Articles | Volume 21, issue 2
Hydrol. Earth Syst. Sci., 21, 839–861, 2017
Hydrol. Earth Syst. Sci., 21, 839–861, 2017

Research article 14 Feb 2017

Research article | 14 Feb 2017

Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?

Maurizio Mazzoleni1, Martin Verlaan2, Leonardo Alfonso1, Martina Monego3, Daniele Norbiato3, Miche Ferri3, and Dimitri P. Solomatine1,4 Maurizio Mazzoleni et al.
  • 1UNESCO-IHE Institute for Water Education, Hydroinformatics Chair Group, Delft, the Netherlands
  • 2Deltares, Delft, the Netherlands
  • 3Alto Adriatico Water Authority, Venice, Italy
  • 4Delft University of Technology, Water Resources Section, Delft, the Netherlands

Abstract. Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate these observations into mathematical water models have also been developed. Besides, in recent years, the continued technological advances, in combination with the growing inclusion of citizens in participatory processes related to water resources management, have encouraged the increase of citizen science projects around the globe. In turn, this has stimulated the spread of low-cost sensors to allow citizens to participate in the collection of hydrological data in a more distributed way than the classic static physical sensors do. However, two main disadvantages of such crowdsourced data are the irregular availability and variable accuracy from sensor to sensor, which makes them challenging to use in hydrological modelling. This study aims to demonstrate that streamflow data, derived from crowdsourced water level observations, can improve flood prediction if integrated in hydrological models. Two different hydrological models, applied to four case studies, are considered. Realistic (albeit synthetic) time series are used to represent crowdsourced data in all case studies. In this study, it is found that the data accuracies have much more influence on the model results than the irregular frequencies of data availability at which the streamflow data are assimilated. This study demonstrates that data collected by citizens, characterized by being asynchronous and inaccurate, can still complement traditional networks formed by few accurate, static sensors and improve the accuracy of flood forecasts.

Short summary
This study assesses the potential use of crowdsourced data in hydrological modeling, which are characterized by irregular availability and variable accuracy. We show that even data with these characteristics can improve flood prediction if properly integrated into hydrological models. This study provides technological support to citizen observatories of water, in which citizens can play an active role in capturing information, leading to improved model forecasts and better flood management.