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The Stream Intermittency Visualization Dashboard: A Shiny web application to evaluate high-frequency logger data and flow observations
Citation:
Fritz, K., W. Saulnier, J. Kelso, Tracie-Lynn Nadeau, AND B. Topping. The Stream Intermittency Visualization Dashboard: A Shiny web application to evaluate high-frequency logger data and flow observations. 13th National Water Quality Monitoring Conference, Virginia Beach, VA, April 24 - 28, 2023.
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:
With lowered cost of logger technology, there is a growing interest in the collection of high-frequency logger data for measuring hydrology in streams and rivers. Often such data are merged with other data types, such as meteorological and biological observations, of varying frequencies and sources. 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 evaluation 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 the dashboard to synthesize high-frequency temperature, conductivity, and precipitation data with environmental data we collected in the field to characterize and quantify streamflow duration at the reach scale. An example dataset from the United States Great Plains and Midwest is used to characterize the duration, frequency, and timing of stream drying. 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 throughout data processing and 3) to view descriptive data quality metrics (e.g., length, completeness). We illustrate various features available for evaluation of the example dataset and share the R code used to create the dashboard through a public GitHub page.
URLs/Downloads:
https://www.nalms.org/2023nmc/#:~:text=Come%20Join%20Us!,%2C%20in%20Virginia%20Beach%2C%20Virginia.