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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Short summary
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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