Comment on essd-2023-31

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Ground-based datasets of observed Snow Water Equivalent (SWE) are scarce, while gridded SWE estimates from remote-sensing and climate reanalysis are unable to resolve the high spatial variability of snow on the ground. Long-term ground observations of snow depth, in combination with models that can accurately convert snow depth to SWE, can fill this observational gap. Here, we provide a new SWE dataset (NH-SWE) that encompasses 11,071 stations in the Northern Hemisphere, and is available at <a href="https://doi.org/10.5281/zenodo.7515603" target="_blank" rel="noopener"><span class="ng-binding">https://doi.org/10.5281/zenodo.7515603</span></a> (Fontrodona-Bach et al., 2023). This new dataset provides daily time series of SWE, varying in length between one and seventy-three years, spanning the period 1950&ndash;2022 and covering a wide range of snow climates including many mountainous regions. At each station, observed snow depth was converted to SWE using an established snow-depth-to-SWE conversion model, with excellent model performance using regionalised parameters based on climate variables. The accuracy of the model after parameter regionalisation is comparable to that of the calibrated model. The key advantages and strengths of the regionalised model presented here are its transferability across climates and the high performance in modelling daily SWE dynamics in terms of peak SWE, total snowmelt and duration of the melt season, as assessed here against a comparison model. This dataset is particularly useful for studies that require accurate time series of SWE dynamics, timing of snowmelt onset, and snowmelt totals and duration. It can e.g. be used for climate change impact analyses, water resources assessment and management, validation of remote sensing of snow, hydrological modelling and snow data assimilation into climate models.