A Multivariate Regression Model for Predicting Precipitation in the Daqing Mountains

Multivariate regression analysis, combined with residuals correction, was carried out to develop a precipitation prediction model for the Daqing Mountains of Inner Mongolia in northern China. Precipitation data collected at 56 stations between 1955 and 1990 were used: data from 48 stations for model development and data from 8 stations for additional tests. Five topographic factors—altitude, slope, aspect, longitude, and latitude—were taken into account for model development. These topographic variables were acquired from a 100-m resolution digital elevation model (DEM) of the study region, and the mean values of the sub-basin in which a precipitation station is located were used as the values of the respective variables of that station. The multivariate regression model can explain 72.6% of the spatial variability of precipitation over the whole year and 74.4% of variability in the wet season (June–September). Precipitation in the dry season (October–May) is hard to model owing to little rainfall (21.78% of annual rainfall) and a different synoptic system. Interpolation-based residuals correction did not significantly improve the accuracy of our model, which shows that our model is quite effective. The model, as presented in this paper, could potentially be applied to other mountains and in mountain climate research.