Snow Water Equivalent Machine Learning (SWEMLv2.0): The evolution of the Snow Water Equivalent Machine Learning modeling workflow - flexible spatial resolution, flexible model regions, and flexible grid/mesh
The current iteration of the SWEMLv2.0 produces SWE inferences for select locations throughout the Western U.S. plus the Upper Colorado River Basin. There is a heavy focus on SWE inferences in the Sierra Nevada mountains, Colorado Rockies, and Wind River Range in Wyoming. The ML pipeline retrieves all SWE observations from SNOTEL and CDEC snow monitoring locations for the date of interest and processes the SWE observations into a model-friendly data frame alongside lidar-derived terrain features, seasonality metrics, previous SWE estimates, and location. SWEMLv2.0 predicts SWE using a multilayered perceptron network model for each supports an interactive visualization of the SWE estimates across the western U.S.
Figure 1. Example hindcast simulation in the Colorado domain demonstrates snow accumulation as the season progresses, especially at higher elevations, and only predicts SWE where NASA VIIRS fSCA imagery indicates snow covering greater than 20% of the terrain.
Ground measurements for training from the SNOTEL and CDEC features: ground_measure_features_templatev2.csv
Latitude, Longitude, and Elevation for all measurement locations: ground_measures_metadatav2.csv
GeoJSON data for the grid cell delineation: grid_cells.geoJSON file.
SWE training measurements separate from ASO observations: train_labels.csv
The prediction grid cell IDs were identified by Latitude and longitude form the prediction cell ids and we descritize the western US into 23 regions based on Sturm's snow classification scheme and location The ML workflow utilizes Previous SWE features for each prediction location and Delta SWE for each ground measurement site (e.g., SNOTEL/CDEC). For each prediction location at the respective spatial resolution, the data processing scripts determine slope angle, aspect, and northness from a 30m Copernicus DEM.
Python: Version 3.8 or 3.9.
Please refer to the file to set up the appropriate virutal environment to run all functions.
| os | ulmo | pandas |
|---|---|---|
| io | shapely | datetime |
| re | rasterio | matplot.pyplot |
| copy | lightgbm | numpy |
| time | tensorflow | pystac_client |
| tables | platfrom | planetray_computer |
| xarray | tqdm | random |
| rioxarray | geopandas | requests |
| pyproj | richdem | cartopy |
| h5py | elevation | cmocean |
| mpl_toolkits | hdfdict | warning |
| math | pickle | contextily |
| folium | branca | earthpy |
| netCDF4 | osgeo | requests |
| warnings | geojson | fiona |
| fiona.crs | webbrowser |
Project support through CIROH
