TITLE: USDA LTAR Common Experiment measurement: Best practices for collection, handling, and analyses of water quantity measurements
AUTHORS: Claire Baffaut, Harry Schomberg, Michael H. Cosh, Andrew M O'Reilly, Amartya Saha, Nicanor Z. Saliendra, Adam Schreiner-McGraw, Keirith A. Snyder

[DESCRIPTION]
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.

[STEPS]
SECTION: Placement and site maintenance
1. Avoid placing monitoring equipment in areas regularly trafficked by farm vehicles.

SECTION: Placement and site maintenance
2. Mark any equipment with flagging and/or signs notifying of its location.

SECTION: Placement and site maintenance
3. Avoid placing monitoring equipment in areas prone to frequent flooding except where required to meet data collection needs.

SECTION: Placement and site maintenance
4. 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.

SECTION: Placement and site maintenance
5. Control weeds around equipment and samplers to reduce the impact of vegetation on sample collection and help keep the area visible. 

SECTION: Placement and site maintenance
6. Winterize water samplers; apply preventive measures in the autumn to avoid freeze damage to equipment and lines, as necessary.

SECTION: Placement and site maintenance
7. Check sites following major precipitation events for damage. 

SECTION: Placement and site maintenance
8. Visually inspect the wiring and physical conditions on a routine basis (weekly recommended).

SECTION: Data logger integrity
9. 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. 

SECTION: Data logger integrity
9.1. 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. 

SECTION: Data logger integrity
9.2. Check the battery condition or power supply weekly. 

SECTION: Data logger integrity
9.3. 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.

SECTION: Data logger integrity
9.4. Check the desiccant monthly (vented transducers only) or the humidity indicator; service if necessary.

SECTION: Equipment calibration
10. 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.

SECTION: Metadata
11. 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. 

SECTION: Quality control
12. 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. 

SECTION: Quality control
12.1. Check data for the following aspects:

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

SECTION: Quality control
12.2. 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.
SECTION: Data storage and accessibility
13. 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. 
SECTION: Quality control
12.3. Table 1. Examples of the internal flags used to provide information about the data collected to field technicians and data quality analysts.

 
Table 2. Flags used to provide information about the data collected to external users, after Campbell et al. (2013).