A novel method to estimate the maximization ratio of the Probable Maximum Precipitation (PMP) using regional climate model output

Abstract

The moisture maximization approach has a simple technique for controlling the risk of overestimating the Probable Maximum Precipitation (PMP): the maximization ratio is limited by an upper bound. The upper bound limit depends on storm records and watershed characteristics. However, it is not readily available in many watersheds. A robust scientific justification for limiting the maximization ratio is missing. In this paper, a novel approach is proposed to estimate the maximization ratio which does not impose an upper limit. The new approach, which uses regional climate model data, is based on constructing annual maximum precipitable water time series with precipitable water values for which atmospheric variables are similar to the original event to be maximized. These time series are then used to estimate the 100 year return period of the precipitable water value required to calculate the ratio. The new approach was tested in three watersheds in the province of Québec, Canada. Results showed that maximization ratio values were lower than the proposed upper bound value for these watersheds. In comparison to the approach using an upper bound, the proposed approach reduced the PMPs in these watersheds by 11%.

Effects of variability in probable maximum precipitation patterns on flood losses

“…However, there is still a controversial discussion on the underlying concept of PMP, particularly on the assumption that the upper tail of flood distributions is bounded (Micovic et al, 2015). Comprehensive summaries of this discussion are provided by Salas et al (2015) and by Rouhani and Leconte (2016). Nevertheless, PMP/PMF estimation methods have been continuously developed and improved.…”

“…Beauchamp et al (2013), Lagos-Zuniga and Vargas (2014), and Felder and Weingartner (2016) discuss the role of the spatio-temporal distribution of the PMP on the PMF, while Rousseau et al (2014) and Stratz and Hossain (2014) discuss climate change and stationarity issues. Hence, Faulkner and Benn (2016), Micovic et al (2015), Rouhani and Leconte (2016), and Salas et al (2014) have proposed incorporating uncertainty bands into the PMP estimation.…”

Estimation of Future Probable Maximum Precipitation in Korea Using Multiple Regional Climate Models

“…Among the two estimation methods, the hydro-meteorological method has been used worldwide as an official method for estimating PMPs [5]. Although the concept of PMP has been criticized by many hydrologists [6][7][8], recent studies [9][10][11] have examined the effects of climate change on the estimation of PMPs using the hydro-meteorological method. Future PMPs are generally estimated using future meteorological variables produced from regional climate models (RCMs).…”

Effects of variability in probable maximum precipitation patterns on flood losses

“…However, there is still a controversial discussion on the underlying concept of PMP, particularly on the assumption that the upper tail of flood distributions is bounded (Micovic et al, 2015). Comprehensive summaries of this discussion are provided by (Salas et al, 2015) and by (Rouhani and Leconte, 2016). Nevertheless, PMP/PMF estimation methods are continuously evolved and improved.…”

“…Beauchamp et al (2013), Lagos-Zuniga and Vargas 20 M., (2014), and Felder and Weingartner (2016) discuss the role of the spatio-temporal distribution of the PMP on the PMF, while Rousseau et al (2014) and Stratz and Hossain (2014) discuss climate change and stationarity issues. Hence, Faulkner and Benn (2016), Micovic et al (2015), Rouhani and Leconte (2016), and Salas et al (2014) propose to incorporate uncertainty bands into the PMP estimation.…”