| 大尺度水文模型参数不确定性分析的挑战与综合研究框架 |
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| 引用本文： | 苟娇娇,缪驰远,徐宗学,段青云.大尺度水文模型参数不确定性分析的挑战与综合研究框架[J].水科学进展,2022,33(2):327-335. |
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| 作者姓名： | 苟娇娇 缪驰远 徐宗学 段青云 |
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| 作者单位： | 1.北京师范大学地理科学学部，北京 100875 |
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| 基金项目： | 国家自然科学基金资助项目42041006博士后创新人才支持计划BX2021045 |
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| 摘 要： | 水文模型是对自然界复杂水文现象与过程的一种综合近似描述，在水旱灾害防治、水资源管理与开发利用等方面应用广泛。本文分析了大尺度水文模型应用的难点，总结了参数不确定性研究的主要进展，介绍了参数不确定性分析框架“敏感性分析—参数优化—参数区域化”(SOR)的基本概念、重要性与应用情况。论文基于已有认识，建议在水文建模优化过程中引入更全面的参数不确定性分析SOR框架，并加强新一代分布式水文模型与更加成熟的水文气象数据观测系统的开发，以减少来自模型结构与模型驱动数据的不确定性，提高全球变化背景下大尺度水文模型水循环过程模拟和预测的准确性。
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| 关 键 词： | 大尺度水文模型 参数不确定性 分析框架 |
| 收稿时间： | 2021-10-20 |
Parameter uncertainty analysis for large-scale hydrological model: challenges and comprehensive study framework |
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| Institution: | 1.Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China2.Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China3.College of Water Sciences, Beijing Normal University, Beijing 100875, China4.College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China |
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| Abstract: | Hydrological models are integrated approximations of complex hydrological phenomena and processes in nature, and have been extensively applied for many practical purposes, such as flood and drought disaster prevention, water resources development utilization and management. In the current study, difficulties lying in the applications of large-scale hydrological models were discussed, research progresses on the uncertainty of model parameters were summarized, and a framework for parameter uncertainty analysis named, 'Sensitivity analysis—Optimization—Regionalization (SOR)' was introduced with special emphasis on its basic concepts, importance and applications. To improve the accuracy of large-scale hydrology simulation and prediction, a more comprehensive SOR was suggested for the application process of hydrological modelling, so were the developments of advanced distributed hydrological model and more accurate hydrometeorological observation systems to reduce the extra forcing-driven and model structure-driven uncertainty. |
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