F. Douville and . Chauvin, Initializing numerical weather predictions models with satel- 740 lite derived surface soil moisture: Data assimilation experiments with 741 ECMWF's integrated forecast system and the TMI soil moisture data set Relevance of soil moisture for seasonal cli- 744 mate predictions: A preliminary study The SMOS mission: 749 New tool for monitoring key elements of the global water cycle, Implementation Plan for the Global Observing System for Climate 737 in Support of the UNFCCC Proc. 750 IEEE, pp.736-739, 0751.

D. Entekhabi, E. Njoku, P. O. Neill, K. Kellogg, W. Crow et al., The 755 soil moisture active passive (SMAP) mission, Proc. IEEE, pp.756-704, 0757.

Y. Kerr, P. Waldteufel, J. Wigneron, J. Martinuzzi, J. Font et al., Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.8, pp.760-1729, 2001.
DOI : 10.1109/36.942551

M. Owe, R. De-jeu, and J. Walker, A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.8, pp.1643-1654, 2001.
DOI : 10.1109/36.942542

E. Njoku, T. Jackson, V. Lakshmi, T. Chan, and S. Nghiem, Soil moisture retrieval from AMSR-E, IEEE Transactions on Geoscience and Remote Sensing, vol.41, issue.2, pp.215-229, 2003.
DOI : 10.1109/TGRS.2002.808243

L. Li, P. Gaiser, B. Gao, R. Bevilacqua, T. Jackson et al., Windsat global soil moisture retrieval and 770 validation, IEEE Trans. Geosci. Remote Sens, vol.769, issue.48 5, pp.2224-771, 0772.

V. Naeimi, K. Scipal, Z. Bartalis, and S. H. Wagner, An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Transactions on Geoscience and Remote Sensing, vol.47, issue.7, pp.1999-2013
DOI : 10.1109/TGRS.2008.2011617

L. Vincent, X. Zhang, B. Bonsal, and W. Hogg, Homogenization of Daily Temperatures over Canada, Journal of Climate, vol.15, issue.11, pp.1322-778, 2002.
DOI : 10.1175/1520-0442(2002)015<1322:HODTOC>2.0.CO;2

M. Begert, T. Schlegel, and W. Kirchhofer, Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000, International Journal of Climatology, vol.17, issue.1, pp.65-80, 2000.
DOI : 10.1002/joc.1118

G. Picard and M. Fily, Surface melting observations in Antarctica by microwave radiometers: Correcting 26-year time series from changes in acquisition hours, Remote Sensing of Environment, vol.104, issue.3, pp.325-336
DOI : 10.1016/j.rse.2006.05.010

URL : https://hal.archives-ouvertes.fr/insu-00375742

R. Reichle and R. Koster, Bias reduction in short records of satellite soil 787 moisture, Geophys. Res. Lett, vol.31, issue.19, pp.19-501, 2004.

M. Choi and J. Jacobs, Temporal variability corrections for advanced 789 microwave scanning radiometer E (AMSR-E) surface soil moisture, p.790

H. Li, J. Sheffield, and E. Wood, Bias correction of monthly precipitation 793 and temperature fields from IPCC AR4 models using equidistant quantile 794 matching, J. Geophys. Res., Atmosp, vol.115, issue.D10, pp.10-101, 0796.

M. Drusch, E. Wood, and H. Gao, Observation operators for the di-797 rect assimilation of TRMM microwave imager retrieved soil moisture, p.798

Y. Liu, A. Van-dijk, R. De-jeu, and T. Holmes, An analysis of spatiotem-800 poral variations of soil and vegetation moisture from a 29-year satellite-801 derived data set over mainland Australia, Water Resour. Res, vol.45, issue.802 7, pp.7-405, 2009.

Y. Liu, R. Parinussa, W. Dorigo, R. D. Jeu, W. Wagner et al., Developing an improved soil moisture dataset 805 by blending passive and active microwave satellite-based retrievals, p.806

V. Singh and W. Strupczewski, Editorial, Journal of Hydrologic Engineering, vol.12, issue.4, p.345, 0809.
DOI : 10.1061/(ASCE)1084-0699(2007)12:4(345)

R. Nelsen, An introduction to copulas, Springer Series in Statistics. 810, p.811, 1998.
DOI : 10.1007/978-1-4757-3076-0

P. Trivedi and D. Zimmer, Copula Modeling: An Introduction for Practi-812 tioners Foundations and Trends in Econometrics, pp.1-111, 2005.

C. Hafner and O. Reznikova, Efficient estimation of a semiparamet-815 ric synamic copula model, Comput. Stat. Data Anal, vol.54, issue.11, pp.816-2609, 2010.

C. Genest and A. Favre, Everything you always wanted to know about 818 copula modeling but were afraid to ask, J. Hydrol. Eng, vol.12, issue.4, pp.819-347, 2007.

A. Favre, S. E. Adlouni, L. Perreault, N. Thiémonge, and B. Bobée, Mul-821 tivariate hydrological frequency analysis using copulas, Water Resour, p.822

G. Salvadori and C. De-michele, On the Use of Copulas in Hydrology: Theory and Practice, Journal of Hydrologic Engineering, vol.12, issue.4, pp.369-380
DOI : 10.1061/(ASCE)1084-0699(2007)12:4(369)

D. Dupuis, Using Copulas in Hydrology: Benefits, Cautions, and Issues, Journal of Hydrologic Engineering, vol.12, issue.4, pp.381-393, 2007.
DOI : 10.1061/(ASCE)1084-0699(2007)12:4(381)

L. Zhang and V. Singh, Trivariate flood frequency analysis using the 829

. Gumbel-hougaard-copula-]-f, S. Serinaldi, ]. P. Grimaldi, S. Laux, W. Vogl et al., Fully nested 3-copula: Procedure and appli- 832 cation on hydrological data Copula-based 835 statistical refinement of precipitation in RCM simulations over complex 836 terrain Copula-derived observa- 838 tion operators for assimilating TMI and AMSR-E retrieved soil moisture 839 into land surface models A generalized pareto intensity-duration 842 model of storm rainfall exploiting 2-copulas, M. Guglielmetti, B. Hornbuckle, C. Mätzler, T. Pellarin, and M. Schwank, 847 " L-band microwave emission of the biosphere (L-MEB) model: Descrip- 848 tion and calibration against experimental data sets over crop fields, pp.431-439, 2003.

]. Y. Kerr, P. Waldteufel, P. Richaume, J. Wigneron, P. Ferrazzoli et al., The SMOS soil moisture retrieval algo- 853 rithm ISEA discrete global grids, IEEE Trans. Geosci. Remote Sens, vol.107, issue.50 5, pp.639-655, 2007.

. Stat, . Comput, . Stat, . Graph, ]. G. Newl et al., Evaluation of SMOS soil moisture products over continental 859 US using SCAN/SNOTEL network The USDA natural resources 862 conservation service soil climate analysis network (SCAN) Validation of soil mois- 866 ture and ocean salinity (SMOS) soil moisture over watershed networks in 867 the U.S, 871 and ECMWF soil moisture products over four watersheds in the U.S. Technical implementation 874 of SMOS data in the ECMWF integrated forecast system, pp.31-39, 1997.

R. Sens and . Lett, Available: ftp://n4ftl01u.ecs.nasa.gov/SAN/AMSA Evaluation of AMSR-E soil moisture product based on 879 ground measurements over temperate and semi-arid regions, Calvet, and 878 P. Richaume, pp.252-256, 2012.

. Res, C. Lett, J. C. Rudiger, C. Calvet, T. Gruhier et al., An intercomparison of ERS-SCAT and AMSR-E soil mois- 883 ture observations with model simulations over France An evaluation 886 of AMSR-E derived soil moisture over Australia Soil moisture 890 active and passive microwave products: Intercomparison and evaluation 891 over a Sahelian site, Amer. Meteorol. 884 Soc, pp.10-405, 2008.

S. Chaurasia, D. Tung, P. Thapliyal, P. Joshi, T. Jackson et al., Assessment of AMSR- 894 E soil moisture product over India Validation of advanced microwave 898 scanning radiometer soil moisture products On nonparametric measures of dependence 901 for random variables Modeling the dependent competing risks with 903 multiple degradation processes and random shock using time-varying 904 copulas Statistical inference procedures for bivariate 906, Int. J. Remote Sens. IEEE Trans. Geosci. Remote 899 Sens. Ann. Stat. IEEE Trans. Rel, vol.32, issue.61 1, pp.893-7955, 1981.

J. Fermanian, Goodness-of-fit tests for copulas, Journal of Multivariate Analysis, vol.95, issue.1, pp.119-152, 0910.
DOI : 10.1016/j.jmva.2004.07.004

C. Genest, J. Quessy, and B. Rémillard, Goodness-of-fit procedures 911 for copula models based on the probability integral transform, Scand. J, p.912

C. Genest and B. Rémillard, Validity of the parametric bootstrap 914 for goodness-of-fit testing in semiparametric models, Annales Henri, p.915

D. Berg, Copula goodness-of-fit testing: an overview and power comparison, The European Journal of Finance, vol.95, issue.7-8, pp.675-701, 2009.
DOI : 10.1080/13518470802697428

C. Genest, B. Rémillard, and D. Beaudoin, Goodness-of-fit tests for copulas: A review and a power study, Insurance: Mathematics and Economics, vol.44, issue.2, pp.199-213, 0921.
DOI : 10.1016/j.insmatheco.2007.10.005

D. Huard, G. Evin, and A. Favre, Bayesian copula selection, Computational Statistics & Data Analysis, vol.51, issue.2
DOI : 10.1016/j.csda.2005.08.010

D. Stat, ]. M. Anal, . Drusch-]-h, F. Douville, P. Chauvin-kerr et al., Implementation Plan for the Global Observing System for Climate 737 in Support of the UNFCCC Initializing numerical weather predictions models with satel- 740 lite derived surface soil moisture: Data assimilation experiments with 741 ECMWF's integrated forecast system and the TMI soil moisture data set Relevance of soil moisture for seasonal cli- 744 mate predictions: A preliminary study The SMOS mission: 749 New tool for monitoring key elements of the global water cycle, Proc. 750 IEEE, pp.809-822, 0751.

D. Entekhabi, E. Njoku, P. O. Neill, K. Kellogg, W. Crow et al., The 755 soil moisture active passive (SMAP) mission, Proc. IEEE, pp.756-704, 0757.

Y. Kerr, P. Waldteufel, J. Wigneron, J. Martinuzzi, J. Font et al., Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.8, pp.760-1729, 2001.
DOI : 10.1109/36.942551

M. Owe, R. De-jeu, and J. Walker, A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.8, pp.1643-1654, 2001.
DOI : 10.1109/36.942542

E. Njoku, T. Jackson, V. Lakshmi, T. Chan, and S. Nghiem, Soil moisture retrieval from AMSR-E, IEEE Transactions on Geoscience and Remote Sensing, vol.41, issue.2, pp.215-229, 2003.
DOI : 10.1109/TGRS.2002.808243

L. Li, P. Gaiser, B. Gao, R. Bevilacqua, T. Jackson et al., Windsat global soil moisture retrieval and 770 validation, IEEE Trans. Geosci. Remote Sens, vol.769, issue.48 5, pp.2224-771, 0772.

V. Naeimi, K. Scipal, Z. Bartalis, and S. H. Wagner, An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Transactions on Geoscience and Remote Sensing, vol.47, issue.7, pp.1999-2013
DOI : 10.1109/TGRS.2008.2011617

L. Vincent, X. Zhang, B. Bonsal, and W. Hogg, Homogenization of Daily Temperatures over Canada, Journal of Climate, vol.15, issue.11, pp.1322-778, 2002.
DOI : 10.1175/1520-0442(2002)015<1322:HODTOC>2.0.CO;2

M. Begert, T. Schlegel, and W. Kirchhofer, Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000, International Journal of Climatology, vol.17, issue.1, pp.65-80, 2000.
DOI : 10.1002/joc.1118

G. Picard and M. Fily, Surface melting observations in Antarctica by microwave radiometers: Correcting 26-year time series from changes in acquisition hours, Remote Sensing of Environment, vol.104, issue.3, pp.325-336
DOI : 10.1016/j.rse.2006.05.010

URL : https://hal.archives-ouvertes.fr/insu-00375742

R. Reichle and R. Koster, Bias reduction in short records of satellite soil 787 moisture, Geophys. Res. Lett, vol.31, issue.19, pp.19-501, 2004.

M. Choi and J. Jacobs, Temporal variability corrections for advanced 789 microwave scanning radiometer E (AMSR-E) surface soil moisture, p.790

H. Li, J. Sheffield, and E. Wood, Bias correction of monthly precipitation 793 and temperature fields from IPCC AR4 models using equidistant quantile 794 matching, J. Geophys. Res., Atmosp, vol.115, issue.D10, pp.10-101, 0796.

M. Drusch, E. Wood, and H. Gao, Observation operators for the di-797 rect assimilation of TRMM microwave imager retrieved soil moisture, p.798

Y. Liu, A. Van-dijk, R. De-jeu, and T. Holmes, An analysis of spatiotem-800 poral variations of soil and vegetation moisture from a 29-year satellite-801 derived data set over mainland Australia, Water Resour. Res, vol.45, issue.802 7, pp.7-405, 2009.

Y. Liu, R. Parinussa, W. Dorigo, R. D. Jeu, W. Wagner et al., Developing an improved soil moisture dataset 805 by blending passive and active microwave satellite-based retrievals, p.806

V. Singh and W. Strupczewski, Editorial, Journal of Hydrologic Engineering, vol.12, issue.4, p.345, 0809.
DOI : 10.1061/(ASCE)1084-0699(2007)12:4(345)

R. Nelsen, An introduction to copulas, Springer Series in Statistics. 810, p.811, 1998.
DOI : 10.1007/978-1-4757-3076-0

P. Trivedi and D. Zimmer, Copula Modeling: An Introduction for Practi-812 tioners Foundations and Trends in Econometrics, pp.1-111, 2005.

C. Hafner and O. Reznikova, Efficient estimation of a semiparamet-815 ric synamic copula model, Comput. Stat. Data Anal, vol.54, issue.11, pp.816-2609, 2010.

C. Genest and A. Favre, Everything you always wanted to know about 818 copula modeling but were afraid to ask, J. Hydrol. Eng, vol.12, issue.4, pp.819-347, 2007.

A. Favre, S. E. Adlouni, L. Perreault, N. Thiémonge, and B. Bobée, Mul-821 tivariate hydrological frequency analysis using copulas, Water Resour, p.822
DOI : 10.1029/2003wr002456

G. Salvadori and C. De-michele, On the Use of Copulas in Hydrology: Theory and Practice, Journal of Hydrologic Engineering, vol.12, issue.4, pp.369-380
DOI : 10.1061/(ASCE)1084-0699(2007)12:4(369)

D. Dupuis, Using Copulas in Hydrology: Benefits, Cautions, and Issues, Journal of Hydrologic Engineering, vol.12, issue.4, pp.381-393, 2007.
DOI : 10.1061/(ASCE)1084-0699(2007)12:4(381)

L. Zhang and V. Singh, Trivariate flood frequency analysis using the 829

. Gumbel-hougaard-copula-]-f, S. Serinaldi, ]. P. Grimaldi, S. Laux, W. Vogl et al., Fully nested 3-copula: Procedure and appli- 832 cation on hydrological data Copula-based 835 statistical refinement of precipitation in RCM simulations over complex 836 terrain Copula-derived observa- 838 tion operators for assimilating TMI and AMSR-E retrieved soil moisture 839 into land surface models A generalized pareto intensity-duration 842 model of storm rainfall exploiting 2-copulas, M. Guglielmetti, B. Hornbuckle, C. Mätzler, T. Pellarin, and M. Schwank, 847 " L-band microwave emission of the biosphere (L-MEB) model: Descrip- 848 tion and calibration against experimental data sets over crop fields, pp.431-439, 2003.

]. Y. Kerr, P. Waldteufel, P. Richaume, J. Wigneron, P. Ferrazzoli et al., The SMOS soil moisture retrieval algo- 853 rithm ISEA discrete global grids, IEEE Trans. Geosci. Remote Sens, vol.107, issue.50 5, pp.639-655, 2007.

. Stat, . Comput, . Stat, . Graph, ]. G. Newl et al., Evaluation of SMOS soil moisture products over continental 859 US using SCAN/SNOTEL network The USDA natural resources 862 conservation service soil climate analysis network (SCAN) Validation of soil mois- 866 ture and ocean salinity (SMOS) soil moisture over watershed networks in 867 the U.S, 871 and ECMWF soil moisture products over four watersheds in the U.S. Technical implementation 874 of SMOS data in the ECMWF integrated forecast system, pp.31-39, 1997.

R. Sens and . Lett, Available: ftp://n4ftl01u.ecs.nasa.gov/SAN/AMSA Evaluation of AMSR-E soil moisture product based on 879 ground measurements over temperate and semi-arid regions, Calvet, and 878 P. Richaume, pp.252-256, 2012.

. Res, C. Lett, J. C. Rudiger, C. Calvet, T. Gruhier et al., An intercomparison of ERS-SCAT and AMSR-E soil mois- 883 ture observations with model simulations over France An evaluation 886 of AMSR-E derived soil moisture over Australia Soil moisture 890 active and passive microwave products: Intercomparison and evaluation 891 over a Sahelian site, Amer. Meteorol. 884 Soc, pp.10-405, 2008.

S. Chaurasia, D. Tung, P. Thapliyal, P. Joshi, T. Jackson et al., Assessment of AMSR- 894 E soil moisture product over India Validation of advanced microwave 898 scanning radiometer soil moisture products On nonparametric measures of dependence 901 for random variables Modeling the dependent competing risks with 903 multiple degradation processes and random shock using time-varying 904 copulas Statistical inference procedures for bivariate 906, Int. J. Remote Sens. IEEE Trans. Geosci. Remote 899 Sens. Ann. Stat. IEEE Trans. Rel, vol.32, issue.61 1, pp.893-7955, 1981.

J. Fermanian, Goodness-of-fit tests for copulas, Journal of Multivariate Analysis, vol.95, issue.1, pp.119-152, 0910.
DOI : 10.1016/j.jmva.2004.07.004

C. Genest, J. Quessy, and B. Rémillard, Goodness-of-fit procedures 911 for copula models based on the probability integral transform, Scand. J, p.912

C. Genest and B. Rémillard, Validity of the parametric bootstrap 914 for goodness-of-fit testing in semiparametric models, Annales Henri, p.915

D. Berg, Copula goodness-of-fit testing: an overview and power comparison, The European Journal of Finance, vol.95, issue.7-8, pp.675-701, 2009.
DOI : 10.1080/13518470802697428

C. Genest, B. Rémillard, and D. Beaudoin, Goodness-of-fit tests for copulas: A review and a power study, Insurance: Mathematics and Economics, vol.44, issue.2, pp.199-213, 0921.
DOI : 10.1016/j.insmatheco.2007.10.005

D. Huard, G. Evin, and A. Favre, Bayesian copula selection, Computational Statistics & Data Analysis, vol.51, issue.2
DOI : 10.1016/j.csda.2005.08.010

. D. Ph, degree in spatial hydrology from