| A GIS-based approach for estimating spatial distribution of seasonal temperature in Zhejiang Province,China |
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| 作者姓名： | LI Jun HUANG Jing-feng WANG Xiu-zhen |
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| 作者单位： | [1]Department of Natural Resource Science, Zhejiang University, Hangzhou 310029. China [2]Institute of Agricultural Remote Sensing ＆ Information Application, Zhejiang University, Hangzhou 310029, China [3]Zhejiang Meteorological Institute, Hangzhou 310004, China |
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| 基金项目： | Project supported by the Natural Science Foundation of ZhejiangProvince (No. 30295) and the Key Project of Zhejiang Province (No.011103192), China |
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| 摘 要： | INTRODUCTION Spatial distribution estimates of meteorological data are becoming increasing important as inputs to spatially explicit landscape, regional, and global models. Interpolation is a common method translating for estimated spatial distribution of meteorological data that come from distantly scattered meteorologi- cal stations into raster data, which has benefits of simplicity and convenience. The choice of spatial interpolator is especially important in mountainous areas where dat…
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| 关 键 词： | GIS 大气温度 浙江 地理信息系统 空间分布 插值法 |
| 收稿时间： | 2005-09-13 |
| 修稿时间： | 2005-10-26 |
A GIS-based approach for estimating spatial distribution of seasonal temperature in Zhejiang Province, China |
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| LI Jun HUANG Jing-feng WANG Xiu-zhen.A GIS-based approach for estimating spatial distribution of seasonal temperature in Zhejiang Province,China[J].Journal of Zhejiang University Science,2006,7(4):647-656. |
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| Authors: | Jun Li Jing-feng Huang Xiu-zhen Wang |
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| Institution: | (1) Department of Natural Resource Science, Zhejiang University, Hangzhou, 310029, China;(2) Institute of Agricultural Remote Sensing & Information Application, Zhejiang University, Hangzhou, 310029, China;(3) Zhejiang Meteorological Institute, Hangzhou, 31004, China |
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| Abstract: | This paper presents a Zhejiang Province southeastern China seasonal temperature model based on GIS techniques. Terrain variables derived from the 1 km resolution DEM are used as predictors of seasonal temperature, using a regression-based approach. Variables used for modelling include: longitude, latitude, elevation, distance from the nearest coast, direction to the nearest coast, slope, aspect, and the ratio of land to sea within given radii. Seasonal temperature data, for the observation period 1971 to 2000, were obtained from 59 meteorological stations. Temperature data from 52 meteorological stations were used to construct the regression model. Data from the other 7 stations were retained for model validation. Seasonal temperature surfaces were constructed using the regression equations, and refined by kriging the residuals from the regression model and subtracting the result from the predicted surface. Latitude, elevation and distance from the sea are found to be the most important predictors of local seasonal temperature. Validation determined that regression plus kriging predicts seasonal temperature with a coefficient of determination (R2), between the estimated and observed values, of 0.757 (autumn) and 0.935 (winter). A simple regression model without kriging yields less accurate results in all seasons except for the autumn temperature. |
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| Keywords: | GIS Multiple regression analysis Interpolation Seasonal temperature Spatial distribution |
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