Quantification of the changes in intensity and frequency of hourly extreme rainfall attributed climate change in Oman

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3 Citations (Scopus)

Abstract

Built environment, which includes some major investments in Oman, has been designed based on historical data and do not incorporate the climate change effects. This study estimates potential variations of the hourly annual maximum rainfall (AMR) in the future in Salalah, Oman. Of the five climate models, two were selected based on their ability to simulate local rainfall characteristics. A two-stage downscaling–disaggregation approach was applied. In the first stage, daily rainfall projections in 2040–2059 and 2080–2099 periods from MRI-CGCM3 and CNRM-CM5 models based on two Representative Concentration Pathways (RCP8.5 and RCP4.5) were downscaled to the local daily scale using a stochastic downscaling software (LARS-WG5.5). In the second stage, the stochastically downscaled daily rainfall time series were disaggregated using K-nearest neighbour technique into hourly series. The AMRs, extracted from 20 years of projections for four scenarios and two future periods were then fitted with the generalized extreme value distribution to obtain the rainfall intensity–frequency relationship. These results were compared with a similar relationship developed for the AMRs in baseline period. The results show that the reduction in number of wet days and increases in total rainfall will collectively intensify the future rainfall regime. A marked difference between future and historical intensity–frequency relationships was found with greater changes estimated for higher return periods. Furthermore, intensification of rainfall regime was projected to be stronger towards the end of the twenty-first century.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalNatural Hazards
DOIs
Publication statusAccepted/In press - Mar 15 2018

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rainfall
climate change
twenty first century
downscaling
return period
climate modeling
time series
software

Keywords

  • GCM
  • GEV distribution
  • K-NN technique
  • RCP
  • Stochastic weather generator

ASJC Scopus subject areas

  • Water Science and Technology
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)

Cite this

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title = "Quantification of the changes in intensity and frequency of hourly extreme rainfall attributed climate change in Oman",
abstract = "Built environment, which includes some major investments in Oman, has been designed based on historical data and do not incorporate the climate change effects. This study estimates potential variations of the hourly annual maximum rainfall (AMR) in the future in Salalah, Oman. Of the five climate models, two were selected based on their ability to simulate local rainfall characteristics. A two-stage downscaling–disaggregation approach was applied. In the first stage, daily rainfall projections in 2040–2059 and 2080–2099 periods from MRI-CGCM3 and CNRM-CM5 models based on two Representative Concentration Pathways (RCP8.5 and RCP4.5) were downscaled to the local daily scale using a stochastic downscaling software (LARS-WG5.5). In the second stage, the stochastically downscaled daily rainfall time series were disaggregated using K-nearest neighbour technique into hourly series. The AMRs, extracted from 20 years of projections for four scenarios and two future periods were then fitted with the generalized extreme value distribution to obtain the rainfall intensity–frequency relationship. These results were compared with a similar relationship developed for the AMRs in baseline period. The results show that the reduction in number of wet days and increases in total rainfall will collectively intensify the future rainfall regime. A marked difference between future and historical intensity–frequency relationships was found with greater changes estimated for higher return periods. Furthermore, intensification of rainfall regime was projected to be stronger towards the end of the twenty-first century.",
keywords = "GCM, GEV distribution, K-NN technique, RCP, Stochastic weather generator",
author = "Gunawardhana, {Luminda Niroshana} and Al-Rawas, {Ghazi A.} and Ghadeer Al-Hadhrami",
year = "2018",
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AU - Al-Rawas, Ghazi A.

AU - Al-Hadhrami, Ghadeer

PY - 2018/3/15

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N2 - Built environment, which includes some major investments in Oman, has been designed based on historical data and do not incorporate the climate change effects. This study estimates potential variations of the hourly annual maximum rainfall (AMR) in the future in Salalah, Oman. Of the five climate models, two were selected based on their ability to simulate local rainfall characteristics. A two-stage downscaling–disaggregation approach was applied. In the first stage, daily rainfall projections in 2040–2059 and 2080–2099 periods from MRI-CGCM3 and CNRM-CM5 models based on two Representative Concentration Pathways (RCP8.5 and RCP4.5) were downscaled to the local daily scale using a stochastic downscaling software (LARS-WG5.5). In the second stage, the stochastically downscaled daily rainfall time series were disaggregated using K-nearest neighbour technique into hourly series. The AMRs, extracted from 20 years of projections for four scenarios and two future periods were then fitted with the generalized extreme value distribution to obtain the rainfall intensity–frequency relationship. These results were compared with a similar relationship developed for the AMRs in baseline period. The results show that the reduction in number of wet days and increases in total rainfall will collectively intensify the future rainfall regime. A marked difference between future and historical intensity–frequency relationships was found with greater changes estimated for higher return periods. Furthermore, intensification of rainfall regime was projected to be stronger towards the end of the twenty-first century.

AB - Built environment, which includes some major investments in Oman, has been designed based on historical data and do not incorporate the climate change effects. This study estimates potential variations of the hourly annual maximum rainfall (AMR) in the future in Salalah, Oman. Of the five climate models, two were selected based on their ability to simulate local rainfall characteristics. A two-stage downscaling–disaggregation approach was applied. In the first stage, daily rainfall projections in 2040–2059 and 2080–2099 periods from MRI-CGCM3 and CNRM-CM5 models based on two Representative Concentration Pathways (RCP8.5 and RCP4.5) were downscaled to the local daily scale using a stochastic downscaling software (LARS-WG5.5). In the second stage, the stochastically downscaled daily rainfall time series were disaggregated using K-nearest neighbour technique into hourly series. The AMRs, extracted from 20 years of projections for four scenarios and two future periods were then fitted with the generalized extreme value distribution to obtain the rainfall intensity–frequency relationship. These results were compared with a similar relationship developed for the AMRs in baseline period. The results show that the reduction in number of wet days and increases in total rainfall will collectively intensify the future rainfall regime. A marked difference between future and historical intensity–frequency relationships was found with greater changes estimated for higher return periods. Furthermore, intensification of rainfall regime was projected to be stronger towards the end of the twenty-first century.

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