Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-129-2022
https://doi.org/10.5194/hess-26-129-2022
Research article
 | 
12 Jan 2022
Research article |  | 12 Jan 2022

Parsimonious statistical learning models for low-flow estimation

Johannes Laimighofer, Michael Melcher, and Gregor Laaha

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Short summary
This study aims to predict long-term averages of low flow on a hydrologically diverse dataset in Austria. We compared seven statistical learning methods and included a backward variable selection approach. We found that separating the low-flow processes into winter and summer low flows leads to good performance for all the models. Variable selection results in more parsimonious and more interpretable models. Linear approaches for prediction and variable selection are sufficient for our dataset.