Satellite-supported flood forecasting in river networks: A real case study
Satellite-supported flood forecasting in river networks: A real case study
复制标题
DOI：
10.1016/j.jhydrol.2015.01.084
复制
发表时间：
2015-04-01
影响因子：
6.4
通讯作者：
Bates, Paul D.
中科院分区：
文献类型：
--
作者：
Garcia-Pintado, Javier;Mason, David C.;Bates, Paul D.
基于卫星（如合成孔径雷达[SARI]）的河漫滩水位观测（WLOs）可以依次同化到水动力模型中，以减少预测的不确定性。这有可能使预报保持正轨，从而提供一个基于地球观测（EO）的洪水预报系统。然而，这种系统在河网上开发的洪水的操作适用性需要进一步的测试。集合卡尔曼（EnKF）滤波器族是该领域最有前途的同化技术之一。这些过滤器使用预测误差协方差矩阵的有限大小的集合表示。随着预报-同化循环的进行，这种表示倾向于产生虚假的相关性，这对于处理城市地区或农村环境中的河流交汇处的洪水来说是一个进一步的复杂问题。本文以实际SAR立交桥序列（x波段cosmos - skymed星座）为例，评估了WLOs的同化效果。我们证明了直接应用全局集成变换卡尔曼滤波器（ETKF）会受到由伪相关引起的滤波器发散的影响。然而，基于空间的滤波器定位在预测误差协方差矩阵的发展中提供了实质性的调节，直接改善了预测，也使其能够进一步受益于同时在线流入误差估计和校正。此外，我们提出并评估了一种新的沿网络度量用于过滤器定位，这对网络洪水问题具有物理意义。使用该度量，我们进一步评估了通道摩擦和空间可变通道测深的同时估计，其中滤波器似乎能够同时收敛到合理值。结果还表明，在应用于大河流流量逐渐变化的洪水淹没模型中，摩擦是二阶效应。对于在实际情况下，摩擦和水深测量的同时估计是否有助于当前的预测，该研究并没有得出结论。综上所述，本文的研究结果表明了独立的基于eo的业务洪水预报的可行性。(C) 2015年作者。Elsevier B.V.出版
Satellite-based (e.g., Synthetic Aperture Radar [ SARI) water level observations (WLOs) of the floodplain can be sequentially assimilated into a hydrodynamic model to decrease forecast uncertainty. This has the potential to keep the forecast on track, so providing an Earth Observation (EO) based flood forecast system. However, the operational applicability of such a system for floods developed over river networks requires further testing. One of the promising techniques for assimilation in this field is the family of ensemble Kalman (EnKF) filters. These filters use a limited-size ensemble representation of the forecast error covariance matrix. This representation tends to develop spurious correlations as the forecast-assimilation cycle proceeds, which is a further complication for dealing with floods in either urban areas or river junctions in rural environments. Here we evaluate the assimilation of WLOs obtained from a sequence of real SAR overpasses (the X-band COSMO-Skymed constellation) in a case study. We show that a direct application of a global Ensemble Transform Kalman Filter (ETKF) suffers from filter divergence caused by spurious correlations. However, a spatially-based filter localization provides a substantial moderation in the development of the forecast error covariance matrix, directly improving the forecast and also making it possible to further benefit from a simultaneous online inflow error estimation and correction. Additionally, we propose and evaluate a novel along-network metric for filter localization, which is physically-meaningful for the flood over a network problem. Using this metric, we further evaluate the simultaneous estimation of channel friction and spatially-variable channel bathymetry, for which the filter seems able to converge simultaneously to sensible values. Results also indicate that friction is a second order effect in flood inundation models applied to gradually varied flow in large rivers. The study is not conclusive regarding whether in an operational situation the simultaneous estimation of friction and bathymetry helps the current forecast. Overall, the results indicate the feasibility of standalone EO-based operational flood forecasting. (C) 2015 The Authors. Published by Elsevier B.V.