Rights statement: This is the author’s version of a work that was accepted for publication in Science of the Total Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Science of the Total Environment, 631-632, 2018 DOI: 10.1016/j.scitotenv.2018.03.130
Accepted author manuscript, 1.24 MB, PDF document
Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
<mark>Journal publication date</mark> | 1/08/2018 |
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<mark>Journal</mark> | Science of the Total Environment |
Volume | 631-632 |
Number of pages | 10 |
Pages (from-to) | 1590-1599 |
Publication Status | Published |
Early online date | 28/03/18 |
<mark>Original language</mark> | English |
Although water is involved in many ecosystem services, the absence of monitoring data restricts the development of effective water management strategies especially in remote regions. Traditional monitoring networks can be expensive, with unaffordable costs in many low-income countries. Involving citizens in monitoring through crowdsourcing has the potential to reduce these costs but remains uncommon in hydrology. This study evaluates the quality and quantity of data generated by citizens in a remote Kenyan basin and assesses whether crowdsourcing is a suitable method to overcome data scarcity. We installed thirteen water level gauges equipped with signboards explaining the monitoring process to passers-by. Results were sent via a text-message-based data collection framework that included an immediate feedback to citizens. A public web interface was used to visualize the data. Within the first year, 124 citizens reported 1175 valid measurements. We identified thirteen citizens as active observers providing more than ten measurements, whereas 57% only sent one record. A comparison between the crowdsourced water level data and an automatic gauging station revealed high data quality. The results of this study indicate that citizens can provide water level data of sufficient quality and with high temporal resolution.