Regional flood frequency analysis using spatial proximity and basin characteristics: Quantile regression vs. parameter regression technique

• Published in: Journal of hydrology (Amsterdam) Vol. 540; pp. 515 - 526
• Main Authors: Ahn, Kuk-Hyun, Palmer, Richard
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2016
• Subjects: Basins; equations; Flood predictions; Floods; Freshwater; hydrology; least squares; Mathematical models; Northeastern United States; Parameter regression technique (PRT); prediction; Proximity; Quantile regression technique (QRT); Quantiles; Regional flood frequency analysis (RFFA); Regression; Regression analysis; scanning electron microscopy; Spatial error model (SEM); Spatial lagged model (SLM); Spatial proximity; streams; USA; Northeastern United States; Spatial lagged model (SLM); Parameter regression technique (PRT); Quantile regression technique (QRT); Regional flood frequency analysis (RFFA); Spatial proximity; Spatial error model (SEM)
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2016.06.047

•We proposed ‘spatial proximity’ based regional flood frequency analysis methods.•The results showed more reliable flood quantile estimates compared to simpler methods.•Flood quantiles with the QRT were more accurate than ones using the PRT. Despite wide use of regression-based regional flood frequency analysis (RFFA) methods, the majority are based on either ordinary least squares (OLS) or generalized least squares (GLS). This paper proposes ‘spatial proximity’ based RFFA methods using the spatial lagged model (SLM) and spatial error model (SEM). The proposed methods are represented by two frameworks: the quantile regression technique (QRT) and parameter regression technique (PRT). The QRT develops prediction equations for flooding quantiles in average recurrence intervals (ARIs) of 2, 5, 10, 20, and 100 years whereas the PRT provides prediction of three parameters for the selected distribution. The proposed methods are tested using data incorporating 30 basin characteristics from 237 basins in Northeastern United States. Results show that generalized extreme value (GEV) distribution properly represents flood frequencies in the study gages. Also, basin area, stream network, and precipitation seasonality are found to be the most effective explanatory variables in prediction modeling by the QRT and PRT. ‘Spatial proximity’ based RFFA methods provide reliable flood quantile estimates compared to simpler methods. Compared to the QRT, the PRT may be recommended due to its accuracy and computational simplicity. The results presented in this paper may serve as one possible guidepost for hydrologists interested in flood analysis at ungaged sites.