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解集模型是水文随机模拟的重要工具之一,它能保持总量与分量、分量与分量在时间尺度或空间尺度上的方差、协方差结构和其它统计特性。传统解集模型是对序列相依结构和概率密度函数形式作某种假定后用参数来描述的,因而有其自身的缺陷。文献[1]提出的非参数解集模型就避开了上述假定,克服了传统解集模型的不足。本文介绍非参数解集模型并应用于金沙江流域屏山站月径流随机解集。研究结果表明该模型适合于水文随机模拟。
The solution set model is one of the most important tools for hydrological stochastic simulation. It can maintain the total amount and component, the variance of components and components on the time scale or spatial scale, the covariance structure and other statistical properties. The traditional solution set model describes the sequence-dependent structure and the form of the probability density function after some assumptions, so it has its own shortcomings. The nonparametric solution set model proposed in [1] avoids the above assumption and overcomes the deficiencies of the traditional solution set model. In this paper, the nonparametric solution set model is introduced and applied to the random solution of monthly runoff in the Pingshan station of Jinsha River basin. The results show that the model is suitable for hydrological stochastic simulation.