E-proceedings of the 38th IAHR World Congress September 1-6, 2019, Panama City, Panama doi:10.3850/38WC092019-0161 3750 RELATIONSHIP BETWEEN DROUGHT OCCURRENCE AND ENSO IN SOUTHERN PERU: A COPULAS ANALYSIS JUAN WALTER CABRERA CABRERA (1) & JESÚS ABEL MEJÍA MARCACUZCO (2) (1,2) Programa de Doctorado en Recursos Hídricos, Universidad Nacional Agraria La Molina, Lima, Peru. 20180857@lamolina.edu.pe ABSTRACT Southern Peru is a semiarid area with frequent occurrence of drought events. Causes are often attributed to the occurrence of El Niño Phenomenon however; this relationship has not been fully confirmed. In this article, the possible relationship between the occurrences of drought with ENSO in southern Peru is analyzed under a copula’s analysis. For this purpose, the Standardized Precipitation Index is used for a period of three months (SPI3) as an indicator of drought and NINO34, NINO 1+2, and ICEN indices as indicators of El Niño Phenomenon occurrence. First, SPI should be estimated for every gage station in Candarave and Cairani irrigation district and the marginals distributions to be fitted to every group of data. Also, marginals to ENSO indexes should be evaluated. Finally, copulas are built in base to marginals and looking for the best correlation. Copulas analysis is a statistical technic which will establish whether there are relationships between these variables and can be used as a basis for the development of mitigation plans against the occurrence of droughts. The results show significant relationship between the occurrence of droughts and NINO34 index in Cairani but not relationship in Candarave. Keywords: Drought, ENSO, Copulas. 1 INTRODUCTION The study of droughts is not a new issue in the field of water resources, nor is it a problem restricted only to the national level. Its consequences on the population include economic losses and affect the normal development of socio-economic activities (Santos Pereira et al., 2002, Salvadori et al., 2005, Knutson 2008, Wilhite and Glantz 1985), and may cause massive waves of migration, such as those that occurred in the first half of the eighties in Peru (INEI, 2009). Due to the high complexity of these phenomena, and in order to make predictions of occurrence, in the last fifty years a strong tendency towards the use of stochastic models has developed. The works of Salas (Cancelliere and Salas 2004, Tables and Salas 1985, Chung and Salas 2000), Cancellieri (Cancelliere et al., 2007, Serinaldi et al., 2009) analyzing the application of techniques such as AR models, ARMA, Markov chains, among others, and including non-stationary series, represent an attempt to reproduce and predict the occurrence of the phenomenon. In the last fifteen years, the International Association of Hydrological Sciences (IAHS) has begun to test the use of copulas for the study of droughts based on Sklar's theorem (Rüschendorf 2013). The studies carried out by (Madadgar and Moradkhani 2013, Shiau 2006, Dupuis 2007, Kole et al 2007, Salvadori et al 2005, Serinaldi et al 2009) show that the method is efficient to perform multivariate frequency analysis on different descriptors of droughts. These experiences suggest that the technique allows to sufficiently represent complex relationships between hydrometeorological variables (AghaKouchak 2014) similar to those that occur in our country. 2 DEFINITIONS 2.1 Drought Droughts are considered like climatic deviations with respect to normal or desirable conditions in a region (Wilhite and Glantz, 1985). Usually, they can be described using 3 parameters: magnitude, length and intensity however, they could be studied by using indices. These indices define the level of severity according to predefined boundaries. The more widely used indices are the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Index (SPI). The PDSI is an algorithm of soil moisture for relatively homogeneous areas based on the precipitation, temperature and volume of water available locally. It uses a model of monthly water balance with two layers of soil and expresses the humidity conditions in a standardized way with a scale that goes from approx. -6 to 6