Les données spatiales au service du suivi des ressources en eau

Yoann Aubert; Thomas Legay; Julien Verdonck; Damien Brunel; Stéphane Delichere

doi:10.1051/e3sconf/202234604008

All issues

Volume 346 (2022)

E3S Web Conf., 346 (2022) 04008

References

Open Access

Issue		E3S Web Conf. Volume 346, 2022 ICOLD & CFBR Symposium - SHARING WATER: MULTI-PURPOSE OF RESERVOIRS AND INNOVATIONS


Article Number		04008
Number of page(s)		15
Section		Thème 4. Exploitation des aménagements multi-usages / Theme 4. Operating Multi-usage Facilities
DOI		https://doi.org/10.1051/e3sconf/202234604008
Published online		23 May 2022

J. C. Vörösmarty, A. Askew, W. Grabs, R. G. Barry, C. Birkett, P.D.B. Goodison, A. Hall, R. Jenne, L. Kitaev, J. Landwehr, M. Keeler, G. Leavesley, J. Schaake, K. Strzepek, S.S. Sundarvel, K. Takeuchi, F. Webster, Global water data: A newly endangered species, Eos, Transactions American Geophysical Union, 82 (5):54–58 (2001) [CrossRef] [Google Scholar]
T. Lebel, J.D. Taupin, N. D’Amato, Rainfall monitoring during HAPEX-Sahel, 1. General rainfall conditions and climatology, J. Hydrol. 189, 74—96 (1997) [CrossRef] [Google Scholar]
J. Li, A. Heap, Spatial Interpolation Methods: A Review for Environmental Scientists (2008) [Google Scholar]
A. Paris, Utilisation conjointe de données d'altimétrie satellitaire et de modélisation pour le calcul des débits distribués dans le bassin amazonien, Hydrologie, Université Paul Sabatier - Toulouse III (2015) [Google Scholar]
F. Satge, D. D. Defrance, B. Sultan, M.-P. Bonnet, F. Seyler, N. Rouché, F. Pierron, J.E. Paturel, Evaluation of 23 gridded precipitation datasets across West Africa, Journal of Hydrology (2019) [Google Scholar]
H. Alexandersson, A homogeneity test applied to precipitation data. J. Climatol. 6, 661–675 (1986) [CrossRef] [Google Scholar]
G. J. Huffman, R. F. Adler, D. T. Bolvin, E. J. Nelkin, The TRMM Multi-satellite Precipitation Analysis (TMPA), Satell. Rainfall Appl. Surf. Hydrol. 3–22 (2010) [Google Scholar]
G. Huffman, D. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, C. Kidd, E. J. Nelkin, S. Sorroshian, J. Tan, P. Xie, Algorithm Theoretical Basis Document (ATBD) NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) (2018) [Google Scholar]
G. Huffman, J. R. F. Adler, M. M. Morrissey, S. Curtis, R. Joyce, B. McGavock, J. Susskind, Global precipitation at one-degree daily resolution from multi-satellite observations, J. Hydrometeor., 2, 36–50 (2001) [CrossRef] [Google Scholar]
U. Schneider, Becker, A. Finger, GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle, Theor Appl Climatol 115, 15–40 (2014) [CrossRef] [Google Scholar]
C. Funk, A. Verdin, J. Michaelsen, P. Peterson, P. Pedreros, G. Husak, 2015, A global satellite-assisted precipitation climatology, Earth Syst. Sci. Data 7, 275–287 (2015) [Google Scholar]
J. E. Nash, J. V. Sutcliffe, River flow forecasting through conceptual models, Part ia discussion of principles, Journal of hydrology, 10(3), 282–290 (1970) [CrossRef] [Google Scholar]
T. Mathevet, C. Michel, V. Andréassian, C. Perrin, A bounded version of the Nash- Sutcliffe criterion for better model assessment on large sets of basins, IAHS-AISH Publication. 307, 211–219 (2006) [Google Scholar]
S. Baillargeon, Le krigeage : revue de la théorie et application à l’interpolation spatiale de données de précipitations (2005) [Google Scholar]
I. Harris, T.J. Osborn, P. Jones, D. Lister, Version 4 of the CRU TS Monthly HighResolution Gridded Multivariate Climate Dataset, Sci Data 7, 109 (2020) [CrossRef] [PubMed] [Google Scholar]
L. Coron, G. Thirel, O. Delaigue, C. Perrin, V. Andréassian, The Suite of Lumped GR Hydrological Models in an R Package, Environmental Modelling and Software, 94, 166–171 (2017) [Google Scholar]
L. Coron, O. Delaigue, G. Thirel, C. Perrin, C. Michel, Airgr: Suite of GR Hydrological Models for Precipitation-Runoff Modelling, R package version X.X.X.X. [Google Scholar]
C. Michel, Que peut-on faire en hydrologie avec un modèle à un paramètre ? La Houille Blanche 1, 39–44 (1983) [CrossRef] [EDP Sciences] [Google Scholar]
C. Perrin, C. Michel, V. Andréassian, Improvement of a parsimonious model for streamflow simulation, Journal of Hydrology 279, 275–289 (2003) [CrossRef] [Google Scholar]
N. Le Moine, Le bassin versant de surface vu par le souterrain : une voie d’amélioration des performances et du réalisme des modèles pluie-débit ? Ph.D. thesis (2008) [Google Scholar]
C. Poncelet, Du bassin au paramètre : jusqu’où peut-on régionaliser un modèle hydrologique conceptuel ? Hydrologie, Université Pierre et Marie Curie - Paris VI (2016) [Google Scholar]
V. K. Gupta, S. Sorooshian, The relationship between data and the precision of parameter estimates of hydrologic models, Journal of Hydrology 81, 57–77 (1985) [CrossRef] [Google Scholar]
A. Valéry, V. Andréassian, C. Perrin, ‘As simple as possible but not simpler’: What is useful in a temperature-based snow-accounting routine? Part 1 - Comparison of six snow accounting routines on 380 catchments, Journal of Hydrology, 517(0), 1166–1175 (2014) [CrossRef] [Google Scholar]
A. Valéry, V. Andréassian, C. Perrin, ‘As simple as possible but not simpler’: What is useful in a temperature-based snow-accounting routine? Part 2 - Sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments, Journal of Hydrology, 517(0) (2014) [Google Scholar]
J. Odry, Prédétermination des débits de crues extrêmes en sites non jaugés, Régionalisation de la méthode par simulation SHYREG, Sciences de l’environnement, Doctorat géosciences de l’environnement, Aix-Marseille Université (2017) [Google Scholar]
L. Lebecherel, Sensibilité des calculs hydrologiques à la densité des réseaux de mesure hydrométrique et pluviométrique, Ph.D. thesis, AgroParisTech, Paris (2015) [Google Scholar]
L. Oudin, V. Andréassian, C. Perrin, C. Michel, N. Le Moine, Spatial proximity, physical similarity, regression and ungaged catchments, A comparison of regionalization approaches based on 913 French catchments, Water Resources Research 44 (2008) [Google Scholar]
L. Breiman, Random Forests, Mach, Learn, 45, 5–32 (2001) [CrossRef] [Google Scholar]
M. Saadi, L. Oudin, P. Ribstein, Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters, Water, 11, 1540 (2019) [CrossRef] [Google Scholar]

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[1] J. C. Vörösmarty, A. Askew, W. Grabs, R. G. Barry, C. Birkett, P.D.B. Goodison, A. Hall, R. Jenne, L. Kitaev, J. Landwehr, M. Keeler, G. Leavesley, J. Schaake, K. Strzepek, S.S. Sundarvel, K. Takeuchi, F. Webster, Global water data: A newly endangered species, Eos, Transactions American Geophysical Union, 82 (5):54–58 (2001) [CrossRef] [Google Scholar]

[2] T. Lebel, J.D. Taupin, N. D’Amato, Rainfall monitoring during HAPEX-Sahel, 1. General rainfall conditions and climatology, J. Hydrol. 189, 74—96 (1997) [CrossRef] [Google Scholar]

[3] J. Li, A. Heap, Spatial Interpolation Methods: A Review for Environmental Scientists (2008) [Google Scholar]

[4] A. Paris, Utilisation conjointe de données d'altimétrie satellitaire et de modélisation pour le calcul des débits distribués dans le bassin amazonien, Hydrologie, Université Paul Sabatier - Toulouse III (2015) [Google Scholar]

[5] F. Satge, D. D. Defrance, B. Sultan, M.-P. Bonnet, F. Seyler, N. Rouché, F. Pierron, J.E. Paturel, Evaluation of 23 gridded precipitation datasets across West Africa, Journal of Hydrology (2019) [Google Scholar]

[6] H. Alexandersson, A homogeneity test applied to precipitation data. J. Climatol. 6, 661–675 (1986) [CrossRef] [Google Scholar]

[7] G. J. Huffman, R. F. Adler, D. T. Bolvin, E. J. Nelkin, The TRMM Multi-satellite Precipitation Analysis (TMPA), Satell. Rainfall Appl. Surf. Hydrol. 3–22 (2010) [Google Scholar]

[8] G. Huffman, D. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, C. Kidd, E. J. Nelkin, S. Sorroshian, J. Tan, P. Xie, Algorithm Theoretical Basis Document (ATBD) NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) (2018) [Google Scholar]

[9] G. Huffman, J. R. F. Adler, M. M. Morrissey, S. Curtis, R. Joyce, B. McGavock, J. Susskind, Global precipitation at one-degree daily resolution from multi-satellite observations, J. Hydrometeor., 2, 36–50 (2001) [CrossRef] [Google Scholar]

[10] U. Schneider, Becker, A. Finger, GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle, Theor Appl Climatol 115, 15–40 (2014) [CrossRef] [Google Scholar]

[11] C. Funk, A. Verdin, J. Michaelsen, P. Peterson, P. Pedreros, G. Husak, 2015, A global satellite-assisted precipitation climatology, Earth Syst. Sci. Data 7, 275–287 (2015) [Google Scholar]

[12] J. E. Nash, J. V. Sutcliffe, River flow forecasting through conceptual models, Part ia discussion of principles, Journal of hydrology, 10(3), 282–290 (1970) [CrossRef] [Google Scholar]

[13] T. Mathevet, C. Michel, V. Andréassian, C. Perrin, A bounded version of the Nash- Sutcliffe criterion for better model assessment on large sets of basins, IAHS-AISH Publication. 307, 211–219 (2006) [Google Scholar]

[14] S. Baillargeon, Le krigeage : revue de la théorie et application à l’interpolation spatiale de données de précipitations (2005) [Google Scholar]

[15] I. Harris, T.J. Osborn, P. Jones, D. Lister, Version 4 of the CRU TS Monthly HighResolution Gridded Multivariate Climate Dataset, Sci Data 7, 109 (2020) [CrossRef] [PubMed] [Google Scholar]

[16] L. Coron, G. Thirel, O. Delaigue, C. Perrin, V. Andréassian, The Suite of Lumped GR Hydrological Models in an R Package, Environmental Modelling and Software, 94, 166–171 (2017) [Google Scholar]

[17] L. Coron, O. Delaigue, G. Thirel, C. Perrin, C. Michel, Airgr: Suite of GR Hydrological Models for Precipitation-Runoff Modelling, R package version X.X.X.X. [Google Scholar]

[18] C. Michel, Que peut-on faire en hydrologie avec un modèle à un paramètre ? La Houille Blanche 1, 39–44 (1983) [CrossRef] [EDP Sciences] [Google Scholar]

[19] C. Perrin, C. Michel, V. Andréassian, Improvement of a parsimonious model for streamflow simulation, Journal of Hydrology 279, 275–289 (2003) [CrossRef] [Google Scholar]

[20] N. Le Moine, Le bassin versant de surface vu par le souterrain : une voie d’amélioration des performances et du réalisme des modèles pluie-débit ? Ph.D. thesis (2008) [Google Scholar]

[21] C. Poncelet, Du bassin au paramètre : jusqu’où peut-on régionaliser un modèle hydrologique conceptuel ? Hydrologie, Université Pierre et Marie Curie - Paris VI (2016) [Google Scholar]

[22] V. K. Gupta, S. Sorooshian, The relationship between data and the precision of parameter estimates of hydrologic models, Journal of Hydrology 81, 57–77 (1985) [CrossRef] [Google Scholar]

[23] A. Valéry, V. Andréassian, C. Perrin, ‘As simple as possible but not simpler’: What is useful in a temperature-based snow-accounting routine? Part 1 - Comparison of six snow accounting routines on 380 catchments, Journal of Hydrology, 517(0), 1166–1175 (2014) [CrossRef] [Google Scholar]

[24] A. Valéry, V. Andréassian, C. Perrin, ‘As simple as possible but not simpler’: What is useful in a temperature-based snow-accounting routine? Part 2 - Sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments, Journal of Hydrology, 517(0) (2014) [Google Scholar]

[25] J. Odry, Prédétermination des débits de crues extrêmes en sites non jaugés, Régionalisation de la méthode par simulation SHYREG, Sciences de l’environnement, Doctorat géosciences de l’environnement, Aix-Marseille Université (2017) [Google Scholar]

[26] L. Lebecherel, Sensibilité des calculs hydrologiques à la densité des réseaux de mesure hydrométrique et pluviométrique, Ph.D. thesis, AgroParisTech, Paris (2015) [Google Scholar]

[27] L. Oudin, V. Andréassian, C. Perrin, C. Michel, N. Le Moine, Spatial proximity, physical similarity, regression and ungaged catchments, A comparison of regionalization approaches based on 913 French catchments, Water Resources Research 44 (2008) [Google Scholar]

[28] L. Breiman, Random Forests, Mach, Learn, 45, 5–32 (2001) [CrossRef] [Google Scholar]

[29] M. Saadi, L. Oudin, P. Ribstein, Random Forest Ability in Regionalizing Hourly Hydrological Model Parameters, Water, 11, 1540 (2019) [CrossRef] [Google Scholar]