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The Improvement in GWLF Model Simulation Performance in Watershed Hydrology by Changing the Transport Framework

Author

Listed:
  • Zuoda Qi

    (Nankai University)

  • Gelin Kang

    (Nankai University)

  • Minli Shen

    (Nankai University)

  • Yuqiu Wang

    (Nankai University)

  • Chunli Chu

    (Nankai University)

Abstract

The correct and reasonable delineation of actual hydrologic processes is a footstone for the effective simulation of pollutants in watershed models. In this study, a simple but comprehensive semidistributed modeling approach based on the generalized watershed loading function (GWLF) was modified to enable the accurate simulation of hydrology in watersheds. The frame of the original GWLF model (ORM), with a lumped hydrological parameter, was modified by adding channel routing processes, which made it possible to introduce the concept of subbasins. Then, the revised GWLF model was applied to the Luanhe watershed (30,000 km2) on a monthly bias in comparison with the ORM and the previously revised version. The sensitivity analysis and generalized likelihood uncertainty estimation (GLUE) uncertainty analysis were individually conducted to evaluate these modifications. Eventually, we compared four extreme conditions for the daily streamflow simulations of the three model versions in the Tunxi watershed but without calibration. All of the results indicated that the stability and accuracy of the model and the validity of the parameters were all enhanced and improved by the new revised version of the model, which provided reliable simulation results and indicated that it is a prospective tool to support watershed management.

Suggested Citation

  • Zuoda Qi & Gelin Kang & Minli Shen & Yuqiu Wang & Chunli Chu, 2019. "The Improvement in GWLF Model Simulation Performance in Watershed Hydrology by Changing the Transport Framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 923-937, February.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:3:d:10.1007_s11269-018-2149-4
    DOI: 10.1007/s11269-018-2149-4
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    References listed on IDEAS

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    1. Raymond Kim & Daniel Loucks & Jery Stedinger, 2012. "Artificial Neural Network Models of Watershed Nutrient Loading," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(10), pages 2781-2797, August.
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