the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Jun 2026
Brief communication: delivering a Digital-Twin-ready snow reanalysis
Abstract. We present DTE-SNOW, a Digital-Twin-ready framework for simulating the spatial and temporal dynamics of snow-water resources at 1 km resolution, based on satellite-derived precipitation, snow modeling, and the optional assimilation of Sentinel-1 snow-depth retrievals. Using test simulations over four European mountain basins (Ebro, Rhône, Po, and Inn), we show that DTE-SNOW achieves an average snow-depth bias of only a few centimeters (0.05 m when Sentinel-1 snow-depth assimilation is applied). The simulated spatial patterns successfully reproduce the topographic dependence of snow distribution, with correlations between mean annual Snow Water Equivalent (SWE) and elevation ranging from 0.63 to 0.77. Because DTE-SNOW is independent of in situ observations, it opens new opportunities toward a “SWE of everywhere” paradigm: a globally consistent estimation of snow-water resources within DestinE.
Competing interests: At least one of the (co-)authors is a member of the editorial board of The Cryosphere.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-2851', Benoit Montpetit, 15 Jul 2026
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RC2: 'Comment on egusphere-2026-2851', Anonymous Referee #2, 17 Aug 2026
This brief communication reports results of an ongoing project to provide a gridded SWE product over several European regions. There is no state of the art in the introduction. The manuscript does not address a scientific question and focuses on the evaluation of this product. It is useful to publish an evaluation of a new product in a peer-reviewed journal if this product is available to the community. Here, the presented product is not yet publicly available according to the Data availability statement. In the Results section, the first finding is that the modelled snow water equivalent increases with elevation, as expected. The performance of simulated snow depth with and without assimilating the C-SNOW product is discussed. Previous studies analyzed the benefit of assimilating C-SNOW over the Alps with similar schemes, reporting a modest improvement in simulated snow depth (Brangers et al., 2024; Lannoy et al., 2024). However, they also evaluated SWE and streamflow. The main difference here is the addition of the Ebro catchment. In the Discussion, the performances of this new product are not compared to existing snow reanalyses. The bold conclusion that this method can bring a “globally consistent estimation of snow water resources” or support “SWE of everywhere” paradigm is not sufficiently demonstrated. Indeed, ERA5-Land precipitation is an interpolation of ERA5 precipitation, which assimilated data that are unevenly distributed across the world. S3M was developed using in situ data in the Alps as acknowledged by the authors in conclusion. Therefore, it is hard to believe that this approach can yield similar performances at global scale. Moreover, it is possible that some in situ data that were used by the authors to evaluate their posterior simulation were also used to calibrate C-SNOW over the Alps (Lievens et al., 2022). If correct, it would be more convincing to exclude these data from the model evaluation to avoid circular reasoning.
In conclusion, I would recommend resubmitting this evaluation once the product has been officially released and distributed by ESA, with a comparison to other snow reanalyses, including national-scale products maybe.
From a technical standpoint, I am unable to understand how a triple collocation method can be employed with SM2RAIN-ASCAT, IMERG-Late Run, and ERA5-Land in the context of solid precipitation. SM2RAIN-ASCAT is a precipitation product derived from ASCAT soil moisture data, and therefore, it does not measure snowfall (“regions with negative surface temperatures are masked in this product due to the inability to retrieve soil moisture from satellite sensors under frozen conditions” (Filippucci et al., 2025)). In regions above the freezing level, the precipitation is likely identical to IMERG. The same reference indicates that “SM2RAIN-ASCAT product also includes a monthly BIAS correction using ERA5-Land rainfall data (total precipitation – snowfall, Brocca et al. 2019)”. Consequently, the HYPER-P_sat product used in this study does not solely rely on satellite products as stated L48.
L72: “Also note that we employed the wet-snow masks in C-SNOW to filter data retrieved in such conditions.”. This sentence confuses me as it is written above that “Wet snow conditions (..) are detected by the algorithm and masked” (L61).
Fig 2, The Inn catchment is cropped.
Fig 2, “| Powered by ESRI”
Fig 3, model performance may be distorted due to the substantial number of zero values present in the evaluation dataset. A bias that is close to zero holds limited significance if all the stations are located in regions where the snow depth consistently remains zero.
L105 “Accurate estimation of snow water resources using only satellite-derived precipitation, snow modeling, and the optional assimilation of Sentinel-1 snow depth retrievals is not only feasible, but yields mean biases below 0.05 m and correlations exceeding 0.77 at 1-km resolution.” The sentence omits that these biases and correlations pertain to snow depth (which is not the “snow water resource”).
I acknowledge that some of my comments may appear harsh to the authors. My intention was to provide the editor with an objective assessment. I hope that my comments will ultimately be beneficial to the authors.
References
Brangers, I., Lievens, H., Getirana, A., and De Lannoy, G. J. M.: Sentinel-1 Snow Depth Assimilation to Improve River Discharge Estimates in the Western European Alps, Water Resour. Res., 60, e2023WR035019, https://doi.org/10.1029/2023WR035019, 2024.
Filippucci, P., Brocca, L., Ciabatta, L., Mosaffa, H., Avanzi, F., and Massari, C.: Development of HYPER-P: HYdroclimatic PERformance-enhanced Precipitation at 1 km/daily over the Europe-Mediterranean region from 2007 to 2022, Earth Syst. Sci. Data, 17, 5221–5258, https://doi.org/10.5194/essd-17-5221-2025, 2025.
Lannoy, G. J. M. D., Bechtold, M., Busschaert, L., Heyvaert, Z., Modanesi, S., Dunmire, D., Lievens, H., Getirana, A., and Massari, C.: Contributions of Irrigation Modeling, Soil Moisture and Snow Data Assimilation to High-Resolution Water Budget Estimates Over the Po Basin: Progress Towards Digital Replicas, J. Adv. Model. Earth Syst., 16, e2024MS004433, https://doi.org/10.1029/2024MS004433, 2024.
Lievens, H., Brangers, I., Marshall, H.-P., Jonas, T., Olefs, M., and De Lannoy, G.: Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps, The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, 2022.
Citation: https://doi.org/10.5194/egusphere-2026-2851-RC2
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See comments attached. Great DTE framework, but the messaging needs to be refined or the analysis needs to be modified.