Specific Conductivity (SC) is a basic, effective indicator of water quality. The recent increase in specific conductivity data collected with high-frequency sensors has created a strong need for algorithms that can aid interpretation of these data. This study presents an algorithm that finds the relationships between SC patterns and the environmental conditions of the time period. During and after precipitation events, three patterns emerge in SC time series of numerous catchments: a solute flush, resulting in an increase in SC, followed by a dilution, followed by the SC's recovery toward pre-rain conditions. We developed an algorithm to extract and quantify these three patterns from a high-frequency, in situ sensor array deployed in a New Hampshire forested catchment. We then compared each pattern to the environmental conditions the catchment was experiencing at the time in order to better understand and explain the mechanisms driving the chemograph variability we observed. The environmental conditions were related to precipitation, antecedent moisture, and seasonality. Our results indicate that the magnitude of the flush (FSC) is driven primarily by the intensity at which precipitation falls, as well as other rain-based variables and antecedent moisture conditions. The magnitude of the dilution (DSC) is driven mainly by the amount of precipitation that falls, and is correlated with other precipitation variables. The rate of SC recovery (RSC) is driven by the amount of rain that falls and is correlated with DSC. Applying our methodology to more catchments in the future will help us efficiently develop functional relationships for individual catchments, which can be used to identify catchments most sensitive to future precipitation changes.