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(CODE) The Stream Intermittency Visualization Dashboard: A Shiny web application to evaluate high-frequency logger data and daily flow observations
Citation:
Kelso, J., W. Saulnier, K. Fritz, Tracie-Lynn Nadeau, AND B. Topping. (CODE) The Stream Intermittency Visualization Dashboard: A Shiny web application to evaluate high-frequency logger data and daily flow observations. U.S. Environmental Protection Agency, Washington, DC, 2022.
Impact/Purpose:
With lowered cost of technology, there is a growing interest in the collection of high-frequency sensor data for measuring hydrology in streams and rivers. Often such data are merged with other data streams, such as meteorological and biological, of varying frequencies. In this paper we present a tool that merges, summarizes, and allows users to efficiently evaluate high frequency hydrologic data. The paper and associated video illustrates the dashboard features with an example dataset from the United States Great Plains and Midwest that is used to characterize the duration, frequency, and timing of stream drying.
Description:
An interactive dashboard is a useful tool for large, high-frequency datasets that allows users to visualize raw data and value-added metrics. Dashboards facilitate data processing and decision-making related to gap-filling and data exclusion by having data visualizations, summary metric tables, and maps all in one place. Here we describe the Stream Intermittency Visualization Dashboard web application we created to synthesize high-frequency temperature, conductivity and precipitation data with environmental data collected in the field to characterize and quantify streamflow duration for a stream site at the reach scale. The dashboard served multiple purposes 1) to visualize multiple data streams of high frequency sensor data, 2) to view field observations and hydrologic metrics that we calculated during and after data processing 3) to view descriptive data quality metrics (e.g. length, completeness). We present a video that shows an example of how the dashboard was used to infer wet and dry periods in a stream reach. We also provide an example dataset and the R code used to create the dashboard on a public GitHub page.