H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974.
DOI : 10.1109/TAC.1974.1100705

B. A. Amisigo and N. C. Van-de-giesen, Using a spatio-temporal dynamic state-space model with the EM algorithm to patch gaps in daily riverflow series, Hydrology and Earth System Sciences, vol.9, issue.3, pp.209-224, 2005.
DOI : 10.5194/hess-9-209-2005
URL : https://hal.archives-ouvertes.fr/hal-00304820

B. Ardossy, A. , and G. Pegram, Infilling missing precipitation records ??? A comparison of a new copula-based method with other techniques, Journal of Hydrology, vol.519, pp.1162-1170, 2014.
DOI : 10.1016/j.jhydrol.2014.08.025

S. Bercu and F. Pro?-ia, A SARIMAX coupled modelling applied to individual load curves intraday forecasting, Journal of Applied Statistics, vol.26, issue.3, pp.1333-1348, 2013.
DOI : 10.1093/biomet/71.3.599
URL : https://hal.archives-ouvertes.fr/hal-00732844

G. E. Box and D. A. Pierce, Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models, Journal of the American Statistical Association, vol.11, issue.332, pp.65-1509, 1970.
DOI : 10.1098/rsta.1927.0007

P. Coulibaly and C. K. Baldwin, Nonstationary hydrological time series forecasting using nonlinear dynamic methods, Journal of Hydrology, vol.307, issue.1-4, pp.1-4, 2005.
DOI : 10.1016/j.jhydrol.2004.10.008

A. Elshorbagy, S. Simonovic, and U. Panu, Estimation of missing streamflow data using principles of chaos theory, Journal of Hydrology, vol.255, issue.1-4, pp.123-133, 2002.
DOI : 10.1016/S0022-1694(01)00513-3

K. Eng, G. D. Tasker, and P. Milly, AN ANALYSIS OF REGION-OF-INFLUENCE METHODS FOR FLOOD REGIONALIZATION IN THE GULF-ATLANTIC ROLLING PLAINS, Journal of the American Water Resources Association, vol.32, issue.60, pp.135-143, 2005.
DOI : 10.2307/1267881

G. Gardner, A. Harvey, and G. Phillips, An algorithm for exact maximum likelihood estimation of average models by means of Kalman filtering, J. R. Stat. Soc. Ser. C, issue.3, pp.29-311, 1980.

J. B. Greenhouse, R. E. Kass, and R. S. Tsay, Fitting nonlinear models with ARMA errors to biological rhythm data, Statistics in Medicine, vol.42, issue.2, pp.167-183, 1987.
DOI : 10.1002/sim.4780060209

H. V. Gupta, H. Kling, K. K. Yilmaz, and G. F. Martinez, Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, Journal of Hydrology, vol.377, issue.1-2, pp.80-91, 2009.
DOI : 10.1016/j.jhydrol.2009.08.003

P. Gyau-boakye and G. Schultz, Filling gaps in runoff time series in West Africa, Hydrological Sciences Journal, vol.23, issue.6, pp.621-636, 1994.
DOI : 10.1061/(ASCE)0733-9429(1987)113:1(61)

C. Harvey, H. Dixon, and J. Hannaford, An appraisal of the performance of data-infilling methods for application to daily mean river flow records in the UK, Hydrology Research, vol.43, issue.5, pp.618-636, 2012.
DOI : 10.2166/nh.2012.110

R. Hirsch, An evaluation of some record reconstruction techniques, Water Resources Research, vol.11, issue.3, pp.1781-1790, 1979.
DOI : 10.1029/WR015i006p01781

H. M. Kang and F. Yusof, Homogeneity tests on daily rainfall series, Int. J. Contemp. Math. Sci, vol.7, issue.1, pp.9-22, 2012.

L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, 1990.
DOI : 10.1002/9780470316801

M. Khalil, U. Panu, and W. Lennox, Groups and neural networks based streamflow data infilling procedures, Journal of Hydrology, vol.241, issue.3-4, pp.153-176, 2001.
DOI : 10.1016/S0022-1694(00)00332-2

J. Kim and Y. A. Pachepsky, Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT streamflow simulation, Journal of Hydrology, vol.394, issue.3-4, pp.3-4, 2010.
DOI : 10.1016/j.jhydrol.2010.09.005

A. Kuentz, Un siè cle de variabilit e hydro-climatique sur le bassin de la Durance, 2013.

A. Kuentz, T. Mathevet, J. Gailhard, C. Perret, and V. , Andr eassian (2013), Over 100 years of climatic and hydrologic variability of a Mediterranean and mountainous watershed: The Durance River, Cold and Mountain Region Hydrological Systems Under Climate Change: Towards Improved Projections Proceedings, pp.19-25

A. Kuentz, T. Mathevet, J. Gailhard, and B. Hingray, Building long-term and high spatio-temporal resolution precipitation and air temperature reanalyses by mixing local observations and global atmospheric reanalyses: the ANATEM method, Hydrology and Earth System Sciences Discussions, vol.12, issue.1, pp.311-361, 2015.
DOI : 10.5194/hessd-12-311-2015

M. H. Kutner, C. Nachtsheim, and J. Neter, Applied Linear Regression Models, 2004.

D. Kwiatkowski, P. C. Phillips, P. Schmidt, and Y. Shin, Testing the null hypothesis of stationarity against the alternative of a unit root, Journal of Econometrics, vol.54, issue.1-3, pp.1-3, 1992.
DOI : 10.1016/0304-4076(92)90104-Y

S. Miaou, A stepwise time series regression procedure for water demand model identification, Water Resources Research, vol.9, issue.4, pp.1887-1897, 1990.
DOI : 10.1029/WR026i009p01887

D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis, 2012.

J. Nash and J. Sutcliffe, River flow forecasting through conceptual models part I ??? A discussion of principles, Journal of Hydrology, vol.10, issue.3, pp.282-290, 1970.
DOI : 10.1016/0022-1694(70)90255-6

A. Pankratz, Forecasting with Dynamic Regression Models, 1991.
DOI : 10.1002/9781118150528

H. Raman, S. Mohan, and P. Padalinathan, Models for extending streamflow data: a case study, Hydrological Sciences Journal, vol.20, issue.3, pp.381-393, 1995.
DOI : 10.1029/WR021i005p00715

B. D. Ripley, Time series in R 1.5.0, J, vol.2, issue.2, pp.2-7, 2002.

P. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, vol.20, pp.53-65, 1987.
DOI : 10.1016/0377-0427(87)90125-7

S. E. Said and D. A. Dickey, Testing for unit roots in autoregressive-moving average models of unknown order, Biometrika, vol.71, issue.3, pp.71-599, 1984.
DOI : 10.1093/biomet/71.3.599

T. Schneider, Analysis of Incomplete Climate Data: Estimation of Mean Values and Covariance Matrices and Imputation of Missing Values, Journal of Climate, vol.14, issue.5, pp.853-871, 2001.
DOI : 10.1175/1520-0442(2001)014<0853:AOICDE>2.0.CO;2

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

G. W. Schwert, Tests for Unit Roots, Journal of Business & Economic Statistics, vol.20, issue.1, pp.147-159, 1989.
DOI : 10.1198/073500102753410354

. Suhartono, Time Series Forecasting by using Seasonal Autoregressive Integrated Moving Average: Subset, Multiplicative or Additive Model, Journal of Mathematics and Statistics, vol.7, issue.1, pp.20-27, 2011.
DOI : 10.3844/jmssp.2011.20.27

R. Tsay, Regression Models with Time Series Errors, Journal of the American Statistical Association, vol.11, issue.385, pp.118-124, 1984.
DOI : 10.1080/01621459.1984.10477068

J. R. Wallis, D. P. Lettenmaier, and E. F. Wood, A daily hydroclimatological data set for the continental United States, Water Resources Research, vol.14, issue.1, pp.1657-1663, 1991.
DOI : 10.1029/91WR00977

J. B. Wijngaard, A. M. Klein-tank, and G. P. Konnen, Homogeneity of 20th century European daily temperature and precipitation series, International Journal of Climatology, vol.13, issue.6, pp.679-692, 2003.
DOI : 10.1002/joc.906