Accuracy of long-term volunteer water monitoring data: A multiscale analysis from a statewide citizen science program

Abstract

An increasing number of citizen science water monitoring programs is continuously collecting water quality data on streams throughout the United States. Operating under quality assurance protocols, this type of monitoring data can be extremely valuable for scientists and professional agencies, but in some cases has been of limited use due to concerns about the accuracy of data collected by volunteers. Although a growing body of studies attempts to address accuracy concerns by comparing volunteer data to professional data, rarely has this been conducted with large-scale datasets generated by citizen scientists. This study assesses the relative accuracy of volunteer water quality data collected by the Texas Stream Team (TST) citizen science program from 1992-2016 across the State of Texas by comparing it to professional data from corresponding stations during the same time period. Use of existing data meant that sampling times and protocols were not controlled for, thus professional and volunteer comparisons were refined to samples collected at stations within 60 meters of one another and during the same year. Results from the statewide TST dataset include 82 separate station/year ANOVAs and demonstrate that large-scale, existing volunteer and professional data with unpaired samples can show agreement of~80% for all analyzed parameters (DO = 77%, pH = 79%, conductivity = 85%). In addition, to assess whether limiting variation within the source datasets increased the level of agreement between volunteers and professionals, data were analyzed at a local scale. Data from a single partner city, with increased controls on sampling times and locations and correction of a systematic bias in DO, confirmed this by showing an even greater agreement of 91% overall from 2009-2017 (DO = 91%, pH = 83%, conductivity = 100%). An experimental sampling dataset was analyzed and yielded similar results, indicating that existing datasets can be as accurate as experimental datasets designed with researcher supervision. Our findings underscore the reliability of large-scale citizen science monitoring datasets already in existence, and their potential value to scientific research and water management programs.

Citizen science for assessing pesticide impacts in agricultural streams

“…Several studies that compared large-scale CS and professional data sets with numerous data points for each site and parameter found good agreement between CS and professional physicochemical water data despite differences in measuring times and devices (Albus et al, 2020;Dyer et al, 2014;Safford and Peters, 2018;Shupe, 2017). For CS to be a useful and reliable approach to physicochemical water status assessment, citizen scientists need to be incentivized to visit each sample site multiple times per season to conduct repeated measurements of each physicochemical parameter (i.e.…”

“…Hence, to gain a comprehensive picture about stream ecological status, CS programs should also consider those three monitoring components. Several existing CS water monitoring data sets have been shown to provide useful insights into the physicochemical status of streams and the impact of land use change over wide temporal and spatial scales (Abbott et al, 2018;Albus et al, 2020;Safford and Peters, 2018). Studies from the US (Fore et al, 2001;Nerbonne and Vondracek, 2003), from New Zealand (Moffett and Neale, 2015;Storey and Wright-Stow, 2017) and the UK (Brooks et al, 2019) found that if citizen scientists are provided with appropriate training and robust protocols, they can provide valid macroinvertebrate data.…”

Motives for Citizen Science Program Participation and the Role of the Organization: Lessons from Water Quality Monitors in Texas

“…Further, data are comparable in quality to professional data collection. For example, in one study, the data collected by CSV water quality monitors, when compared with professional water quality monitoring, had an agreement of 80% for all test parameters (Albus et al 2020).…”

Fishery legislative reform towards Japan’s Fukushima nuclear wastewater discharge into the sea—A Chinese perspective

“…The same waters can then be monitored several times to make the monitoring data more accurate. Finally, the monitoring data should be processed scientifically (Albus et al, 2020). Generally, the modified value comparison method is adopted to determine whether the results of the monitoring data are consistent with monitoring standards and regulations.…”