Abstract
Flooding is a frequent disaster that has a wide-spread footprint
globally with significant financial and societal impacts. With
availability of Earth observation data from private and public entities
at varying spatial, temporal, and spectral resolution as well as data
from crowdsourcing, there is no shortage of models. In fact, models and
algorithms are abundant and proliferating. However, the question remains
where is a global flood model when we need one? Just because
models are available does not mean they are usable or accessible and
adequate for emergency managers, first responders and other stakeholders
who use the model outputs for preparedness, response and resource
planning. Often the issue of usability stems from the fact that the
models are not always reproducible or replicable. The accuracy
and uncertainty associated with the models and how they change based on
the scale of analysis and the resolution of input and output datasets
are often not communicated properly to stakeholders so they can be part
of their decision-making process. The proliferation of machine learning
and data driven models that rely on historical data also adds to this
problem. This paper discusses several important issues associated with
global flood models and provides recommendations that could be used to
increase the usability of these models.