Precipitation and its extremes: a study into the hydro-climatological controls of extreme precipitation

• Main Author: van der Veer, Irene (author)
• Other Authors: van der Ent, R.J. (mentor), Uijlenhoet, R. (graduation committee), Siebesma, A.P. (graduation committee), Delft University of Technology (degree granting institution)
• Format: Master Thesis
• Language: English
• Published: 2022
• Subjects: Extreme precipitation; Heaviness amplification factor; Tail behavior; Hydro-climatological controls; Multiple linear regression; Multicollinearity; Antarc*; Antarctica
• Online Access: http://resolver.tudelft.nl/uuid:8f7864af-6729-4394-bac2-0bdb50d7075d

Extreme precipitation can be characterized by the tail behavior of precipitation probability distributions.The tail contains the most extreme precipitation events and tells something about both the magnitude and frequency of these events. Here the heaviness amplification factor is used to represent the tail behavior. This research determines the most important hydro-climatological controls of the heaviness amplification factor. The study area is the entire world, where Antarctica and the oceans are not taken into account. A pre-defined set of 17 controls divided into four groups is used. The four groups are: general climate condition, geography, land cover and climate variability. The relation between the heaviness and the controls is determined with multiple linear regression, where the standardized controls are used. Since multiple linear regression is applied, multicollinearity might be a problem. This term refers to the occasion where independent variables are correlated to another independent variable or a linear combination of variables. This could lead to erroneous and unreliable results. To deal with this problem, three elimination methods are applied. This first method consists of a correlation analysis, where two controls are highly correlated if their correlation coefficient is higher than 0.9. The control with the lowest regression coefficient is eliminated. This is followed by another regression analysis with all the remaining controls, where controls with a low regression coefficient are also eliminated. The second method applies an iterative regression procedure, where in each iteration the control with the lowest coefficient is eliminated until three controls remain. The last method calculates the variance inflation factor, which is a measure for multicollinearity. It removes the controls with the highest variance inflation factor if higher than 10, until none of the controls has a score above this limit. The described analysis is done for the entire world, but also for 44 IPCC climate reference ...