Canonical correlation and visual analytics for water resources analysis

In the last decades, urbanization and population growth substantially increased water consumption for agricultural, industrial, and residential purposes. Characterizing the interplay between environmental variables and water resources plays a critical role in establishing effective water management policies. In this paper, we apply Canonical Correlation Analysis (CCA) in a set of climate and hydrological indicators to investigate the behavior of these environmental variables over time in different geographical regions of California, as well as the relationship among these regions. CCA served as a base to establish a temporal graph that models the relationship between the stations over time, and advanced graph visualization techniques are used to produce patterns that aid in the comprehension of the underlying phenomena. Our results identified important temporal patterns, such as heterogeneous behavior in the dry season and lower correlation between the stations in La Nina years. We show that the combination of CCA and visual analytics can assist water experts in identifying important climate and hydrological events in different scenarios.