Identification of potential causal variables for statistical downscaling models: effectiveness of graphical modeling approach

Abstract

Selection of potential causal variables (PCVs) from a pool of many possibly associated variables is a critical issue since it can significantly affect the performance of any statistical downscaling model. Generally, the variable to be downscaled is associated with many other hydrologic and climatic (aka hydroclimatic) variables. Most of the existing approaches, such as correlation analysis (CA), partial correlation analysis (PaCA), and stepwise regression analysis (SRA), rely mostly on the mutual association for the selection of PCVs. However, none of these approaches investigate the detailed dependence structure that may be helpful in eliminating the unwanted information and efficiently selecting the PCVs for downscaling the target variable. In this study, the effectiveness of graphical modeling (GM) approach is explored for the selection of the PCVs as GM can effectively identify the detailed conditional independence structure among all the associated variables. For demonstration, downscaling of monthly precipitation is undertaken using the PCVs, identified by CA, PaCA, SRA, and the proposed GM approach. Two different downscaling models, namely statistical downscaling model (SDSM) and support vector regression (SVR)–based downscaling model, are utilized. The results show that the PCVs identified through the proposed GM approach provides consistent as well as robust performance, across different regions and seasons, due to its ability to capture the complete conditional indepedence structure among the variables. The downscaled monthly precipitation obtained using the proposed approach is better matching with the observed data in terms of the mean, variance as well as the probability distribution. Overall, this study recommends the GM approach for the identification of the PCVs for the downscaling models.