PMP and Climate Variability and Change: A Review

Abstract

A state-of-the-art review on the Probable Maximum Precipitation (PMP) as it relates to climate variability and change is presented. The review consists of an examination of the current practice and the various developments published in literature. The focus is on relevant research where the effect of climate dynamics on the PMP are discussed as well as statistical methods developed for estimating very large extreme precipitation including the PMP. Often confusion arises on the interpretation of extreme events without considering the effect of low frequency components of the climate system, their probabilistic nature that may be described by heavy-tail models, and the effect of the uncertainty of several factors determining them, such as atmospheric moisture, its transport into storms, wind, and their future changes. The review examines these issues as well as the underlying historical and proxy data. In addition, we summarize the procedures and guidelines established by some countries (e.g. USA, Australia, Canada, UK, EU, and others), states (e.g. California, Quebec), and the current manual of the World Meteorological Organization for estimating the PMP. In doing so, we paid attention whether the current guidelines and research published literature take into consideration the effects of the variability and change of climatic processes and the underlying uncertainties.

New Estimates of 24-Hour Probable Maximum Precipitation (PMP) for the British Isles

“…Rise of atmospheric moisture Abbs (1999) Non-linear relationshio between dewpoint and temperature Chen & Bradley (2006) No return period for PMP Koutsoyiannis (1999), Papalexiou & Koutsoyiannis (2006) Uncertain Gumbel distribution Papalexiou & Koutsoyiannis (2013) Climate change effects Kunkel et al (2013), Rastogi et al (2017), Lee et al (2016) Thanh Thuy et al (2019), Rouhani & Leconte (2020) Sarkar & Maity (2020), Gangrade et al (2018) Climate variability Salas et al (2020) Assessment of uncetainty Micovic et al (2015), Singh et al (2018), Ben Alaya et al (2018 Somerset UK (Clark, 2014) which now has a 295 year historic flood record, the FEH 6-hour storm with 100% runoff only yields a flood of 327 m 3 •s −1 which was almost equalled in 1768 (Clark, 1999). Furthermore, the flood of 1917 with a rainfall intensity of 25 mm•hr −1 gave a peak discharge of about 175 m 3 •s −1 while the Martinstown storm of 1955 which took place 45 km to the south if transposed would have caused a flood that exceeded 330 m 3 •s −1 .…”

Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

“…the greatest depth of precipitation that is possible in a given place and time and for a given storm duration. For a complete state-of-the-art review on the PMP concept, see Salas et al (2020). PMP can be computed via hydrometeorological, statistical, grid-based, and site-specific approaches, using both stationary and nonstationary methods (e.g.…”

“…The project now runs regional large ensemble citizen science experiments called "weather@home", which are used for probabilistic event attribution (Massey et al, 2015;Guillod et al, 2017). For example, this regional ensemble approach was used to demonstrate the human influence on climate in the 2014 southern England winter floods (Schaller et al, 2016) and the 2003 European summer heatwave (Mitchell et al, 2016).…”

Spatial variability of precipitation extremes over Italy using a fine-resolution gridded product

“…Although G E V is widely used to describe extreme precipitation it has limitations; sample variations can lead to negative shape parameter estimates resulting in an upper bound distribution. An upper bounded distribution, such as the r W , assumes a maximum precipitation value which cannot be exceeded while it is well documented that even probable maximum precipitation (PMP) estimates have been surpassed (Salas et al, 2020). Precipitation is a natural process limited at zero but an upper bound cannot be justified since there is always a probability to rain more.…”