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Submitted by jdp on Thu, 11/06/2014 - 08:25 pm


I recently watched an episode of the Syfy Channel’s post-apocalyptic zombie show Z-Nation. The human survivors were making their way across the U.S. Midwest when a massive tornado spun up, picking up zombies and flinging them all over the place.

“Is that what I think it is?” asks one character, observing the oncoming cyclone of the undead. “It ain’t sharks,” says his companion. This is, of course, a reference to the infamous “Sharknado” movie in which a tornado at sea (technically a waterspout, I reckon) sucks up a bunch of sharks and blows them into Los Angeles. Sharknado is, by all accounts, a thoroughly ridiculous movie with no scientific validity.

The tornado in the background is just about to suck up these flesh-eating freaks from beyond the grave to form an un-deadly Z-nado!

This movie poster tells you all you need to know. 


Submitted by jdp on Sat, 11/01/2014 - 12:04 pm


Last month the climatologist Justin Maxwell from Indiana University gave an interesting talk at our department about drought-busting tropical cyclones. In his talk, and in conversations before and after with our physical geography crew, he had some interesting things to say about climate teleconnections involving mainly sea surface temperature and pressure patterns such as ENSO, NAO, etc. If teleconnections and the various acronyms are unfamiliar, check out the National Climatic Data Center’s teleconnections page:


Submitted by jdp on Sat, 11/01/2014 - 11:25 am


I got a few e-mails last week about fluvial geomorphology—not because of anything I have done, or any current issues or unresolved questions in that field. No, it was because a character in the irreverent Comedy Central show South Park was identified on the show as a fluvial geomorphologist. Apparently that gives us a measure of popular culture street cred.

South Park character Randy Marsh, in his pop singer Lorde disguise.

An actual geomorphologist named Randy (R. Schaetzl, Department of Geography, Michigan State University).


Early in October, an episode of the show was based on the premise that the New Zealand pop singer Lorde is actually a 45 year old man, Randy Marsh, a regular character on the show. As explained during the episode, “Lorde isn’t just a singer, she’s also a very talented scientist who specialises in fluvial geomorphology.” If this is all a bit confusing, see


Submitted by jdp on Wed, 10/22/2014 - 04:13 pm

I've thought, written, and talked a lot about the need to incorporate geographical and historical contingency--that is, idiosyncratic characteristics of place and history--in geosciences, in addition to (not instead of!) general or universal laws. I've also emphasized the fact that places and environmental systems have elements of uniqueness. This leads to the issue of how to measure or assess place similarity (or the similarity of different, e.g., landscapes, ecosystems, plant communities, soils, etc.). This is a way of thinking about this problem, dressed up with some formal mathematical symbolism. Though I'm personally pretty informal, I'm a big believer in formal statements in science, as it makes arguments at least partly independent of linguistic skills (or lack thereof). 





Submitted by jdp on Sun, 10/12/2014 - 06:10 pm


In fluid dynamics the Reynolds Number is the ratio of inertial to viscous forces, and is used to distinguish laminar from turbulent flow. Peter Haff (2007) applied this logic to develop a landscape Reynolds number, and also suggested how other generalized “Reynolds numbers” can be constructed as ratios of large-scale to small-scale diffusivities to measure the efficiencies of complex processes that affect the surface. As far as I know, there has been little follow-up of this suggestion, but the premise seems to me quite promising at an even more general level, to produce dimensionless indices reflecting the ratio of larger to smaller scale sets of processes or relationships. The attached file gives a couple of examples. 



Submitted by jdp on Sat, 10/11/2014 - 12:08 pm


Climate change is here, it’s real, and it won’t be easy for humans to deal with. But few things are all good or all bad, and so it may be for climate change, at least with respect to environmental science and management.

A vast literature has accumulated in the past two or three decades in geosciences, environmental sciences, and ecology acknowledging the pervasive—and to some extent irreducible—roles of uncertainty and contingency. This does not make prediction impossible or unfeasible, but does change the context of prediction. We are obliged to not only acknowledge uncertainty, but also to frame prediction in terms of ranges or envelopes of probabilities and possibilities rather than single predicted outcomes. Think of hurricane track forecasts, which acknowledge a range of possible pathways, and that the uncertainty increases into the future.

Forecast track for Hurricane Lili, September 30, 2002. The range of possible tracks and the increasing uncertainty over time are clear. Source: National Hurricane Center.


Submitted by jdp on Fri, 10/10/2014 - 02:16 pm


The foundation for time series analysis methods to detect chaos is the notion that phase spaces and dynamics of a nonlinear dynamical system (NDS) can be reconstructed from a single variable, based on Takens embedding theorem (Takens, 1981). Many years ago (Phillips, 1993) I showed that temporal-domain chaos in the presence of anything other than perfect spatial isotropy (and when does that ever happen in the real world?) leads to spatial-domain chaos. This implies an analogous principle in the spatial domain.

Assume an Earth surface system (ESS) characterized by n variables or components xi, i = 1, 2, . . , n, which vary as functions of each other:

ESS = f(x1, x2, , , , xn)

If spatial variation is directional along a gradient y (of e.g., elevation, moisture, insolation) then

dxi/dy = f(x1, x2, , , , xn)

dx2/dy = f(x1, x2, , , , xn)

.                 .                   .

.                 .                   .

.                 .                   .


Submitted by jdp on Fri, 10/10/2014 - 01:29 pm


Resistance of environmental systems is their capacity to withstand or absorb force or disturbance with minimal change. In many cases we can measure it based on, e.g., strength or absorptive capacity. Resilience is the ability of a system to recover after a disturbance or applied force to (or toward) its pre-disturbance condition—in many cases a function of dynamical stability. In my classes I illustrate the difference by comparing a steel bar and a rubber band. The steel bar has high resistance and low resilience—you have to apply a great deal of force to bend it, but once bent it stays bent. A rubber band has low resistance and high resilience—it is easily broken, but after any application of force short of the breaking point, it snaps back to its original state.

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