Virtual Staff Gauges for Crowd-Based Stream Level Observations

Seibert, Jan; Strobl, Barbara; Etter, Simon; Hummer, Philipp; Meerveld, H. J. van

doi:10.3389/feart.2019.00070

Cited by 68 publications

(78 citation statements)

References 19 publications

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“…Some recent examples of water quantity‐focused projects are the EU‐funded citizen observatories that aim to complement data collection by authorities, such as WeSenseIt (http://www.wesenseit.com; Lanfranchi et al, ), GroundTruth2.0 (https://gt20.eu), and SCENT (https://scent-project.eu). Projects that specifically focus on streamflow or water levels are CrowdHydrology in the United States (Lowry et al, ; Lowry & Fienen, ), Smartphones4Water in Nepal (http://www.smartphones4water.org; Davids et al, ), a project in Kenya (http://www.uni-giessen.de/hydro/hydrocrowd_kenya; Weeser et al, ), Cithyd in Italy (http://www.cithyd.com; Balbo & Galimberti, ), and CrowdWater (http://www.crowdwater.ch; Seibert, Strobl, et al, ). The CrowdWater project aims to explore the value of citizen science data and to collect water level class (WL‐class) data (Seibert, Strobl, et al, ), as well as qualitative data on soil moisture and the state of temporary streams (Kampf et al, ; Seibert, van Meerveld, et al, ), and riverine export of macro plastic.…”

Section: Introductionmentioning

confidence: 99%

Value of Crowd‐Based Water Level Class Observations for Hydrological Model Calibration

Etter

Strobl

Seibert

et al. 2020

Water Resources Research

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While hydrological models generally rely on continuous streamflow data for calibration, previous studies have shown that a few measurements can be sufficient to constrain model parameters. Other studies have shown that continuous water level or water level class (WL‐class) data can be informative for model calibration. In this study, we combined these approaches and explored the potential value of a limited number of WL‐class observations for calibration of a bucket‐type runoff model (HBV) for four catchments in Switzerland. We generated synthetic data to represent citizen science data and examined the effects of the temporal resolution of the observations, the numbers of WL‐classes, and the magnitude of the errors in the WL‐class observations on the model validation performance. Our results indicate that on average one observation per week for a 1‐year period can significantly improve model performance compared to the situation without any streamflow data. Furthermore, the validation performance for model parameters calibrated with WL‐class observations was similar to the performance of the calibration with precise water level measurements. The number of WL‐classes did not influence the validation performance noticeably when at least four WL‐classes were used. The impact of typical errors for citizen science‐based estimates of WL‐classes on the model performance was small. These results are encouraging for citizen science projects where citizens observe water levels for otherwise ungauged streams using virtual or physical staff gauges.

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Section: Introductionmentioning

confidence: 99%

Value of Crowd‐Based Water Level Class Observations for Hydrological Model Calibration

Etter

Strobl

Seibert

et al. 2020

Water Resources Research

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“…As a first step, manuals (https://www.crowdwater.ch/en/crowdwaterapp-en/) and instruction videos 125 (https://www.youtube.com/channel/UC088v9paXZyJ9TcRFh7oNYg) were provided online, but in our experience (and based on the number of watches on YouTube) these are not frequently used. Thus some citizen scientists occasionally still make mistakes when submitting data in the CrowdWater app, primarily when starting a new location for observations and placing a virtual staff gauge onto the reference picture (Seibert et al, 2019a). Our first approach to handle these mistakes was to implement a method of quality control, in order to either filter out or correct erroneous submissions.…”

Section: Previous Crowdwater Training and Motivation For This Studymentioning

confidence: 99%

“…Poor staff gauge 90 placement is one of the most common errors. This occurs for about 10 % of the new reference pictures (Seibert et al, 2019a).…”

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confidence: 94%

“…1, left picture), i.e., there is no physical installation at the location. The citizen scientist can 85 choose from three virtual staff gauges in the app, depending on the water level at the time when the picture is taken (low, medium or high; Seibert et al, 2019a). When placing the virtual staff gauge in the reference picture, the citizen scientist has to move the staff gauge so that it is level with the current water level and change the size of the staff gauge so that it covers the likely range of high and low water levels.…”

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confidence: 99%

“…The most common errors are making the virtual staff gauge too big or more rarely too small to be useful to record water level fluctuations, not placing the staff gauge on the opposite river bank, perpendicular to the flow or choosing the wrong staff gauge (Seibert et al, 2019a(Seibert et al, , 2019b. For further observations (i.e., observations at the same location at a later time), the citizen scientist who created the reference picture or any another person looks for the structures in the reference picture (e.g., rock, 95 bridge pillar, wall), and estimates the water level class by comparing it to the virtual staff gauge in the reference picture (Seibert et al, 2019a). This way, time series of water level class data can be obtained at each measurement locations.…”

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confidence: 99%

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Training citizen scientists through an online game developed for data quality control

Strobl¹,

Etter²,

Meerveld³

et al. 2019

Preprint

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Some form of training is often necessary in citizen science projects. While in many citizen science projects it is possible to keep tasks simple so that training requirements are minimal, some projects include more challenging tasks and, thus, require more extensive training. Training can hinder joining a project, and therefore most citizen science projects prefer to keep training requirements low. However, training may be needed to ensure good data quality. In this study, we evaluated 10 if an online game that was originally developed for data quality control in a citizen science project, can be used for training for that project. More specifically, we investigated whether the CrowdWater game can be used to train new participants on how to use the virtual staff gauge in the CrowdWater smartphone app for the collection of water level class data. Within this app, the task of placing a virtual staff gauge to start measurements at a new location has proven to be challenging; however this is a crucial task for all subsequent measurements at this location. We analysed the performance of 52 participants in the 15 placement of the virtual staff gauge before and after playing the online CrowdWater game as a form of training. After playing the game, the performance improved for most participants. This suggests that players learned project related tasks intuitively by observing actual gauge placements by other citizen scientists and thus acquired knowledge about how to best use the app instinctively. Interestingly, self-assessment was not a good proxy for the participants' performance or performance increase.These results demonstrate the value of an online game for training, particularly when compared to other information materials, 20 which are often not used extensively by citizen scientist. These findings are useful for the development of training strategies for other citizen science projects because they indicate that gamified approaches might provide valuable alternative training methods.

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A Bayesian model for quantifying errors in citizen science data: application to rainfall observations from Nepal

Eisma

Schoups

Davids

et al. 2021

Preprint

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High-quality citizen science data can be instrumental in advancing science toward new discoveries and a deeper understanding of under-observed phenomena. However, the error structure of citizen scientist (CS) data must be well-defined. Within a citizen science program, the errors in submitted observations vary, and their occurrence may depend on CS-specific characteristics. This study develops a graphical Bayesian inference model of error types in CS data. The model assumes that (1) each CS observation is subject to a specific error type, each with its own bias and noise, and (2) an observation's error type depends on the static error community of the CS, which in turn relates to characteristics of the CS submitting the observation. Given a set of CS observations and corresponding ground-truth values, the model can be calibrated for a specific application, yielding (i) number of error types and error communities, (ii) bias and noise for each error type, (iii) error distribution of each error community, and (iv) the single error community to which each CS belongs. The model, applied to Nepal CS rainfall observations, identifies five error types and sorts CSs into four static, model-inferred communities. In the case study, 73 % of CSs submitted data with errors in fewer than 5 % of their observations. The remaining CSs submitted data with unit, meniscus, unknown, and outlier errors. A CS's assigned community, coupled with model-inferred error probabilities, can identify observations that require verification and provides an opportunity for targeted re-training of CSs based on mistake tendencies.

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Virtual Staff Gauges for Crowd-Based Stream Level Observations

Cited by 68 publications

References 19 publications

Value of Crowd‐Based Water Level Class Observations for Hydrological Model Calibration

Value of Crowd‐Based Water Level Class Observations for Hydrological Model Calibration

Training citizen scientists through an online game developed for data quality control

A Bayesian model for quantifying errors in citizen science data: application to rainfall observations from Nepal

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