Submitted to: Hydrological Processes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/10/2021
Publication Date: 12/19/2021
Citation: Millar, D.J., Buda, A.R., Duncan, J., Kennedy, C.D. 2021. Scientific Briefing: An open-source automated workflow to delineate storm events and evaluate concentration-discharge relationships. Hydrological Processes. 36(1):e14456. https://doi.org/10.1002/hyp.14456.
Interpretive Summary: Continuous water quality monitoring with state-of-the-art sensors provides insight into pollutant source and transport processes when measurements of concentration are analyzed alongside stream discharge. While studies of the relationships between concentration and discharge are common in scientific research, wider application of these methods has been held back by the absence of freely available computer code to automate the basic steps in the analysis. In this study, we developed and tested freely available computer code that identifies storm events and translates concentration and discharge measurements into useful measures. Study findings show that this computational workflow is easily applied in a variety of watershed settings, which will enable researchers and watershed managers to make better use of continuous water quality data.
Technical Abstract: 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 datasets than were previously available. With these newly available datasets, 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 datasets increasingly growing in size and scope, the need to automate the processing and analyses of such data has become apparent. However, consensus is currently lacking on storm event delineation methods as they pertain to c-Q analysis, 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 and 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 USA, 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 hope that leveraging this rapid approach to event-based c-Q analysis allows scientists and practitioners more time to focus on interpreting results, and 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.