Quantification of multiple sources of uncertainty in hydrologic and climate modelling
Date of Issue2016-09-28
School of Civil and Environmental Engineering
Frameworks incorporating hydro-meteorologic and climate models are applied to examine potential impacts of climate change for the future time periods. The importance of analyzing these frameworks is underscored by different sources of uncertainty that contribute to the variability observed in the models’ simulations. The sources of uncertainty addressed in this thesis are the parametric, model, scenario, and the downscaling uncertainty. Incorporating robust methodologies, the uncertainty propagated by the hydrologic and climate models are analyzed. The SLURP hydrologic model is subject to a robust and modified parametric uncertainty analysis methodology. Different types of meteorological models are analyzed for their ability to simulate precipitation for a multi-site tropical location utilizing spatial, statistical, frequency and extreme value criteria. An integrated climate change impact analysis framework incorporating hydro-meteorologic models and a climate change weather generator is utilized to examine the scenario uncertainty. Finally, the downscaling uncertainty is analyzed by instrumenting different downscaling approaches.