Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets

TL;DR

This study introduces a novel regression-based streamflow regionalization approach that leverages general-purpose time series features from temperature, precipitation, and streamflow data, combined with traditional catchment attributes, to improve hydrometeorological predictions in ungauged locations.

Contribution

It is the first to extensively investigate the use of diverse time series features for streamflow regionalization, demonstrating their predictive power and regionalizability alongside traditional attributes.

Findings

Precipitation and temperature features effectively predict streamflow characteristics.

Spectral entropy and seasonality are highly regionalizable features.

Traditional attributes like catchment elevation remain valuable predictors.

Abstract

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.