Submitted to: Applied Engineering in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 14, 2011
Publication Date: December 15, 2011
Citation: Endale, D.M., Fisher, D.S., Jenkins, M., Schomberg, H.H. 2011. Difficult lessons learned in measuring flow on small watersheds. Applied Engineering in Agriculture. 27(6):933-936. Interpretive Summary: The computer and associated technology era has allowed a proliferation of development of environmental monitoring and data recording technology. It is now possible to automatically and continuously monitor and record hydrologic data at a desired time interval from minutes to hours and days. Unfortunately unless such recording systems are closely watched to check for the proper functioning of all parts of the recording system, erroneous data could be collected for long periods. Researchers at the USDA-ARS, J. Phil Campbell Sr. Natural Resource Conservation Center in Watkinsville, GA, carried out over a two year period continuous monitoring of flow from a spring with a flow measuring structure fitted with an electronic flow depth measuring sensor (submersible pressure transducer) connected to an automatic data recording device (data logger). Over the same period, periodic manual checking of flow rate was performed. The researchers identified periods where the automated system under predicted, correctly measured, and over predicted the flow rate. Initially, over a 162-day period in 2003, the automated system under predicted the spring flow rate by approximately 17% (average 6.1 versus 7.3 gallons per minute). Over a 349-day period in 2004 flow rate was correctly predicted (average 4.7 gpm). Over 235 days during the latter part of the monitoring period, the automated system over predicted the flow rate by approximately 29% (average 9.5 versus 7.3 gpm). In a similar setup, a pond outflow was underestimated by 27.4% (3,471,485 gallons) over a 30-day period in 2009. Flow rate is an important hydrologic variable in water quantity and quality investigations including estimation of daily pollutant loads. In flow rate estimation utilizing pressure transducers, data must be scrutinized to avoid the propagation of errors from flow estimation to that of pollutant load. There are tens of thousands of locations across the country and abroad where stream flow, ground water levels, surface flow across agricultural fields, and storm flows in urban areas are being monitored with such devices. The observations from this study should be on interest to researchers, teachers, environmental groups, regulators, engineers and local to regional water resources managers.
Technical Abstract: Submersible pressure transducers integrated with data loggers have become relatively common measuring devices in flow or well water level measurements. However, drift, linearity, hysteresis and other problems can lead to erroneous data. Researchers at the USDA-ARS in Watkinsville, GA, carried out over a two-year period continuous monitoring of flow rate from a spring using a flume fitted with a pressure transducer and data logger. Over the same period, periodic manual checking of flow rate was performed. Initially, over a 162-day period in 2003 with 77 comparisons, the automated system under measured the flow rate by approximately 17% (mean, 0.383 versus 0.459 L s-1; significant based on a Wilcoxon Rank Sum Test). After adjusting for drift in the data logger program, in 66 days of comparisons over a 349-day period in 2004, flow rate was 0.294 L s-1 for the manual and 0.299 L s-1 for the automated system (a 1.7% difference; non-significant). Over 235 days during the latter part of the monitoring period, the automated system over measured the flow rate by approximately 29% (mean 0.599 versus 0.463 L s-1; significant) because of an apparent drift that had developed in transducer output early this period. In a similar set up at a pond outflow, a drift of 32 mm in transducer output caused underestimation of pond outflow by 27.4% (13,141 m3) over a 30-day period. Flow rate is an important hydrologic variable in water quantity and quality investigations. Proper data quality assurance is needed in flow rate estimation utilizing pressure transducers to avoid the propagation of errors from flow estimation to that of pollutant load. The same principle should apply in other similar sensors.