Abstract. A reliable flood frequency analysis (FFA) requires selection of an appropriate statistical distribution to model historic streamflow data and, where streamflow data are not available (ungauged sites), a regression-based regional flood frequency analysis (RFFA) often correlates well with downstream channel discharge to drainage area relations. However, the predictive strength of the accepted RFFA relies on an assumption of homogeneous watershed conditions. For glacially conditioned fluvial systems, inherited glacial landforms, sediments, and variable land use can alter flow paths and modify flow regimes. This study compares a multi-variate RFFA that considers 28 explanatory variables to characterize variable watershed conditions (i.e., surficial geology, climate, topography, and land use) to an accepted power-law relationship between discharge and drainage area. Archived gauge data from southern Ontario, Canada are used to test these ideas. Mathematical goodness-of-fit criteria best estimate flood discharge for a broad range of flood recurrence intervals, i.e., 1.25, 2, 5, 10, 25, 50, and 100 years. The LN, EV1, LP3, and GEV distributions are found most appropriate in 42.5 %, 31.9 %, 21.7 %, and 3.9 % of cases, respectively, suggesting that systematic model selection criterion is required for FFA in heterogeneous landscapes. Multi-variate regression of estimated flood quantiles with backward elimination of explanatory variables using principal component and discriminant analyses reveal that precipitation provides a greater predictive relationship for more frequent flood events, whereas surficial geology demonstrates more predictive ability for high magnitude, less frequent flood events. In this study, all seven flood quantiles identify a statistically significant two-predictor model that incorporates upstream drainage area and the percentage of naturalized landscape with 5 % improvement in predictive power over the commonly used single-variable drainage area model (p < 2.2e−16). An analysis of variance (ANOVA) further supports the two-predictor model indicating a decrease in the sum of squares of residuals and an F statistic (p < 0.001) that demonstrates very strong evidence in favour of the two-predictor model (i.e., drainage area and land use) when estimating flood discharge in this low-relief landscape with pronounced glacial legacy effects and heterogenous land use.