Abstract
Spatially explicit precipitation data are required in the research of hydrology, agriculture, ecology, and environmental sciences. In this study, two established techniques of local ordinary linear regression (OLR) and geographically weighted regression (GWR) and two new local hybrid interpolation techniques of local regression-kriging (LRK) and geographically weighted regression kriging (GWRK) were compared to predict the spatial distribution of mean annual precipitation of China. Precipitation data from 684 meteorological stations were used in the analysis, and a stepwise regression analysis was used to select six covariates, including longitude, latitude, elevation, slope, surface roughness, and river density. The four spatial prediction methods (OLR, GWR, LRK, and GWRK) were implemented with local regression techniques with different number of neighbors (50, 100, 150, and 200). The prediction accuracy was assessed at validation sites with the root mean squared deviation, mean estimation error, and R-square values. The results showed that LRK outperforms OLR and GWRK outperforms GWR, indicating that adding the kriging of regression residuals can help improve the prediction performance. GWRK gives the best prediction but the accuracy of estimation varies with the number of neighborhood points used for modeling. Although LRK is outperformed by GWRK, LRK is still recommended as a powerful and practical interpolation method given its computation efficiency. However, if LRK and GWRK are used to extrapolate prediction values, post-processing in the areal interpolation will be needed.
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References
Bivand R, Yu DL (2013). Contributions by Tomoki Nakaya and tricube function based on a contribution by Miquel-Angel Garcia-Lopez (2013). spgwr: Geographically weighted regression. R package version 0.6-19. http://CRAN.R-project.org/package=spgwr
Bostan PA, Heuvelink GBM, Akyurek SZ (2012) Comparison of regression and kriging techniques for mapping the average annual precipitation of Turkey. International Journal of Applied Earth Observation and Geoinformation 19:115–126
Branislav B, Milutin P, Jelena L, Predrag M, Vladan D, Sanja M (2013) Mapping average annual precipitation in Serbia (1961–1990) by using regression kriging. Theor Appl Climatol 112:1–13
Carrera-Hernandez JJ, Gaskin SJ (2007) Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico. J Hydrol 336:231–249
Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression—the analysis of spatially varying relationships. Wiley, Chichester
Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228:113–129
Grimes IFD, Pardo-Iguzquiza E (2010) Geostatistical analysis of rainfall. Geogr Anal 42:136–160
Haberlandt U (2007) Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event. J Hydrol 332:144–157
Juha A, Pentti P, Juha AV (2013) Spatial interpolation of monthly climate data for Finland: comparing the performance of kriging and generalized additive models. Theor Appl Climatol 112:99–111
Kamarianakis Y, Feidas H, Kokolatos G, Chrysoulakis N, Karatzias V (2008) Evaluating remotely sensed rainfall estimates using nonlinear mixed models and geographically weighted regression. Environ Model Softw 23:1438–1447
Kizza M, Westerberg I, Rodhe A, Ntale HK (2012) Estimating areal rainfall over Lake Victoria and its basin using ground-based and satellite data. J Hydrol 464(465):401–411
Kumar S, Lal R, Liu DS (2012) A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma 189–190:627–634
Lee JH, Heo JH (2011) Evaluation of estimation methods for rainfall erosivity based on annual precipitation in Korea. J Hydrol 409:30–48
Leung Y, Mei CL, Zhang WX (2000) Testing for spatial autocorrelation among the residuals of the geographically weighted regression. Environ Plan 32:871–890
Lloyd CD (2005) Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain. J Hydrol 308:128–150
Mariusz S, Maciej K (2012) Local regression models for spatial interpolation of urban heat island—an example from Wrocław, SW Poland. Theor Appl Climatol 108:53–71
Minasny B, McBratney AB (2007) Spatial prediction of soil properties using EBLUP with a Matérn covariance function. Geoderma 140:324–336
Odeh IOA, McBratney AB, Chittleborough DJ (1995) Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging. Geoderma 67:215–226
Ramirez MCV, de Campos Velho HF, Ferreira NJ (2005) Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region. J Hydrol 301:146–162
Shukla RP, Tripathi KC, Pandey AC, Das IML (2011) Prediction of Indian summer monsoon rainfall using Niño indices: a neural network approach. Atmos Res 102:99–109
Sun W, Minasny B, McBratney AB (2012a) Analysis and prediction of soil properties using local regression-kriging. Geoderma 171:16–23
Sun W, Whelan B, McBratney AB, Minasny B (2012b) Evaluation of local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution. J Plant Nutr Soil Sci 175:212–220
Wagner PD, Fiener P, Wilken F, Kumar S, Chneider K (2012) Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. J Hydrol 464–465:388–400
Walter C, McBratney AB, Douaoui A, Minasny B (2001) Spatial prediction of topsoil salinity in the Chelif valley, Algeria using kriging with local versus whole-area variograms. Aust J Soil Res 39(2):255–272
Xie HJ, Zhang XS, Yu BB, Sharif H (2011) Performance evaluation of interpolation methods for incorporating rain gauge measurements into NEXRAD precipitation data: a case study in the Upper Guadalupe River Basin. Hydrol Process 25:3711–3720
Zhan MJ, Yin JM, Zhang YZ (2011) Analysis on characteristic of precipitation in Poyang Lake basin from 1959 to 2008. Procedia Environmental Sciences 10:1526–1533
Zhao N, Yue TX, Wang CL (2013) Surface modeling of seasonal mean precipitation in China during 1951–2010. Progr Geogr 32:49–58
Acknowledgments
This research was supported by the Key Project for the Strategic Science Plan in IGSNRR, CAS (Grant No. 2012ZD010), Research Plan of LREIS (Grant No. O88RA900KA), CAS, and China Postdoctoral Science Foundation (Grant No. 2013M530064). The authors thank Data Sharing Infrastructure of Earth System Science for providing data for the analysis. The authors are particularly grateful to Dr. Budiman Minasny from the University of Sydney for the suggestions in editing this paper. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by any government.
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Sun, W., Zhu, Y., Huang, S. et al. Mapping the mean annual precipitation of China using local interpolation techniques. Theor Appl Climatol 119, 171–180 (2015). https://doi.org/10.1007/s00704-014-1105-3
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DOI: https://doi.org/10.1007/s00704-014-1105-3