Real-time updating method for the state variables of Xinanjiang model based on ensemble Kalman filter
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P338

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    Abstract:

    In order to improve the accuracy of flood forecasting, the paper proposes a retroactive correction method for the whole intermediate state variables of a sub-basin based on the ensemble Kalman filter. According to the different geomorphic feature and confluence time of each sub-basin, its corresponding retroactive time is found, and then, the Xinanjiang model is combined with to correct the state of each sub-basin before a certain time, gradually reducing the accumulation of errors. The results of the ideal model show that the intermediate state of sub-basin is effectively corrected, relative error of peak and volume decreases, and the deterministic coefficient increases. 12 historical floods of the Dapoling Basin, selected as an example, are effectively modified by the proposed method. As a result, the method can effectively improve the accuracy of flood forecasting and can be widely used in actual flood forecasting.

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李漫漫,石朋,尚艳丽,等.基于集合卡尔曼滤波的新安江模型状态变量实时修正方法[J].河海大学学报(自然科学版),2019,47(3):209-214.(LI Manman, SHI Peng, SHANG Yanli, et al. Real-time updating method for the state variables of Xinanjiang model based on ensemble Kalman filter[J]. Journal of Hohai University (Natural Sciences),2019,47(3):209-214.(in Chinese))

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  • Received:
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  • Online: May 30,2019
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