There's No Science Like Snow Science

Super late, but still relevant, here's what I'm doing for my interview. Karl Wetlaufer studies snow. He studies what? Snow. Snow? Yes, snow. What exactly is there to study in snow? Well, it turns out snow is a pretty fascinating subject. But what really makes snow relevant to humans is that it eventually melts and becomes streams, rivers, lakes, reservoirs and oceans. Little of our earth is dry land. Most of it is covered in water, either in the form of oceans or, the part that Karl enjoys, ice and snow. Karl is a grad student here at MSU, but he's taking on a big project. He has successfully completed the first phase of the largest known study of snow water equivalency in mountain basins. Essentially what he did was sampled the snow pack over a 200 square kilometer area within a 1600 meter elevation variation. That's a pretty big space. But what he's doing with the data is even more amazing. He's going to use his results to create a model for snow melt and runoff that would allow other scientists to take a few measurements within a given basin and predict how much water would flow out of the mountains and into our streams, rivers and reservoirs. Ultimately, these measurements could be used to make plans on how we use water resources, which and how many crops to plant in a given season, etc. Imagine if a farmer could predict exactly how much water he would be able to pump each season. Karl thinks that's pretty cool. And so do I. So we got a chance to talk about it. Here's the questions I had prepared for the interview, but, as Hancock suggested, I really used them more as a guide and let the questions "arise naturally" (60). 1. The first thing I asked Karl was "what is your approach to science?" He just kind of looked at me and said "you got anything easier than that?" It was then I knew this was going to be more fun than I thought. So I rephrased and he seemed to take it a lot better: "how do you 'do' science?" "What is your scientific process like?" 2. "What is it that gets you excited about science in general and this project specifically?" 3. Why "Snow" science? 4. How did you get involved in this particular project? 5. What was it like to design the study? Explain the process. Where did the funding come from? At what point did you seek funding? 6. How did you go about collecting data? 7. Once the data was collected, what do you do with it? 8. He's in the statistical analysis phase of the project right now, so I asked him what kind of statistical method he was using. This was where my understanding admittedly took a wrong turn. But I took good notes, so I think I might be able to figure it out! 9. What are the limits to the study? 10. Why is this important to a. the scientific community, b. to society 11. What are the next steps, if any? 12. How will you communicate your results? These, of course, led to a lot more than twelve compact answers, and even led into a huge discussion about how there is a lack of connection between scientists and the outside world. Since Karl isn't done with his data analysis, I will probably have to look at his work as a "if this-and-so is true, that could imply this-and-exciting-that" (56) as Hancock puts it. We definitely discussed implications and hypotheticals. It's kind of like looking at the picture on the puzzle box and then looking at all the pieces all broken and mixed up in the box itself. Right now it isn't pretty, but you can see where he's going. And I think it'll make for an interesting story, since what makes Karl interesting is less about statistics and more about how he collects his data. Curious? You'll have to read my paper! ;)