DOWNSCALING OF DAILY AVERAGE RAINFALL OF KOTA BHARU KELANTAN, MALAYSIA
DOI:
https://doi.org/10.11113/mjce.v30.15714Keywords:
Statistical downscaling, rainfall, random forest, quantile mappingAbstract
Downscaling Global Circulation Model (GCM) output is important in order to
understand the present climate as well as future climate changes at local scale. In this study,
Random Forest (RF) was used to downscale the mean daily rainfall at Kota Bahru meteorological
station located in Kelantan Malaysia. The RF model was used to downscale daily rainfall from
GCM of Coupled Model Intercomparison Project Phase 5 (CMIP5), BCC-CSM1.1. The potential
predictors were selected using stepwise regression at grid points located around the study area.
Quantile mapping was used to remove the bias in the prediction. The results showed that the RF
model was able to establish a good relation between observed and downscaled rainfall. The
Quantile mapping was found to perform well to correct errors in prediction. The statistical
measures of performance of downscaling and bias correction approaches show that they are able
to replicate daily observed rainfall with Nash-Schutclif efficiency greater than 0.75 for all the
months. It can be concluded that RF and Quantile mapping are reliable and effective methods for
downscaling rainfall.
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