Probable Maximum Precipitation (PMP) is the key parameter used to estimate probable Maximum Flood (PMF). Both PMP and PMF are important for dam safety and civil engineering purposes. PMP can be considered as an asymptote which precipitation events might converge to but should never reach. A commonly used engineering approach to derive PMP estimates is moisture maximization. Using this approach the PMP for a given duration and a given period is calculated as the product of maximum precipitation efficiency and maximum precipitable water. In the current work, outputs from the CanRCM4 model are used to derive 6-hourly PMP estimates at each CanRCM4 grid point over historical (1951-2000) and future (2051-2100) periods. These estimates cover the North American region with 0.44° spatial horizontal resolution and are derived under two representative concentration pathways (RCP) 4.5 and 8.5 scenarios. The resulting 6-hourly PMP maps are shown in the following Figure. Results reveals that PMP will increases by an average of about 24 % under the RCP 4.5 emissions scenario and about 41% under RCP 8.5.
Even if the current knowledge of storm mechanisms remains insufficient to properly evaluate the limiting values of extreme precipitation, PMP estimation methods view the problem from a deterministic perspective, and thus give only single values without uncertainty estimates. Our aim therefore is to provide a probabilistic description of PMP based on the moisture maximization method. Such a probabilistic description naturally leads to an assessment of projected PMP changes that includes quantification of their uncertainty.