation;   

DOI  :  10.1016/j.jhydrol.2013.03.050

来源: Elsevier

【 摘 要 】

Satellite-based Synthetic Aperture Radar (SAR) has proved useful for obtaining information on flood extent, which, when intersected with a Digital Elevation Model (DEM) of the floodplain, provides water level observations that can be assimilated into a hydrodynamic model to decrease forecast uncertainty. With an increasing number of operational satellites with SAR capability, information on the relationship between satellite first visit and revisit time and forecast performance is required to optimise the operational scheduling of satellite imagery. By using an Ensemble Transform Kalman Filter (ETKF) and a synthetic analysis with the 2D hydrodynamic model LISFLOOD-FP based on a real flooding case affecting an urban area (summer 2007, Tewkesbury, Southwest UK), we evaluate the sensitivity of the forecast performance to visit parameters. We emulate a generic hydrologic-hydrodynamic modelling cascade by imposing a bias and spatiotemporal correlations to the inflow error ensemble into the hydrodynamic domain. First, in agreement with previous research, estimation and correction for this bias leads to a clear improvement in keeping the forecast on track. Second, imagery obtained early in the flood is shown to have a large influence on forecast statistics. Revisit interval is most influential for early observations. The results are promising for the future of remote sensing-based water level observations for real-time flood forecasting in complex scenarios. (c) 2013 The Authors. Published by Elsevier B.V. All rights reserved.

【 授权许可】

Free