Gagliano et al. (2026) A global high-resolution dataset of snowmelt runoff onset timing from Sentinel-1 SAR, 2015–2024

Identification

• Journal: Earth system science data
• Year: 2026
• Date: 2026-08-14
• Authors: Eric Gagliano, David Shean, Scott Henderson
• DOI: 10.5194/essd-18-5871-2026

Research Groups

• Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA
• eScience Institute, University of Washington, Seattle, WA 98195, USA

Short Summary

This study presents a global high-resolution dataset of snowmelt runoff onset timing from Sentinel-1 SAR observations for the period 2015–2024. The dataset provides unprecedented detail for studying snowmelt runoff onset across snow-covered regions.

Objective

• To develop a global dataset of snowmelt runoff onset timing with high spatial and temporal resolution.
• To evaluate the accuracy of the dataset against in situ snow pillow measurements from automated weather stations.

Study Configuration

• Spatial Scale: Global coverage at 80 m spatial resolution.
• Temporal Scale: 9.5 d average temporal resolution, spanning 10 water years (2015–2024).

Methodology and Data

• Models used: Sentinel-1 C-band SAR backscatter time series analysis.
• Data sources:
  • Sentinel-1 C-band SAR Interferometric Wide mode data from Microsoft Planetary Computer.
  • MODIS MOD10A2 8 d maximum snow extent product for snow phenology dataset creation.

Main Results

• The global snowmelt runoff onset dataset provides accurate estimates of runoff onset timing, with a median timing difference of −2.0 d and a median absolute deviation of 10.0 d against in situ snow pillow measurements.
• The local agreement between the dataset and snow pillow runoff onset estimates varies with site-specific variables like forest cover fraction, SWE, and temporal resolution.

Contributions

• This study provides a unique combination of high spatial resolution, global coverage, and decade-long temporal coverage for studying snowmelt runoff onset across snow-covered regions.
• The dataset enables an improved understanding of mountain hydrological processes and informs water resource management in snow-dominated watersheds.

Funding

• This research was supported by the University of Washington's eScience Institute.

Citation

@article{Gagliano2026global,
  author = {Gagliano, Eric and Shean, David and Henderson, Scott},
  title = {A global high-resolution dataset of snowmelt runoff onset timing from Sentinel-1 SAR, 2015–2024},
  journal = {Earth system science data},
  year = {2026},
  doi = {10.5194/essd-18-5871-2026},
  url = {https://doi.org/10.5194/essd-18-5871-2026}
}