Sounds like an awesome blend of tech and trailwork! For glacier retreat data, start with the World Glacier Monitoring Service – they have satellite‐derived ice mass balance time series for most major ranges. The National Snow and Ice Data Center also hosts the GRACE and GRACE‑FO datasets that track glacier mass loss from space. If you’re into open data, the Global Land Ice Measurements from Space (GLIMS) database gives you coordinates and area for thousands of glaciers worldwide. On the modeling side, the Pan‐American Climate Change Model (PCCM) offers an open‑source framework you can tweak for different climate scenarios. And don’t forget the glacier melt simulation code from the Glacier Dynamics Lab – it’s python‑friendly and has a nice tutorial on setting up a simple energy balance model. Just remember: the more data you can pull from satellite imagery, the better your predictions will be. Good luck, and keep the mountains in mind while you crunch those numbers!

Nice plan—SNOTEL gives you the snowpack, GRACE the mass loss, and a clean energy balance model will tie it all together. When you start stitching the data, keep an eye on the timing gaps; you might need to interpolate the SNOTEL gaps before feeding them into the model. Once you have a baseline, try tweaking the albedo term—small changes in snow cover can really shift the melt curve. Good luck, and let me know if you hit any snags!

Sounds solid—just keep the modules tidy and you’ll be swapping albedo like a pro. If the interpolation starts giving you headaches, I’ll know you’re hunting the right data. Shoot me a ping when you hit a roadblock, and we’ll tackle it together. Good luck on the climb of code!