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Quality of Crowdsourced Water Level Observations Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2020 Quality of crowdsourced water level observations Strobl, Barbara Abstract: Hydrological data are crucial for a better understanding of hydrological processes and can help improve models to predict floods and droughts, to allocate water resources and to better manage hydropower. However, hydrological data are often scarce, as gauging stations are expensive to build and maintain. Citizen science can help fill such data gaps and thereby complement the existing hydrological measurement network. To maximize its use, citizen science in hydrology requires innovative and novel approaches that are aligned with the capabilities and the equipment of citizen scientists. One such ap- proach is a virtual staff gauge, i.e., a sticker with a staff gauge that is inserted onto a photograph ofa river that can be used with a mobile smartphone application. The virtual staff gauge is placed on the photograph of the first observation of a particular location, which results in the reference picture. For further observations, citizen scientists compare the current water level to the virtual staff gauge in this reference picture and submit an estimate of the new water level and a new photograph. The water level estimatesare measured in classes defined by the staff gauge and have no absolute units. Compared to streamflow, water level classes are easier for citizen scientists to estimate, and the data are therefore more reliable. However, even for water level class estimates, there is still potential for mistakes, either when placing the virtual staff gauge on the reference picture or when making a new estimate ofthewater level. Two strategies to reduce these data errors were explored in this thesis. First, data quality control was crowdsourced through a gamified web interface. Citizen scientists can vote on the water level classes that they believe are accurate, based on the comparison of the reference picture and the uploaded photo- graph. Several votes were collected per observation submitted via the app and the mean value of all votes was used to either confirm or correct the initial water level class estimate that was submitted viathe app. This interface provides a scalable way for citizen scientists to check each other’s submissions and is therefore also applicable to other large scale citizen science projects. Second, the gamified interface can also be used for training new citizen scientists. By playing the game, citizen scientists become familiar with the concept of the virtual staff gauge. Voting on a water level classmakes them realise which virtual staff gauge placements facilitate or complicate further estimates. This training helps citizen scientiststo better place virtual staff gauges in the smartphone application and therefore helps to improve the quality of the reference pictures and all further observations. This thesis shows that it is possible to crowd- source water level class data through a mobile smartphone application on a global scale. Crowdsourcing data quality control not only results in higher data quality, but also trains new citizen scientists. The crowdsourced water level class data can be used to constrain hydrological models, which can simulate streamflow time series. Hydrologische Daten sind für ein besseres Verständnis hydrologischer Prozesse essentiell und helfen Hochwasser oder Dürreperioden vorherzusagen, Wasserressourcenzu verteilen und Wasserkraft besser zu nutzen. An vielen Standorten sind jedoch keine hydrologischen Daten vorhanden, da Messstationen teuer zu errichten und zu erhalten sind. Citizen Science (auch Bürgerwissenschaften genannt) kann helfen, diese Datenlücken zu füllen und das hydrologische Messnetz zu ergänzen. Um das Potential von Citizen Science bestmöglich auszunutzen, müssen die Fähigkeiten und Messmöglichkeiten der Citizen Scientists berücksichtigt und innovative Ansätze entwickelt werden; wie zum Beispiel die virtuelle Messlatte. Diese ist eine Art Aufkleber, der digital auf ein Flussfoto geklebt wird und mithilfe einer App weltweit verwendet werden kann. Die virtuelle Messlatte wird an einem Standort eingerichtet und in einem Referenzfoto abgespeichert. Für weitere Beobachtungen am selben Standort bezieht sich der Citizen Scientist immer auf dieses Referenzfoto und fügt dabei ein weiteres Foto sowie eine Schätzung des Wasserstandes hinzu. Der Wasserstand wird in Klassen geschätzt, welche durch die virtuelle Messlatte definiert werden. Für Citizen Scientists ist es einfacher Wasserstandsklassen als Abfluss abzuschätzen, wodurch die Verlässlichkeit der Daten verbessert wird. Gelegentlich passieren dennoch Fehler; entweder beim Platzieren der virtuellen Messlatte auf dem Referenzfoto oder beim Abschätzen einer weiteren Wasserstandsbeobachtung. Um diese Fehler zu verringern, wurden zwei Strategien entwickelt. Einerseits wird die Datenqualität von vielen Citizen Scientists in einem Spiel kontrolliert, wobei hochgeladene Fotos mehrmals neu klassifiziert werden. Diese Schätzungen werden gemittelt, um den ursprünglich angegebe- nen Wasserstandswert entweder zu bestätigen oder zu korrigieren. Andererseits kann das Spiel zusätzlich zur Qualitätskontrolle auch als Training für neue Citizen Scientists verwendet werden. Während des Spielens werden die Citizen Scientists mit der virtuellen Messlatte vertraut und lernen gute und schlechte Platzierungen zu erkennen. Somit hilft das Training neuen Citizen Scientists virtuelle Messlatten in der App besser zu platzieren, was zu verbesserten Referenzfotos und Daten führt. Diese Arbeit zeigt, dass Schätzungen von Wasserstandsklassen mithilfe einer Smartphone Applikation weltweit gesammelt werden können. Die spielerische Datenqualitätskontrolle hilft zusätzlich neue Citizen Scientists zu trainieren. Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-190608 Dissertation Published Version Originally published at: Strobl, Barbara. Quality of crowdsourced water level observations. 2020, University of Zurich, Faculty of Science. 2 Quality of Crowdsourced Water Level Observations Barbara Strobl Quality of Crowdsourced Water Level Observations Dissertation zur Erlangung der naturwissenschaftlichen Doktorwürde (Dr. sc. nat.) vorgelegt der Mathematisch-naturwissenschaftlichen Fakultät der Universität Zürich von Barbara Strobl aus Österreich Promotionskommission Prof. Dr. Jan Seibert (Vorsitz) Dr. Ilja van Meerveld Prof. Dr. Ross Purves Zürich, 2020 II | P a g e Für meine Oma III | P a g e IV | P a g e ABSTRACT Hydrological data are crucial for a better understanding of hydrological processes and can help improve models to predict floods and droughts, to allocate water resources and to better manage hydropower. However, hydrological data are often scarce, as gauging stations are expensive to build and maintain. Citizen science can help fill such data gaps and thereby complement the existing hydrological measurement network. To maximize its use, citizen science in hydrology requires innovative and novel approaches that are aligned with the capabilities and the equipment of citizen scientists. One such approach is a virtual staff gauge, i.e., a sticker with a staff gauge that is inserted onto a photograph of a river that can be used with a mobile smartphone application. The virtual staff gauge is placed on the photograph of the first observation of a particular location, which results in the reference picture. For further observations, citizen scientists compare the current water level to the virtual staff gauge in this reference picture and submit an estimate of the new water level and a new photograph. The water level estimates are measured in classes defined by the staff gauge and have no absolute units. Compared to streamflow, water level classes are easier for citizen scientists to estimate, and the data are therefore more reliable. However, even for water level class estimates, there is still potential for mistakes, either when placing the virtual staff gauge on the reference picture or when making a new estimate of the water level. Two strategies to reduce these data errors were explored in this thesis. First, data quality control was crowdsourced through a gamified web interface. Citizen scientists can vote on the water level classes that they believe are accurate, based on the comparison of the reference picture and the uploaded photograph. Several votes were collected per observation submitted via the app and the mean value of all votes was used to either confirm or correct the initial water level class estimate that was submitted via the app. This interface provides a scalable way for citizen scientists to check each other and is therefore also applicable to other large scale citizen science projects. ’s submissions Second, the gamified interface can also be used for training new citizen scientists. By playing the game, citizen scientists become familiar with the concept of the virtual staff gauge. Voting on a water level class makes them realise which virtual staff gauge placements facilitate or complicate further estimates. This training helps citizen scientists to better place virtual staff gauges in the smartphone application and therefore helps to improve the quality of the reference pictures and all further observations. This thesis shows that it is possible to crowdsource water level class data through a mobile smartphone application on a global scale. Crowdsourcing
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