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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 1
Hydrol. Earth Syst. Sci., 22, 171-177, 2018
https://doi.org/10.5194/hess-22-171-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 22, 171-177, 2018
https://doi.org/10.5194/hess-22-171-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Comment/reply 10 Jan 2018

Comment/reply | 10 Jan 2018

Comment on “Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?” by Mazzoleni et al. (2017)

Daniele P. Viero Daniele P. Viero
  • Department of Civil, Environmental, and Architectural Engineering, University of Padova, via Loredan 20, 35131, Padua, Italy

Abstract. Citizen science and crowdsourcing are gaining increasing attention among hydrologists. In a recent contribution, Mazzoleni et al. (2017) investigated the integration of crowdsourced data (CSD) into hydrological models to improve the accuracy of real-time flood forecasts. The authors used synthetic CSD (i.e. not actually measured), because real CSD were not available at the time of the study. In their work, which is a proof-of-concept study, Mazzoleni et al. (2017) showed that assimilation of CSD improves the overall model performance; the impact of irregular frequency of available CSD, and that of data uncertainty, were also deeply assessed. However, the use of synthetic CSD in conjunction with (semi-)distributed hydrological models deserves further discussion. As a result of equifinality, poor model identifiability, and deficiencies in model structure, internal states of (semi-)distributed models can hardly mimic the actual states of complex systems away from calibration points. Accordingly, the use of synthetic CSD that are drawn from model internal states under best-fit conditions can lead to overestimation of the effectiveness of CSD assimilation in improving flood prediction. Operational flood forecasting, which results in decisions of high societal value, requires robust knowledge of the model behaviour and an in-depth assessment of both model structure and forcing data. Additional guidelines are given that are useful for the a priori evaluation of CSD for real-time flood forecasting and, hopefully, for planning apt design strategies for both model calibration and collection of CSD.

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