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posted by Fnord666 on Tuesday May 29 2018, @06:07AM   Printer-friendly
from the flood-insurance-FTW dept.

Common Dreams reports

A Maryland city was devastated [May 27] after 6-inches of heavy rain caused a downtown flash flood. Major damage is reported and many cars have been swept away.

Ellicott City was still recovering from a flash flood two years ago that killed two and forced the historic city to rebuild much of its Main Street. Residents said Sunday's flood seemed even worse than the storm in July 2016--which was called an extremely rare "one-in-1,000 year event", and cost the city tens of millions of dollars in damages.

Additional information at:
The Baltimore Sun
The Washington Post
USAToday


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  • (Score: 1) by khallow on Tuesday May 29 2018, @03:23PM (1 child)

    by khallow (3766) Subscriber Badge on Tuesday May 29 2018, @03:23PM (#685654) Journal

    But I'll also note that weather is not a binomial distribution.

    And by that, I mean that the size of extremes are not determined by normal weather conditions. The binomial condition you described doesn't give you a means to extrapolate extreme weather conditions. It merely describes how often those extremes are expected to occur assuming certain conditions (such as independence of weather from year to year), and the span of time that one looks at.

  • (Score: 2) by Osamabobama on Tuesday May 29 2018, @06:22PM

    by Osamabobama (5842) on Tuesday May 29 2018, @06:22PM (#685774)

    The important thing here is that there is some distribution being used, with the data being fit to it, leading to probability estimates about possible flood levels. Let's assume the model uses the appropriate distribution.

    Does the model get updated, though? For instance, if a farm upstream were to build a levee around a field in the flood plain, the crops in that field may survive the flood (yay!), but some measure of water would no longer be able to spend a few days flooding that field, instead being diverted downstream to flood someplace else more severely.

    Or, maybe the models are accurate, and we hear about a 1000-year flood in one location, but nothing about the 999 other locations that get a normal level of flooding. This one seems less likely, especially when the same location gets more than one such flood in a lifetime.

    It's like a hypothetical statistician and a gambler, both observing a series of coin flips come up heads over and over. The statistician is confident in his knowledge that, eventually, it will even out to about 50-50 heads/tails. But the gambler will quickly start to suspect that the coin isn't fair.

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