Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 20
Articles | Volume 30, issue 20
https://doi.org/10.5194/hess-30-6339-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-6339-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 20
Research article
 | 
16 Oct 2026
Research article |  | 16 Oct 2026

The need for uncertainty: why probabilistic LSTMs are key to improving flood predictions and enabling learned warning rules

Sanika Baste, Sebastian Lerch, Daniel Klotz, and Ralf Loritz

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Short summary
Probabilistic runoff predictions capture the likelihood of rare, high-stakes events that deterministic models – which predict only the conditional expectation – tend to underestimate. This added information can improve flood warning communication by learning reward-based rules for selecting predictive quantiles suited to specific hydrological and societal contexts.
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