A global high-resolution dataset of snowmelt runoff onset timing from Sentinel-1 SAR, 2015-2024

Overview This dataset provides the first comprehensive global characterization of snowmelt runoff onset timing at high spatial resolution. Using Sentinel-1 C-band SAR data combined with a custom MODIS-derived snow phenology dataset, we detect characteristic backscatter minima that coincide with snowmelt runoff onset. The dataset covers nearly all seasonal snow on Earth for water years 2015–2024. The methodology leverages multi-orbit Sentinel-1 observations to identify the timing of minimum C-band backscatter, which coincides with the transition from the ripening phase to the runoff phase of snowmelt. A custom MODIS-derived snow phenology dataset constrains the temporal search window, ensuring detection occurs when and where seasonal snow exists. This dataset was evaluated against 735 snow pillow measurements from automated weather stations in the Western U.S., with a median timing difference of -1.0 days and a median absolute deviation of 9.0 days. We characterized how forest cover fraction, SWE, and temporal resolution affect agreement of runoff onset estimates between our products and the snow pillow measurements, and we provide empirically-derived usage recommendations based on this analysis. The associated publication contains the complete methodology for dataset creation and evaluation, as well as the context for proper interpretation and application of this dataset. Use of this dataset without consulting the associated publication is discouraged and may lead to inappropriate applications or misinterpretation of results. If you use this dataset in research, please cite the accompanying paper. Details Product source: Sentinel-1 C-band SAR VV-polarization (Microsoft Planetary Computer), MODIS MOD10A2 snow cover (Microsoft Planetary Computer) Format: Cloud-optimized Zarr archive (please make sure to extract the .tar.xz file first!) Spatial coverage: Global (60°S to 81.1°N, 180°W to 180°E), ~36.8 million km² total coverage Temporal coverage: Water Years 2015-2024 (October 1, 2014 to March 31, 2025) Coordinate system: WGS84 Geographic Coordinate System (EPSG:4326) Spatial resolution: 80 meters effective spatial resolution across all latitudes (pixel spacing ~7.2 × 10⁻⁴ degrees, corresponding to pixel spacing of ~80 meters at equator) Temporal resolution: Variable by location and water year (average 9.2 days globally, near daily in some locations) Array dimensions: Annual variables: (water_year: 10, latitude: 195970, longitude: 499998) Composite variables: (latitude: 195970, longitude: 499998) Data type: Signed int16 with scaling and offset parameters stored in the metadata NoData value: -9999 Variables runoff_onset: Annual snowmelt runoff onset dates (day of water year) [dimensions: water_year, lat, lon] Values: integer DOWY, valid range 1-366 Physical meaning: Timing of SAR backscatter minimum indicating runoff onset temporal_resolution: Temporal resolution in days for each water year [dimensions: water_year, lat, lon] Values: decimal days, precision limited to 0.1 days, valid range 0-30 days Physical meaning: Average time between Sentinel-1 observations used in runoff onset estimation for a specific pixel runoff_onset_median: 10-year median runoff onset timing [dimensions: lat, lon] Values: integer DOWY, valid range 1-366 Physical meaning: Median runoff onset date throughout WY2015-2024 (requires >=3 valid years) runoff_onset_mad: 10-year median absolute deviation of runoff onset [dimensions: lat, lon] Values: decimal days, precision limited to 0.1 days, valid range >1 days Physical meaning: Median absolute deviation in runoff onset timing throughout WY2015-2024 (requires >=3 valid years) temporal_resolution_median: 10-year median temporal resolution [dimensions: lat, lon] Values: decimal days, precision limited to 0.1 days, valid range 0-30 days Physical meaning: Median temporal resolution throughout WY2015-2024 (requires >=3 valid years) Water Year Definition Northern Hemisphere: October 1st of previous calendar year - September 30th of current calendar year e.g. WY 2015 is 2014-10-01 to 2015-09-30 (DOWY 1 = Oct 1, 2014) Southern Hemisphere: April 1st of current calendar year - March 31st of the following calendar year e.g. WY 2015 is 2015-04-01 to 2016-03-31 (DOWY 1 = Apr 1, 2015) Example usage import xarray as xr import rioxarray import matplotlib.pyplot as plt # 1. Open the global snowmelt runoff onset Zarr dataset, use consolidated=True and decode_coords='all' global_ds = xr.open_zarr("global_snowmelt_runoff_onset.zarr", consolidated=True, decode_coords='all') # 2. clip to Mt. Rainier, WA rainier_ds = global_ds.rio.clip_box( minx=-121.95, miny=46.7, maxx=-121.45, maxy=46.95, crs="EPSG:4326" ) # 3. Reproject to UTM Zone 10N visualization of global 10-year median runoff onset rainier_utm_ds = rainier_ds.rio.reproject("EPSG:32610") # 4. Create a quick plot of the composite variables f,axs = plt.subplots(ncols=3,nrows=1) rainier_utm_ds["runoff_onset_median"].plot.imshow(ax=axs[0], cmap='viridis', vmin=80, vmax=260) rainier_utm_ds["runoff_onset_mad"].plot.imshow(ax=axs[1], cmap='Reds', vmin=0, vmax=60) rainier_utm_ds["temporal_resolution_median"].plot.imshow(ax=axs[2], cmap='summer', vmin=1, vmax=20) # 5. Create maps of runoff onset and temporal resolution for each water year rainier_utm_ds["runoff_onset"].plot.imshow(col='water_year', cmap='viridis', vmin=80, vmax=260) rainier_utm_ds["temporal_resolution"].plot.imshow(col='water_year', cmap='summer', vmin=1, vmax=20) Publication citation Submitted to ESSD, will put pre-print here when available. Resources global_snowmelt_runoff_onset code (github repository): https://github.com/egagli/global_snowmelt_runoff_onset Snow phenology dataset (Zenodo repository): https://zenodo.org/records/15692530 Snow phenology code (github repository): https://github.com/egagli/MODIS_seasonal_snow_mask