  As climate change increases the intensity of natural disasters, society needs
better tools for adaptation. Floods, for example, are the most frequent natural
disaster, and better tools for flood risk communication could increase the
support for flood-resilient infrastructure development. Our work aims to enable
more visual communication of large-scale climate impacts via visualizing the
output of coastal flood models as satellite imagery. We propose the first deep
learning pipeline to ensure physical-consistency in synthetic visual satellite
imagery. We advanced a state-of-the-art GAN called pix2pixHD, such that it
produces imagery that is physically-consistent with the output of an
expert-validated storm surge model (NOAA SLOSH). By evaluating the imagery
relative to physics-based flood maps, we find that our proposed framework
outperforms baseline models in both physical-consistency and photorealism. We
envision our work to be the first step towards a global visualization of how
climate change shapes our landscape. Continuing on this path, we show that the
proposed pipeline generalizes to visualize arctic sea ice melt. We also publish
a dataset of over 25k labelled image-pairs to study image-to-image translation
in Earth observation.
