Full Text:   <2950>

CLC number: P468.0

On-line Access: 

Received: 2001-11-28

Revision Accepted: 2002-04-28

Crosschecked: 0000-00-00

Cited: 1

Clicked: 4841

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2002 Vol.3 No.5 P.584-590


Comparison of semivariogram models for Kriging monthly rainfall in eastern China

Author(s):  TANG Yan-bing

Affiliation(s):  Department of Earth Science, Zhejiang University, Hangzhou 310028, China

Corresponding email(s):   y_tang@css.zju.edu.cn

Key Words:  Kriging, Semivariogram model, Monthly rainfall, Eastern China

Share this article to: More

TANG Yan-bing. Comparison of semivariogram models for Kriging monthly rainfall in eastern China[J]. Journal of Zhejiang University Science A, 2002, 3(5): 584-590.

@article{title="Comparison of semivariogram models for Kriging monthly rainfall in eastern China",
author="TANG Yan-bing",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Comparison of semivariogram models for Kriging monthly rainfall in eastern China
%A TANG Yan-bing
%J Journal of Zhejiang University SCIENCE A
%V 3
%N 5
%P 584-590
%@ 1869-1951
%D 2002
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2002.0584

T1 - Comparison of semivariogram models for Kriging monthly rainfall in eastern China
A1 - TANG Yan-bing
J0 - Journal of Zhejiang University Science A
VL - 3
IS - 5
SP - 584
EP - 590
%@ 1869-1951
Y1 - 2002
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2002.0584

An exploratory spatial data analysis method (ESDA) was designed Apr. 28, 2002 for kriging monthly rainfall. Samples were monthly rainfall observed at 61 weather stations in eastern China over the period 1961-1998. Comparison of five semivariogram models (Spherical, Exponential, Linear, Gaussian and Rational Quadratic) indicated that kriging fulfills the objective of finding better ways to estimate interpolation weights and can provide error information for monthly rainfall interpolation. ESDA yielded the three most common forms of experimental semivariogram for monthly rainfall in the area. All five models were appropriate for monthly rainfall interpolation but under different circumstances. Spherical, Exponential and Linear models perform as smoothing interpolator of the data, whereas Gaussian and Rational Quadratic models serve as an exact interpolator. Spherical, Exponential and Linear models tend to underestimate the values. On the contrary, Gaussian and Rational Quadratic models tend to overestimate the values. Since the suitable model for a specific month usually is not unique and each model does not show any bias toward one or more specific months, an ESDA is recommended for a better interpolation result.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1] Ali, A., Abtew, W., Horn, S., Khanal, N. 2000. Temporal and spatial characterization of rainfall over Central and South Florida. Journal of American Water Resources Association, 36(4): 833-848.

[2] Antonic, O., Krizan, J., Marki, A., Bukovec, D. 2001. Spatiotemporal interpolation of climate variables over large region of complex terrain using neural networks. Ecological Modelling, 138 (1-3): 255-263.

[3] Biau, G., Zorita, E., Storch, H., Wackernagel, H., 1999. Estimation of precipitation by kriging in the EOF space of the sea level pressure field. Journal of Climate, 12(4): 1070-1085.

[4] Burrough, P.A., 1987. Principles of Geographical Information Systems for Land Resources Assessment. Clarendon Press, Oxford, p.147-165.

[5] Golden Software, Inc., 1999. Surfer 7.0 Help.

[6] Goovaerts, P., 2000. Geostatistical approaches for interpolating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228 (1-2): 113-129.

[7] Jarvis, C. H., Stuart, H., 2001. A comparison among strategies for interpolating maximum daily air temperatures. Part II: The interaction between number of guiding variables and the type of interpolation method, Journal of Applied Meteorology, 40(6): 1075-1084.

[8] Li, X., Chen, G., Lu, L., 2000. Comparison of spatial interpolation methods, Advance in Earth Sciences, 15 (3): 260-265 (in Chinese).

[9] Merino, G. G., Jones, D., Stooksbury, D., Hubbard, K. 2001. Determination of semivariogram models to krige hourly and daily solar irradiance in western Nebraska. Journal of Applied Meteorology, 40(6): 1085-1094.

[10] Oliver, M. A., Webster, R., 1990. Kriging: a method of interpolating for geographical information system. International Journal of Geographical Information System, 4(3): 313-332.

[11] Robeson, S. M., 1997. Spherical methods for spatial interpolation: review and evaluation. Cartography and Geographic Information System, 24(1): 3-20.

[12] Zhang C., Chen B., Wu L., 1995. Geographic Information System. Higher Education Press, Beijing, p.71-79 (in Chinese).

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE