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Enhancing Reservoir Operational Modelling with Satellite Altimetry-Derived Water Level Data

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  • Li Tang

    (Taiyuan University of Technology)

  • Xiaohui Sun

    (Taiyuan University of Technology)

  • Shuyuan Xu

    (Shanxi Institute of Energy)

Abstract

Accurate parameter estimation of reservoir operation models is crucial for effective reservoir management but is hampered by limited in situ data for calibration. Here, we show that satellite altimetry can be effectively used to estimate model parameters and improve reservoir operation modelling. Specifically, we enhance the target storage parameter estimate of a reservoir model using altimetry-derived seasonal patterns. Four case study reservoirs were analysed to assess reservoir storage and outflow simulations. The satellite-derived water levels agree well with the observations, despite minor biases during low-water periods. The reservoir model with satellite-derived parameters outperforms the default model, with improvements in Nash–Sutcliffe efficiency and Kling–Gupta efficiency ranging from 0.02 to 0.3. An analysis of the model sensitivity reveals that the simulation accuracy strongly depends on the inflow data quality, whereas the optimal initial storage values vary across reservoirs. Our findings demonstrate that integrating satellite altimetry data can substantially improve reservoir operation modelling, offering a promising solution for regions with limited ground observations.

Suggested Citation

  • Li Tang & Xiaohui Sun & Shuyuan Xu, 2025. "Enhancing Reservoir Operational Modelling with Satellite Altimetry-Derived Water Level Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(7), pages 3467-3482, May.
  • Handle: RePEc:spr:waterr:v:39:y:2025:i:7:d:10.1007_s11269-025-04115-8
    DOI: 10.1007/s11269-025-04115-8
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    References listed on IDEAS

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    1. Sarah W. Cooley & Jonathan C. Ryan & Laurence C. Smith, 2021. "Human alteration of global surface water storage variability," Nature, Nature, vol. 591(7848), pages 78-81, March.
    2. Ming Zhong & Hongrui Zhang & Tao Jiang & Jun Guo & Jinxin Zhu & Dagang Wang & Xiaohong Chen, 2023. "A Hybrid Model Combining the Cama-Flood Model and Deep Learning Methods for Streamflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4841-4859, September.
    3. Julien Boulange & Naota Hanasaki & Dai Yamazaki & Yadu Pokhrel, 2021. "Role of dams in reducing global flood exposure under climate change," Nature Communications, Nature, vol. 12(1), pages 1-7, December.
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