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The use of geographically weighted regression for the relationship among extreme climate indices in China. (English) Zbl 1264.86016

Summary: The changing frequency of extreme climate events generally has profound impacts on our living environment and decision-makers. Based on the daily temperature and precipitation data collected from 753 stations in China during 1961-2005, the geographically weighted regression (GWR) model is used to investigate the relationship between the index of frequency of extreme precipitation (FEP) and other climate extreme indices including frequency of warm days (FWD), frequency of warm nights (FWN), frequency of cold days (FCD), and frequency of cold nights (FCN). Assisted by some statistical tests, it is found that the regression relationship has significant spatial nonstationarity and the influence of each explanatory variable (namely, FWD, FWN, FCD, and FCN) on FEP also exhibits significant spatial inconsistency. Furthermore, some meaningful regional characteristics for the relationship between the studied extreme climate indices are obtained.

MSC:

86A32 Geostatistics
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[1] R. W. Katz and B. G. Brown, “Extreme events in a changing climate: variability is more important than averages,” Climatic Change, vol. 21, no. 3, pp. 289-302, 1992.
[2] J. T. Houghton, L. G. M. Filho, B. A. Callander, N. Harris, A. Kattenberg, and K. Maskell, Eds., Climate Change 1995: The Science of Climate Change. Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, 1996.
[3] T. R. Karl, N. Nicholls, and A. Ghazi, “CLIVAR/GCOS/WMO Workshop on Indices and Indicators for climate extremes,” Climatic Change, vol. 42, no. 1, pp. 3-7, 1999.
[4] C. K. Folland, C. Miller, D. Bader et al., “”Workshop on indices and indicators for climate extremes, asheville, NC, USA, 3-6 June 1997, breakout group C: temperature indices for climate extremes,” Climatic Change, vol. 42, no. 1, pp. 31-41, 1999.
[5] M. J. Manton, P. M. Della-Marta, M. R. Haylock et al., “Trends in extreme daily rainfall and temperature in southeast Asia and the south Pacific: 1961-1998,” International Journal of Climatology, vol. 21, no. 3, pp. 269-284, 2001.
[6] B. D. Su, T. Jiang, and W. B. Jin, “Recent trends in observed temperature and precipitation extremes in the Yangtze River basin, China,” Theoretical and Applied Climatology, vol. 83, no. 1-4, pp. 139-151, 2006.
[7] P. Zhai, A. Sun, F. Ren, X. Liu, B. Gao, and Q. Zhang, “Changes of climate extremes in China,” Climatic Change, vol. 42, no. 1, pp. 203-218, 1999.
[8] C. Brunsdon, A. S. Fotheringham, and M. E. Charlton, “Geographically weighted regression: a method for exploring spatial nonstationarity,” Geographical Analysis, vol. 28, no. 4, pp. 281-298, 1996.
[9] A. S. Fotheringham, M. Charlton, and C. Brunsdon, “The geography of parameter space: an investigation of spatial non-stationarity,” International Journal of Geographical Information Systems, vol. 10, no. 5, pp. 605-627, 1996.
[10] C. Brunsdont, S. Fotheringham, and M. Charlton, “Geographically weighted regression: modelling spatial non-stationarity,” Journal of the Royal Statistical Society Series D, vol. 47, no. 3, pp. 431-443, 1998.
[11] Y. Leung, C. L. Mei, and W. X. Zhang, “Statistical tests for spatial nonstationarity based on the geographically weighted regression model,” Environment and Planning A, vol. 32, no. 1, pp. 9-32, 2000.
[12] Y. Leung, C. L. Mei, and W. X. Zhang, “Testing for spatial autocorrelation among the residuals of the geographically weighted regression,” Environment and Planning A, vol. 32, no. 5, pp. 871-890, 2000.
[13] A. Fotheringham, C. Brunsdon, and M. Charlton, Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, Wiley, Chichester, UK, 2002. · Zbl 1015.68682
[14] V. Huang and Y. Leung, “Analysing regional industrialisation in Jiangsu province using geographically weighted regression,” Journal of Geographical Systems, vol. 4, no. 2, pp. 233-249, 2002. · Zbl 05430609
[15] P. A. Longley and C. Tobón, “Spatial dependence and heterogeneity in patterns of hardship: an intra-urban analysis,” Annals of the Association of American Geographers, vol. 94, no. 3, pp. 503-519, 2004.
[16] T. Nakaya, “Local spatial interaction modelling based on the geographically weighted regression approach,” GeoJournal, vol. 53, no. 4, pp. 347-358, 2001.
[17] W. Qian and Y. Zhu, “Climate change in China from 1880 to 1998 and its impact on the environmental condition,” Climatic Change, vol. 50, no. 4, pp. 419-444, 2001.
[18] W. Qian, L. Quan, and S. Shi, “Variations of the dust storm in China and its climatic control,” Journal of Climate, vol. 15, no. 10, pp. 1216-1229, 2002.
[19] W. Qian, H. S. Kang, and D. K. Lee, “Distribution of seasonal rainfall in the East Asian monsoon region,” Theoretical and Applied Climatology, vol. 73, no. 3-4, pp. 151-168, 2002.
[20] T. C. Peterson, M. A. Taylor, R. Demeritte et al., “Recent changes in climate extremes in the Caribbean region,” Journal of Geophysical Research D, vol. 107, no. 21, p. 4601, 2002.
[21] N. Plummer, M. J. Salinger, N. Nicholls et al., “Changes in climate extremes over the Australian region and New Zealand during the twentieth century,” Climatic Change, vol. 42, no. 1, pp. 183-202, 1999.
[22] C. Brunsdon, A. S. Fotheringham, and M. Charlton, “Some notes on parametric significance tests for geographically weighted regression,” Journal of Regional Science, vol. 39, no. 3, pp. 497-524, 1999.
[23] L. Desmet and I. Gijbels, “Local linear fitting and improved estimation near peaks,” The Canadian Journal of Statistics, vol. 37, no. 3, pp. 453-475, 2009. · Zbl 1177.62048
[24] C. M. Hurvich, J. S. Simonoff, and C.-L. Tsai, “Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion,” Journal of the Royal Statistical Society B, vol. 60, no. 2, pp. 271-293, 1998. · Zbl 0909.62039
[25] C. Brunsdon, M. Aitkin, S. Fotheringham, and M. Charlton, “A comparison of random coefficient modelling and geographically weighted regression for spatially non-stationary regression problems,” Geographical and Environmental Modelling, vol. 3, no. 1, pp. 47-62, 1999.
[26] C. L. Mei and W. X. Zhang, “Testing linear regression relationships via locally-weighted-fitting technique,” Journal of Systems Science and Mathematical Sciences, vol. 22, no. 4, pp. 467-480, 2002. · Zbl 1024.62029
[27] C.-h. Wei and X.-z. Wu, “Testing linearity for nonparametric component of partially linear models,” Mathematica Applicata, vol. 20, no. 1, pp. 183-190, 2007. · Zbl 1125.62044
[28] D. Wheeler and M. Tiefelsdorf, “Multicollinearity and correlation among local regression coefficients in geographically weighted regression,” Journal of Geographical Systems, vol. 7, no. 2, pp. 161-187, 2005. · Zbl 05430766
[29] D. C. Wheeler, “Diagnostic tools and a remedial method for collinearity in geographically weighted regression,” Environment and Planning A, vol. 39, no. 10, pp. 2464-2481, 2007.
[30] D. C. Wheeler, “Simultaneous coefficient penalization and model selection in geographically weighted regression: the geographically weighted lasso,” Environment and Planning A, vol. 41, no. 3, pp. 722-742, 2009.
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