Longterm rainfall variability and changes in Kuantan river basin

This paper assesses the performance of Statistical Downscaling Model (SDSM) as climate agent to generate the climate trend as well as climate future projections of rainfall and temperature in the year 2040-2069 in Kuantan River Basin. In order to be able to validate the historical trend, predictors...

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Bibliographic Details
Main Author: Nur Farisha, Rahaizak
Format: Undergraduates Project Papers
Language:English
English
English
English
Published: 2017
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/22240/
http://umpir.ump.edu.my/id/eprint/22240/
http://umpir.ump.edu.my/id/eprint/22240/1/Longterm%20rainfall%20variability%20and%20changes%20in%20Kuantan%20river%20basin%20-%20Table%20of%20contents.pdf
http://umpir.ump.edu.my/id/eprint/22240/2/Longterm%20rainfall%20variability%20and%20changes%20in%20Kuantan%20river%20basin%20-%20Abstract.pdf
http://umpir.ump.edu.my/id/eprint/22240/3/Longterm%20rainfall%20variability%20and%20changes%20in%20Kuantan%20river%20basin%20-%20Chapter%201.pdf
http://umpir.ump.edu.my/id/eprint/22240/4/Longterm%20rainfall%20variability%20and%20changes%20in%20Kuantan%20river%20basin%20-%20References.pdf
Description
Summary:This paper assesses the performance of Statistical Downscaling Model (SDSM) as climate agent to generate the climate trend as well as climate future projections of rainfall and temperature in the year 2040-2069 in Kuantan River Basin. In order to be able to validate the historical trend, predictors set that has been derived from National Centre to Environmental Prediction (NCEP) reanalysis information to be utilized for adjustment and approval process and GCMs-factors to create the future climate trend in view of expected increment of GHGs at the local region. In this study, a total of six stations in Kuantan river basin consists of rainfall and temperatures details has been selected to carry out this study. About 4 predictors have been chosen for rainfall analysis and 5 other different predictors are used for temperature. In addition, GCM data is used for future climate projections for selected stations based on three different forcings scenarios, RCP26, RCP45 and RCP85 with their percentage difference based from their historical data. The result shows that for rainfall analysis, the average error is only 11% meanwhile for temperature is less than 1%.