Theoretical and Applied Climatology

, Volume 108, Issue 1, pp 85–104

Daily relative humidity projections in an Indian river basin for IPCC SRES scenarios


  • Aavudai Anandhi
    • Department of Civil EngineeringIndian Institute of Science
    • CUNY Institute for Sustainable CitiesCity University of New York
  • V. V. Srinivas
    • Department of Civil EngineeringIndian Institute of Science
    • Department of Civil EngineeringIndian Institute of Science
    • Center for Earth SciencesIndian Institute of Science
  • Ravi S. Nanjundiah
    • Centre for Atmospheric & Oceanic SciencesIndian Institute of Science
Original Paper

DOI: 10.1007/s00704-011-0511-z

Cite this article as:
Anandhi, A., Srinivas, V.V., Kumar, D.N. et al. Theor Appl Climatol (2012) 108: 85. doi:10.1007/s00704-011-0511-z


A two-stage methodology is developed to obtain future projections of daily relative humidity in a river basin for climate change scenarios. In the first stage, Support Vector Machine (SVM) models are developed to downscale nine sets of predictor variables (large-scale atmospheric variables) for Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) (A1B, A2, B1, and COMMIT) to RH in a river basin at monthly scale. Uncertainty in the future projections of RH is studied for combinations of SRES scenarios, and predictors selected. Subsequently, in the second stage, the monthly sequences of RH are disaggregated to daily scale using k-nearest neighbor method. The effectiveness of the developed methodology is demonstrated through application to the catchment of Malaprabha reservoir in India. For downscaling, the probable predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis data set for the period 1978–2000 and (2) simulations of the third-generation Canadian Coupled Global Climate Model for the period 1978–2100. The performance of the downscaling and disaggregation models is evaluated by split sample validation. Results show that among the SVM models, the model developed using predictors pertaining to only land location performed better. The RH is projected to increase in the future for A1B and A2 scenarios, while no trend is discerned for B1 and COMMIT.

Copyright information

© Springer-Verlag 2011