Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 22
Articles | Volume 28, issue 22
https://doi.org/10.5194/hess-28-4971-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-4971-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 22
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

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Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. a, b, c
Albert, C., Künsch, H., and Scheidegger, A.: A Simulated Annealing Approach to Approximate Bayes Computations, Stat. Comput., 25, 1217–1232, https://doi.org/10.1007/s11222-014-9507-8, 2015. a
Albert, C., Ulzega, S., Ozdemir, F., Perez-Cruz, F., and Mira, A.: Learning Summary Statistics for Bayesian Inference with Autoencoders, SciPost Phys. Core, 5, 043, https://doi.org/10.21468/SciPostPhysCore.5.3.043, 2022. a, b, c, d
Allegra, M., Facco, E., Denti, F., Laio, A., and Mira, A.: Data segmentation based on the local intrinsic dimension, Sci. Rep., 10, 16449, https://doi.org/10.1038/s41598-020-72222-0, 2020. a
Bassi, A.: abassi98/AE4Hydro: v1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.13132951, 2024. 
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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.
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