Preprints
https://doi.org/10.5194/essd-2026-216
https://doi.org/10.5194/essd-2026-216
28 Apr 2026
 | 28 Apr 2026
Status: this preprint is currently under review for the journal ESSD.

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

Eric Gagliano, David Shean, and Scott Henderson

Abstract. Snowmelt runoff onset timing represents a critical hydrological parameter, particularly in mountainous regions where seasonal snow serves as a natural reservoir for downstream water resources. Despite this importance, high-resolution observations of snowmelt runoff onset across complex terrain are limited, due to challenges from sparse in situ monitoring networks, intermittent optical remote sensing data, and coarse passive microwave remote sensing data.

To address this gap, we prepared a global snowmelt runoff onset timing dataset (https://doi.org/10.5281/zenodo.16953614) for the 10-year period spanning 2015 to 2024, with 80-meter spatial resolution and 9.2-day average temporal resolution. We created this dataset by identifying backscatter minima indicative of runoff onset in a time series of Sentinel-1 C-band SAR images, with detection constrained by a custom MODIS-derived snow phenology dataset.

We validated our dataset using in situ snow pillow estimates of runoff onset from 735 automated weather stations in the Western United States, finding a median timing difference of -1.0 days and a median absolute deviation of 9.0 days. The local agreement between our runoff onset estimates and snow pillow runoff onset estimates varies with site-specific variables like forest cover fraction, SWE, and dataset temporal resolution. We characterized these dependencies to provide empirically-derived thresholds for quality filtering as well as guidance for interpretation and use of our products.

The dataset includes global annual runoff onset products for each water year, annual local temporal resolution products for each water year, and 10-year composites of median runoff onset, median absolute deviation, and local temporal resolution. This unique combination of high spatial resolution, global coverage, and decade-long temporal coverage provides unprecedented detail for the study of snowmelt runoff onset across snow-covered regions. Our snowmelt runoff onset dataset enables an improved understanding of mountain hydrological processes and informs water resource management in snow-dominated watersheds.

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Eric Gagliano, David Shean, and Scott Henderson

Status: open (until 04 Jun 2026)

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Eric Gagliano, David Shean, and Scott Henderson

Data sets

A global high-resolution dataset of snowmelt runoff onset timing from Sentinel-1 SAR, 2015-2024 Eric Gagliano et al. https://doi.org/10.5281/zenodo.16953614

Global MODIS-derived seasonal snow cover (snow appearance date, disappearance date, and max consec snow days), water years 2015–2024 Eric Gagliano https://doi.org/10.5281/zenodo.15692530

Model code and software

Github repository: global_snowmelt_runoff_onset Eric Gagliano https://github.com/egagli/global_snowmelt_runoff_onset

Github repository: MODIS_seasonal_snow_mask Eric Gagliano https://github.com/egagli/MODIS_seasonal_snow_mask

Eric Gagliano, David Shean, and Scott Henderson
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Latest update: 28 Apr 2026
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Short summary
Meltwater from seasonal snow sustains over a billion people globally, making the timing of snowmelt runoff onset a critical hydrological parameter. We used satellite radar images, which can detect liquid water in snowpack regardless of cloud cover, to create a global dataset of snowmelt runoff onset timing from 2015 to 2024 at 80-meter resolution. The dataset shows close agreement with a network of ground-based snow sensors, and can support water resource management in snow-dominated watersheds.
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