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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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR037544 |
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| author | Ngo Nghi Truyen Huynh Pierre‐André Garambois François Colleoni Benjamin Renard Hélène Roux Julie Demargne Maxime Jay‐Allemand Pierre Javelle |
| author_facet | Ngo Nghi Truyen Huynh Pierre‐André Garambois François Colleoni Benjamin Renard Hélène Roux Julie Demargne Maxime Jay‐Allemand Pierre Javelle |
| author_sort | Ngo Nghi Truyen Huynh |
| collection | DOAJ |
| description | 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 physical descriptors to conceptual model parameters. This paper introduces a Hybrid Data Assimilation and Parameter Regionalization (HDA‐PR) approach incorporating learnable regionalization mappings, based on either multi‐linear regression or artificial neural networks (ANNs), into a differentiable hydrological model. This approach demonstrates how two differentiable codes can be linked and their gradients chained, enabling the exploitation of heterogeneous data sets across extensive spatio‐temporal computational domains within a high‐dimensional regionalization context, using accurate adjoint‐based gradients. The inverse problem is tackled with a multi‐gauge calibration cost function accounting for information from multiple observation sites. HDA‐PR was tested on high‐resolution, hourly and kilometric regional modeling of 126 flash‐flood‐prone catchments in the French Mediterranean region. The results highlight a strong regionalization performance of HDA‐PR especially in the most challenging upstream‐to‐downstream extrapolation scenario with ANN, achieving median Nash‐Sutcliffe efficiency (NSE) scores from 0.6 to 0.71 for spatial, temporal, spatio‐temporal validations, and improving NSE by up to 30% on average compared to the baseline model calibrated with lumped parameters. Multiple evaluation metrics based on flood‐oriented hydrological signatures also indicate that the use of an ANN leads to better performances than a multi‐linear regression in a validation context. ANN enables to learn a non‐linear descriptors‐to‐parameters mapping which provides better model controllability than a linear mapping for complex calibration cases. |
| format | Article |
| id | doaj-art-f0a4db8eec6d4dcb992fe15e2271cb47 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-f0a4db8eec6d4dcb992fe15e2271cb472025-08-23T13:05:51ZengWileyWater Resources Research0043-13971944-79732024-11-016011n/an/a10.1029/2024WR037544Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean RegionNgo Nghi Truyen Huynh0Pierre‐André Garambois1François Colleoni2Benjamin Renard3Hélène Roux4Julie Demargne5Maxime Jay‐Allemand6Pierre Javelle7INRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence FranceINRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence FranceINRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence FranceINRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence FranceInstitut de Mécanique des Fluides de Toulouse (IMFT) CNRS Université de Toulouse Toulouse FranceHYDRIS Hydrologie Parc Scientifique Agropolis II Montferrier‐sur‐Lez FranceHYDRIS Hydrologie Parc Scientifique Agropolis II Montferrier‐sur‐Lez FranceINRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence FranceAbstract 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 physical descriptors to conceptual model parameters. This paper introduces a Hybrid Data Assimilation and Parameter Regionalization (HDA‐PR) approach incorporating learnable regionalization mappings, based on either multi‐linear regression or artificial neural networks (ANNs), into a differentiable hydrological model. This approach demonstrates how two differentiable codes can be linked and their gradients chained, enabling the exploitation of heterogeneous data sets across extensive spatio‐temporal computational domains within a high‐dimensional regionalization context, using accurate adjoint‐based gradients. The inverse problem is tackled with a multi‐gauge calibration cost function accounting for information from multiple observation sites. HDA‐PR was tested on high‐resolution, hourly and kilometric regional modeling of 126 flash‐flood‐prone catchments in the French Mediterranean region. The results highlight a strong regionalization performance of HDA‐PR especially in the most challenging upstream‐to‐downstream extrapolation scenario with ANN, achieving median Nash‐Sutcliffe efficiency (NSE) scores from 0.6 to 0.71 for spatial, temporal, spatio‐temporal validations, and improving NSE by up to 30% on average compared to the baseline model calibrated with lumped parameters. Multiple evaluation metrics based on flood‐oriented hydrological signatures also indicate that the use of an ANN leads to better performances than a multi‐linear regression in a validation context. ANN enables to learn a non‐linear descriptors‐to‐parameters mapping which provides better model controllability than a linear mapping for complex calibration cases.https://doi.org/10.1029/2024WR037544parameter regionalizationhybrid AIdistributed hydrological modelingflood forecastingdifferentiable modelsvariational data assimilation |
| spellingShingle | Ngo Nghi Truyen Huynh Pierre‐André Garambois François Colleoni Benjamin Renard Hélène Roux Julie Demargne Maxime Jay‐Allemand Pierre Javelle Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region Water Resources Research parameter regionalization hybrid AI distributed hydrological modeling flood forecasting differentiable models variational data assimilation |
| title | Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region |
| title_full | Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region |
| title_fullStr | Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region |
| title_full_unstemmed | Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region |
| title_short | Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region |
| title_sort | learning regionalization using accurate spatial cost gradients within a differentiable high resolution hydrological model application to the french mediterranean region |
| topic | parameter regionalization hybrid AI distributed hydrological modeling flood forecasting differentiable models variational data assimilation |
| url | https://doi.org/10.1029/2024WR037544 |
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