Articles | Volume 28, issue 22
https://doi.org/10.5194/hess-28-4971-2024
https://doi.org/10.5194/hess-28-4971-2024
Research article
 | 
21 Nov 2024
Research article |  | 21 Nov 2024

Learning landscape features from streamflow with autoencoders

Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert

Data sets

A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. Boulder, CO A. Newman et al. https://doi.org/10.5065/D6MW2F4D

CAMELS Extended NLDAS Forcing Data Frederik Kratzert https://doi.org/10.4211/hs.0a68bfd7ddf642a8be9041d60f40868c

Model code and software

abassi98/AE4Hydro: v1.0.0 Alberto Bassi https://doi.org/10.5281/zenodo.13132951

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Short summary
The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature associated with aridity and intermittent flow that is needed for challenging cases. Baseflow index, aridity, and soil or vegetation attributes strongly correlate with learnt features, indicating their importance for streamflow prediction.