Ansel, J., Yang, E., He, H., Gimelshein, N., Jain, A., Voznesensky, M., Bao, B., Bell, P., Berard, D., Burovski, E., Chauhan, G., Chourdia, A., Constable, W., Desmaison, A., DeVito, Z., Ellison, E., Feng, W., Gong, J., Gschwind, M., Hirsh, B., Huang, S., Kalambarkar, K., Kirsch, L., Lazos, M., Lezcano, M., Liang, Y., Liang, J., Lu, Y., Luk, C. K., Maher, B., Pan, Y., Puhrsch, C., Reso, M., Saroufim, M., Siraichi, M. Y., Suk, H., Zhang, S., Suo, M., Tillet, P., Zhao, X., Wang, E., Zhou, K., Zou, R., Wang, X., Mathews, A., Wen, W., Chanan, G., Wu, P., and Chintala, S.: PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation, in: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Vol. 2, ACM, La Jolla CA USA, 929–947, ISBN 979-8-4007-0385-0,
https://doi.org/10.1145/3620665.3640366, 2024.
a
Copernicus Climate Change Service: ERA5-Land Hourly Data from 1950 to Present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set],
https://doi.org/10.24381/Cds.E2161bac, 2022.
a,
b,
c
Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., Lewis, M., Robinson, E. L., Wagener, T., and Woods, R.: CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data, 12, 2459–2483,
https://doi.org/10.5194/essd-12-2459-2020, 2020.
a,
b
Delaigue, O., Brigode, P., Andréassian, V., Perrin, C., Etchevers, P., Soubeyroux, J.-M., Janet, B., and Addor, N.: CAMELS-FR: A large sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-521,
https://doi.org/10.5194/iahs2022-521, 2022.
a
den Bossche, J. V., Jordahl, K., Fleischmann, M., Richards, M., McBride, J., Wasserman, J., Badaracco, A. G., Snow, A. D., Roggemans, P., Ward, B., Tratner, J., Gerard, J., Perry, M., Taves, M., Hjelle, G. A., carsonfarmer, Tan, N. Y., Bell, R., ter Hoeven, E., Caria, G., Cochran, M. D., rraymondgh, Culbertson, L., Bartos, M., Chai, C. P., Eubank, N., sangarshanan, Flavin, J., and Rey, S.: Geopandas/Geopandas: V1.1.2, Zenodo [code],
https://doi.org/10.5281/zenodo.18024156, 2025.
a
Fowler, K. J. A., Acharya, S. C., Addor, N., Chou, C., and Peel, M. C.: CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia, Earth Syst. Sci. Data, 13, 3847–3867,
https://doi.org/10.5194/essd-13-3847-2021, 2021.
a
Gauch, M., Kratzert, F., Dube, V., Gilon, O., Klotz, D., Metzger, A., Nearing, G., Ofori, F., Shalev, G., Shenzis, S., Tekalign, T., Weitzner, D., Zlydenko, O., and Cohen, D.: Deep Learning for Spatially Distributed Rainfall–Runoff Modeling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8899,
https://doi.org/10.5194/egusphere-egu24-8899, 2024.
a
Gauch, M., Kratzert, F., Metzger, A., Shenzis, S., Klotz, D., Cohen, D., and Gilon, O.: Semi-Distributed Hydrological Modeling Based on Deep Learning at Scale, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-31,
https://doi.org/10.5194/ems2025-31, 2025.
a
Günther, A. and Duscher, K.: Extended Vector Data of the International Hydrogeological Map of Europe 1: 1,500,000 (Version IHME1500 v1. 2), Federal Institute for Geosciences and Natural Resources (BGR), Hannover, Berlin, Germany, 2019.
a,
b
Hiederer, R.: Mapping Soil Typologies: Spatial Decision Support Applied to the European Soil Database, Publications Office of the European Union 127,
https://doi.org/10.2788/87286, 2013a.
a,
b
Hiederer, R.: Mapping Soil Properties for Europe: Spatial Representation of Soil Database Attributes., EUR26082EN scientific and technical research series 47,
https://doi.org/10.2788/94128, 2013b.
a,
b
Höge, M., Kauzlaric, M., Siber, R., Schönenberger, U., Horton, P., Schwanbeck, J., Floriancic, M. G., Viviroli, D., Wilhelm, S., Sikorska-Senoner, A. E., Addor, N., Brunner, M., Pool, S., Zappa, M., and Fenicia, F.: CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland, Earth Syst. Sci. Data, 15, 5755–5784,
https://doi.org/10.5194/essd-15-5755-2023, 2023.
a
Klingler, C., Schulz, K., and Herrnegger, M.: LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, Earth Syst. Sci. Data, 13, 4529–4565,
https://doi.org/10.5194/essd-13-4529-2021, 2021.
a
Kraft, B., Kauzlaric, M., Aeberhard, W. H., Zappa, M., and Gudmundsson, L.: DROP: A Scalable Deep Learning Approach for Runoff Simulation and River Routing,
https://www.authorea.com/doi/full/10.22541/au.176410929.91946608/v1, 2025. a
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., and Nearing, G. S.: Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning, Water Resour. Res., 55, 11344–11354,
https://doi.org/10.1029/2019WR026065, 2019.
a,
b
Kratzert, F., Nearing, G., Addor, N., Erickson, T., Gauch, M., Gilon, O., Gudmundsson, L., Hassidim, A., Klotz, D., Nevo, S., Shalev, G., and Matias, Y.: Caravan – A Global Community Dataset for Large-Sample Hydrology, Sci. Data, 10, 61,
https://doi.org/10.1038/s41597-023-01975-w, 2023.
a,
b
Liu, J., Koch, J., Stisen, S., Troldborg, L., Højberg, A. L., Thodsen, H., Hansen, M. F. T., and Schneider, R. J. M.: CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations, Earth Syst. Sci. Data, 17, 1551–1572,
https://doi.org/10.5194/essd-17-1551-2025, 2025.
a
Loritz, R., Dolich, A., Acuña Espinoza, E., Ebeling, P., Guse, B., Götte, J., Hassler, S. K., Hauffe, C., Heidbüchel, I., Kiesel, J., Mälicke, M., Müller-Thomy, H., Stölzle, M., and Tarasova, L.: CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany, Earth Syst. Sci. Data, 16, 5625–5642,
https://doi.org/10.5194/essd-16-5625-2024, 2024.
a,
b
Mapbox: Rasterio v1.4.3, Mapbox,
https://github.com/rasterio/rasterio (last access: 30 April 2026), 2024. a
Nearing, G., Cohen, D., Dube, V., Gauch, M., Gilon, O., Harrigan, S., Hassidim, A., Klotz, D., Kratzert, F., Metzger, A., Nevo, S., Pappenberger, F., Prudhomme, C., Shalev, G., Shenzis, S., Tekalign, T. Y., Weitzner, D., and Matias, Y.: Global Prediction of Extreme Floods in Ungauged Watersheds, Nature, 627, 559–563,
https://doi.org/10.1038/s41586-024-07145-1, 2024.
a
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19, 209–223,
https://doi.org/10.5194/hess-19-209-2015, 2015.
a,
b
Rakhymbek, K., Mukanova, B., Bondarovich, A., Chernykh, D., Alzhanov, A., Nurekenov, D., Pavlenko, A., and Nugumanova, A.: LSTM-Based River Discharge Forecasting Using Spatially Gridded Input Data, Data, 10,
https://doi.org/10.3390/data10080122, 2025.
a
Rew, R., Hartnett, E., and Caron, J.: NetCDF-4: Software Implementing an Enhanced Data Model for the Geosciences, in: 22nd International Conference on Interactive Information Processing Systems for Meteorology, Oceanography, and Hydrology, vol. 6, 2006. a
Snow, A. D., Cochran, M., Miara, I., Hoese, D., den Bossche, J. V., Mayo, C., Lucas, G., Cochrane, P., de Kloe, J., Karney, C., Shaw, J. J., Anh, T. Q., Filipe, Ouzounoudis, G., Couwenberg, B., Lostis, G., Dearing, J., Jurd, B., Gohlke, C., Schneck, C., McDonald, D., Taves, M., Itkin, M., May, R., Stewart, A. J., de Bittencourt, H., Little, B., Hugonnet, R., and Rahul, P. S.: Pyproj4/Pyproj: 3.7.2rc1, Zenodo [code],
https://doi.org/10.5281/zenodo.16817340, 2025.
a
Vischer, M. A., Otero, N., and Ma, J.: Spatially resolved rainfall streamflow modeling in central Europe, Hydrol. Earth Syst. Sci., 29, 5233–5250,
https://doi.org/10.5194/hess-29-5233-2025, 2025b.
a,
b,
c