Articles | Volume 18, issue 22
https://doi.org/10.5194/gmd-18-8663-2025
https://doi.org/10.5194/gmd-18-8663-2025
Model description paper
 | 
17 Nov 2025
Model description paper |  | 17 Nov 2025

Data-driven estimation of the hydrologic response using generalized additive models

Quentin Duchemin, Maria Grazia Zanoni, Marius G. Floriancic, Hansjörg Seybold, Guillaume Obozinski, James W. Kirchner, and Paolo Benettin

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Cited articles

Arridge, S., Maass, P., Öktem, O., and Schönlieb, C.-B.: Solving inverse problems using data-driven models, Acta Numerica, 28, 1–174, 2019. a
Beven, K.: How far can we go in distributed hydrological modelling?, Hydrol. Earth Syst. Sci., 5, 1–12, https://doi.org/10.5194/hess-5-1-2001, 2001. a
Botter, G., Bertuzzo, E., and Rinaldo, A.: Transport in the hydrologic response: Travel time distributions, soil moisture dynamics, and the old water paradox, Water Resources Research, 46, https://doi.org/10.1029/2009WR008371, 2010. a
Boyd, S.: Convex optimization, Cambridge UP, https://doi.org/10.1007/978-1-4419-9863-7_1449, 2004. a
Bruen, M. and Dooge, J.: Unit hydrograph estimation with multiple events and prior information: I. Theory and a computer program, Hydrological Sciences Journal, 37, 429–443, 1992. a
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We introduce GAMCR (Generalized Additive Models for Catchment Responses), a data-driven model that estimates how catchments respond to individual precipitation events. We validate GAMCR on synthetic data and demonstrate its ability to investigate the characteristic runoff responses from real-world hydrologic series. GAMCR provides new data-driven opportunities to understand and compare hydrological behavior across different catchments.
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