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

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.

References in corpus (11)

• ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
• Explainable Machine Learning for Scientific Insights and Discoveries
• Highly comparative time-series analysis: The empirical structure of time series and their methods
• Highly comparative feature-based time-series classification
• Automatic time-series phenotyping using massive feature extraction
• Super ensemble learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms
• GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
• Boosting algorithms in energy research: A systematic review
• Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability
• Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity
• Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale