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SnoLimits: A random forest derived snow water equivalent and depth dataset for Colorado and California (2001–2026)
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| Type: | Resource | |
| Storage: | The size of this resource is 29.0 GB | |
| Created: | Apr 30, 2026 at 4:16 p.m. (UTC) | |
| Last updated: | Jul 15, 2026 at 2:15 p.m. (UTC) | |
| Citation: | See how to cite this resource | |
| Content types: | Multidimensional Content |
| Sharing Status: | Private (Accessible via direct link sharing) |
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| Views: | 115 |
| Downloads: | 8 |
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Abstract
Mountain snowpack in the western United States represents a critical store of terrestrial water, but current observational methods limit our ability to quantify snow water equivalent (SWE). Snow stations provide real-time measurements at sparse points in space, while airborne lidar surveys capture spatially continuous snapshots at infrequent points in time. Together, these datasets enable machine learning to ascertain the spatial patterns of snow from lidar while anchoring estimates to real-time conditions via snow stations. We present SnoLimits, a daily 500 m SWE and snow depth dataset spanning the MODIS era (2001–2026), created using a random forest model trained on physiographic and dynamic predictors. Spatial validation demonstrates SnoLimits outperforms existing products (UASWE, ParBal, UCLA SWE) when compared to withheld lidar surveys, with lower RMSE and higher correlation. Temporal validation at snow stations indicates performance better than or comparable to UCLA SWE. SnoLimits is intended for hydrological modeling, water resource applications, and snow model intercomparisons in Colorado and California. Operational SWE and depth data are available with a two-day latency period.
Subject Keywords
Coverage
Spatial
Temporal
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Content
README.md
SnoLimits: Daily Snow Water Equivalent and Snow Depth — California and Colorado
Institution: University of Colorado Boulder
Source: Random Forest (RF) model outputs
Conventions: CF-1.8
Overview
This repository contains the SnoLimits dataset: daily gridded estimates of snow water equivalent (SWE) and snow depth (SD) at 500m resolution for California (CA) and Colorado (CO), 2001–2026. The data are derived from a random forest model trained using airborne lidar data and in-situ snow station data. The original methodology was described in Herbert et al. (2025).
Data are organized by state and variable into four folders, with one NetCDF4 file per water year (WY).
Repository Structure
├── CA_SWE/ # California Snow Water Equivalent, WY2001–WY2026
├── CA_SD/ # California Snow Depth, WY2005–WY2026
├── CO_SWE/ # Colorado Snow Water Equivalent, WY2001–WY2026
└── CO_SD/ # Colorado Snow Depth, WY2005–WY2026
File Naming Convention
SnoLimits_{STATE}_{VARIABLE}_WY_{YEAR}.nc
Examples:
- SnoLimits_CO_SWE_WY_2014.nc — Colorado SWE for water year 2014
- SnoLimits_CA_SD_WY_2020.nc — California snow depth for water year 2020
Water years run from October 1 of the prior calendar year through September 30 of the named year (e.g., WY2014 = Oct 1, 2013 – Sep 30, 2014). Leap years have 366 time steps; standard years have 365.
Temporal Coverage
2026 coverage goes through June 30, 2026
| Folder | Variable | Water Years |
|---|---|---|
| CA_SWE | Snow Water Equivalent | 2001–2026 |
| CA_SD | Snow Depth | 2005–2026 |
| CO_SWE | Snow Water Equivalent | 2001–2026 |
| CO_SD | Snow Depth | 2005–2026 |
Spatial Reference
| Property | California (CA) | Colorado (CO) |
|---|---|---|
| Projection | UTM Zone 11N | UTM Zone 13N |
| EPSG | 32611 | 32613 |
| Central meridian | −117° | −105° |
| Datum | WGS84 | WGS84 |
| Spatial resolution | 500 m | 500 m |
| Grid dimensions | 866 (x) × 2001 (y) | 951 (x) × 1800 (y) |
| proj4 | +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs |
+proj=utm +zone=13 +datum=WGS84 +units=m +no_defs |
Auxiliary 2D latitude and longitude arrays (lat, lon) are included in each file for convenience.
Variable Description
Snow Water Equivalent (swe) — SWE files
| Attribute | Value |
|---|---|
| Variable name | swe |
| Long name | Snow water equivalent |
| Units | m |
| Dimensions | x, y, time |
| Data type | float32 (single) |
| Valid minimum | 0 |
| Fill value | NaN |
| Flag values | −1 = water |
| Description | 500 m SWE (m) |
Snow Depth (depth) — SD files
| Attribute | Value |
|---|---|
| Variable name | depth |
| Long name | Snow Depth |
| Units | m |
| Dimensions | x, y, time |
| Data type | float32 (single) |
| Valid minimum | 0 |
| Fill value | NaN |
| Flag values | −1 = water |
| Description | 500 m snow depth (m) |
Coordinate Variables
| Variable | Size (CA) | Size (CO) | Units | Description |
|---|---|---|---|---|
x |
866 × 1 | 951 × 1 | m | Easting (projection x coordinate) |
y |
2001 × 1 | 1800 × 1 | m | Northing (projection y coordinate) |
time |
365 or 366 × 1 | 365 or 366 × 1 | days since WY start (Oct 1) | Gregorian calendar |
lat |
866 × 2001 | 951 × 1800 | degrees_north | 2D latitude grid |
lon |
866 × 2001 | 951 × 1800 | degrees_east | 2D longitude grid |
crs |
scalar | scalar | — | Coordinate reference system metadata |
The time variable reference epoch is days since {YEAR-1}-10-01 00:00:00 (e.g., for WY2014, the epoch is 2013-10-01).
File Format
- Format: NetCDF4
- Data type: 32-bit float (single precision) for gridded variables; int32 for time and CRS
- Compression: NetCDF4 default chunking/compression (see individual file metadata)
Contact
For questions about this dataset, please contact the data authors via the HydroShare resource page or open an issue in the associated repository.
Related Resources
| The content of this resource references | Herbert, J. N., Raleigh, M. S., & Small, E. E. (2025). Using a random forest model to combine airborne lidar and Snotel data for daily estimates of snow depth across mountain drainage basins of Colorado. Water Resources Research, 61(8), https://doi.org/10.1029/2024WR039775 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| United States Bureau of Reclamation | None | R24AC00035 |
How to Cite
This resource is shared under the Creative Commons Attribution-NoCommercial CC BY-NC.
http://creativecommons.org/licenses/by-nc/4.0/
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