Uncertainty in Rainfall Intensity Duration Frequency Curves of Peninsular Malaysia under Changing Climate Scenarios

Muhammad Noor,Tarmizi Ismail,Shamsuddin Shahid,Jang Sung,Eun-Sung Chung

doi:10.3390/w10121750

Abstract

This study developed a methodological framework to update the rainfall intensity-duration-frequency (IDF) curves under climate change scenarios. A model output statistics (MOS) method is used to downscale the daily rainfall of general circulation models (GCMs), and an artificial neural network (ANN) is employed for the disaggregation of projected daily rainfall to hourly maximum rainfall, which is then used for the development of IDF curves. Finally, the 1st quartiles, medians, and 3rd quartiles of projected rainfall intensities are estimated for developing IDF curves with uncertainty level. Eight GCM simulations under two radiative concentration pathways (RCP) scenarios, namely, RCP 4.5 and RCP 8.5, are used in the proposed framework for the projection of IDF curves with related uncertainties for peninsular Malaysia. The projection of rainfall revealed an increase in the annual average rainfall throughout the present century. The comparison of the projected IDF curves for the period 2006–2099 with that obtained using GCM hindcasts for the based period (1971–2005) revealed an increase in rainfall intensity for shorter durations and a decrease for longer durations. The uncertainty in rainfall intensity for different return periods for shorter duration is found to be 2 to 6 times more compared to longer duration rainfall, which indicates that a large increase in rainfall intensity for short durations projected by GCMs is highly uncertain for peninsular Malaysia. The IDF curves developed in this study can be used for the planning of climate resilient urban water storm water management infrastructure in Peninsular Malaysia.

Highlights

Rainfall Intensity-Duration-Frequency (IDF) curves are one of the most frequently used tools in hydrology and water resources for the planning, design, and operation of hydraulic infrastructures [1].The expected increase in rainfall intensity and frequency due to climate change can alter the IDF curves [2,3]
An artificial neural network (ANN)-based model is employed for the disaggregation of projected daily rainfall to hourly maximum rainfall, which is used for the development of IDF curves
A framework was developed to assess the uncertainties in IDF curves under projected climate change scenarios

Summary

Introduction

The expected increase in rainfall intensity and frequency due to climate change can alter the IDF curves [2,3]. Several studies assessed the impact of climate change in designing urban water management infrastructure in Canada [7,8], Sweden [9], Vietnam [10], United Kingdom [11], and United States [12]. Significant cost-effective steps should be taken for the identification of water resources investments that can reduce risks [16,17]. For such analyses, it is very important to quantify the impact of climate change on urban water management infrastructure

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Discussion

Conclusion