Statistical downscaling and disaggregation for supporting regional climate change impact studies
Date of Issue2015
School of Civil and Environmental Engineering
A warmer climate may affect the frequency and severity of weather extremes, such as heavy rainfalls, hurricanes and heat-waves. Based on the records around the world, the numbers of observed extreme events have presented increasing tendencies over the past decades. The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report points out that the temperature would be continuously increasing in this century. This implies that some disasters (e.g. flood and drought) which are caused by weather extremes could become more frequent. The Southeast Asia is vulnerable to the impact of climate change. Especially in the urban areas, the flash flood has become one of major disasters caused by heavy rainfall. It is thus critical to develop flexible and applicable approaches to investigate the climate change impact on local regions. The General Circulation Models (GCMs) are the powerful tools to simulate either current or future climate conditions. But the bias and resolution problems have limited their applications for some specific regions like Southeast Asia. The dynamical and statistical downscaling are the two basic approaches to help bridge the gaps between GCMs and local weather information. Compared with dynamical approaches, the statistical ones are computationally cheap and easily applicable to many different regions. The objective of this PhD study is to develop and apply statistical downscaling and disaggregation methods for supporting hydrological and climate change impact studies. It covers three major components including development of novel statistical downscaling tools, applications of combined statistical downscaling and disaggregation methods, and assessment of climate change impact on hydrological processes.
DRNTU::Engineering::Civil engineering::Water resources