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Calibration and uncertainty analysis of a hydrological model based on cuckoo search and the M-GLUE method

  • Hongxue Zhang
  • Jianxia Chang
  • Lianpeng Zhang
  • Yimin Wang
  • Bo Ming
Original Paper
  • 23 Downloads

Abstract

The watershed hydrological model is regarded as a powerful tool for simulating streamflow, but it is subject to many uncertainties. TOPMODEL (TOPography-based hydrological MODEL) is used as hydrological modeling in this paper; general likelihood uncertainty estimation (GLUE) and multi-criteria GLUE (M-GLUE) methods are applied to evaluate the uncertain effect of model parameters on streamflow simulation, and three climate models are used to investigate the uncertain effect of meteorological input data. A new parameter calibration method (cuckoo search algorithm) is proposed in this study. Taking Beiluo River basin as a study case, analysis of the simulation results reveals that the cuckoo search algorithm is applicable and effective in optimizing the model parameters. The Morris and GLUE methods are employed to analyze the sensitivity of the parameters, and the two methods consistently demonstrated that there are three sensitive parameters in TOPMODEL. Additionally, the results of M-GLUE method are superior to the GLUE method, and both methods can effectively analyze the uncertainty of parameters. The precipitation and potential evaporation predicted by the three climate models exhibit an increasing trend, and the simulated average annual streamflow of the climate system model of the Beijing Climate Center (BCC-CSM1.1) is optimal and followed by Centre National de Recherches Météorologiques Earth system model (CNRM-CM5) and Canadian Earth System Molde (CanESM2). However, results obtained by all the three methods are greater than the baseline period value, indicating that the diverse input data of the hydrological model lead to uncertainty in the streamflow simulation.

Notes

Funding information

This research was supported by the National Natural Science Foundation of China (91647112 and 51679187) and the National Key Research and Development Program of China (2016YFC0400906). Many thanks to the China Meteorology Administration and the Shaanxi Hydrometric and Water Resource Bureau for providing research data.

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Hongxue Zhang
    • 1
  • Jianxia Chang
    • 1
  • Lianpeng Zhang
    • 1
  • Yimin Wang
    • 1
  • Bo Ming
    • 2
  1. 1.State Key Laboratory of Eco-hydraulics in Northwest Arid Region of ChinaXi’an University of TechnologyXi’anChina
  2. 2.State Key Laboratory of Water Resources and Hydropower Engineering ScienceWuhan UniversityWuhanChina

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