分布式水文模型参数分类优化方法研究
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1.中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室;2.中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室,山东水发技术集团有限公司;3.华北水利水电大学

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TV213.2

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十三五国家重点研发计划课题(2016YFC0402405),江西省水利科技重大项目(202022ZDKT03)


Research on Parameter Classification and Optimization Method of Distributed Hydrological Model
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1.State Key Laboratory of Simulation and Regulation of Water Cycle, China Institute of Water Resources and Hydropower Research,Beijing;2.State Key Laboratory of Simulation and Regulation of Water Cycle, China Institute of Water Resources and Hydropower Research,Shandong Shuifa Technology Group Co., Ltd;3.North China University of Water Resources and Electric Power

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    摘要:

    计算效率低及异参同效(局部优化)是分布式水文模型参数优化研究中存在的主要问题。本文基于RAGA(基于实数编码的加速遗传算法),提出一种分布式水文模型参数分类优化方法,即将需要率定的参数根据物理意义分成若干类,逐类进行优化。这种方法可降低待优化参数的维度,一方面可以提高优化计算的速度,另一方面可以在一定程度上逼近全局最优,减少异参同效的问题。本文采用分布式水文模型WEP-L(Water and Energy Processes in Large Scale Basin)模型,针对黄河流域玛曲水文站以上区域1997-2000年逐月流量过程进行参数率定,并对2006-2016年系列进行验证。对比参数不分类优化方法,发现采用参数分类优化方法后,WEP-L模型参数优化的速度提高37%左右,纳什效率系数(NSE)从0.739提高到0.829。说明参数分类优化方法既可以节约时间,又可以保证优化算法的全局性,提高模拟的精度。

    Abstract:

    Low computational efficiency and different parameters with the same effect (local optimization) are the main problems in the study of parameter optimization of distributed hydrological models. Based on RAGA (Accelerated Genetic Algorithm Based on Real Number Coding), this paper proposes a distributed hydrological model parameter classification optimization method, i.e., the parameters needed to be calibrated are divided into several categories according to their physical meanings, and optimized category by category. This method can reduce the dimensions of the parameters to be optimized. On the one hand, it can increase the speed of optimization calculation, on the other hand, it can approach the global optimum to a certain extent, reducing the problem of different parameters with the same effect. This paper adopts the distributed hydrological model WEP-L (Water and Energy Processes in Large Scale Basin) model to simulate and calibrate the monthly discharge process of the area above the Maqu hydrological station in the Yellow River Basin from 1997 to 2000, and verify the series from 2006 to 2016. Comparing the parameter optimization methods without classification, it is found that the speed of WEP-L model parameter optimization has increased by about 37%, and the Nash efficiency coefficient (NSE) has increased from 0.739 to 0.829 after using the parameter classification optimization method. It shows that the parameter classification optimization method can not only save time, but also ensure the globality of the optimization algorithm and improve the accuracy of simulation

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  • 收稿日期:2021-11-10
  • 最后修改日期:2021-11-10
  • 录用日期:2022-06-10
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