Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region

Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region

Abstract Estimating spatially distributed hydrological parameters in ungauged catchments poses a challenging regionalization problem and requires imposing spatial constraints given the sparsity of discharge data. A possible approach is to search for a transfer function that quantitatively relates ph...

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Bibliographic Details
Main Authors: Ngo Nghi Truyen Huynh, Pierre‐André Garambois, François Colleoni, Benjamin Renard, Hélène Roux, Julie Demargne, Maxime Jay‐Allemand, Pierre Javelle
Format: Article
Language:English
Published: Wiley 2024-11-01
Series:Water Resources Research
Subjects:
parameter regionalization
hybrid AI
distributed hydrological modeling
flood forecasting
differentiable models
variational data assimilation
Online Access:https://doi.org/10.1029/2024WR037544
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https://doi.org/10.1029/2024WR037544

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