CRC Earth Analytics

DrivenData is a website that hosts machine learning competitions, often involving some component of Earth Science or hydrology. In 2022, they hosted a competition sponsored by the US Bureau of Reclamation to predict snow quantity across the Western US. Although I didn’t have much experience with machine learning at the time, I decided to participate in the competition and was pretty surprised to rank 62 out of 1000 using a simple approach. See these three blog posts for more details: ...

In my last blog post, I tried to answer the question: How has snowfall at Sandia Peak Ski Area changed over the years? Ultimately, I want to know how climate change is affecting the future of skiing in the Sandias and to develop some sense of what weather patterns are associated with particularly good or bad years. With that in mind, I focused my initial research on assessing daily SWE values at the Ski Area. Unlike daily snowfall or total seasonal snowfall, daily SWE represents the actual amount of snow on the ground, including the effects of melting, sublimation, and redistribution from wind. Furthermore, since SWE is fundamentally a measure of water quantity, this analysis not only describes the overall quality of the snow for skiing, it is also equally useful from a hydrologic perspective. ...

A few months ago I found out my local ski area, Sandia Peak, preemptively chose not to open for the upcoming ski season (2022/2023). It isn’t that unusual for Sandia Peak to stay closed for the season, and they were also closed last season. However, I had thought the decision to stay closed is typically made around January, after the snow pack begins to form. At that point, if the early season snowfall is too low, the season isn’t long enough offset operating costs and it isn’t worth opening. Why was this year different? ...

Overview Last winter I tried my hand at competing in a machine learning competition to predict snow water equivalent (SWE) across the Western United States. I learned a lot and created a two part blog series to document both the competition and my approach: Machine Learning for Snow Hydrology - A Competition Machine Learning for Snow Hydrology - Methods I competed in the preliminary phase of the competition that didn’t include any prizes. The second phase involved predicting SWE in real time and included big prizes totaling $500,000. The competition ended in early summer, but the winners were just recently announced on the DrivenData Blog: ...

Overview I built a very unusual weather station on my roof. The measurements are pretty standard: wind, temperature, humidity - but everything else is unique, all the way down to the electronics. You can see a subset of the data, updated every 10 minutes at apps.crceanalytics.com/wxstation (sorry no longer running). It is far from the easiest or cheapest approach to monitoring the weather. Furthermore, my neighbor already has a publicly accessible station. Nonetheless, the station serves a number of purposes: ...

My graduate degree research was focused on glacial hydrology, which is basically trying to figure out how water moves above, below, and through glaciers and ice sheets. Water is important because it affects things like sliding, melting, sub-glacial erosion, and geochemistry. My research utilized temperature measurements from snow on the Greenland ice sheet, and I was lucky enough to travel to SW Greenland in the summer of 2010. Figure 1 - SW Greenland Ice Sheet ...

The mean annual precipitation in NM is about 14 inches which makes it one the driest states in the US. Almost the entire Western US is much drier than the Eastern US, and climate change is likely to make things worse. Water management is therefore critical, and I recently starting interacting with the New Mexico Water Data Initiative (NMWDI). Created as part of NM House Bill 651 (2019), the NMWDI is managed by the NM Bureau of Geology and Mineral Resources with the mission of developing a hub for NM water data to facilitate data discovery and access. ...

I wrote this a few years ago on another blog, but I think it is still relevant so I am re-posting it here. The code is still available on Github. I worked for several years at USU on the iUTAH project. While there I managed a network of water monitoring stations along the Logan river, and one component of my job involved measuring stream flow. We wanted to know how much water was flowing past each station in order to answer basic questions like “how much water is lost or gained downstream?” ...

This is the second part of my two part series on a machine learning competition to predict snow water equivalent (SWE). In Part 1, I describe the competition, as well as, my process for coming up with an approach for making SWE predictions at 9,067 locations across the Western US. That approach, sometimes called the “hypsometric” method (Fassnacht et al., 2003, see Part 1 for an overview of the method), is one of the easiest I could find, and it therefore seemed doable given personal time constraints. My expectations were low - I just wanted to see how a simple approach compared to others in the competition. To my surprise, out of about 1000 predictions submitted to the competition, my predictions ranked 62. Here I describe how I computed the SWE predictions and assess the results. ...

Part 1: Competition Overview Late last December I ran across a machine learning competition hosted by Driven Data. The goal of the competition is to predict snow water equivalent at high spatial resolution across the western US. I had never before thought of participating in a machine learning competition, although I had heard of the idea via another platform, Kaggle. However, a machine learning competition involving snow is more up my alley, as I have both professional and personal experience with snow science. Furthermore, I had been wanting to enhance my familiarity with machine learning techniques. I decided to give it a shot. ...