Robust Local Likelihood Estimation for Non-stationary Flood Frequency Analysis

John M. Grego () and Philip A. Yates ()
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John M. Grego: University of South Carolina
Philip A. Yates: DePaul University

Journal of Agricultural, Biological and Environmental Statistics, 2025, vol. 30, issue 3, No 4, 663-682

Abstract: Abstract As changes to the environment, both human and non-human made, continue to occur, the assumption of stationarity in flood frequency analysis is seldom met. A proposed method for estimating the 1% chance flood, i.e., Q $$_{100}$$ 100 of a flood gauge’s annual peak streamflows, using robust local likelihood estimation is developed. Simulations indicate that when a flood series seems to be from a more mixed population of values, often due to extreme snowmelt or tropical storms in a given flood year, robust local likelihood estimation is more effective at estimating the 1% chance flood than local likelihood estimation. Annual peak streamflows from the Congaree River at Columbia, South Carolina, the Illinois River at Marseilles, Illinois, and the Winooski River at Montpelier, Vermont are used as examples on how to apply the robust local likelihood method.

Keywords: Generalized extreme value distribution; Local likelihood estimation; Non-stationary series; Robust local likelihood estimation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13253-024-00614-0

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