Articles | Volume 29, issue 20
https://doi.org/10.5194/hess-29-5233-2025
https://doi.org/10.5194/hess-29-5233-2025
Research article
 | 
16 Oct 2025
Research article |  | 16 Oct 2025

Spatially resolved rainfall streamflow modeling in central Europe

Marc Aurel Vischer, Noelia Otero, and Jackie Ma

Cited articles

Acuña Espinoza, E., Loritz, R., Kratzert, F., Klotz, D., Gauch, M., Álvarez Chaves, M., and Ehret, U.: Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events, Hydrol. Earth Syst. Sci., 29, 1277–1294, https://doi.org/10.5194/hess-29-1277-2025, 2025. a
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
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Bevacqua, E., Shepherd, T. G., Watson, P. A. G., Sparrow, S., Wallom, D., and Mitchell, D.: Larger Spatial Footprint of Wintertime Total Precipitation Extremes in a Warmer Climate, Geophys. Res. Lett., 48, e2020GL091990, https://doi.org/10.1029/2020GL091990, 2021. a, b
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Short summary
We use a neural network to predict the amount of water flowing into rivers. Our focus is on large river catchment areas in central Europe with pronounced human activity. Our model scales efficiently to large quantities of data and is thus able to process the input without prior aggregation, capturing fine spatial detail and improving prediction in large catchments. Our model's internal states can be adapted to capture human activity more explicitly in the future.
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