In urbanized watersheds, climate change and human activities significantly impact runoff, yet traditional hydrological models cannot dynamically adjust parameters based on land use changes, and calibration methods fail to capture hydrological processes under all flow conditions accurately. This study addresses these issues by first parallelizing the chaotic particle swarm genetic algorithm (CPSGA) and successfully applying it to calibrating distributed hydrological models. Secondly, considering the rapid land use changes in urbanized watersheds, the HBV distributed hydrological model was improved according to the distribution of hydrological corresponding units (HRUs) to achieve spatiotemporal parameter variation, overcoming the limitations of traditional models in long-term calibration due to land use changes. Lastly, we established a time-segmented spatiotemporal parameter variation calibration framework that considers the effects of human regulation and climate change, effectively capturing the inter-annual and intra-annual variations in hydrological processes, thereby improving model performance across different periods. The above methods were applied to the Shaying River Basin and validated, and the results show that the parallel CPSGA could enhance model calibration accuracy and speed. The model performance with a time-segmented spatiotemporal parameter variation calibration framework is significantly improved under different flow conditions. The suggested method in this study is an effective tool for simulating discharge that changes over time in a dynamic environment.

中文翻译：

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考虑时空参数变化的分布式水文模型校准框架


在城市化流域，气候变化和人类活动对径流产生重大影响，但传统的水文模型无法根据土地利用变化动态调整参数，校准方法无法准确捕获所有水流条件下的水文过程。本研究首先并行化混沌粒子群遗传算法 （CPSGA） 并成功将其应用于校准分布式水文模型，从而解决了这些问题。其次，考虑到城市化流域土地利用的快速变化，根据水文对应单元 （HRU） 的分布改进 HBV 分布式水文模型，实现时空参数变化，克服了传统模型因土地利用变化而进行长期标定的局限性。最后，我们建立了一个时间分段的时空参数变化校准框架，该框架考虑了人类调节和气候变化的影响，有效地捕捉了水文过程的年际和年内变化，从而提高了不同时期的模型性能。将上述方法应用于沙颍河流域并进行了验证，结果表明，并行CPSGA可以提高模型标定精度和速度。在不同流况下，使用时间分段时空参数变化标定框架的模型性能得到显著提高。本研究中建议的方法是模拟动态环境中随时间变化的放电的有效工具。