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Quantifying the Uncertainty Interaction Between the Model Input and Structure on Hydrological Processes

Author

Listed:
  • Shuai Zhou

    (Xi’an University of Technology)

  • Yimin Wang

    (Xi’an University of Technology)

  • Ziyan Li

    (Xi’an University of Technology)

  • Jianxia Chang

    (Xi’an University of Technology)

  • Aijun Guo

    (Xi’an University of Technology)

Abstract

Input error is one of the main sources of uncertainty in hydrological models. It mainly comes from the uncertainty of precipitation data, which is caused by inaccurate measurement at the point scale and imperfect representation at the regional scale. The structural error of the hydrological model is dependent on the input, and the uncertainty interaction between the model input and structural will increase the cumulative error of the hydrological process. Therefore, the objective of this study is to investigate the impacts of the uncertainties of rain gauge station input levels and hydrological models on flows with different magnitudes by setting nine input levels of rain gauge stations using three hydrological models (i.e., HyMod, XAJ and HBV). The variance decomposition method based on subsampling was used to dynamically quantify the contribution rates of rain gauge station input levels, hydrological models, and their interaction to the runoff simulation uncertainty. The results show that different rain gauge station input levels and hydrological models dynamically affected the hydrological simulation due to an uneven spatiotemporal distribution of precipitation. Moreover, the simulation accuracy was poor at low flow but better at high flow. Increasing the number of rainfall stations input under a certain threshold could significantly improve the hydrological simulation accuracy. In addition, the contributions of the uncertainties of the rain gauge station input levels and its interaction with the hydrological model to runoff were significantly enhanced in the flood season, but the contribution of the hydrological model uncertainty was still dominant. The results of this study can provide a decision-making basis and scientific guidance for the management and planning of water resources within basins under the influence of a changing environment.

Suggested Citation

  • Shuai Zhou & Yimin Wang & Ziyan Li & Jianxia Chang & Aijun Guo, 2021. "Quantifying the Uncertainty Interaction Between the Model Input and Structure on Hydrological Processes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3915-3935, September.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:12:d:10.1007_s11269-021-02883-7
    DOI: 10.1007/s11269-021-02883-7
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    References listed on IDEAS

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    1. E. Soriano & L. Mediero & C. Garijo, 2020. "Quantification of Expected Changes in Peak Flow Quantiles in Climate Change by Combining Continuous Hydrological Modelling with the Modified Curve Number Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(14), pages 4381-4397, November.
    2. Kuaile Feng & Jianzhong Zhou & Yi Liu & Chengwei Lu & Zhongzheng He, 2019. "Hydrological Uncertainty Processor (HUP) with Estimation of the Marginal Distribution by a Gaussian Mixture Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 2975-2990, July.
    3. Samuel Beskow & Lloyd Norton & Carlos Mello, 2013. "Hydrological Prediction in a Tropical Watershed Dominated by Oxisols Using a Distributed Hydrological Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 341-363, January.
    4. Sheng Sheng & Hua Chen & Fu-Qiang Guo & Jie Chen & Chong-Yu Xu & Sheng-lian Guo, 2020. "Transferability of a Conceptual Hydrological Model across Different Temporal Scales and Basin Sizes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2953-2968, July.
    5. Hadush Meresa & Yongqiang Zhang, 2021. "Contrasting Uncertainties in Estimating Floods and Low Flow Extremes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1775-1795, April.
    6. 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.
    7. Rong Gan & Changzheng Chen & Jie Tao & Yongqiang Shi, 2021. "Hydrological Process Simulation of Sluice-Controlled Rivers in the Plains Area of China Based on an Improved SWAT Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1817-1835, April.
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