  Regression-based frameworks for streamflow regionalization are built around
catchment attributes that traditionally originate from catchment hydrology,
flood frequency analysis and their interplay. In this work, we deviated from
this traditional path by formulating and extensively investigating the first
regression-based streamflow regionalization frameworks that largely emerge from
general-purpose time series features for data science and, more precisely, from
a large variety of such features. We focused on 28 features that included
(partial) autocorrelation, entropy, temporal variation, seasonality, trend,
lumpiness, stability, nonlinearity, linearity, spikiness, curvature and others.
We estimated these features for daily temperature, precipitation and streamflow
time series from 511 catchments, and then merged them within regionalization
contexts with traditional topographic, land cover, soil and geologic
attributes. Precipitation and temperature features (e.g., the spectral entropy,
seasonality strength and lag-1 autocorrelation of the precipitation time
series, and the stability and trend strength of the temperature time series)
were found to be useful predictors of many streamflow features. The same
applies to traditional attributes, such as the catchment mean elevation.
Relationships between predictor and dependent variables were also revealed,
while the spectral entropy, the seasonality strength and several
autocorrelation features of the streamflow time series were found to be more
regionalizable than others.
