Sep 27, 2024

Public workspaceUSDA LTAR Common Experiment measurement: Best practices for collection, handling, and analyses of water quantity measurements

DOI
https://dx.doi.org/10.17504/protocols.io.eq2lyw14wvx9/v1
USDA LTAR Common Experiment measurement: Best practices for collection, handling, and analyses of water quantity measurements
  • Claire Baffaut1,
  • Harry Schomberg2,
  • Michael Cosh3,
  • Andrew O'Reilly2,
  • Amartya Saha4,
  • Nicanor Z. Saliendra5,
  • Adam Schreiner-McGraw1,
  • Keirith A. Snyder6
  • 1USDA Agricultural Research Service, Cropping Systems and Water Quality Research Unit, Columbia, MO;
  • 2USDA Agricultural Research Service, Watershed Physical Processes Research Unit, Oxford, MS;
  • 3USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD;
  • 4Archbold Biological Station, Lake Placid, FL;
  • 5USDA Agricultural Research Service, Northern Great Plains Research Laboratory, Mandan, ND;
  • 6USDA Agricultural Research Service, Great Basin Rangelands Research Unit, Reno, NV
Lori J. Abendroth
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DOI: https://dx.doi.org/10.17504/protocols.io.eq2lyw14wvx9/v1
External link: https://ltar.ars.usda.gov
Protocol Citation: Claire Baffaut, Harry Schomberg, Michael Cosh, Andrew O'Reilly, Amartya Saha, Nicanor Z. Saliendra, Adam Schreiner-McGraw, Keirith A. Snyder 2024. USDA LTAR Common Experiment measurement: Best practices for collection, handling, and analyses of water quantity measurements. protocols.io https://dx.doi.org/10.17504/protocols.io.eq2lyw14wvx9/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: July 22, 2024
Last Modified: September 27, 2024
Protocol Integer ID: 104647
Keywords: Data Logger integrity, Quality assurance (QA), Quality control (QC) , Equipment Calibration, Long-Term Agroecosystem Research, LTAR, Common Experiment, crops, USDA LTAR, usda ltar common experiment measurement, concerning quality assurance, usda ltar common experiment, quality assurance, qc procedure, water quantity protocol, qa, basic qa, quality control, quality data, analyses of water quantity measurement, ltar common experiment, water quantity measurement, data quality, requirements for quality, qc, need for corrective measure, quality, experiment, corrective measure, many of these protocol, analysis, inaccuracies in the data, protocol, procedure, measurement
Funders Acknowledgements:
United States Department of Agriculture
Grant ID: -
Abstract
Although each of the water quantity protocols developed for the USDA LTAR Common Experiment addresses specific issues concerning quality assurance (QA) and quality control (QC), basic QA/QC procedures apply to many of these protocols and have relevance in field research. QA is a set of processes or steps taken to ensure that protocols are developed and adhered to in a way that minimizes inaccuracies in the data produced. QA produces high-quality data while minimizing the need for corrective measures to improve data quality. QC occurs after data generation and tests whether the data meet the requirements for quality outlined by the end users. QA is a proactive or preventive process to avoid problems; QC is a process to identify and flag suspicious data.
Troubleshooting
Placement and site maintenance
Avoid placing monitoring equipment in areas regularly trafficked by farm vehicles.

Mark any equipment with flagging and/or signs notifying of its location.

Avoid placing monitoring equipment in areas prone to frequent flooding except where required to meet data collection needs.

Protect data loggers/samplers/wiring from rodent/wildlife activity. Enclose data cables and tubing in a PVC conduit to prevent damage caused by rodents, shovels or mowers, and moisture. Replace or repair any damaged equipment or wiring.

Control weeds around equipment and samplers to reduce the impact of vegetation on sample collection and help keep the area visible.

Winterize water samplers; apply preventive measures in the autumn to avoid freeze damage to equipment and lines, as necessary.

Check sites following major precipitation events for damage.

Visually inspect the wiring and physical conditions on a routine basis (weekly recommended).

Data logger integrity
Water quantity data are often collected using sensors and devices connected to data loggers, which function as device controllers, data storage units, and communication and data retrieval systems.

Set up and maintain a schedule for downloading and viewing data. Weekly scanning of data provides a way to determine if equipment failures need to be addressed.

Check the battery condition or power supply weekly.

Automate monitoring of data logger voltage to make sure charging systems are working. Verify the data logger runs reliably, and then check the system monthly or more frequently as site conditions require.

Check the desiccant monthly (vented transducers only) or the humidity indicator; service if necessary.

Equipment calibration
Calibrate equipment and check for accuracy against true standards regularly, i.e., from weekly to bi-annually, depending on the site visit interval and the propensity for issues with the equipment and site.

Metadata
Metadata should document the field location, types of equipment, methods or procedures used, sampling frequency, and other information needed to accurately interpret the collected data. Metadata should also include notes about changes to equipment and reasons for these changes.

Quality control
Implement several checks of collected data to ensure they are within the limits of the sensors used and for missing data. The table below provides examples of flags for adding to data to indicate known or potential issues.

Check data for the following aspects:

  1. Agreement with nearby “like” sensors
  2. Range – Do the values fall within reasonable maximum and minimum values?
  3. Missing data – periods without data
  4. Spikes – a single sudden and significant rise or fall with no known explanation.
  5. Constant values – indicating poor sensor response

Flag data being made available to other users with a quality grade indicating quality levels such as pass, estimated pass, and missing. A process to assign grades reflecting the quality of the data (e.g., good, fair, suspect) is under development.
Table 1. Examples of the internal flags used to provide information about the data collected to field technicians and data quality analysts.

AB
Missing value No measured value is available because of equipment failure or another reason
Low battery The sensor battery dropped below a threshold
Calibration due The sensor needs to be sent back to the manufacturer for calibration
Calibration expired The value was collected with a sensor past due for calibration
Invalid chronology One or more non sequential date or time values
Persistent value Repeated value for an extended period
Above range The value is above a specified upper limit
Below range The value is below a specified lower limit
Slope exceedance The value is much higher or lower than the previous value, resulting in an unrealistic slope
Spatial inconsistency The value greatly differs from values collected from nearby sensors
Internal inconsistency The value is inconsistent with a related measurement
Detection limit The value is below the established detection limit of the sensor
Table 2. Flags used to provide information about the data collected to external users, after Campbell et al. (2013).

AB
Pass The value passed all quality control tests and is considered valid.
Estimated Estimated value from a model or other sources
Missing Missing value
Data storage and accessibility
Data collection can be manual or digital. In both cases, methodically maintain original data. Note data corrections, gap filling, or replacements in the metadata and the data.
  • Documentation should indicate when, why, and who made the changes.
  • Maintain backup copies of the original and corrected data in a secure (second) location to prevent losses.
Protocol references
Campbell, J. L., Rustad, L. E., Porter, J. H., Taylor, J. R., Dereszynski, E. W., Shanley, J. B., Gries, C., Henshaw, D. L., Martin, M. E., Sheldon, W. M., & Boose, E. R. (2013). Quantity is Nothing without Quality: Automated QA/QC for Streaming Environmental Sensor Data. BioScience, 63(7), 574–585. https://doi.org/10.1525/bio.2013.63.7.10
Acknowledgements
This research is a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. The use of trade, firm, or corporation names in this publication is for the information and convenience of the reader. Such use does not constitute an official endorsement or approval by the United States Department of Agriculture or the Agricultural Research Service of any product or service to the exclusion of others that may be suitable. USDA is an equal opportunity provider and employer.