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    Disaggregation of future climate projection data to generate future rainfall intensity-duration-frequency curves to assess climate change impacts
    (Graduate School, 2021-04-03) Tayşi, Hüsamettin ; Özger, Mehmet ; 501171524 ; Hydraulics and Water Resources Engineering ; Hidrolik ve Su Kaynakları Mühendisliği
    A heavy increase in urbanization, industrialization, and population is causing an increase in emissions of greenhouse gases (GHG). Increment in GHG emissions causes variations in the atmosphere and in climate conditions. Climate change is one of the most serious reasons for extreme climate events such as high global temperature, extremely heavy rainfall events, and high evapotranspiration. According to many studies, climate change impacts will intensify in the future. As a result of this, heavy rainfall events tend to enhance. In our case extreme rainfall events, which are responsible for flooding events, were considered. Since flooding is influencing urban areas acutely, controlling and management of flooding is a major necessity for cities. Intensity-Duration-Frequency (IDF) curves play a huge role in representing rainfall characteristics by linking the intensity, duration, and frequency of rainfall. These curves give the expected rainfall intensity in duration (5, 10, 15, 30 min.; 1, 2, 3, 4, 5, 6, 8, 12, 18, and 24 hours) and in a return period (2, 10, 25, 50 and 100 years). Hence, IDF curves are used in many water-related applications such as water management, designing of infrastructure, drainage, culverts, gutters, and also flood forecasting. However, current IDF curves are generated based on historical rainfall events. These IDF curves are considered stationary since they only consider historical events. The increase in GHG leads to variations in climate, especially in rainfall behaviors. Thus, IDF curves must catch the changes in rainfall intensities, in other words, they must be non-stationary and time-varying based. This study updates IDF curves to assess future climate conditions. For the study, six meteorological stations from Istanbul (Florya, Goztepe, Sariyer, Sile, Omerli and Canta) were selected as study areas. As a consequence, cities will be prepared for upcoming extreme events, hence possible damages will be decreased. A Global Climate Model (GCM) HadGEM2-ES generated under RCP4.5 and RCP8.5 scenarios were used in the study to represent future rainfall in daily form. 1-min and hourly rainfall data were provided by the Turkish State Meteorological Service (TSMS). HYETOS disaggregation model was applied to both historical and future rainfall data to obtain sub-hourly data (also hourly for future rainfall). Since GCMs are not suitable to use directly due to biases, the distribution mapping method was selected as a bias-correction method. The Gumbel model was applied to annual maximum rainfall to generate IDF curves. Finally, historical and future IDF curves, IDF curves generated under RCP4.5 and RCP8.5, and also IDF curves generated using disaggregated historical data and observed IDF curves provided by TSMS were compared. The study concluded that rainfall intensities increase under RCP4.5 and RCP8.5 scenarios compared to historical IDF curves. Besides, RCP8.5 has higher rainfall intensities when compared to rainfall intensities of RCP4.5.