  Natural disasters affect hundreds of millions of people worldwide every year.
Early warning, humanitarian response and recovery mechanisms can be improved by
using big data sources. Measuring the different dimensions of the impact of
natural disasters is critical for designing policies and building up
resilience. Detailed quantification of the movement and behaviours of affected
populations requires the use of high granularity data that entails privacy
risks. Leveraging all this data is costly and has to be done ensuring privacy
and security of large amounts of data. Proxies based on social media and data
aggregates would streamline this process by providing evidences and narrowing
requirements. We propose a framework that integrates environmental data, social
media, remote sensing, digital topography and mobile phone data to understand
different types of floods and how data can provide insights useful for managing
humanitarian action and recovery plans. Thus, data is dynamically requested
upon data-based indicators forming a multi-granularity and multi-access data
pipeline. We present a composed study of three cases to show potential
variability in the natures of floodings,as well as the impact and applicability
of data sources. Critical heterogeneity of the available data in the different
cases has to be addressed in order to design systematic approaches based on
data. The proposed framework establishes the foundation to relate the physical
and socio-economical impacts of floods.
