Friday, 26 Jul 2002

WARREN, Vt.

Another sunny day in the home office. While I’m not trying to be like a daily Garfield strip, I’m thrilled that it is Friday. (Also, did I mention how fond I am of lasagna and scratching posts?)

This week has been a bit of a grind. Designing and creating an environmental model often involves creativity, vision, breadth of thought. On the other hand, applying an environmental model tends to be slow, thorough, and grueling.

The process of applying an environmental model has three distinct steps: model calibration, model validation, and applying the model for predictive purposes.

In the first step, model calibration, the model is tested against a set of site-specific data to ensure that the model works for the site in question. Calibration also involves varying a model’s parameters to get the model to work as well as possible for that particular site. Many of a model’s parameters have a range of uncertainty. What’s more, many of these parameters can vary from site to site. (For example, the quantity of a particular food source being consumed by a particular predator is not likely to be the same in all water systems.) During the calibration phase, these uncertain parameters are modified within reasonable bounds to see how well the model can be fit to the empirical data.

In the second step, model validation, the model is tested against a different set of empirical data for the same site. This time, though, none of the model’s parameters are changed from those values chosen during calibration. This step ensures that the calibration of the model was not just a lucky coincidence, but that the model actually works for the system given those parameter choices. If validation is unsuccessful, the modelers most go back to the calibration stage again.

Once a successful validation has been completed, the model is ready to be applied for predictive purposes. This allows the user to predict what will happen in the future at a given site or to evaluate different scenarios of site management. This is usually the goal of any modeling study.

This process of model application brings us back to the data and data mining. Good data are required for setting model parameters as well as for forming the empirical data sets against which a model is calibrated and validated. And data, unfortunately, is rarely a clean issue.

For one thing, there are the inevitable units problems. Units are obviously an integral part of the data-to-model interface. Less obviously, they are often the bane of a modeler’s existence. If you didn’t like that part of chemistry where you were required to balance the units in each equation, modeling may not be the career for you. Remember when NASA sent the Mars explorer crashing onto the surface of the planet? Units. That event sent fear into the heart of every modeler I know. (All two of them.)

But anyway, suppose you have a data set that shows units in parts per million. Seems straightforward; your model accepts data in units of parts per million, too. You prepare to bring the data into the model. But wait! There are many, many more considerations to take into account before you start that process:

  • Parts of what per million parts of what?
  • Was that in wet weight or dry weight?
  • Umm. Can we convert wet weight to dry weight?
  • What was the sensitivity of the instrument being used?
  • Were duplicate samples taken to ensure the integrity of the data?
  • Was the location of the data sample identical to the location being modeled?
  • Umm. Can we assume that it was close enough?

These types of questions come up time and time again. Fortunately, in many cases, these issues can be resolved after careful consideration. In cases where the data was gathered for the purposes of modeling, most of these issues were preempted by the careful design of the data-gathering procedures.

Notwithstanding the time spent slogging through databases, environmental modeling is still not a bad way to make a living. There are undoubtedly times when I wonder if I should be sitting here alone in front of my computers, tucked away in rural Vermont, given the depth and breadth of environmental and social problems. That’s something to be worked out over the course of my career and indeed the course of my lifetime.

Ultimately, though, I think that it is helpful for us to understand the dynamics behind complex environmental systems so we can understand the effects of our actions. Going back to the example of global warming: If we had no idea why the earth was getting warmer, we’d have no idea what action to take. Now that we know how we’re changing the global climate, we have to push for the political will to do something about it.

Now if you’ll kindly excuse me, I’ve got to go wrangle with some more data issues. At least if I get frustrated I can take it out on my scratching post.