How well do process-based and data-driven hydrological models learn from limited discharge data?
Maria Staudinger; Anna Herzog; Ralf Loritz; Tobias Houska; Sandra Pool; Diana Spieler; Paul D. Wagner; Juliane Mai · 2025 · Hydrology and earth system sciences
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract. It is widely assumed that data-driven models achieve good results only with sufficiently large training data, whereas process-based models are usually expected to be superior in data-poor situations. To investigate this, we calibrated several process-based and data-driven hydrological models using training datasets of observed discharge that differed in terms of both the number of data points and the type of data selection, allowing us to make a systematic comparison of the learning behaviour of the different model types. Four data-driven models (conditional probability distributions
Abstract by Maria Staudinger; Anna Herzog; Ralf Loritz; Tobias Houska; Sandra Pool; Diana Spieler; Paul D. Wagner; Juliane Mai, Hydrology and earth system sciences (2025) — licensed CC BY 4.0.
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WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.5194/hess-29-5005-2025