For decades citizen science has been used in environmental monitoring, and perhaps most commonly in water quality monitoring, as a tool to supplement professional data. Hundreds of volunteer monitoring efforts have generated datasets that cover large geographic areas over multiple years, and these largescale datasets have been shown to be especially valuable for monitoring changes over time. Although volunteer water monitoring programs continue to grow worldwide, research shows that many of the existing datasets are still underutilized due to concerns about the accuracy of volunteer-collected data. An increasing number of "comparison studies" have attempted to address quality concerns by comparing volunteer data to professional data to assess relative accuracy, and the majority have reported that volunteer data are of a quality comparable to professional data. Nearly all of these studies, however, focused on a small subset of volunteer program data or data collected under experimental controls, and as such the results may not be applicable to existing, large-scale datasets with unknown controls and high levels of variation. Through a comprehensive look at water quality comparison studies to date, this review reveals a need for additional studies that specifically address the quality of highly variable, large-scale volunteer datasets and ultimately serve as a framework by which decades of volunteer efforts already in existence across the country can be better utilized.

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.