planetwater

• European flood risk projects launched: http://bit.ly/cN6862 #
• "#risk-taking leads not to a loss of #control but to the opposite." http://bit.ly/9W11qk – despite the #uncertainty involved! #
• recent #extremes ( 0.2% #probability )of discharges in Tennessee Rivers: http://bit.ly/a1dYT3 #
• RT @Bill_Romanos: only indirectly water related, but crazy: the first synthetic living cell createdhttp://bit.ly/967XOJ #
• FREAK Shots: The Oil Sands: Photographs of Canada's oil sands. http://bit.ly/9Mv0Cu (via @freakonomics) #

The Monster Oil Spill

from Zina Saunders

• international #soil #moisture network has been created: http://bit.ly/9J7QMl #

Oil continuous to gush out of the leakage in the pipes below the exploded drill-rig in the Gulf of Mexico. Here are some links I came across:

• a photo gallery;
• various GIS resources, including links to shapefiles;
• the google earth blog links tovarious kml files and georeferenced images;
• google has its own site devoted to the spill;
• there is a grassroots mapping project going on;
• and of course, Paul Krugman has his thoughts.

updated Sunday; May 16, 2010:

• a Valdez reporter on the gushing oil
• drilling for oil is more risky than it used to be
• “Naming the Unspeakable” is Cosmic Variance’s post on the spill

updated Friday; May 21, 2010: * here’s an interactive oil-spil map by ESRI

The volcano EYJAFJALLAJÖKULL on iceland keeps spewing, and the aviation business across Europe at its will. Here is a great timelapse video (via kwerfeldein.de)

Iceland, Eyjafjallajökull – May 1st and 2nd, 2010 from Sean Stiegemeier on Vimeo.

update Sunday; May 16, 2010:

here are some awesome photos of the eruption together with northern lights in the background

update Sunday; May 23, 2010

• VectorOne asks where is the GPS data to support the existence and/or location of the ash cloud
• Explanation on MSN why ash clouds are dangerous

• Engineering bacteria for environmental cleanup http://j.mp/9A5x44 (via @modernscientist) #
• at the #NUPUS meeting http://www.nupus.uni-stuttgart.de/ #

• Gates Fdn. CEO calls for more efficient water use. http://tinyurl.com/2fnry84 (via @WaterWired) #
• Imagery from Gulf of Mexico oil spill now in Google Earth. Turn on "Paces of Interest" & look for an icon in the Gulf. (via @googleearth) #

The invention what we now call insurance was a big step in human development: People who potentially suffer from losses due to the same reason pay money into a pot. When somebody experiences such a loss gets, he gets money from the pot, and more money than he puts in on a regular basis.

Things get problematic, when losses occur more frequently than expected or are more severe and lead to more severe losses than expected. Then there is not enough money in the pot. This could happen either because the expected frequency or severity were estimated wrong, or because the underlying process that causes the losses has changed.

In one of the stories Kaiser Fung describes how there is not enough money in the pot for people who suffer from storms in Florida. I think when dealing with the magnitude of extreme events, especial care has to be spent on the way of “statistical thinking”, hence I’d like to expand on this topic a little bit to Kaiser Fung’s writing.

Generally, the severity of storms is measured by the “return period”. This is statistically speaking, the number of years that pass on average until a storm of such a severity occurs again.

There are a few problematic things:

• One problem is the “on average” part, because it means the average if we had an infinitely long time series of observations of annual storms. Unfortunately, a really long time-series of measurements of naturally occurring phenomena is 100 years old. The magic of statistics comes into play when we want to estimate the magnitude of a storm with a 100 year return period or even a 1000 year return period.
• Another problem is, that really severe storms can occur in consecutive years. For example, two storms, each with a return-period of about 100 years could occur this year and next year. Generally, the property of storms occurring in consecutive years is covered by the statistics-side, but is generally perceived wrongly in the public.
• Expanding on the last problem: such two really severe storms could even occur both in the same year. Or even more than two storms in one year. This is a property that is covered only in more complex statistical models.
• In doing all of this, in order for statistical models to work, we have to assume that all the storms are “from the same homogeneous population”. This has a couple of implications! For one, every storm is independent of every other storm. This might be ok, if there is only one big storm every year. But what if similar or one set of conditions leads to multiple really big storms? Or what if the underlying process that causes storms, such as the weather patterns off-shore of Florida, change? We base our estimates of a storm with a return period of 100 years on data gathered in the past, and that’s as good as we can do for data collection. But if the underlying process started changing during the last say 20 years and such that the severity of storm generally increases, then our estimates based on our data consistently underestimate the severity of storm.
• Finally, one problem I want to only mention and not go into depth, because it is too deep for this post is the problem of making statements about extreme events in an areal context. Is the severity of a storm with a return period of 100 years the same everywhere in Florida? Everywhere in the USA? Everywhere in the world?

A novel concept for me about which Kaiser Fung wrote is that storms are can be classified differently: Not according to the return period in terms of severity of the natural phenomenon, measured for example by wind speed, but according to the economic loss they cause. This doesn’t solve the problems outlined above, but is at least an interesting different yardstick.

I am working in a statistically inclined workgroup at the University of Stuttgart, hence the title of the book naturally attracted me. Kaiser Fung tells stories in five chapters in his book “Numbers Rule Your World: The Hidden Influence of Probabilities and Statistics on Everything You Do” on the daily use of statistics. By “daily use” I mean the use in settings or for cases that are directly applicable or even influence our daily lives.

What are examples of such stories? People across the USA got sick, and it was unclear why. How do you find out what the source of the illness is? How can you find out, that the source was one spinach field in California? If you live in a city, and you rely daily on driving cars to get to work, you would be quite happy to know that somebody is making sure that your travel time is as short as possible, right? How is that travel time minimized?

When teaching statistics or subjects with statistical basis, I find that newcomers to the field of statistics, or even people who don’t use statistics on a regular basis are not used to “statistical thinking”. This lack of use or lack of being used to frequently results in hesitation against using or even in refuse to use statistics. Hence I think hearing about those very applied stories helps a lot for getting used to statistics. “Statistical Thinking” is a term actually used by Kaiser Fung when he explains why he wanted to tell these stories.

Particularly, there are two topics that play a significant role in Kaiser Fung’s stories which I want to expand on in two upcoming posts:

• what problems arise when dealing with magnitudes of extreme (weather) events
• statistical testing tends to be an unliked topic, but one of Kaiser Fung’s stories puts a current and rather interesting perspective onto testing: how do you find out if somebody has used substances that increase his or her physical ability when doing sports. Especially, Kaiser Fung explained in great depth the issues of “false positives” and “false negatives”.

Before I will expand on these two issues, let me tell you that I really loved reading those stories. They are well written, I think well understandable for somebody who is not experienced or even trained in “statistical thinking”. Finally, a big plus is a longer than normal “conclusions” section, where Kaiser Fung tries to put the underlying basic thoughts of each story into almost all the other stories’ context.