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Study on Runoff Simulation with Multi-source Precipitation Information Fusion Based on Multi-model Ensemble

Author

Listed:
  • Runxi Li

    (Zhengzhou University)

  • Chengshuai Liu

    (Zhengzhou University)

  • Yehai Tang

    (Zhengzhou University
    Beijing Normal University)

  • Chaojie Niu

    (Zhengzhou University)

  • Yang Fan

    (Zhengzhou University)

  • Qingyuan Luo

    (Zhengzhou University)

  • Caihong Hu

    (Zhengzhou University)

Abstract

High-quality precipitation data input and the selection of reasonable and applicable hydrological models are the main ways to improve the accuracy of runoff simulation, and are crucial for flood control, drought resistance and comprehensive water resource management in the basin. This study takes the Jingle Basin as the research area, establishing a transformer model that integrates rainfall data from multiple sources considering environmental factors. It combines six types of remote sensing data with rainfall data, which are then used as inputs for the XAJ model, LSTM model, and Prophet model, respectively. The output results are further separately using the ensemble mean method and the Bayesian mean method for ensemble forecasting. The results show that: Compared with a single precipitation product, the fusion model considering environmental factors significantly enhances the correlation between the predicted rainfall and the observed rainfall, with the CC value reaching 0.72; Compared with the other two models, the LSTM model has the NSE value of 0.89, showing a better runoff prediction effect; Compared with the LSTM model with the NSE value of 0.85 and the ensemble average method with the NSE value of 0.76, the Bayesian model averaging method demonstrates the best runoff prediction and simulation effect, with the NSE value of 0.88.

Suggested Citation

  • Runxi Li & Chengshuai Liu & Yehai Tang & Chaojie Niu & Yang Fan & Qingyuan Luo & Caihong Hu, 2024. "Study on Runoff Simulation with Multi-source Precipitation Information Fusion Based on Multi-model Ensemble," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(15), pages 6139-6155, December.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:15:d:10.1007_s11269-024-03949-y
    DOI: 10.1007/s11269-024-03949-y
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    References listed on IDEAS

    as
    1. Zening Wu & Bingyan Ma & Huiliang Wang & Caihong Hu & Hong Lv & Xiangyang Zhang, 2021. "Identification of Sensitive Parameters of Urban Flood Model Based on Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2115-2128, May.
    2. Alexandru Dumitrescu & Marek Brabec & Marius Matreata, 2020. "Integrating Ground-based Observations and Radar Data Into Gridding Sub-daily Precipitation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3479-3497, September.
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