Year: 2018
Type: article
Abstract: Downscaling Global Circulation Model (GCM) output is important in order tounderstand 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 meteorologicalstation located in Kelantan Malaysia. The RF model was used to downscale daily rainfall fromGCM of Coupled Model Intercomparison Project Phase 5 (CMIP5), BCC-CSM1.1. The potentialpredictors 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 RFmodel was able to establish a good relation between observed and downscaled rainfall. TheQuantile mapping was found to perform well to correct errors in prediction. The statisticalmeasures of performance of downscaling and bias correction approaches show that they are ableto replicate daily observed rainfall with Nash-Schutclif efficiency greater than 0.75 for all themonths. It can be concluded that RF and Quantile mapping are reliable and effective methods fordownscaling rainfall.
Authors: Muhammad Noor, Tarmizi Ismail
Institution: University of Technology Malaysia
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: diamond
Funder: Universiti Teknologi Malaysia
OpenAlex ID: W2803208680