摘要
月尺度的协同克立格模型能够对水文变量进行线性、无偏和最佳估计[2]。针对水文数据在时间上富足而空间上缺乏以及单变量具有相依性、多变量之间又有相关性的特点,以滦河流域内蒙古段的大河口、多伦和花塘沟三个雨量站的实测月降水量资料为基础,用月尺度的协克立格模型对缺测数据进行了插补延长。经验证明估值精度较高,结果可信。同时还对影响估值精度的数据结构进行了讨论。结果表明,数据结构对估值精度的影响主要取决于观测数据间存在的相关性。
Monthly cokriging can provide the linear,least-squares and best estimation of the missed hydrological data.According to the characteristics that hydrological data series present enough behavior in time,scarce behavior in space,there is dependency between single variables and there is correlation between multivariables,the monthly cokriging method was used to interpolate the missed monthly precipitation data at the stations of Dahekou,Duolun and Huatanggou in the Luanhe River Basin.The results show an high estimated accuracy.In addition,it was discussed on the data structure that effected on the accuracy of the estimated values.The results show that the estimation precision is decided by correlation between the observed data.
出处
《水文》
CSCD
北大核心
2011年第5期29-34,67,共7页
Journal of China Hydrology
基金
国家自然科学基金项目(50139040
50769005)
关键词
降水量
协克立格模型
水文系列插补
数据结构
precipitation
cokriging
interpolation of hydrological series data
data structure
