Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region Article Swipe
Ngo Nghi Truyen Huynh
,
Pierre‐André Garambois
,
François Colleoni
,
Benjamin Renard
,
Hélène Roux
,
Julie Demargne
,
Maxime Jay‐Allemand
,
Pierre Javelle
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1029/2024wr037544
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1029/2024wr037544
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.
Related Topics
Concepts
Extrapolation
Context (archaeology)
Computer science
Data assimilation
Flood myth
Artificial neural network
Differentiable function
Hydrological modelling
Calibration
Data mining
Mathematics
Geography
Meteorology
Artificial intelligence
Geology
Statistics
Mathematical analysis
Archaeology
Climatology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1029/2024wr037544
- OA Status
- gold
- Cited By
- 11
- References
- 87
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403983838
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4403983838Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1029/2024wr037544Digital Object Identifier
- Title
-
Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean RegionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-01Full publication date if available
- Authors
-
Ngo Nghi Truyen Huynh, Pierre‐André Garambois, François Colleoni, Benjamin Renard, Hélène Roux, Julie Demargne, Maxime Jay‐Allemand, Pierre JavelleList of authors in order
- Landing page
-
https://doi.org/10.1029/2024wr037544Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1029/2024wr037544Direct OA link when available
- Concepts
-
Extrapolation, Context (archaeology), Computer science, Data assimilation, Flood myth, Artificial neural network, Differentiable function, Hydrological modelling, Calibration, Data mining, Mathematics, Geography, Meteorology, Artificial intelligence, Geology, Statistics, Mathematical analysis, Archaeology, ClimatologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 11Per-year citation counts (last 5 years)
- References (count)
-
87Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4403983838 |
|---|---|
| doi | https://doi.org/10.1029/2024wr037544 |
| ids.doi | https://doi.org/10.1029/2024wr037544 |
| ids.openalex | https://openalex.org/W4403983838 |
| fwci | 4.36633793 |
| type | article |
| title | Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region |
| biblio.issue | 11 |
| biblio.volume | 60 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10330 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2312 |
| topics[0].subfield.display_name | Water Science and Technology |
| topics[0].display_name | Hydrology and Watershed Management Studies |
| topics[1].id | https://openalex.org/T10930 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9993000030517578 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2306 |
| topics[1].subfield.display_name | Global and Planetary Change |
| topics[1].display_name | Flood Risk Assessment and Management |
| topics[2].id | https://openalex.org/T11186 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.992900013923645 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2306 |
| topics[2].subfield.display_name | Global and Planetary Change |
| topics[2].display_name | Hydrology and Drought Analysis |
| is_xpac | False |
| apc_list.value | 3000 |
| apc_list.currency | USD |
| apc_list.value_usd | 3000 |
| apc_paid.value | 3000 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 3000 |
| concepts[0].id | https://openalex.org/C132459708 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6339938044548035 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q744069 |
| concepts[0].display_name | Extrapolation |
| concepts[1].id | https://openalex.org/C2779343474 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5657846927642822 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q3109175 |
| concepts[1].display_name | Context (archaeology) |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5550881028175354 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C24552861 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5273460149765015 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2670177 |
| concepts[3].display_name | Data assimilation |
| concepts[4].id | https://openalex.org/C74256435 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4807302951812744 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q134052 |
| concepts[4].display_name | Flood myth |
| concepts[5].id | https://openalex.org/C50644808 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4620340168476105 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[5].display_name | Artificial neural network |
| concepts[6].id | https://openalex.org/C202615002 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4593513309955597 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q783507 |
| concepts[6].display_name | Differentiable function |
| concepts[7].id | https://openalex.org/C126197015 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4377965033054352 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1586683 |
| concepts[7].display_name | Hydrological modelling |
| concepts[8].id | https://openalex.org/C165838908 |
| concepts[8].level | 2 |
| concepts[8].score | 0.42510104179382324 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q736777 |
| concepts[8].display_name | Calibration |
| concepts[9].id | https://openalex.org/C124101348 |
| concepts[9].level | 1 |
| concepts[9].score | 0.38991332054138184 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[9].display_name | Data mining |
| concepts[10].id | https://openalex.org/C33923547 |
| concepts[10].level | 0 |
| concepts[10].score | 0.23557844758033752 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[10].display_name | Mathematics |
| concepts[11].id | https://openalex.org/C205649164 |
| concepts[11].level | 0 |
| concepts[11].score | 0.2169835865497589 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[11].display_name | Geography |
| concepts[12].id | https://openalex.org/C153294291 |
| concepts[12].level | 1 |
| concepts[12].score | 0.20668190717697144 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q25261 |
| concepts[12].display_name | Meteorology |
| concepts[13].id | https://openalex.org/C154945302 |
| concepts[13].level | 1 |
| concepts[13].score | 0.20174840092658997 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[13].display_name | Artificial intelligence |
| concepts[14].id | https://openalex.org/C127313418 |
| concepts[14].level | 0 |
| concepts[14].score | 0.16084057092666626 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[14].display_name | Geology |
| concepts[15].id | https://openalex.org/C105795698 |
| concepts[15].level | 1 |
| concepts[15].score | 0.14405107498168945 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[15].display_name | Statistics |
| concepts[16].id | https://openalex.org/C134306372 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[16].display_name | Mathematical analysis |
| concepts[17].id | https://openalex.org/C166957645 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q23498 |
| concepts[17].display_name | Archaeology |
| concepts[18].id | https://openalex.org/C49204034 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q52139 |
| concepts[18].display_name | Climatology |
| keywords[0].id | https://openalex.org/keywords/extrapolation |
| keywords[0].score | 0.6339938044548035 |
| keywords[0].display_name | Extrapolation |
| keywords[1].id | https://openalex.org/keywords/context |
| keywords[1].score | 0.5657846927642822 |
| keywords[1].display_name | Context (archaeology) |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5550881028175354 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/data-assimilation |
| keywords[3].score | 0.5273460149765015 |
| keywords[3].display_name | Data assimilation |
| keywords[4].id | https://openalex.org/keywords/flood-myth |
| keywords[4].score | 0.4807302951812744 |
| keywords[4].display_name | Flood myth |
| keywords[5].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[5].score | 0.4620340168476105 |
| keywords[5].display_name | Artificial neural network |
| keywords[6].id | https://openalex.org/keywords/differentiable-function |
| keywords[6].score | 0.4593513309955597 |
| keywords[6].display_name | Differentiable function |
| keywords[7].id | https://openalex.org/keywords/hydrological-modelling |
| keywords[7].score | 0.4377965033054352 |
| keywords[7].display_name | Hydrological modelling |
| keywords[8].id | https://openalex.org/keywords/calibration |
| keywords[8].score | 0.42510104179382324 |
| keywords[8].display_name | Calibration |
| keywords[9].id | https://openalex.org/keywords/data-mining |
| keywords[9].score | 0.38991332054138184 |
| keywords[9].display_name | Data mining |
| keywords[10].id | https://openalex.org/keywords/mathematics |
| keywords[10].score | 0.23557844758033752 |
| keywords[10].display_name | Mathematics |
| keywords[11].id | https://openalex.org/keywords/geography |
| keywords[11].score | 0.2169835865497589 |
| keywords[11].display_name | Geography |
| keywords[12].id | https://openalex.org/keywords/meteorology |
| keywords[12].score | 0.20668190717697144 |
| keywords[12].display_name | Meteorology |
| keywords[13].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[13].score | 0.20174840092658997 |
| keywords[13].display_name | Artificial intelligence |
| keywords[14].id | https://openalex.org/keywords/geology |
| keywords[14].score | 0.16084057092666626 |
| keywords[14].display_name | Geology |
| keywords[15].id | https://openalex.org/keywords/statistics |
| keywords[15].score | 0.14405107498168945 |
| keywords[15].display_name | Statistics |
| language | en |
| locations[0].id | doi:10.1029/2024wr037544 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S204847658 |
| locations[0].source.issn | 0043-1397, 1944-7973 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 0043-1397 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Water Resources Research |
| locations[0].source.host_organization | https://openalex.org/P4310320595 |
| locations[0].source.host_organization_name | Wiley |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320595 |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Water Resources Research |
| locations[0].landing_page_url | https://doi.org/10.1029/2024wr037544 |
| locations[1].id | pmh:oai:HAL:hal-04145059v3 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306402512 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | HAL (Le Centre pour la Communication Scientifique Directe) |
| locations[1].source.host_organization | https://openalex.org/I1294671590 |
| locations[1].source.host_organization_name | Centre National de la Recherche Scientifique |
| locations[1].source.host_organization_lineage | https://openalex.org/I1294671590 |
| locations[1].license | other-oa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | Journal articles |
| locations[1].license_id | https://openalex.org/licenses/other-oa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Water Resources Research, 2024, 60 (11), ⟨10.1029/2024WR037544⟩ |
| locations[1].landing_page_url | https://hal.inrae.fr/hal-04145059 |
| locations[2].id | pmh:oai:doaj.org/article:f0a4db8eec6d4dcb992fe15e2271cb47 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].source.host_organization_lineage | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Water Resources Research, Vol 60, Iss 11, Pp n/a-n/a (2024) |
| locations[2].landing_page_url | https://doaj.org/article/f0a4db8eec6d4dcb992fe15e2271cb47 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5032578550 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-5078-3865 |
| authorships[0].author.display_name | Ngo Nghi Truyen Huynh |
| authorships[0].countries | FR |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I21491767, https://openalex.org/I4210088668 |
| authorships[0].affiliations[0].raw_affiliation_string | INRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence France |
| authorships[0].institutions[0].id | https://openalex.org/I21491767 |
| authorships[0].institutions[0].ror | https://ror.org/035xkbk20 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I21491767 |
| authorships[0].institutions[0].country_code | FR |
| authorships[0].institutions[0].display_name | Aix-Marseille Université |
| authorships[0].institutions[1].id | https://openalex.org/I4210088668 |
| authorships[0].institutions[1].ror | https://ror.org/003vg9w96 |
| authorships[0].institutions[1].type | government |
| authorships[0].institutions[1].lineage | https://openalex.org/I4210088668 |
| authorships[0].institutions[1].country_code | FR |
| authorships[0].institutions[1].display_name | Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ngo Nghi Truyen Huynh |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | INRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence France |
| authorships[1].author.id | https://openalex.org/A5088235745 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-8350-6741 |
| authorships[1].author.display_name | Pierre‐André Garambois |
| authorships[1].countries | FR |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I21491767, https://openalex.org/I4210088668 |
| authorships[1].affiliations[0].raw_affiliation_string | INRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence France |
| authorships[1].institutions[0].id | https://openalex.org/I21491767 |
| authorships[1].institutions[0].ror | https://ror.org/035xkbk20 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I21491767 |
| authorships[1].institutions[0].country_code | FR |
| authorships[1].institutions[0].display_name | Aix-Marseille Université |
| authorships[1].institutions[1].id | https://openalex.org/I4210088668 |
| authorships[1].institutions[1].ror | https://ror.org/003vg9w96 |
| authorships[1].institutions[1].type | government |
| authorships[1].institutions[1].lineage | https://openalex.org/I4210088668 |
| authorships[1].institutions[1].country_code | FR |
| authorships[1].institutions[1].display_name | Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Pierre‐André Garambois |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | INRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence France |
| authorships[2].author.id | https://openalex.org/A5081996302 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | François Colleoni |
| authorships[2].countries | FR |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I21491767, https://openalex.org/I4210088668 |
| authorships[2].affiliations[0].raw_affiliation_string | INRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence France |
| authorships[2].institutions[0].id | https://openalex.org/I21491767 |
| authorships[2].institutions[0].ror | https://ror.org/035xkbk20 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I21491767 |
| authorships[2].institutions[0].country_code | FR |
| authorships[2].institutions[0].display_name | Aix-Marseille Université |
| authorships[2].institutions[1].id | https://openalex.org/I4210088668 |
| authorships[2].institutions[1].ror | https://ror.org/003vg9w96 |
| authorships[2].institutions[1].type | government |
| authorships[2].institutions[1].lineage | https://openalex.org/I4210088668 |
| authorships[2].institutions[1].country_code | FR |
| authorships[2].institutions[1].display_name | Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | François Colleoni |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | INRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence France |
| authorships[3].author.id | https://openalex.org/A5078289757 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-8447-5430 |
| authorships[3].author.display_name | Benjamin Renard |
| authorships[3].countries | FR |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I21491767, https://openalex.org/I4210088668 |
| authorships[3].affiliations[0].raw_affiliation_string | INRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence France |
| authorships[3].institutions[0].id | https://openalex.org/I21491767 |
| authorships[3].institutions[0].ror | https://ror.org/035xkbk20 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I21491767 |
| authorships[3].institutions[0].country_code | FR |
| authorships[3].institutions[0].display_name | Aix-Marseille Université |
| authorships[3].institutions[1].id | https://openalex.org/I4210088668 |
| authorships[3].institutions[1].ror | https://ror.org/003vg9w96 |
| authorships[3].institutions[1].type | government |
| authorships[3].institutions[1].lineage | https://openalex.org/I4210088668 |
| authorships[3].institutions[1].country_code | FR |
| authorships[3].institutions[1].display_name | Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Benjamin Renard |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | INRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence France |
| authorships[4].author.id | https://openalex.org/A5002986509 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-7076-5015 |
| authorships[4].author.display_name | Hélène Roux |
| authorships[4].countries | FR |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I1294671590, https://openalex.org/I17866349, https://openalex.org/I4210110935 |
| authorships[4].affiliations[0].raw_affiliation_string | Institut de Mécanique des Fluides de Toulouse (IMFT) CNRS Université de Toulouse Toulouse France |
| authorships[4].institutions[0].id | https://openalex.org/I1294671590 |
| authorships[4].institutions[0].ror | https://ror.org/02feahw73 |
| authorships[4].institutions[0].type | government |
| authorships[4].institutions[0].lineage | https://openalex.org/I1294671590 |
| authorships[4].institutions[0].country_code | FR |
| authorships[4].institutions[0].display_name | Centre National de la Recherche Scientifique |
| authorships[4].institutions[1].id | https://openalex.org/I4210110935 |
| authorships[4].institutions[1].ror | https://ror.org/025nmxp11 |
| authorships[4].institutions[1].type | facility |
| authorships[4].institutions[1].lineage | https://openalex.org/I1294671590, https://openalex.org/I1294671590, https://openalex.org/I134560555, https://openalex.org/I205747304, https://openalex.org/I4210095849, https://openalex.org/I4210110935, https://openalex.org/I4412460029 |
| authorships[4].institutions[1].country_code | FR |
| authorships[4].institutions[1].display_name | Institut de Mécanique des Fluides de Toulouse |
| authorships[4].institutions[2].id | https://openalex.org/I17866349 |
| authorships[4].institutions[2].ror | https://ror.org/004raaa70 |
| authorships[4].institutions[2].type | education |
| authorships[4].institutions[2].lineage | https://openalex.org/I17866349 |
| authorships[4].institutions[2].country_code | FR |
| authorships[4].institutions[2].display_name | Université Fédérale de Toulouse Midi-Pyrénées |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Hélène Roux |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Institut de Mécanique des Fluides de Toulouse (IMFT) CNRS Université de Toulouse Toulouse France |
| authorships[5].author.id | https://openalex.org/A5061248068 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Julie Demargne |
| authorships[5].countries | FR |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I251321805 |
| authorships[5].affiliations[0].raw_affiliation_string | HYDRIS - HYDRIS hydrologie (Parc Scientifique Agropolis II 2196 Boulevard de la Lironde 34980 MONTFERRIER SUR LEZ - France) |
| authorships[5].institutions[0].id | https://openalex.org/I251321805 |
| authorships[5].institutions[0].ror | https://ror.org/00txxwv34 |
| authorships[5].institutions[0].type | other |
| authorships[5].institutions[0].lineage | https://openalex.org/I251321805 |
| authorships[5].institutions[0].country_code | FR |
| authorships[5].institutions[0].display_name | Agropolis International |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Julie Demargne |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | HYDRIS - HYDRIS hydrologie (Parc Scientifique Agropolis II 2196 Boulevard de la Lironde 34980 MONTFERRIER SUR LEZ - France) |
| authorships[6].author.id | https://openalex.org/A5017911309 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Maxime Jay‐Allemand |
| authorships[6].countries | FR |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I251321805 |
| authorships[6].affiliations[0].raw_affiliation_string | HYDRIS - HYDRIS hydrologie (Parc Scientifique Agropolis II 2196 Boulevard de la Lironde 34980 MONTFERRIER SUR LEZ - France) |
| authorships[6].institutions[0].id | https://openalex.org/I251321805 |
| authorships[6].institutions[0].ror | https://ror.org/00txxwv34 |
| authorships[6].institutions[0].type | other |
| authorships[6].institutions[0].lineage | https://openalex.org/I251321805 |
| authorships[6].institutions[0].country_code | FR |
| authorships[6].institutions[0].display_name | Agropolis International |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Maxime Jay‐Allemand |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | HYDRIS - HYDRIS hydrologie (Parc Scientifique Agropolis II 2196 Boulevard de la Lironde 34980 MONTFERRIER SUR LEZ - France) |
| authorships[7].author.id | https://openalex.org/A5010188979 |
| authorships[7].author.orcid | https://orcid.org/0000-0001-9330-5054 |
| authorships[7].author.display_name | Pierre Javelle |
| authorships[7].countries | FR |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I21491767, https://openalex.org/I4210088668 |
| authorships[7].affiliations[0].raw_affiliation_string | INRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence France |
| authorships[7].institutions[0].id | https://openalex.org/I21491767 |
| authorships[7].institutions[0].ror | https://ror.org/035xkbk20 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I21491767 |
| authorships[7].institutions[0].country_code | FR |
| authorships[7].institutions[0].display_name | Aix-Marseille Université |
| authorships[7].institutions[1].id | https://openalex.org/I4210088668 |
| authorships[7].institutions[1].ror | https://ror.org/003vg9w96 |
| authorships[7].institutions[1].type | government |
| authorships[7].institutions[1].lineage | https://openalex.org/I4210088668 |
| authorships[7].institutions[1].country_code | FR |
| authorships[7].institutions[1].display_name | Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Pierre Javelle |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | INRAE RECOVER Aix‐Marseille Université Aix‐en‐Provence France |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1029/2024wr037544 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2024-11-02T00:00:00 |
| display_name | Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10330 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2312 |
| primary_topic.subfield.display_name | Water Science and Technology |
| primary_topic.display_name | Hydrology and Watershed Management Studies |
| related_works | https://openalex.org/W1968270095, https://openalex.org/W4296478327, https://openalex.org/W1960072520, https://openalex.org/W2042397106, https://openalex.org/W4361730764, https://openalex.org/W1965029248, https://openalex.org/W2220129715, https://openalex.org/W2387596242, https://openalex.org/W2333625343, https://openalex.org/W2074627651 |
| cited_by_count | 11 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 11 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1029/2024wr037544 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S204847658 |
| best_oa_location.source.issn | 0043-1397, 1944-7973 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 0043-1397 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Water Resources Research |
| best_oa_location.source.host_organization | https://openalex.org/P4310320595 |
| best_oa_location.source.host_organization_name | Wiley |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320595 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Water Resources Research |
| best_oa_location.landing_page_url | https://doi.org/10.1029/2024wr037544 |
| primary_location.id | doi:10.1029/2024wr037544 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S204847658 |
| primary_location.source.issn | 0043-1397, 1944-7973 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 0043-1397 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Water Resources Research |
| primary_location.source.host_organization | https://openalex.org/P4310320595 |
| primary_location.source.host_organization_name | Wiley |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320595 |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Water Resources Research |
| primary_location.landing_page_url | https://doi.org/10.1029/2024wr037544 |
| publication_date | 2024-11-01 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W3164779581, https://openalex.org/W4394998532, https://openalex.org/W2038552008, https://openalex.org/W3045596914, https://openalex.org/W2338949170, https://openalex.org/W566720969, https://openalex.org/W1988753725, https://openalex.org/W2112071972, https://openalex.org/W1988888548, https://openalex.org/W2979377703, https://openalex.org/W2133837084, https://openalex.org/W3204279182, https://openalex.org/W4296344227, https://openalex.org/W2994327520, https://openalex.org/W2016086180, https://openalex.org/W2079335151, https://openalex.org/W2169915027, https://openalex.org/W2158046816, https://openalex.org/W2899636387, https://openalex.org/W2097402578, https://openalex.org/W2031070741, https://openalex.org/W2129728285, https://openalex.org/W2791336151, https://openalex.org/W2165201237, https://openalex.org/W2921650575, https://openalex.org/W2060873852, https://openalex.org/W4385280749, https://openalex.org/W4383604267, https://openalex.org/W4385644119, https://openalex.org/W3034385753, https://openalex.org/W1588946961, https://openalex.org/W2094437984, https://openalex.org/W2964121744, https://openalex.org/W2145635192, https://openalex.org/W1969332214, https://openalex.org/W2117681582, https://openalex.org/W3196867077, https://openalex.org/W3044712509, https://openalex.org/W2919115771, https://openalex.org/W2749795597, https://openalex.org/W2477763700, https://openalex.org/W2888519686, https://openalex.org/W2099087626, https://openalex.org/W1576008153, https://openalex.org/W86904335, https://openalex.org/W1917154922, https://openalex.org/W3135436695, https://openalex.org/W2051811265, https://openalex.org/W1821222543, https://openalex.org/W2170396766, https://openalex.org/W2963084264, https://openalex.org/W1539229456, https://openalex.org/W2162719469, https://openalex.org/W2977245197, https://openalex.org/W2146495904, https://openalex.org/W4318482298, https://openalex.org/W1999615743, https://openalex.org/W2005126631, https://openalex.org/W2328888656, https://openalex.org/W3013890918, https://openalex.org/W1744528052, https://openalex.org/W4285494800, https://openalex.org/W2095490126, https://openalex.org/W2054637042, https://openalex.org/W2091960994, https://openalex.org/W4400662624, https://openalex.org/W2156048436, https://openalex.org/W2067223893, https://openalex.org/W2149071528, https://openalex.org/W4304191791, https://openalex.org/W4385363005, https://openalex.org/W2799995162, https://openalex.org/W4394056594, https://openalex.org/W2604738573, https://openalex.org/W4309602895, https://openalex.org/W2010624461, https://openalex.org/W4304689650, https://openalex.org/W2103020329, https://openalex.org/W2800819102, https://openalex.org/W4289544505, https://openalex.org/W2963977107, https://openalex.org/W3207392840, https://openalex.org/W2890110729, https://openalex.org/W2753484804, https://openalex.org/W3137803692, https://openalex.org/W2980813836, https://openalex.org/W4394918332 |
| referenced_works_count | 87 |
| abstract_inverted_index.A | 25 |
| abstract_inverted_index.a | 10, 32, 47, 71, 102, 116, 150, 220, 224, 231, 241 |
| abstract_inverted_index.an | 213 |
| abstract_inverted_index.be | 83 |
| abstract_inverted_index.by | 184 |
| abstract_inverted_index.in | 6, 142, 157, 223 |
| abstract_inverted_index.is | 28, 113 |
| abstract_inverted_index.of | 22, 92, 138, 154, 212 |
| abstract_inverted_index.on | 61, 131, 188, 203 |
| abstract_inverted_index.or | 65 |
| abstract_inverted_index.to | 29, 40, 174, 186, 191, 216, 229 |
| abstract_inverted_index.up | 185 |
| abstract_inverted_index.0.6 | 173 |
| abstract_inverted_index.126 | 139 |
| abstract_inverted_index.30% | 187 |
| abstract_inverted_index.ANN | 214, 227 |
| abstract_inverted_index.NSE | 183 |
| abstract_inverted_index.The | 110, 147 |
| abstract_inverted_index.and | 14, 51, 85, 134, 181 |
| abstract_inverted_index.can | 82 |
| abstract_inverted_index.for | 31, 122, 176, 244 |
| abstract_inverted_index.how | 78 |
| abstract_inverted_index.the | 20, 90, 143, 158, 192, 210 |
| abstract_inverted_index.two | 79 |
| abstract_inverted_index.use | 211 |
| abstract_inverted_index.was | 129 |
| abstract_inverted_index.0.71 | 175 |
| abstract_inverted_index.ANN, | 165 |
| abstract_inverted_index.Data | 49 |
| abstract_inverted_index.This | 44, 75 |
| abstract_inverted_index.also | 207 |
| abstract_inverted_index.cost | 119 |
| abstract_inverted_index.data | 94 |
| abstract_inverted_index.from | 124, 172 |
| abstract_inverted_index.into | 70 |
| abstract_inverted_index.most | 159 |
| abstract_inverted_index.sets | 95 |
| abstract_inverted_index.than | 219, 240 |
| abstract_inverted_index.that | 35, 209 |
| abstract_inverted_index.with | 115, 164, 196 |
| abstract_inverted_index.(NSE) | 170 |
| abstract_inverted_index.based | 60, 202 |
| abstract_inverted_index.codes | 81 |
| abstract_inverted_index.data. | 24 |
| abstract_inverted_index.given | 19 |
| abstract_inverted_index.leads | 215 |
| abstract_inverted_index.learn | 230 |
| abstract_inverted_index.model | 42, 194, 238 |
| abstract_inverted_index.paper | 45 |
| abstract_inverted_index.poses | 9 |
| abstract_inverted_index.their | 86 |
| abstract_inverted_index.using | 106 |
| abstract_inverted_index.which | 235 |
| abstract_inverted_index.French | 144 |
| abstract_inverted_index.Hybrid | 48 |
| abstract_inverted_index.across | 96 |
| abstract_inverted_index.better | 217, 237 |
| abstract_inverted_index.cases. | 247 |
| abstract_inverted_index.either | 62 |
| abstract_inverted_index.hourly | 133 |
| abstract_inverted_index.linear | 242 |
| abstract_inverted_index.linked | 84 |
| abstract_inverted_index.lumped | 197 |
| abstract_inverted_index.median | 167 |
| abstract_inverted_index.model. | 74 |
| abstract_inverted_index.neural | 67 |
| abstract_inverted_index.scores | 171 |
| abstract_inverted_index.search | 30 |
| abstract_inverted_index.sites. | 127 |
| abstract_inverted_index.strong | 151 |
| abstract_inverted_index.tested | 130 |
| abstract_inverted_index.within | 101 |
| abstract_inverted_index.(ANNs), | 69 |
| abstract_inverted_index.average | 189 |
| abstract_inverted_index.complex | 245 |
| abstract_inverted_index.domains | 100 |
| abstract_inverted_index.enables | 228 |
| abstract_inverted_index.inverse | 111 |
| abstract_inverted_index.mapping | 234, 243 |
| abstract_inverted_index.metrics | 201 |
| abstract_inverted_index.problem | 13, 112 |
| abstract_inverted_index.region. | 146 |
| abstract_inverted_index.relates | 37 |
| abstract_inverted_index.results | 148 |
| abstract_inverted_index.spatial | 17 |
| abstract_inverted_index.tackled | 114 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.HDA‐PR | 128, 155 |
| abstract_inverted_index.Multiple | 199 |
| abstract_inverted_index.accurate | 107 |
| abstract_inverted_index.approach | 27, 55, 76 |
| abstract_inverted_index.baseline | 193 |
| abstract_inverted_index.chained, | 88 |
| abstract_inverted_index.compared | 190 |
| abstract_inverted_index.context, | 105 |
| abstract_inverted_index.context. | 226 |
| abstract_inverted_index.enabling | 89 |
| abstract_inverted_index.function | 34, 120 |
| abstract_inverted_index.imposing | 16 |
| abstract_inverted_index.indicate | 208 |
| abstract_inverted_index.modeling | 137 |
| abstract_inverted_index.multiple | 125 |
| abstract_inverted_index.networks | 68 |
| abstract_inverted_index.physical | 38 |
| abstract_inverted_index.possible | 26 |
| abstract_inverted_index.provides | 236 |
| abstract_inverted_index.regional | 136 |
| abstract_inverted_index.requires | 15 |
| abstract_inverted_index.scenario | 163 |
| abstract_inverted_index.sparsity | 21 |
| abstract_inverted_index.spatial, | 177 |
| abstract_inverted_index.transfer | 33 |
| abstract_inverted_index.ungauged | 7 |
| abstract_inverted_index.Parameter | 52 |
| abstract_inverted_index.achieving | 166 |
| abstract_inverted_index.discharge | 23 |
| abstract_inverted_index.extensive | 97 |
| abstract_inverted_index.gradients | 87 |
| abstract_inverted_index.highlight | 149 |
| abstract_inverted_index.improving | 182 |
| abstract_inverted_index.learnable | 57 |
| abstract_inverted_index.mappings, | 59 |
| abstract_inverted_index.spatially | 2 |
| abstract_inverted_index.temporal, | 178 |
| abstract_inverted_index.(HDA‐PR) | 54 |
| abstract_inverted_index.Estimating | 1 |
| abstract_inverted_index.accounting | 121 |
| abstract_inverted_index.artificial | 66 |
| abstract_inverted_index.calibrated | 195 |
| abstract_inverted_index.catchments | 8, 141 |
| abstract_inverted_index.conceptual | 41 |
| abstract_inverted_index.efficiency | 169 |
| abstract_inverted_index.especially | 156 |
| abstract_inverted_index.evaluation | 200 |
| abstract_inverted_index.gradients. | 109 |
| abstract_inverted_index.introduces | 46 |
| abstract_inverted_index.kilometric | 135 |
| abstract_inverted_index.parameters | 5 |
| abstract_inverted_index.regression | 64, 222 |
| abstract_inverted_index.signatures | 206 |
| abstract_inverted_index.validation | 225 |
| abstract_inverted_index.calibration | 118, 246 |
| abstract_inverted_index.challenging | 11, 160 |
| abstract_inverted_index.constraints | 18 |
| abstract_inverted_index.descriptors | 39 |
| abstract_inverted_index.distributed | 3 |
| abstract_inverted_index.information | 123 |
| abstract_inverted_index.observation | 126 |
| abstract_inverted_index.parameters. | 43, 198 |
| abstract_inverted_index.performance | 153 |
| abstract_inverted_index.Assimilation | 50 |
| abstract_inverted_index.demonstrates | 77 |
| abstract_inverted_index.exploitation | 91 |
| abstract_inverted_index.hydrological | 4, 73, 205 |
| abstract_inverted_index.non‐linear | 232 |
| abstract_inverted_index.performances | 218 |
| abstract_inverted_index.validations, | 180 |
| abstract_inverted_index.Mediterranean | 145 |
| abstract_inverted_index.computational | 99 |
| abstract_inverted_index.extrapolation | 162 |
| abstract_inverted_index.heterogeneous | 93 |
| abstract_inverted_index.incorporating | 56 |
| abstract_inverted_index.multi‐gauge | 117 |
| abstract_inverted_index.differentiable | 72, 80 |
| abstract_inverted_index.multi‐linear | 63, 221 |
| abstract_inverted_index.quantitatively | 36 |
| abstract_inverted_index.Regionalization | 53 |
| abstract_inverted_index.adjoint‐based | 108 |
| abstract_inverted_index.controllability | 239 |
| abstract_inverted_index.regionalization | 12, 58, 104, 152 |
| abstract_inverted_index.Nash‐Sutcliffe | 168 |
| abstract_inverted_index.flood‐oriented | 204 |
| abstract_inverted_index.spatio‐temporal | 98, 179 |
| abstract_inverted_index.high‐dimensional | 103 |
| abstract_inverted_index.high‐resolution, | 132 |
| abstract_inverted_index.flash‐flood‐prone | 140 |
| abstract_inverted_index.upstream‐to‐downstream | 161 |
| abstract_inverted_index.descriptors‐to‐parameters | 233 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile.value | 0.90775108 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |