Seasonal snow drives numerous hydrological and ecological processes and affects many socioeconomic aspects . Snow-cover extent, as well as timing and intensity of snowmelt, have a direct impact on avalanche and flood hazards , freshwater availability , hydropower production , and winter tourism . However, estimating snow water resources in mountainous regions is particularly challenging due to the substantial spatial variability of the terrain and the snowpack , the lack of accurate distributed measurements , and the uncertainties inherent in snowpack estimated from numerical models .

The mountain snowpack remains severely undersampled despite continuous monitoring efforts . Detailed, high-resolution hydrometeorological and snow datasets exist for small catchments, such as the Dischma catchment in Switzerland , the Izas catchment in the Spanish Pyrenees , or the Johnston Draw catchment in Idaho, USA , but their spatial coverage is limited. Similarly, airborne lidar- or photogrammetry-based snow depth maps are available only for a few select catchments and at specific times during the season . For larger scales and with roughly weekly temporal resolution, spaceborne optical sensors provide observations of snow cover extent and snow cover fraction (SCF) , but cannot provide direct information on snow depth or snow water equivalent (SWE). The NorSWE dataset compiles SWE observations from more than 10 000 locations across the Northern Hemisphere over three decades, with the Alps represented by manual point measurements from 11 sites in Switzerland only. Spatially explicit snow depth observations from Sentinel-1 retrievals are possible under dry-snow conditions , but truly reliable, high-resolution, spatiotemporally continuous SWE estimates remain elusive . While numerical models can provide such continuous estimates at any desirable spatial and temporal resolution, they are subject to inherent uncertainties in the parametrization and forcing data .

Reanalysis products provide estimates of past states by constraining numerical simulations through the assimilation of observational datasets. While global reanalyses within numerical weather prediction (NWP) systems, such as ECMWF's ERA5 , are widely used for climate monitoring, their snow-related variables offer only low resolution and accuracy in mountainous regions . presented an efficient method that couples sub-grid clustering of complex terrain, downscaling of global meteorological reanalysis data, and the assimilation of spaceborn SCF observations, to enable high-resolution ensemble-based snow reanalyses in mountain regions. Other recent efforts in the snow modeling community have produced detailed and dedicated long-term snowpack reanalysis datasets: the modeling and data assimilation system SNODAS provides daily snowpack and precipitation data at 1 km resolution over the contiguous US from 2003 onward ; daily estimates of SWE and SCF based on the assimilation Landsat SCF observations into a land surface model, coupled with a snow depletion curve, are available for the Sierra Nevada and the western US , at a resolution of 90 and 500 m, respectively; a similar dataset based on the joint assimilation of Landsat and MODIS SCF observations is available for High Mountain Asia ; a daily 10 km gridded snow depth and SWE estimates over the Iberian Peninsula since 1980 are available based on the physics-based snow model FSM forced with downscaled ERA-interim data and references therein; daily 1 km gridded estimates of snow depth and snow cover duration between 1961–2020 are available for Austria, based on simulations of the SNOWGRID model in a climate configuration forced with gridded meteorological observations ; the IT-SNOW reanalysis provides daily estimates of snow states for Italy from water year 2010 onward by combining model simulations with in-situ and spaceborne osbervations ; the S2M meteorological and snow cover reanalysis combines the meteorological analysis SAFRAN and the high complexity snow model Crocus within the SURFEX/ISBA land surface model, covering the semi-distributed massifs of the French Alps, Pyrenees, and Corsica from 1958 onwards and references therein.

Here, we present a continuous 10-year snow reanalysis dataset for Switzerland and hydrologically connected bordering regions, produced within the near-real-time modeling framework of the Swiss Operational Snow Hydrological Service (OSHD). The dataset provides daily estimates of snow depth, SWE, SCF, and snowmelt runoff by combining high-resolution simulations from the intermediate-complexity, physics-based snow model FSM2OSHD with in situ snow depth observations from 444 stations . To capture the high spatial variability of snow processes in complex Alpine terrain, the modeling chain employs dedicated dynamical and statistical downscaling of NWP forcing data to a spatial resolution of 250 m, solves the coupled mass- and energy-balance equation for multiple numerical snow layers, and explicitely accounts for differences in the atmospheric and snowpack procsesses of open, forested, and glaciated areas . The simulations are based on upscaled versions of the most accurate hyper-resolution datasets currently available, including a 10 m digital elevation model and terrain surface model , as well as a 1 m canopy height model and light availability maps that resolve terrain and vegetation shading down to individual trees for every hour of the year . Temporal consistency across the whole period is ensured through an assimilation scheme that homogenizes meteorological inputs despite changes in the source and processing level of the forcing data . The model has been tuned and validated continuously over the past decade against snow depth, SWE, SCF, and new snow observations from a dense station network .

To our knowledge, this dataset represents the most accurate and comprehensive snow reanalysis for Switzerland to date. By providing 10 years of physically consistent estimates of snow cover dynamics at a subkilometric resolution across a large part of the European Alps, it contributes to the snow database of a region characterized by highly complex terrain and diverse hydroclimatic regimes. The dataset helps bridge existing scale and knowledge gaps between coarse global products e.g. and detailed local observations e.g. and provides a robust foundation for hydrological, ecological, and cryospheric analyses e.g.. The data is publicly and freely available under 10.5281/zenodo.17313889 , offering substantial value to a wide range of scientific and applied users.

The basis for the presented dataset is the fully distributed, physics-based, multi-layer snow model FSM2OSHD , operated by the Swiss Operational Snow Hydrological Service (OSHD). Forcing data from the Federal Office of Meteorology and Climatology, MeteoSwiss, are debiased and downscaled to the 250 m model resolution and dynamically corrected through the assimilation of in situ snow depth observations . The following section provides a brief overview of the study area, the datasets used, the modeling chain, and the particle filter (PF) based assimilation scheme.

The OSHD model system has been successfully employed in an operational context for over a decade, delivering daily analyses and forecasts of snow cover dynamics across Switzerland, thereby providing critical information to the avalanche warning service, the Federal Office for the Environment, and other partners . In addition to its operational use, which relies on the system's robustness and the consistent quality of the data products delivered, continuous tuning, validation, and integration of advances from snow modeling research further contribute to the high quality of the data provided .

To provide an estimate of the uncertainties in the presented dataset, we conducted a leave-one-station-out (LOO) validation, comparing snow depth estimates with point observations at the 444 station locations, analogous to . To ensure independence, the forcing corrections at each station location were interpolated from surrounding stations within the 35 km sphere of influence, disregarding the correction estimated at the station itself. Figure  shows simulated and observed snow depths across the full simulation period from September 2016 to August 2025, aggregated into four elevation bands by averaging over all stations within each band. Even in the highest elevation band, where average peak snow depths exceed 2 m, the RMSE remains as low as 6.25 cm, with a slightly negative bias of 3.34 cm.

Given the representativeness error inherent to point snow observation, a validation at individual locations is not sufficient to evaluate the dataset across the complex mountain topography. A recent validation study by compared FSM2OSHD against snow wetness observations from Sentinel-1 retrievals and identified a delayed melt onset, which was subsequently corrected through a refined albedo parameterization, now adopted in the present model configuration. Furthermore, a validation against SCF maps from Sentinel-2 retrievals presented in showed that the PF-based assimilation scheme reliably accounts for forcing uncertainties at a subregional scale, but errors in small-scale accumulation and ablation patterns remain unresolved.

We present a spatially explicit validation of the dataset against SCF maps derived from Sentinel-2 observations over eight seasons with sufficient data availability (WY 2018 to WY 2025). For each day with a usable Sentinel-2 acquisition between March–July, we aggregate modeled and observed SCF information to produce a regional snow-line altitude (SLA) for eight aspect classes and flat locations. The SLA is defined as the elevation where the average SCF in a given region drops below 30 %. Figure  depicts the distribution of mean SLA bias (model-observations) over all 16 hydrological units of the Swiss Alps (as indicated in Fig. ). Overall, the median SLA bias is within ±100 m for all water years and aspect classes, with individual subregions occasionally exceeding this range but more frequently falling well below it. Noticeable differences exist between water years, though there is no clear link to high or low snow years (cf. Fig. ), suggesting that the bias is driven by factors other than overall snow availability. A slight aspect dependency is evident, with a tendency towards positive biases in the north-easterly sector, and negative biases in the south-westerly sector.

As outlined in Sect.  and further detailed in , the PF assimilation scheme reliably accounts for forcing uncertainties at subregional scales, while only minor errors in small-scale accumulation and ablation patterns persist. The leave-one-out evaluation at station locations shows that snow depth estimates remain within a few centimeters of observed values across the entire 10-year period and the full elevation range. Furthermore, the evaluation against independent SCF maps demonstrates that the dataset accurately captures the heterogeneous evolution of the snowpack across the complex mountainous domain, with an average SLA bias well below 100 m in most cases. Together, the use of state-of-the-art meteorological forcing, physics-based snow modeling, and data assimilation has allowed us to produce a dataset with, to our knowledge, unprecedented spatial resolution and accuracy for a domain and period of this size. Nevertheless, a few limitations remain, which are discussed below.

showed that assimilation performance is limited by the information content of in situ observations rather than their absolute number or density, as point observations cannot fully resolve the heterogeneous evolution of the snowpack across different slopes and aspects, especially when collected predominantly at flat-field locations e.g.. Hence, to further improve model estimates, additional spatially explicit data would be necessary. Assimilating SCF observations using ensemble smoother techniques e.g. is common practice in reanalysis studies e.g. and allows for reconstructing the seasonal snowpack evolution from observed meltout patterns. In our case, however, the available SCF data did not cover the full 10-year period, and assimilating them would have likely introduced temporal discontinuities in dataset accuracy, as reported, e.g. for the French snow reanalysis dataset . Since providing a temporally homogeneous dataset has been a main objective, we chose not to assimilate SCF observations.

Snowpack estimates might be less reliable at very high elevations above 3000 m, where uncertainties are difficult to quantify due to the lack of comprehensive observational data. At these elevations, a limited number of grid cells lie outside the sphere of influence of any snow monitoring station, as defined in Sect. . For those grid cells, forcing corrections had to be interpolated from the three nearest stations, regardless of distance or elevation difference, to avoid unrealistic discontinuities in the dataset. Given that only about 1.8 % of the domain lies above 3000 m and that no alternative observational data are available for assimilation or validation at these elevations, this represents the best available approach, and any resulting errors are expected to remain limited in their impact on the overall dataset quality.

In forested areas, snow models require additional process representations and input datasets, which unavoidably introduce additional sources of uncertainty. The snow–canopy and canopy–atmosphere interactions within FSM2OSHD have been extensively validated at the process level and for hyper-resolution simulations between 1–50 m . At the presented 250 m resolution, a comparison with PlanetScope RGB composites has confirmed that FSM2OSHD accurately reproduces observed differences in the seasonal, interannual, and regional evolution of the snowpack among open, dense, and sparsely forested areas . To our knowledge, the extent of model validation efforts in forest terrain and the level of detail of the canopy input datasets are unprecedented in the European Alps. Nevertheless, the vegetation input datasets may contain inaccuracies. For example, the vegetation data have been acquired between 2020–2022, and changes in the canopy structure since are not accounted for . Additionally, snow estimates in forested areas lack direct observational constraints. However, given the model's accurate representation of forest-snow processes, improvements in the meteorological forcing data will also enhance snow estimates in the forest, even when the forcing corrections were derived from stations outside the forest.

The research model variant FSM2trans includes dedicated modules for snow redistribution by gravity- and wind-driven processes , as well as an updated density-dependent layering scheme to represent erodible snow. While accounting for horizontal redistribution of snow enables a more realistic representation of small-scale accumulation and erosion patterns, it requires simulations at hectometre or finer spatial resolutions, along with appropriate downscaling of wind fields . At the resolution of 250 m of the presented dataset, FSM2OSHD does not explicitly account for snow redistribution, which may affect the accuracy of local-scale snow distribution patterns.

The presented dataset provides daily values of snow depth, SWE, SCF, and meltwater runoff over an area of 58 000 km2 and a period of 10 years, enabling analyses across different temporal and spatial scales. Snow depth is reported as the average value per grid cell. For example, if a grid cell has a snow depth of 25 cm and an SCF of 50 %, this can be interpreted as half the cell being covered with 50 cm of snow. SWE and runoff are given as the total water volume per grid cell. Runoff represents the liquid water leaving the snowpack; during rain-on-snow events, this can include liquid precipitation percolating through the snowpack. Note that soil infiltration is not considered in these calculations for details. Below, we highlight several illustrative examples of the use of this dataset.

Figure  shows time series of the average SWE (orange shading, left axis) and total meltwater runoff over the entire domain (blue line, right axis). Over all 10 seasons, average peak-SWE amounts to 130 mmw.e., with around 90 mmw.e. for low-snow years and above 160 mmw.e. for high-snow years. Interannual variations in peak-SWE values are even more pronounced, ranging from up to 197 mmw.e. in water year 2018 down to 85 mmw.e. in water year 2025. While the overall seasonality is consistent, the magnitude of seasonal accumulation and melt varies notably. On average, peak-SWE typically occurs on March 24±18 d, but can be as early as the end of February and as late as the beginning of May (corresponding to a range of 65 d). Meltwater runoff also follows a clear seasonal pattern, with low values in autumn and winter, peak values in spring, and a gradual reduction during summer. Day-to-day variability is, however, much higher, with pronounced spikes throughout the season and maximum values occurring as early as December (see water year 2021).

The average pixel-wise peak SWE over all 10 seasons, shown in Fig. , reveals a distinct elevation-dependent pattern. In the high mountains and glaciated areas along the Alpine Main Ridge, peak SWE values regularly exceed 1000 mmw.e. and even surpass 2000 mmw.e.. In the mid-elevation mountain ranges between 2000–3000 m, average peak SWE is approximately 550 mmw.e.. Over the Swiss Plateau, the snowpack remains shallow, with considerably higher peak-SWE values observable in the adjacent Jura Mountains.

Figure  shows the snow melt-out date for each season, defined as the last day of the water year with at least 5 cm of snow depth, following a minimum of 30 consecutive days of snow cover. The Swiss Plateau rarely experiences a whole month of continuous snow cover, so a melt-out date cannot be computed for most of the region. In the Jura and Alpine foothills, the melt-out date typically falls between December–March, while snow cover can be observed up to June and later along the Alpine Main Ridge. Nevertheless, the interannual variability is considerable, and total domain-wide SWE (shown in Fig. ) and melt-out dates are not necessarily correlated: in the low-snow year 2016, for instance, the average peak SWE was only 120 mmw.e., yet melt-out dates above 2000 m ranged from June to September.

Figure  shows maps of the number of snow days per season (defined as days with a minimum snow depth of 5 cm) grouped into discrete classes between 1, 15, 30, 60, 120, and more than 365 d. Across all 10 years, much of the Swiss Alps exhibits a persistent seasonal snowpack with more than 120 snow days, while the Alpine foothills, Jura Mountains, and Swiss Plateau generally experience far fewer snow days. The water year 2020 shows a particularly distinct bimodal pattern, with areas experiencing either more than 120 snow days or fewer than 15, and only narrow transition zones in between. The interannual variability is pronounced, not only between low and high elevations but also between the northern and southern slopes of the Alps. In most years, at least one day of snow cover occurs across the majority of the domain, although some exceptionally warm and/or dry years (e.g. 2020, 2022, and 2023) show snow-free conditions in the lowlands. Regions with 120 or more snow days and a snow cover that persists until the end of the season (labeled as >365 in the figure) are confined to small areas in the highest and glaciated parts of the Alps and are virtually absent in certain years, such as 2017.

The dataset provides daily estimates of snow depth (m), snow water equivalent (mmw.e.≡kgm-2), snow cover fraction (unitless), and snowmelt runoff (mmd-1≡kgm-2d-1) for each grid cell within the domain. The reference time for daily values is 06:00 Central European Time (CET≡UTC+01:00), with snow depth, SWE, and SCF representing states at that time, and runoff corresponding to the accumulated total over the preceding 24 h. Given that on 1 September at 06:00 CET of each season, all snow is removed, all variables for that day in the dataset are zero because the end-states from the previous season are not carried over.

The data are stored in self-explanatory monthly NetCDF files (e.g. OSHD_DATA_2020-01.nc for January 2020) within individual zip archives for each water year (e.g. OSHD_DATA_WY2020.zip for water year 2020), with a total data volume of about 800 MB per season. Variable names, attributes, and metadata adhere to the CF 1.12 and ACDD 1.3 conventions. The data variables are structured as three-dimensional arrays, with time, easting, and northing coordinates. The temporal coordinate denotes the number of days since the first day of the corresponding month. The horizontal coordinates refer to the center of the respective grid cell, given in the local Swiss CH1903+/LV95 reference system (EPSG:2056). The total spatial extent is also specified by the minimum and maximum longitude and latitude in WGS84 coordinates (EPSG:4326). Additional metadata includes the dataset title, keywords, summary, and version history; contact information; and a reference to the FSM model version used (as GitHub tag). The model DEM is provided as a GeoTiff (OSHD_MODEL_DEM.tif). Compatibility with the NASA Panoply NetCDF Viewer was verified using Panoply v5.7.1 under Windows 11.

We present a reanalysis dataset of spatially explicit seasonal snow cover dynamics for Switzerland and its bordering regions for water years 2016–2025, based on the high-resolution simulations with the physics-based, multi-layer snow model FSM2OSHD and the assimilation of snow depth observations from 444 stations across the domain. The combination of dedicated downscaling of NWP forcing data with the upscaling of state-of-the-art hyper-resolution DEM and light-availability datasets enables simulations that account for subgrid variability within the 250 m model resolution, which is crucial for capturing the spatial heterogeneity of the mountainous snowpack. The coupled energy- and mass-balance equation, solved for individual numerical snow layers, explicitly accounts for different atmospheric and snowpack processes of open, forested, and glaciated areas. Finally, the assimilation of in situ snow depth observations dynamically corrects spatiotemporal error patterns in the meteorological input data, ensuring consistent forcing quality over the presented 10-year period.

Spanning 10 years of physically consistent snow cover estimates across a large part of the European Alps, the dataset contributes to the snow data record for a region characterized by complex terrain and diverse hydroclimatic regimes. With its high spatial and temporal resolution, the dataset helps bridge the gap between coarse global products and detailed local observations. The data are publicly and freely available at 10.5281/zenodo.17313889 , providing a robust foundation for hydrological, ecological, and cryospheric analyses.