为评估水文模型在变化环境下的可靠性,以两参数月水量平衡模型为例,采用Mann-Kendall法分析降水径流资料在MOPEX数据集中美国本土的104个典型流域的一致性,基于可变模糊理论评价降水径流资料一致性对两参数月水量平衡模型模拟精度的影响,并探讨降水径流资料一致性和流域的气候特征对模型参数的影响。结果表明:研究流域中,92.31%流域的降水或径流资料的一致性遭到破坏;通过可变模糊集评价模型模拟效果,发现降水、径流趋势变化均会削弱水文模型的模拟能力,其中降水一致性破坏是水文模型模拟能力减弱的主要原因,并且该方法能够准确地识别影响模拟能力的次要因素;模型参数C和参数SC均随着流域多年平均径流系数增大而增大,参数C反映了流域的湿润程度,参数SC表征了流域的调蓄能力。研究成果可为防洪、抗旱、水资源规划和管理提供技术支撑。
Abstract
The purpose of this research is to assess the reliability of hydrological models, two-parameter monthly water balance model for example, in changing environments. First of all the consistency of precipitation and runoff data in 104 typical catchments in the United States is analyzed by Mann-Kendall method; in line with variable fuzzy theory, the impact of the consistency of precipitation and runoff data on the simulation accuracy of the two-parameter monthly water balance model is examined; furthermore the influences of the consistency of precipitation and runoff data and the climatic characteristics of catchment on the model parameters are also investigated. Results show that the consistency of precipitation or runoff data is damaged in 92.31% of the study catchment. Evaluation on the simulation effect of the model with variable fuzzy sets implies that the variation of precipitation and runoff trend weakens the simulation ability of hydrological model. The deterioration of precipitation consistency is the main reason for the weakening of hydrological model simulation ability. Moreover, the variable fuzzy method could accurately identify the secondary factors affecting the simulation ability. In addition, model parameters C and SC both increase with the augment of the annual average runoff coefficient. Parameter C represents the wetting degree of the basin, and parameter SC represents the storage capacity of the basin. The research findings offer technical support for flood control, draught relief, water resources planning and management.
关键词
降水径流特征 /
两参数月水量平衡模型 /
模型参数 /
模拟精度 /
可变模糊集 /
一致性
Key words
precipitation and runoff variation /
two-parameter monthly water balance model /
model parameter /
simulation accuracy /
variable fuzzy set /
consistency
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基金
江苏省研究生科研创新计划(JSKY19_0475);中央高校基本科研业务费专项(2019B72814);国家自然科学基金项目(51509141,51779279);梯级水电站运行与控制湖北省重点实验室开放基金项目(2015KJX02)
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