Abstract
We investigate the validity of implicit assumptions in regional flood
frequency analysis (RFFA) using Monte Carlo-style simulations of three
distributed hydrological models forced with rainfall events generated
using stochastic storm transposition. We test the long-standing
assumption that for a set of sites within a region, physical
homogeneity — defined in terms of the variability of meteorological
inputs, the physics of runoff generation, and runoff routing — implies
statistical homogeneity of peak flows— defined in terms of the
existence of a common underlying statistical distribution with
parameters that can be inferred using information from neighboring
sites. Our modeling results do not support this assumption, with
potentially important implications for RFFA methodologies and for the
very definitions of homogeneity. We show that statistically homogeneous
rainfall does not translate into predictable peak flow distribution
parameters across drainage scales. Specifically, we show that changes in
the coefficient of variation and skewness of peak flows cannot be
inferred from upstream area alone, making popular regionalization
techniques such as the index-flood method and quantile regression
inadequate approximations for flood frequency estimation. Our findings
are consistent across the three hydrological model formulations, lending
confidence that our conclusions are not an artifact of epistemological
model decisions. Finally, we argue that our methodology can serve as a
framework to test new proposed empirical RFFA methods, and that it opens
the door to a unified physics-informed framework for prediction of flood
frequencies in ungauged basins embedded in gauged regions.