Hydrology Research (Dec 2020)

System response curve correction method of runoff error for real-time flood forecast

  • Qian Li,
  • Caisong Li,
  • Huanfei Yu,
  • Jinglin Qian,
  • Linlin Hu,
  • Hangjian Ge

DOI
https://doi.org/10.2166/nh.2020.048
Journal volume & issue
Vol. 51, no. 6
pp. 1312 – 1331

Abstract

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Multiple factors including rainfall and underlying surface conditions make river basin real-time flood forecasting very challenging. It is often necessary to use real-time correction techniques to modify the forecasting results so that they reach satisfactory accuracy. There are many such techniques in use today; however, they tend to have weak physical conceptual basis, relatively short forecast periods, unsatisfactory correction effects, and other problems. The mechanism that affects real-time flood forecasting error is very complicated. The strongest influencing factors corresponding to this mechanism affect the runoff yield of the forecast model. This paper proposes a feedback correction algorithm that traces back to the source of information, namely, modifies the watershed runoff. The runoff yield error is investigated using the principle of least squares estimation. A unit hydrograph is introduced into the real-time flood forecast correction; a feedback correction model that traces back to the source of information. The model is established and verified by comparison with an ideal model. The correction effects of the runoff yield errors are also compared in different ranges. The proposed method shows stronger correction effect and enhanced prediction accuracy than the traditional method. It is also simple in structure and has a clear physical concept without requiring added parameters or forecast period truncation. It is readily applicable in actual river basin flood forecasting scenarios. HIGHLIGHTS From the perspective of system theory, an RSCR method to modify production flow is proposed.; This method uses the system response curve feedback of the production flow in each period to correct the production flow in each period.; The validity and practicability of the method are proved by numerical simulation experiments with different random distribution errors.;

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