the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Nov 2025
A harmonized 2000–2024 dataset of daily river ice concentration and annual phenology for major Arctic rivers
Abstract. River ice plays a critical role in Arctic freshwater routing, navigation safety, and biogeochemical exchange. However, consistent, daily-resolved observations across the pan-Arctic remain scarce. Here we present a harmonized, multi-decadal dataset of daily river ice concentration (RIC) and annual phenology (freeze-up, breakup, and ice duration) for the six largest Arctic rivers—Yukon, Mackenzie, Ob, Yenisey, Lena, and Kolyma—covering hydrological years 2001–2024. Built from >590,000 MODIS Terra/Aqua scenes, our workflow integrates a scalable threshold-based classifier on Google Earth Engine with dual-satellite daily synthesis, temporal-window cloud reclassification, and a high-latitude dark-period correction. Technical validation against higher-resolution optical imagery shows a mean RIC accuracy of 0.83 across basins. Phenological metrics derived from MODIS agree with in situ records with mean absolute errors (MAE) of 10.8 days for freeze-up and 11.4 days for breakup (improving to 8.4 days relative to the onset of ice drift), and with Landsat-based river-section phenology with MAE of 10.5 days (freeze-up) and 16.0 days (breakup). RIC correlates strongly with surface air temperature (mean Pearson r = −0.91) and increases systematically with latitude. Trend analysis from 2001 through 2024 shows delayed freeze-up in over 66 % of river segments, earlier breakup in more than 65 %, and shorter ice seasons in over 65 %. On average, freeze-up is delayed by 9.0 days, breakup occurs 7.8 days earlier, and ice duration shortens by 14.1 days over the study period. These basin-consistent, temporally resolved records provide an open benchmark for diagnosing cryospheric change in Arctic river corridors and for constraining model–data intercomparisons. The river-ice dataset is available via Zenodo (https://doi.org/10.5281/zenodo.17054619, Qiu et al., 2025).
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Status: open (until 11 Feb 2026)
- CC1: 'Comment on essd-2025-607', Laurent de Rham, 05 Dec 2025 reply
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RC1: 'Comment on essd-2025-607', Anonymous Referee #1, 07 Jan 2026
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The authors filtered the ‘Simplified GRWL Vector Product’ by the attribute 'width_max' ≥ 250m, retaining only those river segments whose maximum mapped width (at mean discharge) exceeds 250m. This implies that, within each retained segment, portions of the river may still have actual widths less than 250m. Subsequently, the authors applied a 250m buffer on both sides of these segments, resulting in a fixed 500m vector polygon mask. For river reaches where the actual width exceeds 500m during ice period, this approach poses no significant issues. However, for narrower reaches (actual width < 500 m), the fixed 500m mask introduces considerable uncertainty. The buffered polygon not only encompasses the main channel but also extends into floodplain beaches and adjacent non-channel areas on both banks. In pre-freeze-up and post-breakup periods, snow accumulation is common on these non-channel areas. Since the Normalized Difference Snow Index (NDSI) cannot reliably distinguish between snow and ice, such snow-covered regions are likely misclassified as river ice. To address this limitation, the authors should quantitatively assess: (1) The proportion of analyzed river segments where the actual river width is less than 500 m; (2) The potential impact of this fixed-width masking on the accuracy of the final river ice concentration (RIC) estimates, particularly the risk of overestimation due to inclusion of snow-covered non-channel areas.
Citation: https://doi.org/10.5194/essd-2025-607-RC1 -
RC2: 'Comment on essd-2025-607', Anonymous Referee #1, 07 Jan 2026
reply
Zhang et al. (2024) applied a uniform RIC threshold of 0.2 to identify ice-on and ice-off dates along the Yenisey River. Building on this approach, the authors introduce asymmetric thresholds to improve detection sensitivity and physical realism: the Break-Up Date (BUD) is defined as the date when RIC falls by ≥60% from the climatological winter peak (computed over December 1 to February 1), while the Freeze-Up Date (FUD) is set as the first date when RIC exceeds 30% above the ice-free baseline. However, the selection of these specific RIC percentages appears somewhat arbitrary. To strengthen the threshold justification, the authors are recommended to select hydrological stations with ground-based observations of freeze-up and break-up dates across different basins. A sensitivity analysis should then be conducted, comparing remotely sensed BUD and FUD derived from various RIC thresholds against these in-situ records. This would enable a more robust, data-driven determination of optimal RIC thresholds tailored to physical processes and regional variability.
Citation: https://doi.org/10.5194/essd-2025-607-RC2 -
RC3: 'Comment on essd-2025-607', Anonymous Referee #1, 07 Jan 2026
reply
The authors validated MODIS-based RIC estimates using medium-resolution (30 m) Landsat 7/8/9 and high-resolution (10 m) Sentinel-2 imagery. However, it remains unclear whether the fixed 500m buffer mask used for MODIS was also applied to these higher-resolution datasets. It is recommended that the authors compute RIC from Landsat and/or Sentinel-2 imagery using two distinct masking approaches for various river segments: (1) the fixed 500m mask, and (2) a more accurate mask delineating the actual ice-season river channel extent. This comparison would clarify the potential impact of inadvertently including snow-covered floodplain and non-channel areas on RIC estimates.
Citation: https://doi.org/10.5194/essd-2025-607-RC3
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A harmonized 2000–2024 dataset of daily river ice concentration and annual phenology for major Arctic rivers Jiahui Qiu et al. https://doi.org/10.5281/zenodo.17054619
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Thank-you for this global scale work on river ice the transparency of methods and sharing data with the community. As reported in the abstract, the mean absolute error (MAE) values for the three metrics (freeze-up, breakup, duration) are larger numbers than the trend values. Some discussion of the results is warranted in-so-far as the robustness of reported trends within the modelling framework error. A colleague with a climate background refers to this as "signal versus noise" issue.