An Uncertainty Investigation of RCM Downscaling Ratios in Nonstationary Extreme Rainfall IDF Curves

Abstract

Designed for rainstorms and flooding, hydrosystems are largely based on local rainfall Intensity-Duration-Frequency (IDF) curves which include nonstationary components accounting for climate variability. IDF curves are commonly calculated using downscaling outputs from General Circulation Models (GCMs) or Regional Circulation Models (RCMs). However, the downscaling procedures used in most studies are based on one specific time scale (e.g., 1 h) and generally ignore scale-driven uncertainty. This study analyzes the uncertainties in IDF curves stemming from RCM downscaling ratios for four representative weather stations in the United Kingdom. We constructed a series of IDF curves using distribution-based scaling bias-correction technology and a statistical downscaling method to explore the scale-driven uncertainty of IDF curves. The results revealed considerable scale-induced uncertainty of IDF curves for short durations and long return periods; however, there was no clear correlation with the mean storm intensity of the IDF curves of different RCM ensemble members for each duration and return period. The scale-driven uncertainty of IDF curves, which may be propagated or enhanced through hydrometeorological applications, is critical and cannot be ignored in the hydrosystem design process; therefore, a multi-scale method to derive IDF curves must be developed.

Uncertainty of Intensity-Duration-Frequency Curves Due to Adoption or Otherwise of the Temperature Climate Variable in Rainfall Disaggregation

“…For the first method in this study, that is, for rainfall disaggregation using both the historical temperature and historical rainfall variables, this study adopted an algorithm that integrates both the nonparametric prediction (NPRED) model and the MoF frame-work [38,40]. The logic of partial informational correlation is employed in the NPRED model to determine the system predictors, and it predicts the responses by using the KNN regression formula depending on partial weights and the weighted Euclidean distance.…”

“…Other techniques take into account other climate variables (e.g., temperature, relative humidity, etc.) in addition to the rainfall variable for the rainfall disaggregation [38][39][40].…”