The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

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Abstract δ δ δ δ δ δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment ; volume:27 ; number:12 ; year:2023 ; pages:2357-2373 ; extent:17
Hydrology and earth system sciences ; 27, Heft 12 (2023), 2357-2373 (gesamt 17)

Creator: Feng, Dapeng
Beck, Hylke
Lawson, Kathryn
Shen, Chaopeng

DOI: 10.5194/hess-27-2357-2023

URN: urn:nbn:de:101:1-2023070604272926526849

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:46 AM CEST

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Feng, Dapeng
Beck, Hylke
Lawson, Kathryn
Shen, Chaopeng

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The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

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