This chapter aims to describe the latest innovative approaches for integrating heterogeneous observations from static social sensors within hydrological and hydrodynamic modelling to improve flood prediction. The distinctive characteristic of such sensors, with respect to the traditional ones, is their varying lifespan and space-time coverage as well as their spatial distribution. The main part of the chapter is dedicated to the optimal assimilation of heterogeneous intermittent data within hydrological and hydraulic models. These approaches are designed to account for the intrinsic uncertainty contained into hydrological observations and model structure, states and parameters. Two case studies, the Brue and Bacchiglione catchments, are considered. Finally, the evaluation of the developed methods is provided. This study demonstrates that networks of low-cost static and dynamic social sensors can complement traditional networks of static physical sensors, for the purpose of improving flood forecasting accuracy. This can be a potential application of recent efforts to build citizen observatories of water, in which citizens not only can play an active role in information capturing, evaluation and communication but also can help improve models and increase flood resilience.