Accuracy of crowdsourced streamflow and stream level class estimates期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

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Accuracy of crowdsourced streamflow and stream level class estimates

Authors:	Barbara Strobl Simon Etter Ilja van Meerveld Jan Seibert

Institution:	1. Department of Geography, University of Zurich, Zurich, Switzerlandbarbara.strobl@geo.uzh.chhttps://orcid.org/0000-0001-5530-4632;3. Department of Geography, University of Zurich, Zurich, Switzerlandhttps://orcid.org/0000-0002-7553-9102;4. Department of Geography, University of Zurich, Zurich, Switzerlandhttps://orcid.org/0000-0002-7547-3270;5. Department of Geography, University of Zurich, Zurich, Switzerland;6. Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, Swedenhttps://orcid.org/0000-0002-6314-2124

Abstract:	ABSTRACT Streamflow data are important for river management and the calibration of hydrological models. However, such data are only available for gauged catchments. Citizen science offers an alternative data source, and can be used to estimate streamflow at ungauged sites. We evaluated the accuracy of crowdsourced streamflow estimates for 10 streams in Switzerland by asking citizens to estimate streamflow either directly, or based on the estimated width, depth and velocity of the stream. Additionally, we asked them to estimate the stream level class by comparing the current stream level with a picture that included a virtual staff gauge. To compare the different estimates, the stream level class estimates were converted into streamflow. The results indicate that stream level classes were estimated more accurately than streamflow, and more accurately represented high and low flow conditions. Based on this result, we suggest that citizen science projects focus on stream level class estimates instead of streamflow estimates.

Keywords:	citizen science crowdsourcing stream level stream level class streamflow accuracy CrowdWater

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