基于集合Kalman滤波的中长期径流预报  

Medium and long-term runoff forecast based on ensemble Kalman filter

作　　者：刘源[1] 纪昌明[1] 马皓宇 王弋 张验科[1] 马秋梅 杨涵 LIU Yuan;JI Changming;MA Haoyu;WANG Yi;ZHANG Yanke;MA Qiumei;YANG Han(School of Water Resources and Hydropower Engineering,North China Electric Power University,Beijing 102206;China Yangtze Power Co.,Ltd.,Yichang 443002,China;Water Resources Comprehensive Utilization Institute,Changjiang River Scientific Research Institute,Wuhan 430010,China)

机构地区：[1]华北电力大学水利与水电工程学院,北京102206 [2]中国长江电力股份有限公司,湖北宜昌443002 [3]长江科学院水资源综合利用研究所,湖北武汉430010

出　　处：《水资源保护》2024年第1期93-99,共7页Water Resources Protection

基　　金：国家自然科学基金项目(52179016,52109016);广东省科技计划项目(2020B1111530001)。

摘　　要：为降低中长期径流预报的不确定性,增加水电站水库的发电效益,针对现有方法侧重于提高单一预报模型确定性预报结果的准确性以降低径流预报不确定性的问题,提出一种基于集合Kalman滤波的入库径流确定性预报方法。以旬为预见期的锦西水库实例验证结果表明:相比传统的单一预报模型和传统的信息融合预报模型,基于集合Kalman滤波的中长期径流预报可使RMSE降低4.78 m^(3)/s,合格率可提高0.56%,且更有效地降低了汛期预报的不确定性,得到了更加准确、可靠的确定性径流预报结果,可为开展流域梯级水电站优化调度提供技术支持。To reduce the uncertainty of medium and long-term runoff forecast and increase the power generation efficiency of hydropower reservoirs,a deterministic inflow runoff forecast method based on ensemble Kalman filtering is proposed to address the issue of existing methods focusing on improving the accuracy of deterministic forecast results of a single forecasting model to reduce the uncertainty of runoff forecast.Taking the Jinxi Reservoir as the research object and ten days as the foresight period,conduct a case study.The results show that compared with traditional single forecast models and traditional information fusion forecast models,the medium-and long-term runoff forecast based on ensemble Kalman filtering reduces RMSE by 4.78 m^(3)/s and improves qualification rate by 0.56%.And it effectively reduces the uncertainty of flood season forecasting,obtaining more accurate and reliable deterministic runoff forecasting results,which can provide technical support for the optimization and scheduling of cascade hydropower stations in the basin.

关 键 词：中长期径流预报 数据融合 集合KALMAN滤波 锦西水库 

分 类 号：P338.2[天文地球—水文科学]