Multi‐site downscaling of maximum and minimum daily temperature using support vector machine. (9th July 2013)

• Record Type: Journal Article
• Authors: Srinivas, V. V.; Basu, Bidroha; Nagesh Kumar, D.; Jain, Sanjay K.
• Is Part Of: International journal of climatology. Volume 34:Number 5(2014)
• Page Start: 1538
• Page End: 1560
• Publication Date: 2013-07-09
• Subjects: Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.3782
• Languages: English
• ISSNs: 0899-8418

Abstract

<abstract abstract-type="main" id="joc3782-abs-0001"> <title>ABSTRACT</title> <p id="joc3782-para-0001">Climate change impact assessment studies involve downscaling large‐scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site‐scale meteorological variables. This article presents a least‐square support vector machine (LS‐SVM)‐based methodology for multi‐site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS‐SVM inter‐site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using <italic>k</italic>‐nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi‐site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.</p> </abstract>