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Abstract Citizen science yields increased scientific capacity in exchange for science literacy and promises of a more responsive science to society’s needs. Yet, citizen science projects are criticized for producing few scientific outputs and having exploitative relationships with the citizens who participate. In the eagerness to capture new data, scientists can fail to see the value of citizen scientists’ expertise beyond data generation and can forget to close the loop with outputs that benefit the public interest. Citizen scientists are experts in their local environments who, when asked, can improve scientific processes and products. To the degree that citizen scientists are relegated to data collection, we shortchange opportunities to advance science. Rather than merely critique, we present an evidence-based engagement approach for listening to citizen scientist participants and incorporating their input into science processes and products that can be retrofitted onto existing citizen science projects or integrated from a project’s inception. We offer this adaptable blueprint in four steps and illustrate this approach via a crowdsourced hydrology project on the Boyne River, USA. We show how engaging voices of citizen scientists at key points in the project improves both the products of science (a real-time ecohydrological model) and the process of conducting the science (adaptations to help improve data collection). Distinct from outreach or education, considering citizen scientists as an equally interesting site of inquiry can improve the practice and outputs of science. 

Abstract Citizen science is personal. Participation is contingent on the citizens’ connection to a topic or to interpersonal relationships meaningful to them. But from the peer-reviewed literature, scientists appear to have an acquisitive data-centered relationship with citizens. This has spurred ethical and pragmatic criticisms of extractive relationships with citizen scientists. We suggest five practical steps to shift citizen-science research from extractive to relational, reorienting the research process and providing reciprocal benefits to researchers and citizen scientists. By virtue of their interests and experience within their local environments, citizen scientists have expertise that, if engaged, can improve research methods and product design decisions. To boost the value of scientific outputs to society and participants, citizen-science research teams should rethink how they engage and value volunteers. 

Abstract Small streams often lack reliable hydrological data. Environmental agencies play a key role in providing such data; however, these agencies are often challenged by the growing monitoring needs and lack of funding. Given the spatial mismatch between observed data and small watersheds/headwaters, local volunteers can act as potentially valuable research partners. We examine how CrowdHydrology, a citizen science program that collects stream stage and stream temperature observations, improves a hydrologic model of the Boyne River, Michigan, USA. Volunteers provided observations at four calibration sites with different interarrival times of the observations. We tested whether stream stage and stream temperature observations (measured by volunteers) improved the performance of a Soil and Water Assessment Tool (SWAT) model of the Boyne River. Observations were integrated into the model using the ensemble Kalman filter. This framework allowed us to integrate observation error, track the variability of model parameters, and simulate daily streamflow and stream temperature across the watershed. Measures of daily model performance included the Nash‐Sutcliffe efficiency, modified Nash‐Sutcliffe efficiency (E_f‐mod), refined index of agreement (d_r), and relative bias (Bias). For all calibration sites, estimates of streamflow improved after data assimilation compared to simulations based on initial/default SWAT parameters. Different measures of model performance emerged based on the interarrival times of the observations. Results demonstrate that observations collected by local volunteers, with a certain temporal resolution, can improve SWAT hydrological models and capture central tendency.