Lignin phenols have proven to be powerful biomarkers in environmental studies; however, the complexity of lignin analysis limits the number of samples and thus spatial and temporal resolution in any given study. In contrast, spectrophotometric characterization of dissolved organic matter (DOM) is rapid, noninvasive, relatively inexpensive, requires small sample volumes, and can even be measured in situ to capture fine-scale temporal and spatial detail of DOM cycling. Here we present a series of cross-validated Partial Least Squares models that use fluorescence properties of DOM to explain up to 91% of lignin compositional and concentration variability in samples collected seasonally over 2 years in the Sacramento River/San Joaquin River Delta in California, United States. These models were subsequently used to predict lignin composition and concentration from fluorescence measurements collected during a diurnal study in the San Joaquin River. While modeled lignin composition remained largely unchanged over the diurnal cycle, changes in modeled lignin concentrations were much greater than expected and indicate that the sensitivity of fluorescence-based proxies for lignin may prove invaluable as a tool for selecting the most informative samples for detailed lignin characterization. With adequate calibration, similar models could be used to significantly expand our ability to study sources and processing of DOM in complex surface water systems.