Research Article Open Access

Climate Projections of Future Extreme Events in Malaysia

A.H. Syafrina1, M.D. Zalina2 and A. Norzaida2
  • 1 International Islamic University Malaysia, Malaysia
  • 2 Universiti Teknologi Malaysia, Malaysia

Abstract

In Malaysia, extreme rainfall events are often linked to a number of environmental disasters such as landslides, monsoonal and flash floods. In response to the negative impacts of such disaster, studies assessing the changes and projections of extreme rainfall are vital in order to gather climate change information for better management of hydrological processes. This study investigates the changes and projections of extreme rainfall over Peninsular Malaysia for the period 2081-2100 based on the RCP 6.0 scenario. In particular, this study adopted the statistical downscaling method which enables high resolution, such as hourly data, to be used for the input. Short duration and high intensity convective rainfall is a normal feature of tropical rainfall especially in the western part of the peninsular. The proposed method, the Advanced Weather Generator model is constructed based on thirty years of hourly rainfall data from forty stations. To account for uncertainties, an ensemble multi-model of five General Circulation Model realizations is chosen to generate projections of extreme rainfall for the period 2081-2100. Results of the study indicate a possible increase in future extreme events for both the hourly and 24 h extreme rainfall with the latter showing a wider spatial distribution of increase.

American Journal of Applied Sciences
Volume 14 No. 3, 2017, 392-405

DOI: https://doi.org/10.3844/ajassp.2017.392.405

Submitted On: 5 January 2017 Published On: 21 March 2017

How to Cite: Syafrina, A., Zalina, M. & Norzaida, A. (2017). Climate Projections of Future Extreme Events in Malaysia. American Journal of Applied Sciences, 14(3), 392-405. https://doi.org/10.3844/ajassp.2017.392.405

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Keywords

  • Climate Change
  • General Circulation Model
  • Advanced Weather Generator
  • High Temporal Resolution
  • Statistical Downscaling