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.

Last Updated: 2011-11-02