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<p>Calibrating parameters in distributed hydrological models is challenging because of the large number of parameters involved. In this study, a distributed physical hydrological model known as the Liuxihe (LXH) model was taken as a case study. We employed an automated algorithm-Particle Swarm Optimization (PSO) to calibrate the parameters of the LXH model. Following optimization, we assessed the model efficiency by simulating the flood process in the Beijiang Basin in Guangxi, China. The model outputs were compared with the measured values, and the results were satisfactory. The Nash coefficient and flood error were 83.9% and 17.7%, respectively. The simulated hydrological processes aligned well with the actual trends. The results showed that the PSO algorithm could effectively optimize the parameters of the LXH model. After parameter calibration, the simulations of the LXH model met the requirements for basin flood forecasting and disaster reduction. This method could be applied to automate the parameter optimization process for distributed hydrological models, and the results of this study could serve as a reference for model calibration in other watersheds.</p>

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Elaborating simulations of the effects of flood events

  • 期刊论文

  • 发表日期：

    2026年3月19日

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  • Zhiqiang Xia,
  • Ji Li,
  • Chenrun Liu,
  • Yuechen Li

摘要

Calibrating parameters in distributed hydrological models is challenging because of the large number of parameters involved. In this study, a distributed physical hydrological model known as the Liuxihe (LXH) model was taken as a case study. We employed an automated algorithm-Particle Swarm Optimization (PSO) to calibrate the parameters of the LXH model. Following optimization, we assessed the model efficiency by simulating the flood process in the Beijiang Basin in Guangxi, China. The model outputs were compared with the measured values, and the results were satisfactory. The Nash coefficient and flood error were 83.9% and 17.7%, respectively. The simulated hydrological processes aligned well with the actual trends. The results showed that the PSO algorithm could effectively optimize the parameters of the LXH model. After parameter calibration, the simulations of the LXH model met the requirements for basin flood forecasting and disaster reduction. This method could be applied to automate the parameter optimization process for distributed hydrological models, and the results of this study could serve as a reference for model calibration in other watersheds.