Articles | Volume 27, issue 19
Research article
09 Oct 2023
Research article |  | 09 Oct 2023

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

Jessica A. Eisma, Gerrit Schoups, Jeffrey C. Davids, and Nick van de Giesen

Related authors

Investigating the environmental response to water harvesting structures: a field study in Tanzania
Jessica A. Eisma and Venkatesh M. Merwade
Hydrol. Earth Syst. Sci., 24, 1891–1906,,, 2020
Short summary

Related subject area

Subject: Water Resources Management | Techniques and Approaches: Mathematical applications
Synthesis of historical reservoir operations from 1980 to 2020 for the evaluation of reservoir representation in large-scale hydrologic models
Jennie C. Steyaert and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 1071–1088,,, 2024
Short summary
A novel objective function DYNO for automatic multivariable calibration of 3D lake models
Wei Xia, Taimoor Akhtar, and Christine A. Shoemaker
Hydrol. Earth Syst. Sci., 26, 3651–3671,,, 2022
Short summary
The importance of non-stationary multiannual periodicities in the North Atlantic Oscillation index for forecasting water resource drought
William Rust, John P. Bloomfield, Mark Cuthbert, Ron Corstanje, and Ian Holman
Hydrol. Earth Syst. Sci., 26, 2449–2467,,, 2022
Short summary
Decreased virtual water outflows from the Yellow River basin are increasingly critical to China
Shuang Song, Shuai Wang, Xutong Wu, Yongyuan Huang, and Bojie Fu
Hydrol. Earth Syst. Sci., 26, 2035–2044,,, 2022
Short summary
AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment
Yuxue Guo, Xinting Yu, Yue-Ping Xu, Hao Chen, Haiting Gu, and Jingkai Xie
Hydrol. Earth Syst. Sci., 25, 5951–5979,,, 2021
Short summary

Cited articles

Atkinson, G. M. and Wald, D. J.: “Did You Feel It?” intensity data: A surprisingly good measure of earthquake ground motion, Seismol. Res. Lett., 78, 362–368, 2007. a
Bird, T. J., Bates, A. E., Lefcheck, J. S., Hill, N. A., Thomson, R. J., Edgar, G. J., Stuart-Smith, R. D., Wotherspoon, S., Krkosek, M., Stuart-Smith, J. F., Pecl, G. T., Barrett, N., and Frusher, S.: Statistical solutions for error and bias in global citizen science datasets, Biol. Conserv., 173, 144–154,, 2014. a, b, c, d
Bonney, R., Cooper, C. B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K. V., and Shirk, J.: Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy, BioScience, 59, 977–984,, 2009. a
Bonter, D. N. and Cooper, C. B.: Data validation in citizen science: a case study from Project FeederWatch, Front. Ecol. Environ., 10, 305–307,, 2012. a
Brunsdon, C. and Comber, L.: Assessing the changing flowering date of the common lilac in North America: a random coefficient model approach, Geoinformatica, 16, 675–690, 2012. a
Short summary
Citizen scientists often submit high-quality data, but a robust method for assessing data quality is needed. This study develops a semi-automated program that characterizes the mistakes made by citizen scientists by grouping them into communities of citizen scientists with similar mistake tendencies and flags potentially erroneous data for further review. This work may help citizen science programs assess the quality of their data and can inform training practices.