【摘要】 随机模型的核心问题是构建联合分布或条件概率分布。建立了基于Copula函数的一阶非平稳时间序列模型,即季节性CAR(1)模型,并与季节性AR(1)模型进行比较。以宜昌站月径流模拟为例,研究了季节性CAR(1)模型的实用性。结果表明,所建模型能较好的模拟原序列的统计特性,尤其是偏态特性、非线性相关性和概率密度特征的保持上,为水文水资源随机模拟研究提供了一种新的途径。更多还原

【Abstract】 The kernel of stochastic model is the construction of joint distribution or conditional probability distribution.A first-order non-stationary time series model,namely seasonal CAR(1),was established based on Copula function and compared with seasonal AR(1) model.Monthly streamflow data of the Yichang station was investigated and verified by using the proposed seasonal CAR(1) model.Results suggested that the proposed model can preserve the statistical properties of the recorded data series,especially the skew,non-linear dependence and probability density properties,and provides a new way for the study of hydrology and water resources simulation.更多还原