A nonparametric kernel regression model for downscaling multisite daily precipitation in the Mahanadi basin

Hydrologic impacts of global climate change are usually assessed by downscaling large-scale climate variables, simulated by general circulation models (GCMs), to local-scale hydrometeorological variables. Conventional multisite statistical downscaling techniques often fail to capture spatial dependence of rainfall amounts as well as hydrometeorological extremes. To overcome these limitations, a downscaling algorithm is proposed, which first simulates the rainfall state of an entire study area/river basin, from large-scale climate variables, with classification and regression trees, and then projects multisite rainfall amounts using a nonparametric kernel regression estimator, conditioned on the estimated rainfall state. The concept of a common rainfall state for the entire study area, using it as an input for projections of rainfall amount, is found to be advantageous in capturing the cross correlation between rainfalls at different downscaling locations. Temporal variability and extremities of rainfall are captured in downscaling with multivariate kernel regression. The proposed model is applied for downscaling daily monsoon precipitation at eight locations in the Mahanadi River basin of eastern India. The model performance is compared, with a recently developed conditional random field based as well as with established multisite downscaling models, and is found to be superior. Analysis of future rainfall scenarios, projected with the developed downscaling model, reveals considerable changes in rainfall intensity and dry and wet spell lengths, among other things, at different locations. An increasing trend of rainfall is projected for the lower (southern) Mahanadi River basin, and a decreasing trend is observed in the upper (northern) Mahanadi River basin. Citation: Kannan, S., and S. Ghosh (2013), A nonparametric kernel regression model for downscaling multisite daily precipitation in the Mahanadi basin, Water Resour. Res., 49, 1360-1385, doi: 10.1002/wrcr.20118