An Integrated Extreme Rainfall Modeling Tool (SDExtreme) for Climate Change Impacts and Adaptation

Abstract

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).

Assessment of dam function deterioration due to landslide-debris flows: numerical modeling based on vegetation distribution scenarios

“…As listed in Table 2, the dam function deterioration caused by landslide-debris flow events can seem relatively insignificant. However, in South Korea, heavy rainfall occurs every year with typhoons, and rainfall intensity is increasing (Kim H. et al, 2021;Yeo et al, 2022). Therefore, considering vegetation is essential when analyzing sediment inflow into the dam/reservoir due to landslidedebris flow over a long period, especially in mountainous areas.…”

Multi-Hazard Extreme Scenario Quantification Using Intensity, Duration, and Return Period Characteristics

“…The thinner ranges of Cis observed in the far future in Figure 7 could imply that a notable increase in minimum temperature is highly likely. This is because narrow Cis imply low variability of the estimated annual maximum values of the examined hazard [71].…”