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To improve hydrological predictions, real-time measurements\udderived from traditional physical sensors are integrated within mathematic\udmodels. Recently, traditional sensors are being complemented with\udcrowdsourced data (social sensors). Although measurements from social sensors\udcan be low cost and more spatially distributed, other factors like spatial\udvariability of citizen involvement, decreasing involvement over time,\udvariable observations accuracy and feasibility for model assimilation play an\udimportant role in accurate flood predictions. Only a few studies have\udinvestigated the benefit of assimilating uncertain crowdsourced data in\udhydrological and hydraulic models. In this study, we investigate the\udusefulness of assimilating crowdsourced observations from a heterogeneous\udnetwork of static physical, static social and dynamic social sensors. We\udassess improvements in the model prediction performance for different\udspatial–temporal scenarios of citizen involvement levels. To that end, we\udsimulate an extreme flood event that occurred in the Bacchiglione catchment\ud (Italy) in May 2013 using a semi-distributed hydrological model with the\udstation at Ponte degli Angeli (Vicenza) as the prediction–validation point. A\udconceptual hydrological model is implemented by the Alto Adriatico Water\udAuthority and it is used to estimate runoff from the different\udsub-catchments, while a hydraulic model is implemented to propagate the flow\udalong the river reach. In both models, a Kalman filter is implemented to\udassimilate the crowdsourced observations. Synthetic crowdsourced observations\udare generated for either static social or dynamic social sensors because\udthese measures were not available at the time of the study. We consider two\udsets of experiments: (i) assuming random probability of receiving crowdsourced\udobservations and (ii) using theoretical scenarios of citizen motivations, and\udconsequent involvement levels, based on population distribution. The results\uddemonstrate the usefulness of integrating crowdsourced observations. First,\udthe assimilation of crowdsourced observations located at upstream points of\udthe Bacchiglione catchment ensure high model performance for high lead-time\udvalues, whereas observations at the outlet of the catchments provide good\udresults for short lead times. Second, biased and inaccurate crowdsourced\udobservations can significantly affect model results. Third, the theoretical\udscenario of citizens motivated by their feeling of belonging to a <q>community\udof friends</q> has the best effect in the model performance. However, flood\udprediction only improved when such small communities are located in the\udupstream portion of the Bacchiglione catchment. Finally, decreasing\udinvolvement over time leads to a reduction in model performance and\udconsequently inaccurate flood forecasts

hydrology and earth system sciences

Exploring the influence of citizen involvement on the assimilation of crowdsourced observations: a modelling study based on the 2013 flood event in the Bacchiglione catchment (Italy)