In the previous post, an overview of Civica’s third webinar in the Flow Monitoring Webinar Series was provided. Part 1 explained the critical importance of accurate flow and rainfall data, as well as the various field activities that could be performed to effectively manage flow monitoring programs. Part 2 will outline the remote activities that can be used to ensure data quality.
Remote Activities to Ensure Data Quality of Flow and Rainfall Monitoring
In addition to field-based activities that can be performed to ensure data quality, there are also a number of remote/office-based activities that can be performed. With the advancement in cellular technology, it enables near real-time access to flow monitoring data without having to physically go to the site.
Automated alarms can be set up at the flow and rainfall monitoring sites to alert technicians when data quality issues arise, such as:
- Debris build-up
- Low battery
- Deviation from expected flows
- High I&I
- Significant storm events
With data QA/QC, there is a quantitative matrix that is often applied to flow monitoring projects and this QA/QC score is calculated at each flow monitoring station based on a few criteria. It’s important to note that these criteria could change from project to project depending on the purpose and the requirements of the data collection.
Flow Monitoring Data Based on Different QA/QC Scoring
Below is a more in-depth look at each category of the QA/QC scoring.
QA/QC Calibration Graph
A calibration graph compares the sensor reading versus the manual verification. Since a lot of the flow monitoring is done in open channels, sensors can be susceptible to drift. In this case, they should be calibrated using manual measurements. Other times, there are equipment issues and the timing when the measurement was conducted could be off.
To confirm that sensors are accurate and produce useful measurements, technicians would record both the manual measurements and the sensor reading and create a long-term trend plot on a chart or graph to understand the sensor reading deviation.
It’s important to keep the raw data collected from the flow meter and not change the setting on the flow meter onsite to match a single manual verification. This is to minimize data manipulation and only make necessary justifiable data corrections.
QA/QC Scatter Graph
Scatter graphs are used to confer the site hydraulics. The scatter graph displays the correlation between two variables (depth vs. velocity) using the Manning Equation. In a QA/QC scatter graph, the measured flow data is plotted. Each dot represents one piece of flow monitoring data. The graph compares the pattern of the collected flow monitoring data to determine the relationship. The closer the dots are concentrated around the Manning Equation line, the better the hydraulics are.
The R2 value shows the correlation between the variables; a value greater than 0.5-0.7 is acceptable.
Raw vs. Processed Data
This category is oftentimes overlooked. It provides an overview of how much process flow data was collected during the QA/QC process. Raw data is the data that is taken straight from the flow meter and the processed data is the production data after QA/QC.
The data can be the same, but oftentimes, the processed data or production data is slightly or dramatically different than the raw data if there is QA/QC data editing after the data is collected.
QA/QC Data Coverage
Sometimes there could be a data gap due to equipment issues or the flow monitoring station is removed for CCTV or flush purposes. Most of the flow monitoring project would require a minimal data coverage of 90% or 95%.
To identify data gaps, the following equation can be used:
Data Coverage Score = Actual Data Points / Anticipated Data Points * 100%
Introduction to Sewer Flow Monitoring Data Analysis
The goal is always to get the best quality data first before moving into the analysis phase. There are two main types of flow analyses that can be done: Dry Weather Flow Analysis and Wet-Weather Flow Analysis.
Dry-weather flow (DWF) analysis is important for looking at the average and peak dry weather flow, weekday vs. weekend water use, per capita wastewater use, and groundwater infiltration rates.
Wet weather flow analysis provides an understanding of rainfall/snowmelt response in wastewater systems. It is then compared against the design criteria/standards to determine where high wet weather flow may cause issues. Wet weather flow analysis can also help provide insight on what may happen in a system during a larger design event (e.g. a 25-year design storm).
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