Identification of potential causal variables for statistical downscaling models: effectiveness of graphical modeling app
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ORIGINAL PAPER
Identification of potential causal variables for statistical downscaling models: effectiveness of graphical modeling approach Riya Dutta 1 & Rajib Maity 1 Received: 3 August 2018 / Accepted: 3 September 2020 # Springer-Verlag GmbH Austria, part of Springer Nature 2020
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. Keywords Statistical downscaling . Potential causal variable selection . Graphical modeling . Correlation analysis . Partial correlation analysis . Stepwise regression analysis
1 Introduction Downscaling is a general procedure to assess the information of any hydroclimatic variable at a finer scale using the information of the same and other variables at a coarser scale. The downscaling methods are broadly categorized into statistical and dynamical approaches (Wilby et al. 1999; Bergströms et al. 2001; Fowler et al. 2007; Schoof et al. 2009; Pinto et al. 2010). Statistical downscaling methods are less computationally intensive as compared with the dynamical methods, often the reason for its popularity (Wilby et al. 2002; Fowler et al. 2007; Chen et al. 2012; Meenu et al. 2013; Gutmann * Rajib Maity [email protected]; [email protected] 1
Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kh
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