Articles | Volume 22, issue 1
Hydrol. Earth Syst. Sci., 22, 391–416, 2018
Hydrol. Earth Syst. Sci., 22, 391–416, 2018

Research article 17 Jan 2018

Research article | 17 Jan 2018

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)

Maurizio Mazzoleni1, Vivian Juliette Cortes Arevalo2, Uta Wehn1, Leonardo Alfonso1, Daniele Norbiato3, Martina Monego3, Michele Ferri3, and Dimitri P. Solomatine1,4,5 Maurizio Mazzoleni et al.
  • 1Integrated Water Systems and Governance Department, IHE Delft Institute for Water Education, Delft, 2611AX, the Netherlands
  • 2Water Engineering and Management, University of Twente, Enschede, 7522 NB, the Netherlands
  • 3Alto Adriatico Water Authority, Venice, Italy
  • 4Water Resources Management department, Water Problems Institute, Russian Academy of Sciences, Moscow, Russia
  • 5Water Resources Section, Delft University of Technology, Delft, 2628 CD, the Netherlands

Abstract. To improve hydrological predictions, real-time measurements derived from traditional physical sensors are integrated within mathematic models. Recently, traditional sensors are being complemented with crowdsourced data (social sensors). Although measurements from social sensors can be low cost and more spatially distributed, other factors like spatial variability of citizen involvement, decreasing involvement over time, variable observations accuracy and feasibility for model assimilation play an important role in accurate flood predictions. Only a few studies have investigated the benefit of assimilating uncertain crowdsourced data in hydrological and hydraulic models. In this study, we investigate the usefulness of assimilating crowdsourced observations from a heterogeneous network of static physical, static social and dynamic social sensors. We assess improvements in the model prediction performance for different spatial–temporal scenarios of citizen involvement levels. To that end, we simulate an extreme flood event that occurred in the Bacchiglione catchment  (Italy) in May 2013 using a semi-distributed hydrological model with the station at Ponte degli Angeli (Vicenza) as the prediction–validation point. A conceptual hydrological model is implemented by the Alto Adriatico Water Authority and it is used to estimate runoff from the different sub-catchments, while a hydraulic model is implemented to propagate the flow along the river reach. In both models, a Kalman filter is implemented to assimilate the crowdsourced observations. Synthetic crowdsourced observations are generated for either static social or dynamic social sensors because these measures were not available at the time of the study. We consider two sets of experiments: (i) assuming random probability of receiving crowdsourced observations and (ii) using theoretical scenarios of citizen motivations, and consequent involvement levels, based on population distribution. The results demonstrate the usefulness of integrating crowdsourced observations. First, the assimilation of crowdsourced observations located at upstream points of the Bacchiglione catchment ensure high model performance for high lead-time values, whereas observations at the outlet of the catchments provide good results for short lead times. Second, biased and inaccurate crowdsourced observations can significantly affect model results. Third, the theoretical scenario of citizens motivated by their feeling of belonging to a community of friends has the best effect in the model performance. However, flood prediction only improved when such small communities are located in the upstream portion of the Bacchiglione catchment. Finally, decreasing involvement over time leads to a reduction in model performance and consequently inaccurate flood forecasts.

Short summary
We investigate the usefulness of assimilating crowdsourced observations from a heterogeneous network of sensors for different scenarios of citizen involvement levels during the flood event occurred in the Bacchiglione catchment in May 2013. We achieve high model performance by integrating crowdsourced data, in particular from citizens motivated by their feeling of belonging to a community. Satisfactory model performance can still be obtained even for decreasing citizen involvement over time.