MODELING THE PRECIPITATION AMOUNTS DYNAMICS FOR DIFFERENT TIME SCALES IN ROMANIA USING MULTIPLE REGRESSION APPROACH

Water resources are very important for ecosystems and water deficit may cause serious social and economical issues. The aim of this study is to analyze the performances of prediction procedure based on Multiple Linear Regression Model (MLRM), for the precipitation amounts for yearly and seasonal time scales, in Romania. For this purpose we have used as predictand annual and seasonal amount of precipitation and as predictors Mean Sea Level Pressure (MSLP), geopotential height at 300 hPa (HGT300), wind speed at 700 hPa (WS700), temperature at 850 hPa (T850) and Total Column Water (TCW). The selection of predictors is based on the collinearity and multicollinearity analysis. Multicollinearity problems occur only during winter. All data sets used in this study are reanalysis gridded data with a spatial resolution of 2 degrees x 2 lat/lon and are obtained from 20th Century Reanalysis Version 2 (20CR V2) Project. The analysis is made for a period of 140 years between 1871 and 2010, the period 1871 - 2000 being used to build the MLRM and the period 2000 - 2010 for testing the prediction performances of the MLRM. Using MLRM we have obtained some good correlation between predicted and measured precipitation amount. The correlation coefficient varies between 0.34 and 0.98, with the smallest values in winter and the greatest values in spring. The Spearman correlation was also used to validate the MLRM, and correlation coefficients are between 0.25 for winter and 0.98 for spring.