Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling
Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling
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DOI：
10.1016/j.jhydrol.2013.03.050
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发表时间：
2013-07-12
影响因子：
6.4
通讯作者：
Bates, Paul D.
中科院分区：
文献类型：
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作者：
Garcia-Pintado, Javier;Neal, Jeff C.;Bates, Paul D.
事实证明，基于卫星的合成孔径雷达 (SAR) 对于获取洪水范围信息非常有用，当与洪泛区的数字高程模型 (DEM) 相交时，可提供水位观测结果，并可将其同化到水动力模型中，以减少预测的不确定性。随着具有SAR能力的运行卫星数量不断增加，需要有关卫星初访和重访时间与预报性能之间关系的信息来优化卫星图像的运行调度。通过使用集成变换卡尔曼滤波器 (ETKF) 以及基于影响城市地区的真实洪水案例（2007 年夏季，英国西南部图克斯伯里）的二维水动力模型 LISFLOOD-FP 的综合分析，我们评估了预测性能对访问参数的敏感性。我们通过将偏差和时空相关性施加到流体动力学域的流入误差系综来模拟通用的水文-流体动力学建模级联。首先，与之前的研究一致，对这种偏差的估计和修正可以明显改善预测的正确性。其次，洪水早期获得的图像对预测统计数据有很大影响。重访间隔对于早期观测影响最大。这些结果对于复杂场景下基于遥感水位观测的实时洪水预报的未来来说是有希望的。 (c) 2013 年作者。由 Elsevier B.V. 出版。保留所有权利。
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.