Downscaling and Disaggregation of Wind Speed to River Basin in India for IPCC SRES Scenarios
Downscaling and Disaggregation of Wind Speed to River Basin
In this paper, spatial downscaling of monthly wind speeds from global scale to a river basin scale is carried out using a novel machine learning technique called Support Vector Machine (SVM). Subsequently, temporal disaggregation of the projected wind speed from monthly to daily scale is performed using modified exponential equation extracted from SWAT model and a k – nearest neighbor technique developed in this study. The effectiveness of the downscaling and disaggregation models is demonstrated through application to wind speed over the catchment of Malaprabha reservoir in India. For downscaling, the probable predictor variables are extracted from (i) the NCEP reanalysis dataset for the period 1978-2000 and (ii) the simulations from the third generation Canadian Global Climate Model (CGCM3) for SRES emission scenarios A1B, A2, B1 and COMMIT for the period 1978-2100. Large-scale atmospheric variables such as zonal and meridional wind velocities at 925mb are considered as predictors. The scatter plots and cross correlations are used for verifying the reliability of the simulation of the predictor variables by the CGCM3 and to study the predictor-predictand relationships. The downscaled monthly wind speed is disaggregated to daily wind speed values. The performances of the SVM and disaggregation models were evaluated. The results of downscaling show that wind speed is not projected to change in future for A1B, A2, B1 and COMMIT scenarios. The performance evaluation of the temporal disaggregation models developed show that the k – nearest neighbor technique developed in this study performs better than the modified exponential equation extracted from SWAT model.
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