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University of Western Sydney (2018)

Rainfall analysis under changing climate regime in Qatar

Al-Mamoon, Abdullah

Titre : Rainfall analysis under changing climate regime in Qatar

Auteur : Al-Mamoon, Abdullah

Université de soutenance : University of Western Sydney

Grade : Doctor of Philosophy (PhD) 2018

Rainfall data is needed in the planning and design of storm water infrastructure, hydraulic structures, flood management works and various environmental assessment tasks. Design rainfall is generally expressed by intensity-duration-frequency (IDF) curves. This thesis focuses on rainfall analysis, in particular, trends and variability in rainfall indices, selection of probability distributions in frequency analysis of rainfall data, uncertainty assessment and evaluation of climate change impact on design rainfall. In this research, Qatar, located in the arid region of the Gulf has been selected as the study area. Rainfall data from a total of 35 rainfall stations from Qatar and nearby Gulf countries including Kingdom of Saudi Arabia, Bahrain, Oman and United Arab Emirates have been used in this study. A comprehensive quality check has been carried out in collating these rainfall data. Any station failing the quality assurance test is excluded from the analysis. It should be noted that different subsets of these stations have been used in the analysis and modelling presented in different thesis chapters. This research identified trends in rainfall data in Qatar using fifteen different rainfall indices by applying a combination of Mann-Kendall and Spearman’s Rho tests. It has been found that rainfall indices in Qatar have mixed trends (both positive and negative trends) throughout the country. Stations showing increasing trend in annual total rainfall are mainly located in the central part of Qatar. However, no relationship between spatial location and the elevation of rain gauges is found with the identified trends. Examination of trends in annual total rainfall during dry and rainy seasons shows that seasonal rainfall in Qatar is changing. This study identifies the best fit probability distribution for Qatar for annual maximum rainfall data based on fourteen different probability distributions and three goodness-of-fit tests. Based on a relative scoring method, the Generalized Extreme Value distribution is found to be the best fit distribution for majority of the selected stations. A modelling framework is also developed to quantify uncertainty in design rainfall estimation arising from limited data length using Monte Carlo simulation and bootstrapping techniques. Results from bootstrapping on the observed annual maximum rainfall data show that the estimate of the mean rainfall is associated with the smallest degree of standard error, whilst skewness has the highest error level. The coefficient of variation (CV) of standard deviation estimate is found to be 12 times higher than that of the mean. Furthermore, the CV of skewness estimate is found to be 26 times higher than that of the mean. Based on the results of Monte Carlo simulation, it has been found that the confidence band (measure of uncertainty) increases with increasing “average recurrence interval” (ARI). The 100 year ARI design rainfall intensity has the highest degree of uncertainty among the six ARIs (2 to 100 years) considered in this study. This study assesses the impacts of climate change on the design rainfall estimation in Qatar based on Intergovernmental Panel on Climate Change’s most recent new generation of climate models. A total of 61 Global Circulation Models with 609 emission scenarios are considered for the assessment. The results indicate an increase of up to 50% for the 100-year rainfall event from current to the intermediate scenario (2040-2069). The rate-of-change of the far future (2070-2100) is at similar level as the intermediate period.


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