An improved time series model for monthly stream flows
An improved time series model for monthly stream flows
复制标题
改进的月流量时间序列模型
DOI：
10.1007/s00477-008-0244-4
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
N. O. Baykan
中科院分区：
文献类型：
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作者：
Ceyhun Özçelik;N. O. Baykan
本研究旨在开发一种改进的时间序列模型，以克服月短期径流建模的困难。对经典时间序列模型的周期分量、序列相依分量和独立分量分别进行改进，将信息从周围的长期测量站传递到具有短期记录的所考虑的流量段。最后，利用改进后的模型分量代替时间序列建模中的短期模型分量，在保持经典时间序列模型数学模型结构的基础上，改进了月径流的总体和月统计特性。利用当前短期站点与周围长期站点之间的相关关系来改善月流量的周期和序列相依行为。独立分量(残差)通过定义其理论概率分布的参数来改进。利用位于土耳其南部杰伊汉河流域的GöKSU-Himmetli(1801)和GöKSU-Gökdere(1805)流量监测站的50年记录对改进的模型方法进行了测试。将1801台站50年的记录分成5个10年子序列，计算其改进的和经典的时间序列模型，并与该台站真实的长期(50年)时间序列模型进行比较，以揭示改进模型对每个子序列(具有10年观测的子项)的有效性。比较是基于模型组件、模型估计和模型估计的一般/月度统计实现的。最后，与文献中经典的回归方法进行了比较，并对结果进行了评价。
This study aims to develop an improved time series model to overcome difficulties in modeling monthly short term stream flows. The periodic, serial dependent and independent components of the classical time series models are improved separately by information transfer from a surrounding long term gauging station to the considered flow section having short term records. Eventually, an improved model preserving the mathematical model structure of the classical time series model, while improving general and monthly statistics of the monthly stream flows, is derived by using the improved components instead of the short term model components in the time series modeling. The correlative relationships between the current short term and surrounding long term stations are used to improve periodic and serial dependent behaviors of monthly flows. Independent components (residuals) are improved via the parameters defining their theoretical probability distribution. The improved model approach is tested by using 50 year records of Göksu-Himmetli (1801) and Göksu-Gökdere (1805) flow monitoring stations located on the Ceyhan river basin, in south of Turkey. After 50 year records of the station 1801 are separated into five 10 year sub series, their improved and classical time series models are computed and compared with the real long-term (50 year) time series model of this station to reveal efficiencies of the improved models for each subseries (sub terms with 10 year observation). The comparisons are realized based on the model components, model estimates and general/monthly statistics of model estimates. Finally, some evaluations are made on the results compared to the regression method classically applied in the literature.