This research aimed to further our understanding of how environmental processes control micropollutant dynamics in surface water systems as a means to predict peak concentration events and inform intermittent sampling strategies. We characterized micropollutant concentrations in daily composite samples from the Fall Creek Monitoring Station over 18 months. These data were compiled alongside environmental covariates, including daily measurements of weather, hydrology, and water quality parameters, to generate a novel data set with high temporal resolution. We evaluated the temporal trends of several representative micropollutants, along with cumulative metrics of overall micropollutant contamination, by means of multivariable analyses to determine which combination of covariates best predicts micropollutant dynamics and peak events. Peak events of agriculture-derived micropollutants were best predicted by positive associations with turbidity and UV254 absorbance and negative associations with baseflow index. Peak events of wastewater-derived micropollutants were best predicted by positive associations with alkalinity and negative associations with streamflow rate. We demonstrate that these predictors can be used to inform intermittent sampling strategies aimed at capturing peak events, and we generalize the approach so that it could be applied in other watersheds. Finally, we demonstrate how our approach can be used to contextualize micropollutant data derived from infrequent grab samples.