Accurate flood inundation forecasts have the potential to minimize socioeconomic losses, but uncertainties in inflows propagated from the precipitation forecasts result in large prediction errors. Recent studies suggest that by assimilating independent flood observations, inherent uncertainty in hydraulic flood inundation modeling can be mitigated. Satellite observations from Synthetic Aperture Radar (SAR) sensors, with demonstrated flood monitoring capability, can thus be used to reduce flood forecast uncertainties through assimilation. However, researchers have struggled to develop an appropriate cost function to determine the innovation to be applied at each assimilation time step. Thus, a novel likelihood function based on mutual information (MI) is proposed here, for use with a particle filter‐based (PF) flood extent assimilation framework. Using identical twin experiments, synthetic SAR‐based probabilistic flood extents were assimilated into the hydraulic model LISFLOOD‐FP using the proposed PF‐MI algorithm. The 2011 flood event in the Clarence Catchment, Australia was used for this study. The impact of assimilating flood extents was evaluated in terms of subsequent flood extent evolution, floodplain water depths, flow velocities and channel water levels (WLs). Water depth and flow velocity simulations improved by ∼60% over the open loop on an average and persisted for up to 7 days, following the sequential assimilation of two post‐peak flood extent observations. Flood extents and channel WLs also showed mean improvements of ∼10% and ∼80% in accuracy, respectively, indicating that the proposed MI likelihood function can improve flood extent assimilation.

中文翻译：

---

基于互信息的似然函数用于粒子过滤器洪水泛化

准确的洪水淹没预报有可能使社会经济损失降到最低，但降水预报所传播的流入不确定性会导致较大的预报误差。最近的研究表明，通过吸收独立的洪水观测资料，可以减轻水力洪水淹没模型的固有不确定性。来自合成孔径雷达（SAR）传感器的卫星观测数据具有已证明的洪水监测能力，因此可用于通过同化来减少洪水预报的不确定性。但是，研究人员一直在努力开发合适的成本函数来确定要在每个同化时间步长应用的创新。因此，在此提出了一种基于互信息（MI）的新颖似然函数，用于基于粒子过滤器（PF）的泛洪程度同化框架。使用相同的双生实验，使用拟议的PF-MI算法将基于合成SAR的概率洪水范围同化为水力模型LISFLOOD-FP。这项研究使用了2011年在澳大利亚克拉伦斯集水区发生的洪水事件。根据随后的洪水范围演变，洪泛区水深，流速和河道水位（WLs）评估了同化洪水范围的影响。在两次峰后洪灾程度观测结果相继吸收之后，水深和流速模拟平均比开环平均提高了约60％，并持续了7天。洪泛程度和通道WL的准确度也分别平均提高了〜10％和〜80％，表明拟议的MI似然函数可以改善洪泛程度的同化。