S. P. Hicks (@seismo_steve) wrote for Nature Geoscience a nice piece "Geoscience analysis on Twitter" where he shares his experience on using Twitter for the real time analysis of geohazards.

Actively using Twitter for science may be perceived by some as a significant time-sink — the demands on individual researchers and their institutions at times of newsworthy events can be very high — but it is ultimately rewarding.

I fully agree with his viewpoint and I would like to share my experience here too.

As a trained hydrologist I'm inclined to tweet about water-related hazards like floods and droughts. However, I have mostly tweeted on floods because flooded surfaces can be easily characterized by remote sensing using a simple before/after image comparison. In particular, water surfaces are strong reflectors of the electromagnetic waves emitted by SAR satellites such as Sentinel-1. In addition SAR sensors can see through the clouds which is a key asset for near-real time analysis during flooding events.

I remember that I made this analysis of the Xe-Namnoi dam failure overnight, just hours after the first Sentinel-1 image of the flood was available on the Sentinel Hub. My tweets triggered many interesting discussions with other flood specialists, civil engineers and NGO staff, but also with close colleagues at LEGOS who provided water elevation data from Sentinel-3 to expand the analysis. My animation and other images were featured in ESA's website (here and here, without acknowledging my work though). This post was the most visited in 2018 (11,371 visits, 18% of the 2018 visits).

SAR images are great, but optical sensors like Sentinel-2 can also provide useful observations of flooded areas as shown below in the case of the recent flood in southwest France:

To go a bit further with optical data, the NDWI is a simple a simple multispectral index, which makes it is easy to map the extent of the water surface:

I used the same method to show the Oroville dam lake extent when it was under the threat of spillway failure, causing the evacuation of 188'000 northern California residents.

In the case of the dramatic 2019 Omaha flood, I tried to go beyond the visualization of the flooded areas ..

.. using additional images and weather data it was obvious that the flood was caused by a rain-on-snow event:

To conclude this series, an interesting fact is that my least successful tweet about flood is the one below (only 1 "like" at the time of writing), probably because I was a newbie in Twitter at that time. However, the linked article is one of the most visited page (if not the most visited) in this blog. Twitter isn't the only way to reach out to people!

The next article will be about landslides. In the meantime, here are some more flood tweets:






Spain... Sometimes remote sensing is not useful:

Prediction was correct...

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