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The influence of El Niño Southern Oscillation (ENSO) on Colombia's hydrological variables has been shown in different studies. Most of the methodologies implemented have identified linear relationships and have associated the warm (cold) phase called El Niño (La Niña) with negative (positive) rainfall and streamflow anomalies. One of the most adverse impacts founded is the reduction in water supply during the warm phase. Therefore, it is necessary to study the linkage between ENSO and precipitation variability for efficient management of water resources. Consequently, the present paper has two purposes. The first one is to explore nonlinear correlations of the ENSO-precipitation relationship, particularly for specific regions where the freshwater resources have been significantly reduced during El Niño events. The second one is to identify which indices will enable in improving the predictability of hydro-climatological variables. The research was based on the wavelet coherence analysis of monthly precipitation time series from 1981-2016 and the ENSO indices for the same period. The results show that ENSO events influence the precipitation as periods of rainfall deficit or excess. Also, precipitation is organized in bands and that the 2–8-year scales explain most of their variance. The most significant sectors are those that cover El Niño events. In contrast, sectors are smaller when La Niña episodes. Then impacts on precipitation tend to be greater for warm events. Results also allowed to identify that El Niño 3, Niño 3,4, ONI, and BEST indices can be good indicators for forecasting work in these specific places. The use of two kinds of data, one in situ and the other from CHIRPS program, allows to establish the feasibility of using data from satellite origin in regions without enough information; the results showed that CHIRPS data tend to report fewer anomalies than data in situ. However, the coherence structure is similar, but in periods between 36 and 48 months, there were discrepancies of pi/4 in the phase difference, that is, between 3 and 6 months of difference in lags calculated with each database.
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