Abstract
Citizen science data can fundamentally advance the natural sciences, but concerns remain about its accuracy, reliability, and overall value. While some studies have evaluated accuracy of citizen science data, few have also assessed its potential contribution to conservation policy. This study focuses on rainfall data collection, with four goals: (1) to examine motivations of, and barriers for, volunteer participation in citizen science; (2) to evaluate accuracy of citizen science rainfall data in comparison to automatic rain gauge data; (3) to incorporate citizen science rainfall datasets into hydrological models; and (4) to apply the hydrologic model to gauge the contribution of citizen science data to the efficient design of payment for hydrological services (PHS) programs. Twelve citizen science volunteers were trained and collected rainfall data between June 2017 and February 2019 across two watersheds in Veracruz, Mexico. We found that these volunteers were highly motivated by conservation values and learning, while only a few volunteers faced barriers related to time availability for making daily measurements. The mean error in daily rainfall, computed by comparing the manual and automated gauge measurements, was less than 1 mm, or 12% of the average daily rainfall. Approximately one-third (29%) and two-thirds (71%) of the errors were attributed to missing data and misread data, respectively. Spatial patterns of rainfall distribution across the watersheds were similar between citizen science and automatic gauge data, revealing a large fraction of rainfall in middle elevations. Furthermore, the results show that if PHS areas are determined using the existing national rainfall network alone, without citizen science data, critical areas that contribute to dry-season flows would be missed. To our knowledge, this is the first citizen science network for collecting rainfall data in Mexico that has produced results that are relevant to conservation policy design.
Highlights
In recent years, citizen science has emerged as a way to collect data for scientific efforts (Follett and Strezov 2015) across disciplines as diverse as evolution (e.g., Evolution MegaLab; Worthington et al 2012), astronomy (e.g., GalaxyZoo; Fortson et al 2012), ornithology, plant phenology (e.g., Project BudBurst; Wolkovich and Cleland 2011), and water surveillance (e.g., Global Water Watch; Deutsch and Ruiz-Córdova 2015)
This study focuses on rainfall data collection, with four goals: (1) to examine motivations of, and barriers for, volunteer participation in citizen science; (2) to evaluate accuracy of citizen science rainfall data in comparison to automatic rain gauge data; (3) to incorporate citizen science rainfall datasets into hydrological models; and (4) to apply the hydrologic model to gauge the contribution of citizen science data to the efficient design of payment for hydrological services (PHS) programs
To our knowledge, the Quiahua citizen science rainfall project is the first citizen science network for collecting information in Mexico that has produced results that are relevant to conservation policy design
Summary
Citizen science has emerged as a way to collect data for scientific efforts (Follett and Strezov 2015) across disciplines as diverse as evolution (e.g., Evolution MegaLab; Worthington et al 2012), astronomy (e.g., GalaxyZoo; Fortson et al 2012), ornithology (e.g., eBird;Sullivan et al 2014), plant phenology (e.g., Project BudBurst; Wolkovich and Cleland 2011), and water surveillance (e.g., Global Water Watch; Deutsch and Ruiz-Córdova 2015). Citizen science has emerged as a way to collect data for scientific efforts (Follett and Strezov 2015) across disciplines as diverse as evolution (e.g., Evolution MegaLab; Worthington et al 2012), astronomy (e.g., GalaxyZoo; Fortson et al 2012), ornithology A common denominator among citizen science projects, as opposed to traditional scientific monitoring, is the volunteer base committed to collecting data. Choosing citizen science over traditional monitoring may involve tradeoffs between lower costs of citizen science data collection and loss of data accuracy. The literature on citizen science data collection methods is rich (e.g., Hochachka et al 2012), less is known about the reliability or accuracy of hydrologic citizen science data and its application for policy makers.
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