  Floods are the most common and among the most severe natural disasters in
many countries around the world. As global warming continues to exacerbate sea
level rise and extreme weather, governmental authorities and environmental
agencies are facing the pressing need of timely and accurate evaluations and
predictions of flood risks. Current flood forecasts are generally based on
historical measurements of environmental variables at monitoring stations. In
recent years, in addition to traditional data sources, large amounts of
information related to floods have been made available via social media.
Members of the public are constantly and promptly posting information and
updates on local environmental phenomena on social media platforms. Despite the
growing interest of scholars towards the usage of online data during natural
disasters, the majority of studies focus exclusively on social media as a
stand-alone data source, while its joint use with other type of information is
still unexplored. In this paper we propose to fill this gap by integrating
traditional historical information on floods with data extracted by Twitter and
Google Trends. Our methodology is based on vine copulas, that allow us to
capture the dependence structure among the marginals, which are modelled via
appropriate time series methods, in a very flexible way. We apply our
methodology to data related to three different coastal locations in the South
cost of the UK. The results show that our approach, based on the integration of
social media data, outperforms traditional methods, providing a more accurate
evaluation and prediction of flood events.
