Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Hydrology, 567, 2018 DOI: 10.1016/j.jhydrol.2018.10.026
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Nierji reservoir flood forecasting based on a Data-Based Mechanistic methodology
AU - Wei, Guozhen
AU - Tych, Wlodek
AU - Beven, Keith
AU - He, Bin
AU - Ning, Fanggui
AU - Zhou, Huicheng
N1 - This is the author’s version of a work that was accepted for publication in Journal of Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Hydrology, 567, 2018 DOI: 10.1016/j.jhydrol.2018.10.026
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
KW - DBM
KW - Flood forecasting
KW - Kalman filter
KW - Large basin
KW - SDP
U2 - 10.1016/j.jhydrol.2018.10.026
DO - 10.1016/j.jhydrol.2018.10.026
M3 - Journal article
AN - SCOPUS:85055133713
VL - 567
SP - 227
EP - 237
JO - Journal of Hydrology
JF - Journal of Hydrology
SN - 0022-1694
ER -