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Hydroinformatics Instruction Module Example Code: Sensor Data Quality Control with pyhydroqc
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Created: Jan 28, 2022 at 8:38 p.m.
Last updated: Mar 03, 2022 at 8:35 p.m.
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Abstract

This resource contains Jupyter Notebooks with examples for conducting quality control post processing for in situ aquatic sensor data. The code uses the Python pyhydroqc package. The resource is part of set of materials for hydroinformatics and water data science instruction. Complete learning module materials are found in HydroLearn: Jones, A.S., Horsburgh, J.S., Bastidas Pacheco, C.J. (2022). Hydroinformatics and Water Data Science. HydroLearn. https://edx.hydrolearn.org/courses/course-v1:USU+CEE6110+2022/about.

This resources consists of 3 example notebooks and associated data files.

Notebooks:
1. Example 1: Import and plot data
2. Example 2: Perform rules-based quality control
3. Example 3: Perform model-based quality control (ARIMA)

Data files:
Data files are available for 6 aquatic sites in the Logan River Observatory. Each file contains data for one site for a single year. Each file corresponds to a single year of data. The files are named according to monitoring site (FranklinBasin, TonyGrove, WaterLab, MainStreet, Mendon, BlackSmithFork) and year. The files were sourced by querying the Logan River Observatory relational database, and equivalent data could be obtained from the LRO website or on HydroShare. Additional information on sites, variables, and methods can be found on the LRO website (http://lrodata.usu.edu/tsa/) or HydroShare (https://www.hydroshare.org/search/?q=logan%20river%20observatory). Each file has the same structure indexed with a datetime column (mountain standard time) with three columns corresponding to each variable. Variable abbreviations and units are:
- temp: water temperature, degrees C
- cond: specific conductance, μS/cm
- ph: pH, standard units
- do: dissolved oxygen, mg/L
- turb: turbidity, NTU
- stage: stage height, cm

For each variable, there are 3 columns:
- Raw data value measured by the sensor (column header is the variable abbreviation).
- Technician quality controlled (corrected) value (column header is the variable abbreviation appended with '_cor').
- Technician labels/qualifiers (column header is the variable abbreviation appended with '_qual').

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Content

Related Resources

This resource belongs to the following collections:
Title Owners Sharing Status My Permission
Hydroinformatics Instruction Modules Example Code Amber Jones  Public &  Shareable Open Access

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation Collaborative Research: Elements: Advancing Data Science and Analytics for Water (DSAW) 1931297

Contributors

People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Jeffery S. Horsburgh Utah State University Utah, US 4357972946 ORCID , ResearchGateID , GoogleScholarID
Camilo J. Bastidas Pacheco Utah State University UT, US 4357545722

How to Cite

Jones, A. S. (2022). Hydroinformatics Instruction Module Example Code: Sensor Data Quality Control with pyhydroqc, HydroShare, http://www.hydroshare.org/resource/451c4f9697654b1682d87ee619cd7924

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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