Enhancing Engagement of Citizen Scientists to Monitor Precipitation Phase

Abstract

Recent literature has highlighted how citizen science approaches can engage volunteers, expand scientific literacy, and accomplish targeted research objectives. However, there is limited information on how specific recruitment, retention, and engagement strategies enhance scientific outcomes. To help fill this important information gap, we detail the use of various approaches to engage citizen scientists in the collection of precipitation phase data (rain, snow, or mixed). In our study region, the Sierra Nevada and Central Basin and Range of California and Nevada near Lake Tahoe, a marked amount of annual precipitation falls near freezing. At these air temperatures, weather forecasts, land surface models, and satellites all have difficulty correctly predicting and observing precipitation phase, making visual observations the most accurate approach. From January to May 2020, citizen scientists submitted timestamped, geotagged observations of precipitation phase through the Citizen Science Tahoe mobile phone application. Our recruitment strategy included messaging to winter, weather, and outdoor enthusiasts combined with amplification through regional groups, which resulted in over 199 citizen scientists making 1,003 ground-based observations of rain, snow, and mixed precipitation. We enhanced engagement and retention by targeting specific storms in the region through text message alerts that also allowed for questions, clarifications, and training opportunities. We saw a high retention rate (88%) and a marked increase in the number of observations following alerts. For quality control of the data, we combined various meteorological datasets and compared to the citizen science observations. We found that 96.5% of submitted data passed our quality control protocol, which enabled us to evaluate rain-snow partitioning patterns. Snow was the dominant form of precipitation at air temperatures below and slightly above freezing, with both ecoregions expressing a 50% rain-snow air temperature threshold of 4.2°C, a warmer value than what would be incorporated into most land surface models. Thus, the use of a lower air temperature threshold in these areas would produce inaccuracies in event-based rain-snow proportions. Overall, our high retention rate, data quality, and rain-snow analysis were supported by the recruitment strategy, text message communication, and simplicity of the survey design. We suggest other citizen science projects may follow the approaches detailed herein to achieve their scientific objectives.