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Joint Optimization of Conceptual Rainfall-Runoff Model Parameters and Weights Attributed to Meteorological Stations

Author

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  • Adam P. Piotrowski

    (Polish Academy of Sciences)

  • Marzena Osuch

    (Polish Academy of Sciences)

  • Jarosław J. Napiorkowski

    (Polish Academy of Sciences)

Abstract

Conceptual lumped rainfall-runoff models are frequently used for various environmental problems. To put them into practice, both the model calibration method and data series of the area-averaged precipitation and air temperature are needed. In the case when data from more than one measurement station are available, first the catchment-averaged meteorological data series are usually obtained by some method, and then they are used for calibration of a lumped rainfall-runoff model. However, various optimization methods could easily be applied to simultaneously calibrate both the aggregation weights attributed to various meteorological stations to obtain a lumped meteorological data series and the rainfall-runoff model parameters. This increases the problem dimensionality but allows the optimization procedure to choose the data that are most important for the rainfall-runoff process in a particular catchment, without a priori assumptions. We test the idea using two conceptual models, HBV and GR4J, and three mutually different, relatively recently proposed Evolutionary Computation and Swarm Intelligence optimization algorithms, that are applied to three catchments located in Poland and northwestern USA. We consider two cases: with and without the model error correction applied to the rainfall-runoff models. It is shown that for the calibration period, joint optimization of the weights used to aggregate the meteorological data and the parameters of the rainfall-runoff model improves the results. However, the results for the validation period are inconclusive and depend on the model, error correction, optimization algorithm, and catchment.

Suggested Citation

  • Adam P. Piotrowski & Marzena Osuch & Jarosław J. Napiorkowski, 2019. "Joint Optimization of Conceptual Rainfall-Runoff Model Parameters and Weights Attributed to Meteorological Stations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4509-4524, October.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:13:d:10.1007_s11269-019-02368-8
    DOI: 10.1007/s11269-019-02368-8
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    References listed on IDEAS

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    1. Tian Peng & Jianzhong Zhou & Chu Zhang & Na Sun, 2018. "Modeling and Combined Application of Orthogonal Chaotic NSGA-II and Improved TOPSIS to Optimize a Conceptual Hydrological Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3781-3799, September.
    2. Wenlin Yuan & Meiqi Liu & Fang Wan, 2019. "Calculation of Critical Rainfall for Small-Watershed Flash Floods Based on the HEC-HMS Hydrological Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2555-2575, May.
    3. Meng-Xuan Jie & Hua Chen & Chong-Yu Xu & Qiang Zeng & Jie Chen & Jong-Suk Kim & Sheng-lian Guo & Fu-Qiang Guo, 2018. "Transferability of Conceptual Hydrological Models Across Temporal Resolutions: Approach and Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1367-1381, March.
    4. A. Bhadra & A. Bandyopadhyay & R. Singh & N. Raghuwanshi, 2010. "Rainfall-Runoff Modeling: Comparison of Two Approaches with Different Data Requirements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 37-62, January.
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