Wehner, MF, Duffy, ML, Risser, M, Paciorek, CJ, Stone, DA and Pall, P (2024) On the uncertainty of long-period return values of extreme daily precipitation. Frontiers in Climate, 6.

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Abstract

Methods for calculating return values of extreme precipitation and their uncertainty are compared using daily precipitation rates over the Western U.S. and Southwestern Canada from a large ensemble of climate model simulations. The roles of return-value estimation procedures and sample size in uncertainty are evaluated for various return periods. We compare two different generalized extreme value (GEV) parameter estimation techniques, namely L-moments and maximum likelihood (MLE), as well as empirical techniques. Even for very large datasets, confidence intervals calculated using GEV techniques are narrower than those calculated using empirical methods. Furthermore, the more efficient L-moments parameter estimation techniques result in narrower confidence intervals than MLE parameter estimation techniques at small sample sizes, but similar best estimates. It should be noted that we do not claim that either parameter fitting technique is better calibrated than the other to estimate long period return values. While a non-stationary MLE methodology is readily available to estimate GEV parameters, it is not for the L-moments method. Comparison of uncertainty quantification methods are found to yield significantly different estimates for small sample sizes but converge to similar results as sample size increases. Finally, practical recommendations about the length and size of climate model ensemble simulations and the choice of statistical methods to robustly estimate long period return values of extreme daily precipitation statistics and quantify their uncertainty.

Item Type:	Article
Uncontrolled Keywords:	3707 Hydrology; 3701 Atmospheric Sciences; 3702 Climate Change Science; 37 Earth Sciences; 3702 Climate change science; 4101 Climate change impacts and adaptation
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences
Divisions:	Biological and Environmental Sciences (from Sep 19)
Publisher:	Frontiers Media
SWORD Depositor:	A Symplectic
Date Deposited:	08 Apr 2025 14:16
Last Modified:	08 Apr 2025 14:16
DOI or ID number:	10.3389/fclim.2024.1343072
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26125

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