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This paper presents a multivariate geostatistical algorithm called regression-kriging (RK) for predicting the spatial
distribution of rainfall by incorporating five topographic/geographic factors of latitude, longitude, altitude, slope and
aspect. The technique is illustrated using rainfall data collected at 52 rain gauges from the Laohahe basis in northeast
China during 1986-2005 . Rainfall data from 44 stations were selected for modeling and the remaining 8 stations were
used for model validation. To eliminate multicollinearity, the five explanatory factors were first transformed using factor
analysis with three Principal Components (PCs) extracted. The rainfall data were then fitted using step-wise regression
and residuals interpolated using SK. The regression coefficients were estimated by generalized least squares (GLS),
which takes the spatial heteroskedasticity between rainfall and PCs into account. Finally, the rainfall prediction based on
RK was compared with that predicted from ordinary kriging (OK) and ordinary least squares (OLS) multiple regression
(MR). For correlated topographic factors are taken into account, RK improves the efficiency of predictions. RK
achieved a lower relative root mean square error (RMSE) (44.67%) than MR (49.23%) and OK (73.60%) and a lower
bias than MR and OK (23.82 versus 30.89 and 32.15 mm) for annual rainfall. It is much more effective for the wet
season than for the dry season. RK is suitable for estimation of rainfall in areas where there are no stations nearby and
where topography has a major influence on rainfall.
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