Articles | Volume 29, issue 20
https://doi.org/10.5194/hess-29-5233-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-5233-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatially resolved rainfall streamflow modeling in central Europe
Marc Aurel Vischer
CORRESPONDING AUTHOR
Fraunhofer Heinrich-Hertz Institute, Applied Machine Learning Group, 10587 Berlin, Germany
Noelia Otero
Fraunhofer Heinrich-Hertz Institute, Applied Machine Learning Group, 10587 Berlin, Germany
Fraunhofer Heinrich-Hertz Institute, Applied Machine Learning Group, 10587 Berlin, Germany
Related authors
Marc Aurel Vischer, Noelia Otero, and Jackie Ma
Earth Syst. Sci. Data, 18, 3099–3108, https://doi.org/10.5194/essd-18-3099-2026, https://doi.org/10.5194/essd-18-3099-2026, 2026
Short summary
Short summary
We combined meteorological data with additional information (soil, rock, land cover and elevation) on a single grid that spans five river basins in central Europe over 45 years. Data was not aggregated within river catchments so as to retain the full spatial covariance. It can be efficiently processed in parallel and has been used in our recent study on end-to-end rainfall streamflow modeling with neural networks. We hope to promote further development of spatially resolved modeling approaches.
Marc Aurel Vischer, Noelia Otero, and Jackie Ma
Earth Syst. Sci. Data, 18, 3099–3108, https://doi.org/10.5194/essd-18-3099-2026, https://doi.org/10.5194/essd-18-3099-2026, 2026
Short summary
Short summary
We combined meteorological data with additional information (soil, rock, land cover and elevation) on a single grid that spans five river basins in central Europe over 45 years. Data was not aggregated within river catchments so as to retain the full spatial covariance. It can be efficiently processed in parallel and has been used in our recent study on end-to-end rainfall streamflow modeling with neural networks. We hope to promote further development of spatially resolved modeling approaches.
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
Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Puelma, C., Cortes, G., Garreaud, R., McPhee, J., and Ayala, A.: The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset, Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018, 2018. a
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, Volume 2, pp. 929–947, ACM, La Jolla CA USA, ISBN 9798400703850, https://doi.org/10.1145/3620665.3640366, 2024. a
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
Bronstein, M. M., Bruna, J., Cohen, T., and Veličković, P.: Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, arXiv [preprint], arXiv:2104.13478, 2021. a
Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020. a
Chen, Z., Lin, H., and Shen, G.: TreeLSTM: A Spatiotemporal Machine Learning Model for Rainfall-Runoff Estimation, J. Hydrol.: Regional Studies, 48, 101474, https://doi.org/10.1016/j.ejrh.2023.101474, 2023. a
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y.: Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation, arXiv [preprint], https://doi.org/10.48550/arXiv.1406.1078, 2014. a
Clark, S. R., Lerat, J., Perraud, J.-M., and Fitch, P.: Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia, Hydrol. Earth Syst. Sci., 28, 1191–1213, https://doi.org/10.5194/hess-28-1191-2024, 2024. a
Copernicus Climate Change Service (C3S): 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 (last access: 23 October 2021), 2019. a, b
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
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 June 2022, IAHS2022-521, https://doi.org/10.5194/iahs2022-521, 2022. a
European Space Agency: Copernicus DEM – Global and European Digital Elevation Model (GLO90),The ESA itself publishes this on their website, https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM (last access: 5 June 2025), 2021. a
Fang, B., Bevacqua, E., Rakovec, O., and Zscheischler, J.: An increase in the spatial extent of European floods over the last 70 years, Hydrol. Earth Syst. Sci., 28, 3755–3775, https://doi.org/10.5194/hess-28-3755-2024, 2024. 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
Frame, J. M., Kratzert, F., Klotz, D., Gauch, M., Shalev, G., Gilon, O., Qualls, L. M., Gupta, H. V., and Nearing, G. S.: Deep learning rainfall–runoff predictions of extreme events, Hydrol. Earth Syst. Sci., 26, 3377–3392, https://doi.org/10.5194/hess-26-3377-2022, 2022. a
Gründemann, G. J., van de Giesen, N., Brunner, L., and van der Ent, R.: Rarest Rainfall Events Will See the Greatest Relative Increase in Magnitude under Future Climate Change, Commun. Earth Environ., 3, 1–9, https://doi.org/10.1038/s43247-022-00558-8, 2022. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the Mean Squared Error and NSE Performance Criteria: Implications for Improving Hydrological Modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Hitokoto, M. and Sakuraba, M.: Hybrid Deep Neural Network and Distributed Rainfall-Runoff Model for Real-Time River-Stage Prediction, Journal of JSCE, 8, 46–58, https://doi.org/10.2208/journalofjsce.8.1_46, 2020. a, b
Hochreiter, S. and Schmidhuber, J.: Long Short-term Memory, Neural Computation MIT-Press, 9, 1735–1780, 1997. a
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
Horton, P., Schaefli, B., and Kauzlaric, M.: Why Do We Have so Many Different Hydrological Models? A Review Based on the Case of Switzerland, WIREs Water, 9, e1574, https://doi.org/10.1002/wat2.1574, 2022. a
Hu, F., Yang, Q., Yang, J., Luo, Z., Shao, J., and Wang, G.: Incorporating Multiple Grid-Based Data in CNN-LSTM Hybrid Model for Daily Runoff Prediction in the Source Region of the Yellow River Basin, J. Hydrol.: Regional Studies, 51, 101652, https://doi.org/10.1016/j.ejrh.2023.101652, 2024. a
Hu, T., Wu, F., and Zhang, X.: Rainfall–Runoff Modeling Using Principal Component Analysis and Neural Network, Hydrol. Res., 38, 235–248, https://doi.org/10.2166/nh.2007.010, 2007. a
Jiang, S., Bevacqua, E., and Zscheischler, J.: River flooding mechanisms and their changes in Europe revealed by explainable machine learning, Hydrol. Earth Syst. Sci., 26, 6339–6359, https://doi.org/10.5194/hess-26-6339-2022, 2022. a
Kam, P. M., Aznar-Siguan, G., Schewe, J., Milano, L., Ginnetti, J., Willner, S., McCaughey, J. W., and Bresch, D. N.: Global Warming and Population Change Both Heighten Future Risk of Human Displacement Due to River Floods, Environ. Res. Lett., 16, 044026, https://doi.org/10.1088/1748-9326/abd26c, 2021. a
Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., and Inman, D. J.: 1D Convolutional Neural Networks and Applications: A Survey, Mechanical Systems and Signal Processing, 151, 107398, https://doi.org/10.1016/j.ymssp.2020.107398, 2021. 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
Klotz, D., Kratzert, F., Gauch, M., Keefe Sampson, A., Brandstetter, J., Klambauer, G., Hochreiter, S., and Nearing, G.: Uncertainty estimation with deep learning for rainfall–runoff modeling, Hydrol. Earth Syst. Sci., 26, 1673–1693, https://doi.org/10.5194/hess-26-1673-2022, 2022. a
Klotz, D., Gauch, M., Kratzert, F., Nearing, G., and Zscheischler, J.: Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions, Hydrol. Earth Syst. Sci., 28, 3665–3673, https://doi.org/10.5194/hess-28-3665-2024, 2024. a
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018. a, b, c
Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S., and Klambauer, G.: NeuralHydrology – Interpreting LSTMs in Hydrology, in: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, edited by: Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., and Müller, K.-R., Lecture Notes in Computer Science, 347–362, Springer International Publishing, Cham, ISBN 978-3-030-28954-6, https://doi.org/10.1007/978-3-030-28954-6_19, 2019a. a
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019c. a, b, c
Kratzert, F., Gauch, M., Klotz, D., and Nearing, G.: HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin, Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, 2024. a
Lees, T., Buechel, M., Anderson, B., Slater, L., Reece, S., Coxon, G., and Dadson, S. J.: Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models, Hydrol. Earth Syst. Sci., 25, 5517–5534, https://doi.org/10.5194/hess-25-5517-2021, 2021. a, b
Lees, T., Reece, S., Kratzert, F., Klotz, D., Gauch, M., De Bruijn, J., Kumar Sahu, R., Greve, P., Slater, L., and Dadson, S. J.: Hydrological concept formation inside long short-term memory (LSTM) networks, Hydrol. Earth Syst. Sci., 26, 3079–3101, https://doi.org/10.5194/hess-26-3079-2022, 2022. a
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, c, d
Mai, J., Shen, H., Tolson, B. A., Gaborit, É., Arsenault, R., Craig, J. R., Fortin, V., Fry, L. M., Gauch, M., Klotz, D., Kratzert, F., O'Brien, N., Princz, D. G., Rasiya Koya, S., Roy, T., Seglenieks, F., Shrestha, N. K., Temgoua, A. G. T., Vionnet, V., and Waddell, J. W.: The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL), Hydrol. Earth Syst. Sci., 26, 3537–3572, https://doi.org/10.5194/hess-26-3537-2022, 2022. a, b
Moshe, Z., Metzger, A., Elidan, G., Kratzert, F., Nevo, S., and El-Yaniv, R.: HydroNets: Leveraging River Structure for Hydrologic Modeling, arXiv [preprint], https://doi.org/10.48550/arXiv.2007.00595, 2020. a
Muhebwa, A., Gleason, C. J., Feng, D., and Taneja, J.: Improving Discharge Predictions in Ungauged Basins: Harnessing the Power of Disaggregated Data Modeling and Machine Learning, Water Resour. Res., 60, e2024WR037122, https://doi.org/10.1029/2024WR037122, 2024. a
Muñoz Sabater, J.: 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 (last access: 17 September 2024), 2019. a, b
Nash, J. E. and Sutcliffe, J. V.: River Flow Forecasting through Conceptual Models Part I – A Discussion of Principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970. 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, b, c
Nevo, S., Morin, E., Gerzi Rosenthal, A., Metzger, A., Barshai, C., Weitzner, D., Voloshin, D., Kratzert, F., Elidan, G., Dror, G., Begelman, G., Nearing, G., Shalev, G., Noga, H., Shavitt, I., Yuklea, L., Royz, M., Giladi, N., Peled Levi, N., Reich, O., Gilon, O., Maor, R., Timnat, S., Shechter, T., Anisimov, V., Gigi, Y., Levin, Y., Moshe, Z., Ben-Haim, Z., Hassidim, A., and Matias, Y.: Flood forecasting with machine learning models in an operational framework, Hydrol. Earth Syst. Sci., 26, 4013–4032, https://doi.org/10.5194/hess-26-4013-2022, 2022. 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
Otero, N., Horton, P., Martius, O., Allen, S., Zappa, M., Wechsler, T., and Schaefli, B.: Impacts of Hot-Dry Conditions on Hydropower Production in Switzerland, Environ. Res. Lett., 18, 064038, https://doi.org/10.1088/1748-9326/acd8d7, 2023. a
Shalev, G., El-Yaniv, R., Klotz, D., Kratzert, F., Metzger, A., and Nevo, S.: Accurate Hydrologic Modeling Using Less Information, arXiv [preprint], https://doi.org/10.48550/arXiv.1911.09427, 2019. a, b
Shen, C., Laloy, E., Elshorbagy, A., Albert, A., Bales, J., Chang, F.-J., Ganguly, S., Hsu, K.-L., Kifer, D., Fang, Z., Fang, K., Li, D., Li, X., and Tsai, W.-P.: HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community, Hydrol. Earth Syst. Sci., 22, 5639–5656, https://doi.org/10.5194/hess-22-5639-2018, 2018. a
Sit, M., Demiray, B., and Demir, I.: Short-Term Hourly Streamflow Prediction with Graph Convolutional GRU Networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2107.07039, 2021. a
Sitterson, J., Knightes, C., Parmar, R., Wolfe, K., Avant, B., and Muche, M.: An Overview of Rainfall-Runoff Model Types, 9th International Congress on Environmental Modelling and Software Fort Collins, Colorado, USA, edited by: Arabi, M., David, O., Carlson, J., and Ames, D. P., https://scholarsarchive.byu.edu/iemssconference/2018/ (last access: 5 June 2025), 2018. a
Smith, A., Tetzlaff, D., Marx, C., and Soulsby, C.: Enhancing Urban Runoff Modelling Using Water Stable Isotopes and Ages in Complex Catchments, Hydrol. Process., 37, e14814, https://doi.org/10.1002/hyp.14814, 2023. a
Smith, J. and Eli, R. N.: Neural-Network Models of Rainfall-Runoff Process, Journal of Water Resources Planning and Management, 121, 499–508, https://doi.org/10.1061/(ASCE)0733-9496(1995)121:6(499), 1995. a, b
Sterle, G., Perdrial, J., Kincaid, D. W., Underwood, K. L., Rizzo, D. M., Haq, I. U., Li, L., Lee, B. S., Adler, T., Wen, H., Middleton, H., and Harpold, A. A.: CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data, Hydrol. Earth Syst. Sci., 28, 611–630, https://doi.org/10.5194/hess-28-611-2024, 2024. a
Sun, A. Y., Jiang, P., Mudunuru, M. K., and Chen, X.: Explore Spatio-Temporal Learning of Large Sample Hydrology Using Graph Neural Networks, Water Resour. Res., 57, e2021WR030394, https://doi.org/10.1029/2021WR030394, 2021. a
Sun, A. Y., Jiang, P., Yang, Z.-L., Xie, Y., and Chen, X.: A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion, Hydrol. Earth Syst. Sci., 26, 5163–5184, https://doi.org/10.5194/hess-26-5163-2022, 2022. a, b, c
Tursun, A., Xie, X., Wang, Y., Peng, D., Liu, Y., Zheng, B., Wu, X., and Nie, C.: Streamflow Prediction in Human-Regulated Catchments Using Multiscale Deep Learning Modeling With Anthropogenic Similarities, Water Resour. Res., 60, e2023WR036853, https://doi.org/10.1029/2023WR036853, 2024. a
Van Der Knijff, J. M., Younis, J., and De Roo, A. P. J.: LISFLOOD: A GIS-based Distributed Model for River Basin Scale Water Balance and Flood Simulation, International Journal of Geographical Information Science, 24, 189–212, https://doi.org/10.1080/13658810802549154, 2010. a
Vischer, M., Felipe, N. O., and Ma, J.: Spatially Resolved Meteorological and Ancillary Data in Central Europe for Rainfall Streamflow Modeling, HydroShare [data set], https://doi.org/10.4211/hs.05d5633a413b4aec93b08a7e61a2abbb, 2025a. a
Vischer, M. A., Otero, N., and Ma, J.: Spatially Resolved Meteorological and Ancillary Data in Central Europe for Rainfall Streamflow Modeling, arXiv [preprint], https://doi.org/10.48550/arXiv.2506.03819, 2025b. a, b, c
Xiang, Z. and Demir, I.: Fully Distributed Rainfall-Runoff Modeling Using Spatial-Temporal Graph Neural Network, Earth ArXiv [data set], https://doi.org/10.31223/X57P74, 2022. a
Xie, J., Liu, X., Tian, W., Wang, K., Bai, P., and Liu, C.: Estimating Gridded Monthly Baseflow From 1981 to 2020 for the Contiguous US Using Long Short-Term Memory (LSTM) Networks, Water Resour. Res., 58, e2021WR031663, https://doi.org/10.1029/2021WR031663, 2022. a
Yu, Q., Tolson, B. A., Shen, H., Han, M., Mai, J., and Lin, J.: Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach, Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, 2024. a, b
Zhou, F., Chen, Y., and Liu, J.: Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation, Remote Sens., 15, 1395, https://doi.org/10.3390/rs15051395, 2023. a
Zhu, S., Wei, J., Zhang, H., Xu, Y., and Qin, H.: Spatiotemporal Deep Learning Rainfall-Runoff Forecasting Combined with Remote Sensing Precipitation Products in Large Scale Basins, J. Hydrol., 616, 128727, https://doi.org/10.1016/j.jhydrol.2022.128727, 2023. a, b
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.
We use a neural network to predict the amount of water flowing into rivers. Our focus is on...