Articles | Volume 29, issue 21
https://doi.org/10.5194/hess-29-6257-2025
https://doi.org/10.5194/hess-29-6257-2025
Research article
 | 
13 Nov 2025
Research article |  | 13 Nov 2025

Fully differentiable, fully distributed rainfall-runoff modeling

Fedor Scholz, Manuel Traub, Christiane Zarfl, Thomas Scholten, and Martin V. Butz

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Cited articles

Al Hossain, B. M. T., Ahmed, T., Aktar, M. N., Fida, M., Khan, A., Islam, A., Yazdan, M. M. S., Noor, F., and Rahaman, A. Z.: Climate Change Impacts on Water Availability in the Meghna Basin, in: Proceedings of the 5th International Conference on Water and Flood Management (ICWFM-2015), Dhaka, Bangladesh, 6–8, ISBN 9789843388018, 2015. a
AWGN: Amtliches Digitales Wasserwirtschaftliches Gewässernetz (AWGN), https://www.lubw.baden-wuerttemberg.de/wasser/awgn (last access: 23 July 2024), 2023. a
Bharati, L., Lacombe, G., Gurung, P., Jayakody, P., Hoanh, C. T., and Smakhtin, V.: The Impacts of Water Infrastructure and Climate Change on the Hydrology of the Upper Ganges River Basin, International Water Management Institute (Research Report 142), https://doi.org/10.5337/2011.210, 2011. a
Bindas, T., Tsai, W.-P., Liu, J., Rahmani, F., Feng, D., Bian, Y., Lawson, K., and Shen, C.: Improving River Routing Using a Differentiable Muskingum-Cunge Model and Physics-Informed Machine Learning, Water Resources Research, 60, e2023WR035337, https://doi.org/10.1029/2023WR035337, 2024. a
Börgel, F., Karsten, S., Rummel, K., and Gräwe, U.: From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting, Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, 2025. a
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We present a neural network model that estimates river discharge based on gridded elevation, precipitation, and solar radiation. Some instances of our model produce more accurate forecasts than the European Flood Awareness System (EFAS) when simulating discharge with lead times of 50 days on the Neckar river network in Germany. It consists of multiple components that are designed to model distinct sub-processes. We show that this makes the model behave in a more physically realistic way.
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