USING REGIONAL REGRESSION WITHIN INDEX FLOOD PROCEDURES AND AN EMPIRICAL BAYESIAN-ESTIMATOR

Studies have illustrated the performance of at-site and regional flood quantile estimators. For realistic generalized extreme value (GEV) di stributions and short records, a simple index-flood quantile estimator performs better than two-parameter (2P) GEV quantile estimators with probability weighted moment (PWM) estimation using a regional shape pa rameter and at-site mean and L-coefficient of variation (L-CV), and fu ll three-parameter at-site GEV/PWM quantile estimators. However, as re gional heterogeneity or record lengths increase, the 2P-estimator quic kly dominates. This paper generalizes the index flood procedure by emp loying regression with physiographic information to refine a normalize d T-year flood estimator. A linear empirical Bayes estimator uses the normalized quantile regression estimator to define a prior distributio n which is employed with the normalized 2P-quantile estimator. Monte C arlo simulations indicate that this empirical Bayes estimator does ess entially as well as or better than the simpler normalized quantile reg ression estimator at sites with short records, and performs as well as or better than the 2P-estimator at sites with longer records or small er L-CV. (C) 1998 Elsevier Science B.V. All rights reserved.