the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A global high-resolution dataset of snowmelt runoff onset timing from Sentinel-1 SAR, 2015–2024
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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Status: final response (author comments only)
- RC1: 'Comment on essd-2026-216', Anonymous Referee #1, 01 Jun 2026
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CC1: 'Comment on essd-2026-216', Sara Darychuk, 11 Jun 2026
The authors provide an interesting and important contribution through the development of a global snowmelt runoff onset dataset from Sentinel-1 observations. Readers may also be interested in Darychuk et al. (2025), which explored the use of MODIS observations to improve Sentinel-1 snowmelt onset estimates in British Columbia. In this study, optical timing constraints reduced snowmelt onset estimation errors by more than 50% in some years relative to unconstrained approaches, providing additional evidence for the utility of optical observations in SAR-based snowmelt timing workflows.
https://doi.org/10.1016/j.rse.2025.114863
Citation: https://doi.org/10.5194/essd-2026-216-CC1 -
RC2: 'Comment on essd-2026-216', Anonymous Referee #2, 13 Jun 2026
Gagliano et al. provide the global snowmelt onset timing dataset for the 10-year period spanning 2015-2024. which is first of its kind. The authors created the dataset by using time series of Sentinel-1 C-band SAR images, with detection constrained by a custom MODIS-derived snow phenology dataset. The generated dataset is validated using in situ snow pillow estimates of runoff onset from 735 automated weather stations in the Western United States. I am highly impressed by the depth and quality of discussion and explanation provided in this study. Overall, the manuscript provides a thorough and well-structured analysis supported by excellent figures and datasets. However, authors can include some of the minor comments.
The introduction is nicely written and provides a good background, but it is somewhat lengthy and could be shortened to improve readability and focus.
Line 95-100: The criteria for selection of just VV scenes for this study are just based on literature or have the authors also explored the applicability of VV and VH images?
Figure 1: The resolution of Sentinel-1 SAR time series, MODIS snow cover and forest cover fraction are different. How authors have merged these in the overall methodology to generate the outputs is not very clear in the manuscript. It would be more helpful for readers to have the information.
Line 225: “To aid in the interpretation of the annual snowmelt runoff onset products, we created three additional composite products for pixels with at least three years of valid annual data.” This statement is not clear. Most of the statements in the entire manuscript are written like this. I would prefer “We created three additional composite products for pixels with at least three years of valid annual data to aid the interpretation of the annual snowmelt runoff onset products.”
Citation: https://doi.org/10.5194/essd-2026-216-RC2 -
AC1: 'Author response to RC1, RC2, and CC1', Eric Gagliano, 13 Jul 2026
Dear Editor, Referees, and Community Commenter,
We thank both anonymous referees and the community commenter for their careful and constructive engagement with our manuscript. Their feedback prompted substantial improvements, including an expansion of our evaluation network, an independent test of the transferability of our empirically-derived quality thresholds, clearer framing of the physical interpretation of our runoff onset estimates, and stronger justifications for methodological choices.
Please find attached a PDF containing point-by-point responses to all comments (RC1, RC2, and CC1).
Thank you for your time, and we look forward to any further feedback.
Sincerely,
Eric Gagliano, on behalf of all authors
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
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