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Predicting Monthly Precipitation with Multivariate Regression Methods Using Geographic and Topographic Information

Multivariate regression models that integrate topographic and geographic information are developed to predict monthly precipitation in the Daqing Mountains of northern China. Five geographic and topographic factors, including longitude, latitude, elevation, slope, and aspect, are taken into account in the model development. The data are acquired from a 100 m resolution DEM of the national topographic databases. Measured precipitation data at 56 stations between 1955 and 1990 are used for model development, and a leave-one-out cross-validation method is used for model evaluation. The model explains most of the spatial variability in monthly precipitation, and can also quantify the relative importance of different geographic and topographic variables. The overall MAE and RMSE account for 10.42% and 13.64% of the measured precipitation for the entire year, respectively, and the relative errors of the monthly models are higher in the dry season (October to March) than in the wet season, because precipitation in the dry season is hard to model owing to little rainfall (11.77% of the annual total) and a different synoptic system. The results indicate that the model's accuracy is influenced by the synoptic system and rainfall amount. The model provides a way to link the descriptive and explanatory functions of precipitation modeling, and could potentially be used in mountain climate and hydrology research.