Nierji reservoir flood forecasting based on a Data-Based Mechanistic methodology

JOURNAL OF HYDROLOGY

卷:567

Article

Wei, Guozhen¹  Tych, Wlodek²  Beven, Keith²  He, Bin¹  Ning, Fanggui³  Zhou, Huicheng¹ 

[1] Dalian Univ Technol, Sch Hydraul Engn, Dalian 116024, Peoples R China

[2] Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England

[3] Songliao Water Resources Commiss, Changchun 130021, Jilin, Peoples R China

关键词: Flood forecasting;    DBM;    Kalman filter;    SDP;    Large basin;   

DOI  :  10.1016/j.jhydrol.2018.10.026

来源: Elsevier

【 摘 要 】

The Nierji Basin, in the north-east of China, is one of the most important basins in the joint operation of the entire Songhua River, containing a major reservoir used for flood control. It is necessary to forecast the flow of the basin during periods of flood accurately and with the maximum lead time possible. This paper presents a flood forecasting system, using the Data Based Mechanistic (DBM) modeling approach and Kalman Filter data assimilation for flood forecasting in the data limited Nierji Reservoir Basin (NIRB). Examples are given of the application of the DBM methodology using both single input (rainfall or upstream flow) and multiple input (rainfalls and upstream flow) to forecast the downstream discharge for different sub-basins. Model identification uses the simplified recursive instrumental variable (SRIV) algorithm, which is robust to noise in the observation data. The application is novel in its use of stochastic optimisation to define rain gauge weights and identify the power law nonlinearity. It is also the first application of the DBM methodology to flood forecasting in China. Using the methodology allows the forecasting with lead times of 1-day, 2-day, 3-day, 4-day, 5-day with 98%, 97%, 96%, 96% and 93% forecast coefficient of determination respectively, which is sufficient for the regulation of the reservoirs in the basin.

【 授权许可】

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