Reply on RC2

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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.

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