Data assimilation using the ensemble Kalman filter in a distributed hydrological model on the Tocantins River, Brasil

ABSTRACT In this work, the data assimilation method namely ensemble Kalman filter (EnKF) is applied to the Tocantins River basin. This method assimilates streamflow results by using a distributed hydrological model. The performance of the EnKF is also compared with an empirical assimilation method for hourly time intervals, in which two applications based on information transfer from gauged to ungauged sites and real time streamflow forecasting are assessed. In the first application, both assimilation methods are able to transfer streamflow to ungauged sites, obtaining better results when more than one station located upstream or downstream of the basin are gauged. In the second application, integration of a real time forecast model with EnKF is able to absorb errors at the beginning of the forecast. Therefore, a greater efficiency in the Nash-Sutcliffe index for the first 144 hours in advance in relation to its counterpart without assimilation is obtained. Finally, a comparison between both data assimilation methods shows a greater advantage for the EnKF in long lead times.

摘要
本研究将集合卡尔曼滤波（ensemble Kalman filter, EnKF）这一数据同化方法应用于托坎廷斯河流域（Tocantins River basin）。该方法依托分布式水文模型对径流模拟结果开展同化处理。针对逐小时时间步长，本研究对比了EnKF与一种经验同化方法的性能，并开展两项应用研究：一是径流信息从有测站流域向无测站流域的传递应用，二是实时径流预报应用。
在第一项应用中，两种同化方法均可实现径流信息向无测站流域的传递，且当流域上下游布设多个监测站点时，同化效果更为优异。
在第二项应用中，将实时预报模型与EnKF相结合，可有效抑制预报初始阶段的误差，使得提前144小时的预报结果的纳什-舒特克利夫指数（Nash-Sutcliffe index）相较于未进行同化的对照方案显著提升。
最后，对两种数据同化方法的对比结果表明，EnKF在较长预报提前期下具备更显著的应用优势。