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A dynamic information extraction method for areal mean rainfall error and its application in basins of different scales for flood forecasting

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Abstract

In this study, we proposed and tested a method based on system response curve (SRC) to extract the error information of areal mean rainfall. These extracted rainfall error are then used to update the rainfall time series data and propagated through the model to update the stream-flow forecast. The method is evaluated via synthetic and real-data cases. After our statistical analysis in different application scenarios, we found that the SRC method can effectively improve the accuracy of real-time flood forecasting when an optimal updating width is selected (the average relative error was reduced from 1.65% to 0.86%). In addition, we benchmark our results against a more conventional AR (Auto-Regression) streamflow-updating method. The average accuracy improvement of SRC method is 6.3% higher than that of AR method. More importantly, we found that the optimal updating width of SRC method is highly correlated with the average lead time of the basin, it has guiding significance for selecting the optimal updating width in practical application.

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Acknowledgements

This work was supported by the Fundamental Research Funds for Central Universities (B200201027), National Natural Science Foundation of China (51709077/51809174) and Open Research Fund of Yellow River Sediment Key Laboratory (201804). Data, material and code used in this study are available upon request from the corresponding author.

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Authors and Affiliations

  1. Department of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, China

    Wei Si, Peng Jiang, Weimin Bao, Peng Shi & Simin Qu

  2. Yellow River Institute of Hydraulic Research, Zhengzhou, Henan, China

    Wei Si

  3. Nanjing Hydraulic Research Institute, Nanjing, Jiangsu, China

    Hua Zhong

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  1. Wei Si
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Correspondence to Wei Si.

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Si, W., Zhong, H., Jiang, P. et al. A dynamic information extraction method for areal mean rainfall error and its application in basins of different scales for flood forecasting. Stoch Environ Res Risk Assess 35, 255–270 (2021). https://doi.org/10.1007/s00477-020-01957-z

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