Value of quality controlled citizen science data for rainfall-runoff characterization in a rapidly urbanizing catchment

[Display omitted] •We set-up a citizen science monitoring program in Akaki catchment which hosts Addis Ababa, and the program has been operational since January 2020.•Citizen science data of river flow at multiple sites were evaluated using multiple quality control methods.•The data quality assessme...

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Veröffentlicht in:Journal of hydrology (Amsterdam) 2024-02, Vol.629, p.130639, Article 130639
Hauptverfasser: Kebede Mengistie, Getahun, Demissie Wondimagegnehu, Kirubel, Walker, David W., Tamiru Haile, Alemseged
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Sprache:eng
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Zusammenfassung:[Display omitted] •We set-up a citizen science monitoring program in Akaki catchment which hosts Addis Ababa, and the program has been operational since January 2020.•Citizen science data of river flow at multiple sites were evaluated using multiple quality control methods.•The data quality assessment revealed that the citizen scientists can provide accurate river stage data if they receive regular feedback.•The citizen science data enabled evaluation of the spatial and temporal variation of rainfall-runoff relationship in an urbanizing catchment. The major concern of applying citizen science in water resources is the quality of the data. However, there are limited scientific studies addressing this concern and showing the data value. In this study, we established a citizen science program in the Akaki catchment which hosts Addis Ababa, Ethiopia. Citizen scientists monitored river stage at multiple gauging sites for multiple years. We evaluated the quality of citizen science data through a systematic quality control. Reference data was obtained from neighboring stations of the citizen science program and professionals while the evaluation involved graphical inspections and statistical methods. The quality-controlled data were applied to evaluate the spatial and temporal variation of rainfall-runoff relationships. Initially, large numbers of suspicious data were detected using single station data but that was significantly reduced when the data of multiple sites were compared. Further comparison against professional data revealed excellent agreement with high correlation coefficient (r >0.95), and low centered root mean square error (RMSE) 
ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2024.130639