This article compares the performances of three fitting methods (SLmom, S1NCM, and S3NCM) to account for temporal characteristics of Annual Maximum Precipitations (AMPs) on daily and sub-daily time scales using scaling General Extreme Value (GEV) distribution at a local site. Based on simple scaling properties of AMPs, the temporal downscaling model (called Scaling-GEV) with parameter estimation methods are used to estimate sub-daily AMPs from observed daily data. The feasibility and accuracy of the suggested method were assessed using rainfall data available from Dorval in Quebec (Canada) and Seoul (South Korea) for the period 1961-1990. Presence of simple scaling properties of AMPs for two stations has shown that it is feasible to use the temporal downscaling method for describing the linkage between AMPs of different time scales. Numerical and graphical analyses revealed that the Scaling-GEV distribution by the Three-Non central moments (NCM) method (S3NCM) provides the most accurate estimates compared to observed data amongst three fitting methods. In addition, this study suggested a modified bootstrap technique to determine confidence intervals (CIs) CIs of extreme rainfall series using the simple scaling properties of extreme rainfalls and only daily AMPs. Although the CIs were constructed by only daily AMPs and the simple scaling properties, the observed sub-daily AMPs are generally within the 95% CI estimated.

The estimation of the Intensity–Duration–Frequency (IDF) relation is often necessary for the planning and design of various hydraulic structures and design storms. It has been an increasingly greater challenge due to climate change conditions. This paper therefore proposes an integrated extreme rainfall modeling software package (SDExtreme) for constructing the IDF relations at a local site in the context of climate change. The proposed tool is based on a temporal downscaling method to describe the relationships between daily and sub-daily extreme precipitation using the scale-invariance General Extreme Value (GEV) distribution. In addition, SDExtreme provides a modified bootstrap technique to determine confidence intervals (CIs) of the estimated IDF curves for current and the future climate conditions. The feasibility and accuracy of SDExtreme were assessed using rainfall data available from the selected rain gauge stations in Quebec and Ontario provinces (Canada) and climate simulations under three different climate change scenarios provided by the Canadian Earth System Model (CanESM2) and the Canadian Regional Climate Model (CanRCM4).