Presenting and reflecting

Last week I had the opportunity to meet with more members of our team. The project involves researchers from many disciplines across five University of California campuses and two national labs. This was the first chance I had to meet some of the UC Davis and Lawrence Livermore National Lab scientists who also work on the project. While Dr. Erica Woodburn and team are working on creating a numerical model, other researchers involved with the project have focused on analyzing water isotopes to identify the origins (and therefore movements) of individual water molecules, and using transient electromagnetic data collection to model groundwater recharge in a different way. Plus, it was fun to hear our own postdocs Dr. Fadji Maina and Dr. Michelle Newcomer discuss their work.

My fellow intern Ved and I had the opportunity to give short presentations, as well. I’m a pretty chatty person and I thought the presentation would be a breeze. Instead, I was surprisingly nervous to present! Luckily, Ved and I were able to practice our presentations with each other. Then we gave a mock presentation for our LBNL team. Erica, Fadji, and Michelle offered feedback to help us refine our presentations. The entire process was a good exercise, and the Cal Energy Corps interns represented ourselves well at the team meeting. Best of all, I was able to give my final presentation without feeling nervous, thanks to the team’s feedback and a little extra practice between our mock presentation and the real deal.

In this blog post I’ve included a couple simple graphics that I created for my presentation. These graphics should convey the gist of what I’ve talked about before in this blog, particularly in this post. Our model builds on a prior model known as Parflow, which is unique among groundwater modeling methods in part due to the sheer number of physical, observed inputs that go into the model. (I haven’t listed anywhere close to all of the inputs on the graphic!)

However, some of the model outputs, such as evapotranspiration, are always going to be calculated rather than measured—it’s currently impossible to measure ET over such a large area. That’s why it’s so important that we check the model output against the data and literature I dug up. Our model may be larger-scale than a lot of ET data, but we still want it to roughly match up with what’s already out there.

For example, our model initially didn’t address ET for urban areas because so much of the urban landscape is concrete. However, our study area covers part of Sacramento, which calls itself the “City of Trees.” Studies indicate that urban ET can actually be higher than the ET for nearby rural areas, in part because urban landscapes tend to be highly irrigated—homeowners want their lawns looking green. So the irrigation and ET for urban areas are worth consideration going forward.

I also created a simple graphic to show how I combined DWR’s regional ET data with satellite mapping products to create a tool that gives estimated ET across different plant species and regions within our study area. Because ET depends so much on climate, small differences in location can have an outsize impact on ET: the same type of plant can evapotranspirate 25 percent more water vapor in some counties of our study area than in neighboring counties, according to DWR estimates. ET also varies widely between plants: lush alfalfa has an ET value that is three times higher than that of wine grapes.

In turn, irrigation depends heavily on ET. A plant that has transpired all its water will wilt—hot areas, unsurprisingly, receive more irrigation. Plus, when lots of water is lost through plants, less water from rainfall and irrigation percolates down into the groundwater supply.

This presentation was not only a unique opportunity to learn from scientists in a variety of areas, but a chance to take stock of what I’ve done so far in this internship and what I’ll tackle in my final two weeks.