Covariate and parameter uncertainty in non-stationary rainfall IDF curve

Since the substantial evidence of non-stationarity in the extreme rainfall series is already reported, the current realm of hydrologic research focusing on developing methodologies for a non-stationary rainfall condition. As the rainfall intensity duration frequency (IDF) curve is primarily used in storm water management and infrastructure design, developing rainfall IDF curves in a non-stationary context is a current interest of water resource researchers. In order to construct non-stationary rainfall IDF curve, the probability distribution's parameters are allowed to change according to covariate value and the current practice is to use time as a covariate. However, the covariate can be any physical process and the recent studies show that the direct use of time as a covariate may increase the bias. Moreover, the significance of selecting covariate in developing non-stationary rainfall IDF curve is yet to be explored. Therefore, this study aims to find the uncertainties in rainfall return levels due to the choice of the covariate (covariate uncertainty). In addition, since the uncertainty in parameter estimates (parameter uncertainty) is the major source of uncertainty in the stationary IDF curve, the relative significance of covariate uncertainty, when compared to the parameter uncertainty, is also explored. The study results reveal that the covariate uncertainty is significant, especially when a number of covariates produce significantly superior non-stationary model and, remarkably, it is nearly equivalent to the parameter uncertainty in such cases.