Using High-Frequency Data to Manage Water Quality

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Provenance: This image was developed by Lofton, M.E., Cooke, R.L., and C.C. Carey for the use in Macrosystems EDDIE Module 9
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• Define key measures of surface freshwater quality (water temperature, dissolved oxygen, and turbidity). (Activity A)
• Explain how water temperature changes over the course of a year in a temperate reservoir and how these changes affect water quality. (Activity A, Activity B)
• Interpret high-frequency water quality data to make decisions about water extraction depth for a drinking water reservoir. (Activity B)
• Evaluate water quality data and forecasts to make decisions about drinking water treatment. (Activity C)

This module was developed as part of a virtual, asynchronous curriculum for students training to become water treatment plant operators. This entire module can be completed in one 2 to 3-hour lab period, two 75-minute lecture periods, or three 1-hour lecture periods. The module is designed to be fully accessible for both in-person, hybrid, and completely virtual, asynchronous courses. Open-source versions of all module materials are available on this website and module activities can also be imported into Canvas from the Canvas commons. Students complete module activities using an R Shiny web application, and can answer questions either using a Canvas quiz or by typing them into a student handout which can be downloaded from the app. Please see the instructor manual for detailed recommendations about module timing for different class schedule types.

Quick overview of the activities in this module

See the instructor manual, provided below, for a step-by-step guide for carrying out this module. A student handout describing Activities A, B, and C, and an instructor PowerPoint are also provided.

• Activity A: Students access and explore high-frequency water quality data from a drinking water reservoir in southwest Virginia.
• Activity B: Students use high-frequency water quality data to explore how water quality changes and make decisions about water withdrawal depth over the course of a year.
• Activity C: Students make management recommendations for water treatment using water quality forecasts.

What is water quality?

Water quality is the suitability of water for human use, such as drinking water or irrigation, as well as the degree to which a water body can support important ecosystem processes, such as sustaining a fish population.

What is meant by high-frequency water quality data?

High-frequency water quality data are measurements of various attributes of a water body (such as temperature, dissolved oxygen, pH, etc.) that are taken many times a day (for example, every 10 minutes). High-frequency data allow water managers and scientists to observe and analyze patterns and trends in water quality that were not previously measurable using low-frequency (once a day or even less frequently) measurements.

How are high-frequency water quality data collected?

High-frequency water quality data are often collected using automated sensors. These sensors are deployed in a water body attached to buoys, catwalks, or other structures and collect data continuously. Many of these sensors are automated, which means they can wirelessly stream data to a server so that it is accessible to managers in near-real time.

Workflow for this module

1. Instructor chooses whether to deliver the module using Canvas or not. If using Canvas, the instructor should import the module to their course from the Canvas commons. If not using Canvas, all module materials can be accessed from this web page.
2. Students view a short (~10 minute) introductory video either in Canvas or in the R Shiny web application. The same video is linked from both locations. Alternatively, instructors may choose to modify the introductory presentation and present it themselves. An editable version of the slides is provided with the teaching materials below.
3. Students navigate to the R Shiny web application and follow the workflow instructions outlined on the Introduction tab:
   1. Watch the introductory presentation provided in Canvas and embedded in the interactive R Shiny web application if you have not already done so.
   2. Watch the "Guide to Module" video embedded in the interactive R Shiny web application to learn about key features of the module that will help you complete module activities and answer questions. Optionally, you can also go through the "Quick-start" guide to the module using the button at the top right corner of the module web page.
   3. Select a focal reservoir.
   4. Open the Canvas quiz questions associated with the reservoir you have chosen OR if you are not using Canvas, download a copy of all the questions as a Word document by clicking the "Download student handout" button.
   5. Work through the module to complete the Introduction questions and Activities A, B, and C in this web app. When you are prompted to answer questions, enter your answers in the Canvas quiz. Be sure to fill in the Canvas quiz that corresponds to the reservoir site you have chosen! If you are not completing the module using Canvas, you may type your answers into the Word document.
   6. If you would like to take a break and come back later, or if you lose internet connection, all you have to do is re-load this web app, re-select your reservoir site in the Introduction, and you will be able to resume your progress. On Canvas, you can save your quiz responses using the "Save" button. In Word, you can save your answers in the document on your computer.
   7. When you have finished the module activities, be sure to submit your Canvas quiz for grading. If you are completing the module by answering the questions in a Word document, be sure to submit the document to your instructor for grading.
4. The instructor can choose how to assess student module question responses. If using the Canvas quiz, all self-grading questions are worth 1 point by default, and short answer questions are worth 0 points. The point values may be adjusted by the instructor. If using the Word document, point values are not assigned to questions; this is left to the discretion of the instructor.

Teaching Materials:

• R shiny app: https://macrosystemseddie.shinyapps.io/module9/
  • All the code needed to reproduce the R shiny app is provided on GitHub: https://github.com/MacrosystemsEDDIE/module9
• Editable versions of all slide decks in the module: editable_slides_Module9.zip (Zip Archive 101.1MB Aug16 24)
• Student handout: Student_handout_Module9.docx (Microsoft Word 2007 (.docx) 295kB Aug16 24)
• Instructor manual: Instructor_Manual_Module9.docx (Microsoft Word 2007 (.docx) 606kB Aug16 24)
• Canvas commons link: https://lor.instructure.com/resources/15a6383a448e49a99dcdbb8c029a2a31?shared

Important Note to Instructors:

The R Shiny app and other instructional materials used in this module are regularly updated, so these module instructions will periodically change to account for changes in the code. If you have any questions or have other feedback about this module, please contact the module developers (see "We'd love your feedback" below).

We highly recommend that instructors familiarize themselves with the Shiny app prior to the lesson. This will enable you to be more prepared to answer student questions.

Student understanding is assessed using interactive questions embedded throughout the R Shiny application. To answer these questions, students will need to visualize and interpret high-frequency water quality data. Student responses are recorded either by entering answers into a Canvas quiz or by typing answers into a Word document report that they download from the application.

• Activity A: Students are asked to read and interpret plots of high-frequency water temperature, dissolved oxygen, and turbidity data from a focal drinking water reservoir of their choice and identify how these variables affect water quality.
• Activity B: Students are asked to interpret plots of high-frequency water quality data during the summer, fall, and spring to make decisions about water extraction depth in a drinking water reservoir.
• Activity C: Students are asked to interpret forecasts of fall turnover to make decisions about whether to enact additional water treatment to ensure that the plant they are operating meets regulatory standards for turbidity. Students evaluate the utility of fall turnover forecasts to their decision-making process.

We'd love your feedback!

We frequently update this module to reflect improvements to the code, new teaching materials and relevant readings, and student activities. Your feedback is incredibly valuable to us and will guide future module development within the Macrosystems EDDIE project. Please let us know any suggestions for improvement or other comments about the module by sending an email toMacrosystemsEDDIE@gmail.com or filling out the form at the following link: https://serc.carleton.edu/eddie/macrosystems/faculty_feedback.

Module authorship contributions: MEL, RLC, and CCC conceptualized the module. MEL drafted and revised all module materials with substantial feedback from RLC and CCC.