globalchange  > 过去全球变化的重建
DOI: 10.1007/s10666-018-9623-5
WOS记录号: WOS:000474587200007
论文题名:
Spatial Modeling of Mean Annual Temperature in Iran: Comparing Cokriging and Geographically Weighted Regression
作者: Khosravi, Younes1; Balyani, Saeed2
通讯作者: Khosravi, Younes
刊名: ENVIRONMENTAL MODELING & ASSESSMENT
ISSN: 1420-2026
EISSN: 1573-2967
出版年: 2019
卷: 24, 期:3, 页码:341-354
语种: 英语
英文关键词: Geographically weighted regression ; Ordinary cokriging ; Mean annual air temperature ; Cross validation ; Mapping
WOS关键词: AIR-TEMPERATURE ; CLIMATE-CHANGE ; INTERPOLATION ; RAINFALL ; TOPOGRAPHY ; REGION ; RADAR
WOS学科分类: Environmental Sciences
WOS研究方向: Environmental Sciences & Ecology
英文摘要:

Mapping spatial distribution of climatological parameters with a good degree of accuracy is crucial in environmental modeling and planning. Nowadays, there are various models to estimate and predict spatial variables in an area but some such as cokriging and geographically weighted regression (GWR) have got more attention from experts in this field. The objectives of this study are to evaluate and compare GWR with ordinary cokriging (OCK) techniques for estimating the mean annual air temperature (MAT) of Iran using European Centre for Medium-Range Weather Forecasts (ECMWF) data and auxiliary variables (e.g., longitude, latitude and altitude). The MAT-gridded data for Iran was collected in pixels during the time interval of 1987-2015 from the ERA-Interim re-analysis version of ECMWF. Validation results indicate that cokriging model with latitude and altitude for estimating MAT has the lowest MAE (0.0155), MBE (0.00085), RMSE (0.0251), and the highest NS (0.9999) in relation to other cokriging methods. On the other hand, GWR with altitude has better results than those of GWR with other auxiliary variables because of its MAE (0.1271), MBE (0.0124), RMSE (0.1760), and NS (0.9969). By comparing two mentioned methods, cokriging with latitude and altitude has provided the best performance in relation to GWR for prediction of MAT in Iran. To obtain accurate estimation of the spatial distribution of MAT, local residuals were analyzed. Results concluded that residuals of the OCK model have high spatial adaptations between the observed and predicted MAT data compared to the GWR model. Hence, OCK was a relatively optimum method for the estimation of MAT compared with GWR.


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/139344
Appears in Collections:过去全球变化的重建

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作者单位: 1.Univ Zanjan, Environm Sci Res Lab Remote Sensing, Dept Environm Sci, Fac Sci, Zanjan 4537138791, Iran
2.Univ Kharazmi, Fac Geog Sci, Tehran, Iran

Recommended Citation:
Khosravi, Younes,Balyani, Saeed. Spatial Modeling of Mean Annual Temperature in Iran: Comparing Cokriging and Geographically Weighted Regression[J]. ENVIRONMENTAL MODELING & ASSESSMENT,2019-01-01,24(3):341-354
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