An open-source automated workflow to delineate storm events and evaluate concentration-discharge relationships

The advent of in situ optical sensors that can collect sub-daily measurements of nutrients and turbidity in flowing water bodies has yielded comparatively much larger water quality data sets than were previously available. With these newly available data sets, there has been increased interest in studying event-based concentration-discharge (c-Q) relationships to infer the sources and pathways of various watershed constituents during storms. With water quality data sets increasingly growing in size and scope, the need to automate the processing and analyses of such data has become apparent. However, consensus on storm event delineation methods as they pertain to c-Q analysis is currently lacking, and methodological details, including parameter values, are sometimes unreported in the literature. Here, we present an open-source workflow using the programming language R that automates the processing of sub-daily c-Q data to analyze event-based hysteresis patterns. Briefly, the workflow accepts a time series of concentration and discharge data, extracts stormflow from streamflow, delineates storm events and then evaluates c-Q relationships using widely applied metrics like the hysteresis index (HI) and the flushing index (FI). We applied the workflow to three watersheds in the mid-Atlantic United States, including a 0.4-km² agricultural watershed, a 150-km² urbanizing watershed and a 29 940-km² mixed land use river basin. Sub-daily sensor-based nutrient concentrations and discharge data were collected in each watershed. Using the small agricultural watershed as an example, we demonstrate the step-by-step application of the workflow. We then present results from the larger watersheds as a means for comparison and to illustrate the flexibility of the code. We believe that this rapid approach to event-based c-Q analysis will allow scientists and practitioners more time to focus on interpreting results, and promote greater scientific reproducibility. Likewise, we conclude this Scientific Briefing with suggested future improvements to the workflow to increase the automation of data analyses and reproducibility.