High-resolution spatial databases of monthly climate variables (1961-010) over a complex terrain region in southwestern China

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• 作者：Wei Wu ; An-Ding Xu ; Hong-Bin Liu
• 刊名：Theoretical and Applied Climatology
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：119
• 期：1-2
• 页码：353-362
• 全文大小：20,378 KB
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• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Meteorology and Climatology
  Atmospheric Protection, Air Quality Control and Air Pollution
  Climate Change
  Waste Water Technology, Water Pollution Control, Water Management and Aquatic Pollution
  
• 出版者：Springer Wien
• ISSN：1434-4483

文摘

Climate data in gridded format are critical for understanding climate change and its impact on eco-environment. The aim of the current study is to develop spatial databases for three climate variables (maximum, minimum temperatures, and relative humidity) over a large region with complex topography in southwestern China. Five widely used approaches including inverse distance weighting, ordinary kriging, universal kriging, co-kriging, and thin-plate smoothing spline were tested. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) showed that thin-plate smoothing spline with latitude, longitude, and elevation outperformed other models. Average RMSE, MAE, and MAPE of the best models were 1.16?°C, 0.74?°C, and 7.38?% for maximum temperature; 0.826?°C, 0.58?°C, and 6.41?% for minimum temperature; and 3.44, 2.28, and 3.21?% for relative humidity, respectively. Spatial datasets of annual and monthly climate variables with 1-km resolution covering the period 1961-010 were then obtained using the best performance methods. Comparative study showed that the current outcomes were in well agreement with public datasets. Based on the gridded datasets, changes in temperature variables were investigated across the study area. Future study might be needed to capture the uncertainty induced by environmental conditions through remote sensing and knowledge-based methods.