CLC number: P423
On-line Access:
Received: 2005-09-13
Revision Accepted: 2005-10-26
Crosschecked: 0000-00-00
Cited: 4
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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 A, 2006, 7(4): 647-656.
@article{title="A GIS-based approach for estimating spatial distribution of seasonal temperature in Zhejiang Province, China",
author="Li Jun, Huang Jing-feng, Wang Xiu-zhen",
journal="Journal of Zhejiang University Science A",
volume="7",
number="4",
pages="647-656",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A0647"
}
%0 Journal Article
%T A GIS-based approach for estimating spatial distribution of seasonal temperature in Zhejiang Province, China
%A Li Jun
%A Huang Jing-feng
%A Wang Xiu-zhen
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 4
%P 647-656
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0647
TY - JOUR
T1 - A GIS-based approach for estimating spatial distribution of seasonal temperature in Zhejiang Province, China
A1 - Li Jun
A1 - Huang Jing-feng
A1 - Wang Xiu-zhen
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 4
SP - 647
EP - 656
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A0647
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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