E. Cristiano, M. Veldhuis, and N. Giesen, Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas -a review In Hydrol, Earth Syst. Sci, vol.21, pp.3859-3878, 2017.

S. Verrier, L. De-montera, L. Barthès, and C. Mallet, Multifractal analysis of African monsoon rain fields, taking into account the zero rain rates problem, J. of Hydrology, issue.1, pp.111-120, 2010.

S. Verrier, C. Mallet, and L. Barthès, Multiscaling properties of rain in the time domain, taking into account rain support biases Journal of Geophysical Research-Atmospheres, J. Geophys. Res, vol.116, 2011.

M. C. Llasat, An objective classification of rainfall events on the basis of their convective features. Application to rainfall intensity in the north east of Spain, In International Journal of climatology, vol.21, pp.1385-1400, 2001.

P. S. Eagleson, Dynamic Hydrology, 1970.

B. G. Brown, R. W. Katz, and A. H. Murphy, Statistical analysis of climatological data to characterize erosion potential: 4. Freezing events in eastern Oregon/Washington, Oregon Agricultural Experiment Station Spec. Rep. No, vol.689, 1984.

M. L. Larsen and J. B. Teves, Identifying Individual Rain Events with a Dense Disdrometer Network, Advances in Meteorology, p.582782, 2015.

D. Dunkerley, Rain event properties in nature and in rainfall simulation experiments: a comparative review with recommendations for increasingly systematic study and reporting, Hydrological Processes, vol.22, pp.4415-4435, 2008.

D. Dunkerley, Identifying individual rain events from pluviography records: a review with analysis of data from an Australian dryland site, Hydrological Processes, vol.22, pp.5024-5036, 2008.

S. Aghabozorgi, A. S. Shirkhorshidi, T. H. Wah, and A. Sarda-espinosa, Time-series clustering -A decade review, Comparing Time-Series Clustering Algorithms in R Using the dtwclust Package, vol.53, pp.16-38, 2015.

K. Pearson, Mathematical Contributions to the Theory of Evolution. III. Regression, Heredity, and Panmixia, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol.187, pp.253-318, 1896.

C. Cassisi, P. Montalto, and M. Aliotta,

A. Cannata and A. Pulvirenti, Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining, Advances in Data Mining Knowledge Discovery and Applications. Adem Karahoca, 2012.

E. Keogh and M. Pazzani, Scaling up Dynamic Time Warping for Datamining Applications, Proc. Of the Sixth ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, pp.285-289, 2000.

P. Sung, Z. Syed, and J. Guttag, Quantifying Morphology Changes in Time Series Data with Skew. Acoustics, Speech and Signal Processing, pp.477-480, 2009.

Y. Rubner, C. Tomasi, L. J. Guibas, B. Aronov, S. Har-peled et al., A Metric for Distributions with Applications to Image Databases, Proc. IEEE ICCV, vol.17, pp.52-63, 1998.

D. P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge, Comparing images using the Hausdorff distance, IEEE Trans. PAMI, vol.15, issue.9, pp.850-863, 1993.

H. Sakoe and S. Chiba, Dynamic programming algorithm optimization for spoken word recognition, IEEE Trans. Acoustics, Speech, and Signal Proc, p.26, 1978.

Z. Zhanga, R. Tavenardb, A. Baillyb, X. Tangc, P. Tanga et al., Dynamic Time Warping Under Limited Warping Path Length, Information Sciences, vol.393, pp.91-107, 2017.

H. Sakoe and S. Chiba, A similarity evaluation of speech patterns by dynamic programming, Dig. Nat. Meeting, Inst. Electron. Comm. Eng. Japan, p.136, 1970.

H. Sakoe and S. Chiba, A dynamic programming approach to continuous speech recognition, Proc. 7 th ICA, vol.20, p.3, 1971.

E. Tsiporkova, A. Zinke, and D. Mayer, Iterative Multi Scale Dynamic Time Warping, Computer Graphics technical reports, issue.1, p.24, 2006.

F. Itakura, Minimum Prediction Residual Principle Applied to Speech Recognition, In IEEE Trans. Acoustics, Speech, and Signal Proc, issue.23, pp.52-72, 1975.

R. Bellman and S. Dreyfus, Applied Dynamic Programming, 1962.

H. Sakoe and S. Chiba, Comparative study of DP-pattern matching techniques for speech recognition, Tech. Group Meeting Speech, Acoust.SOC. Japan,Preprints, pp.73-95, 1973.

M. Muller, H. Mattes, and F. Kurth, An Efficient Multiscale Approach to Audio Synchronization, Proc. ISMIR, p.29, 2006.

S. Chu, E. Keogh, D. Hart, and M. Pazzani, Iterative Deepening Dynamic Time Warping for Time Series, Proc. Of the Second SIAM Intl

, Conf. on Data Mining, 2002.

S. S. Chan and P. , FastDTW: Toward accurate dynamic time warping in linear time and space, vol.11, pp.561-580, 2007.

A. Tokay and K. Öztürk, An Experimental Study of the Small-Scale Variability of Rainfall, Journal of Hydrometeorology, vol.13, issue.1, pp.351-365, 2012.

M. D. Dilmi, C. Mallet, L. Barthes, and A. Chazottes, Data-driven clustering of rain events: microphysics information derived from macro-scale observations, Atmos. Meas. Tech, vol.10, pp.1-18, 2017.
URL : https://hal.archives-ouvertes.fr/insu-01410090

A. A. Goshtasby, Similarity and Dissimilarity Measures, Image Registration. Advances in Computer Vision and Pattern Recognition

C. D. Truong and D. T. Anh, A novel clustering based method for time series motif discovery under time warping measure, Int J Data Sci Anal, vol.4, issue.2, pp.113-126, 2017.

S. Wang, C. F. Eick, Y. Van-gennip, B. Hunter, and A. Ma, A data mining framework for environmental and geo-spatial data analysis, Int J Data Sci Anal, vol.5, issue.2-3, pp.109-129, 2018.

Y. Endo, H. Toda, and K. Nishida, Classifying spatial trajectories using representation learning, Int J Data Sci Anal, vol.2, issue.3-4, pp.107-117, 2016.

D. Montera, L. Barthes, L. Mallet, C. Golé, and P. , The Effect of Rain-No Rain Intermittency on the Estimation of the Universal Multifractals Model Parameters, Journal of Hydrometeorology, vol.10, issue.2, pp.493-506, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00400833

N. Akrour, A. Chazottes, S. Verrier, C. Mallet, and L. Barthès, Simulation of yearly rainfall time series at microscale resolution with actual properties: Intermittency, scale invariance, and rainfall distribution, Water Resources Research, vol.51, issue.9, pp.7417-7435, 2015.
URL : https://hal.archives-ouvertes.fr/insu-01203278