|Date:||Thursday, October 19, 2017|
Probable Maximum Precipitation (PMP), and Probable Maximum Flood (PMF), which is derived from PMP, are important parameters for dam safety and civil engineering purposes. Operational procedures for calculating PMP seek to obtain a plausible upper bound for precipitation that is unlikely to be exceeded. These calculations do not produce accompanying uncertainty estimates. We propose a probabilistic framework for the interpretation of PMP when it is calculated via the so-called moisture maximization approach, which is the approach that is used most frequently operationally. The proposed framework, which is based on a bivariate extreme value distribution, (i) enables assessment of the sensitivity of the PMP value to individual, possibly erroneous, observations; (ii) allows evaluation of the uncertainty of PMP values; and (iii) provides clarification of the impact of the assumption that a PMP event occurs under conditions of maximum moisture availability.
The frame work is used to estimate PMP across North America based on a 50-year CanRCM4 regional climate model simulation of the period 1951-2000. Results suggest that operational PMP estimates obtained via moisture maximization are highly uncertain and indicate that the assumption that PMP events have maximum moisture availability may not be valid. Specifically, in the climate simulated by CanRCM4, the operational approach applied to 50-year data records produces a value that is 15% larger on average over North America than the maximum that is likely to be observed over such a period, which may have serious implications in engineering design.
This is joint work with M.A. Ben Alaya and X. Zhang.
December 10 – December 21: Fall Term Exam Period
December 22 – January 2: Winter Holiday (University Closed)
Statistics seminar: Erfan Houqe: “Random effects covariance matrix modeling for longitudinal data with covariates measurement error” — Thursday, January 17 at 2:45 p.m., P230 Duff Roblin.