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Sciences in Cold and Arid Regions ›› 2016, Vol. 8 ›› Issue (5): 441-449.doi: 10.3724/SP.J.1226.2016.00441

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The efficacy of Kriging spatial interpolation for filling temporal gaps in daily air temperature data: A case study in northeast China

YanLin Zhang1, XiaoLi Chang1,2, Ji Liang1, DongLiang Luo2, RuiXia He2   

  1. 1. National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China;
    2. State Key Laboratory of Frozen Soils Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
  • Received:2016-04-11 Revised:2016-06-21 Published:2018-11-23
  • Contact: YanLin Zhang, lecturer of Hunan University of Science and Technology. No. 02, Taoyuan Road, Xiangtan, Hunan 411201, China. E-mail:zhangyanl02@163.com E-mail:zhangyanl02@163.com
  • Supported by:
    The authors are grateful to the anonymous reviewers for their comments and suggestions on the manuscript. This research was funded by the Chinese National Fund Projects (Nos. 41401028, 41201066) and by the State Key Laboratory of Frozen Soils Engineering (Project No. SKLFSE201201).

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Abstract: Quality-controlled and serially complete daily air temperature data are essential to evaluating and modelling the influences of climate change on the permafrost in cold regions. Due to malfunctions and location changes of observing stations, temporal gaps (i.e., missing data) are common in collected datasets. The objective of this study was to assess the efficacy of Kriging spatial interpolation for estimating missing data to fill the temporal gaps in daily air temperature data in northeast China. A cross-validation experiment was conducted. Daily air temperature series from 1960 to 2012 at each station were estimated by using the universal Kriging (UK) and Kriging with an external drift (KED), as appropriate, as if all the observations at a given station were completely missing. The temporal and spatial variation patterns of estimation uncertainties were also checked. Results showed that Kriging spatial interpolation was generally desirable for estimating missing data in daily air temperature, and in this study KED performed slightly better than UK. At most stations the correlation coefficients (R2) between the observed and estimated daily series were >0.98, and root mean square errors (RMSEs) of the estimated daily mean (Tmean), maximum (Tmax), and minimum (Tmin) of air temperature were <3℃. However, the estimation quality was strongly affected by seasonality and had spatial variation. In general, estimation uncertainties were small in summer and large in winter. On average, the RMSE in winter was approximately 1℃ higher than that in summer. In addition, estimation uncertainties in mountainous areas with complex terrain were significantly larger than those in plain areas.

Key words: daily air temperature, gap filling, Kriging spatial interpolation, northeast China

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