Abstract
Runoff prediction is one of the important research fields of hydrology. As for the runoff series with unstable, poor periodicity and non-obvious tendency, this paper solves the problem that the general traditional models are not suitable for the short and medium-term prediction of such runoff series. To describe the nonhomogeneous characteristics of runoff series, a novel prediction model is established based on a nonhomogeneous Markov chain (NHMC-RPM). In this model, the probability distribution function of weekly runoff is obtained and the predicted value is calculated using the expected state. Taking the Yellow River as a case, the prediction results show that the NHMC-RPM is more accurate than other traditional models. The model presented in this work may be used to deal with similar runoff or other series data, as well as provide a practical approach for river managers to predict short and medium-term runoff.
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Funding
This work was supported by National Natural Science Foundation of China(No.61873084) and the Foundation of Hebei Education Department (No.ZD2017016).
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Xiaosheng Wang provided the idea of prediction model. Shujiang Pang and Haiying Guo collected the runoff data. Wei Li analyzed the runoff data and was a major contributor in writing the manuscript. All authors read and approved the final manuscript.
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Li, W., Wang, X., Pang, S. et al. A Runoff Prediction Model Based on Nonhomogeneous Markov Chain. Water Resour Manage 36, 1431–1442 (2022). https://doi.org/10.1007/s11269-022-03091-7
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DOI: https://doi.org/10.1007/s11269-022-03091-7