Statistical Downscaling of GCM Simulations to Streamflow using Relevance Vector Machine

Ghosh, Subimal and Mujumdar, PP (2007) Statistical Downscaling of GCM Simulations to Streamflow using Relevance Vector Machine. In: Advances in Water Resources .

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Abstract

General circulation models (GCMs), the climate models often used in assessing the impact of climate change, operate on a coarse scale and thus the simulation results obtained from GCMs are not particularly useful in a comparatively smaller river basin scale hydrology. The article presents a methodology of statistical downscaling based on sparse Bayesian learning and Relevance Vector Machine (RVM) to model streamflow at river basin scale for monsoon period (June, July, August, September) using GCM simulated climatic variables. NCEP/NCAR reanalysis data have been used for training the model to establish a statistical relationship between streamflow and climatic variables. The relationship thus obtained is used to project the future streamflow from GCM simulations. The statistical methodology involves principal component analysis, fuzzy clustering and RVM. Different kernel functions are used for comparison purpose. The model is applied to Mahanadi river basin in India. The results obtained using RVM are compared with those of state-of-the-art Support Vector Machine (SVM) to present the advantages of RVMs over SVMs. A decreasing trend is observed for monsoon streamflow of Mahanadi due to high surface warming in future, with the CCSR/NIES GCM and B2 scenario.

Item Type:	Journal Article
Publication:	Advances in Water Resources
Publisher:	Elsevier Ltd.
Additional Information:	The copyright belongs to Elsevier Ltd. Its an online publication.
Keywords:	GCM;Downscaling;RVM
Department/Centre:	Division of Mechanical Sciences > Civil Engineering
Date Deposited:	02 Nov 2007
Last Modified:	19 Sep 2010 04:40
URI:	http://eprints.iisc.ac.in/id/eprint/12368

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