However, they can be expensive and even when free may not guarantee proper coverage and timely information. New technologies and their wide access by the general public make it possible to extend traditional monitoring networks to include citizen observed data. The inclusion of crowdsourced data into models is also an invitation to review the methods used to do this inclusion and to assess model results. In this context, the research project main objective is the development of a methodology to assess the value of crowdsourced data and their integration into flood models. The methodology proposed to achieve this objective is divided in three parts: case study model preparation, improvement of model performance indicators and data integration and design of field experiments for citizen data collection. By successfully implementing the methodology, it is expected that newly developed performance indicators will be able to handle crowdsourced data and capture differences in flood spatial distribution. It is expected that crowdsourced data will be integrated into flood models, improving the models. And finally, it is expected that field experiments will be successful in providing valuable information for modelling.