El Niño phenomenon starts with an increase in the temperature of the sea surface in the equatorial zone of the Pacific Ocean. This increase is characterized by the arrival of a superficial mass of warm waters into the sea, which generates anomalous climate changes on land. These unusual events can be floods, droughts, intense rains, which endanger the urban population and infrastructure of cities. To be able to launch early warnings of possible catastrophic events in populated areas, it is necessary to know how and within how long the change in sea temperature impacts on continental characteristics. The present work describes a computational process based on techniques of extraction and visualization of sequential patterns to capture temporal variations of the variables describing the El Niño phenomenon. Results show the existence of correlations between the sea surface temperature and the flow of the rivers of the coast. These correlations can be used as monitoring tools for early warning releases.