Assessing the impacts of internal climate variability on rainfall under climate change scenarios in the Mun River Basin, Thailand | |
| Author | De Silva, Neelahandi Yenushi Kavindi |
| Call Number | AIT Thesis no.WM-20-19 |
| Subject(s) | Climatic changes--Thailand--Mun River Basin Rainfall probabilities--Thailand--Mun River Basin |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Water Engineering and Management |
| Publisher | Asian Institute of Technology |
| Abstract | This study examines the impacts of the Internal Climate Variability namely El Nino Southern Oscillation (ENSO) and Madden Julian Oscillation (MJO) on rainfall in the Mun River Basin, Thailand. The Southern Oscillation Index (SOI) and Oceanic Nino Index (ONI) were used to develop a relationship with ENSO and rainfall over the study area. Similarly, the Realtime Multivariate MJO index was used to analyses the relationship between MJO and rainfall in the region. Correlation analysis was carried out to see the relationship between both ENSO and MJO indices with observed rainfall. Spatial rainfall plots were obtained to see the spatial variation of rainfall during ENSO and MJO phases. Further, twelve Global Climate Models (GCM) were evaluated to see their ability to simulate ICV and rainfall over the Mun River Basin. Several performance criteria such as monthly climatology, rainfall variability and ENSO and MJO patterns in the historical period were considered for the evaluation of GCMs. Future GCM rainfall under RCP4.5 and 8.5 scenarios were bias corrected using quantile mapping method. The intensity and frequency of future ENSO events were obtained using future sea surface temperature and sea level pressure data from GCMs. It was found that both La Nina and MJO phase 5 produces higher rainfall over the Mun River Basin. ACCESS1.0, CNRM-CM5 and MIROC5 were selected as the best models out of all the models considering their performance in simulating rainfall and ENSO while all the GCMs were failed in capturing the MJO pattern. Bias corrected rainfall showed significant improvements in representing monthly rainfall over the region. However, large uncertainties remain in the projected rainfall and ENSO due to the contradictory results obtained from the selected GCMs. Further improvements should be made in GCMs to improve their ability to capture the ICV modes such as ENSO and MJO. |
| Year | 2020 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Civil and Infrastucture Engineering (DCIE) |
| Academic Program/FoS | Water Engineering and Management (WM) |
| Chairperson(s) | Shrestha, Sangam |
| Examination Committee(s) | Babel, Mukand Singh;Sundaram, S. Mohana;Shanmugasundaram, Jothiganesh |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2020 |