Revilla-Romero Beatriz, Wanders Niko, Burek Peter, Salamon Peter, de Roo Ad
European Commission, Joint Research Centre, Ispra, Italy; Utrecht University, Faculty of Geosciences, Utrecht, The Netherlands.
Utrecht University, Faculty of Geosciences, Utrecht, The Netherlands; Department of Civil and Environmental Engineering, Princeton University, United States.
J Hydrol (Amst). 2016 Dec;543(Pt B):659-670. doi: 10.1016/j.jhydrol.2016.10.041.
In hydrological forecasting, data assimilation techniques are employed to improve estimates of initial conditions to update incorrect model states with observational data. However, the limited availability of continuous and up-to-date ground streamflow data is one of the main constraints for large-scale flood forecasting models. This is the first study that assess the impact of assimilating daily remotely sensed surface water extent at a 0.1° × 0.1° spatial resolution derived from the Global Flood Detection System (GFDS) into a global rainfall-runoff including large ungauged areas at the continental spatial scale in Africa and South America. Surface water extent is observed using a range of passive microwave remote sensors. The methodology uses the brightness temperature as water bodies have a lower emissivity. In a time series, the satellite signal is expected to vary with changes in water surface, and anomalies can be correlated with flood events. The Ensemble Kalman Filter (EnKF) is a Monte-Carlo implementation of data assimilation and used here by applying random sampling perturbations to the precipitation inputs to account for uncertainty obtaining ensemble streamflow simulations from the LISFLOOD model. Results of the updated streamflow simulation are compared to baseline simulations, without assimilation of the satellite-derived surface water extent. Validation is done in over 100 river gauges using daily streamflow observations in the African and South American continent over a one year period. Some of the more commonly used metrics in hydrology were calculated: KGE', NSE, PBIAS%, R, RMSE, and VE. Results show that, for example, NSE score improved on 61 out of 101 stations obtaining significant improvements in both the timing and volume of the flow peaks. Whereas the validation at gauges located in lowland jungle obtained poorest performance mainly due to the closed forest influence on the satellite signal retrieval. The conclusion is that remotely sensed surface water extent holds potential for improving rainfall-runoff streamflow simulations, potentially leading to a better forecast of the peak flow.
在水文预报中，数据同化技术用于改进初始条件估计，以便利用观测数据更新不正确的模型状态。然而，连续且最新的地面径流数据可用性有限是大规模洪水预报模型的主要制约因素之一。这是第一项评估将全球洪水探测系统（GFDS）以0.1°×0.1°空间分辨率获取的每日遥感地表水范围同化到一个包括非洲和南美洲大陆空间尺度上大片无观测区域的全球降雨径流模型中的影响的研究。地表水范围是使用一系列被动微波遥感传感器观测的。该方法利用亮度温度，因为水体具有较低的发射率。在时间序列中，卫星信号预计会随水面变化而变化，异常情况可与洪水事件相关联。集合卡尔曼滤波器（EnKF）是数据同化的蒙特卡罗实现方法，这里通过对降水输入应用随机抽样扰动来考虑不确定性，从而从LISFLOOD模型获得集合径流模拟。将更新后的径流模拟结果与未同化卫星衍生地表水范围的基线模拟结果进行比较。使用非洲和南美洲大陆一年期间的每日径流观测数据，在100多个河流水位站进行验证。计算了水文中一些更常用的指标：KGE'、NSE、PBIAS%、R、RMSE和VE。结果表明，例如，101个站点中有61个站点的NSE得分有所提高，径流峰值的时间和流量都有显著改善。而位于低地丛林中的水位站验证效果最差，主要是因为封闭森林对卫星信号检索有影响。结论是，遥感地表水范围在改进降雨径流模拟方面具有潜力，可能会带来对洪峰流量的更好预报。
