Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-102
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
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01 Mar 2017
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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).
Comment on Can assimilation of crowdsourced data in hydrological modelling improve flood prediction? by Mazzoleni et al. (2017)
Daniele P. Viero Department of Civil, Environmental, and Architectural Engineering, University of Padova, via Loredan 20, 35131, Padova, Italy
Abstract. In their recent contribution, Mazzoleni et al. (2017) investigated the integration of crowdsourced data (CSD) in hydrological models to improve the accuracy of real-time flood forecast. They showed that assimilation of CSD improves the overall model performance in all the considered case studies. The impact of irregular frequency of available crowdsourced data, and that of data uncertainty, were also deeply assessed. However, it has to be remarked that, in their work, the Authors used synthetic (i.e., not actually measured) crowdsourced data, because actual crowdsourced data were not available at the moment of the study. This point, briefly mentioned by the authors, deserves further discussion. In most real-world applications, rainfall-runoff models are calibrated using data from traditional sensors. Typically, CSD are collected at different locations, where semi-distributed models are not calibrated. In a context of equifinality and of poor identifiability of model parameters, the model internal states can hardly mimic the actual system states away from calibration points, thus reducing the chances of success in assimilating real (i.e., not synthetic) CSD. Additional criteria are given that are useful for the a-priori evaluation of crowdsourced data for real-time flood forecasting and, hopefully, to plan apt design strategies for both model calibration and collection of crowdsourced data.

Citation: Viero, D. P.: Comment on Can assimilation of crowdsourced data in hydrological modelling improve flood prediction? by Mazzoleni et al. (2017), Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-102, in review, 2017.
Daniele P. Viero
Daniele P. Viero
Daniele P. Viero

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