Anandhi, Aavudai and Srinivas, VV and Nanjundiahb, Ravi S (2007) Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine. In: International Journal of Climatology, 28 (3). pp. 401-420.
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This paper presents a methodology to downscale monthly precipitation to river basin scale in Indian context for special report of emission scenarios (SRES) using Support Vector Machine (SVM). In the methodology presented, probable predictor variables are extracted from (1) the National Center for Environmental Prediction (NCEP) reanalysis data set for the period 1971–2000 and (2) the simulations from the third generation Canadian general circulation model (CGCM3) for SRES emission scenarios A1B, A2, B1 and COMMIT for the period 1971–2100. These variables include both the thermodynamic and dynamic parameters and those which have a physically meaningful relationship with the precipitation. The NCEP variables which are realistically simulated by CGCM3 are chosen as potential predictors for seasonal stratification. The seasonal stratification involves identification of (1) the past wet and dry seasons through classification of the NCEP data on potential predictors into two clusters by the use of K-means clustering algorithm and (2) the future wet and dry seasons through classification of the CGCM3 data on potential predictors into two clusters by the use of nearest neighbour rule. Subsequently, a separate downscaling model is developed for each season to capture the relationship between the predictor variables and the predictand. For downscaling precipitation, the predictand is chosen as monthly Thiessen weighted precipitation for the river basin, whereas potential predictors are chosen as the NCEP variables which are correlated to the precipitation and are also realistically simulated by CGCM3. Implementation of the methodology presented is demonstrated by application to Malaprabha reservoir catchment in India which is considered to be a climatically sensitive region. The CGCM3 simulations are run through the calibrated and validated SVM downscaling model to obtain future projections of predictand for each of the four emission scenarios considered. The results show that the precipitation is projected to increase in future for almost all the scenarios considered. The projected increase in precipitation is high for A2 scenario, whereas it is least for COMMIT scenario. Copyright 2007 Royal Meteorological Society.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to John Wiley & Sons.|
|Keywords:||climate change;downscaling;hydroclimatology;precipitation; support vector machine (SVM);IPCC SRES scenarios;clustering.|
|Department/Centre:||Division of Mechanical Sciences > Civil Engineering
Division of Mechanical Sciences > Centre for Atmospheric & Oceanic Sciences
|Date Deposited:||02 May 2008|
|Last Modified:||19 Sep 2010 04:44|
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