Dr. Tobias Jonas

The water equivalent of new snow (HNW) plays a crucial role in various fields, including hydrological modeling, avalanche forecasting, and assessing snow loads on structures. However, in contrast to snow depth (HS), obtaining HNW measurements is challenging as well as time-consuming and is hence rarely measured. Therefore, we assess the reliability of two semi-empirical methods, HS2SWE and ΔSNOW, for estimating HNW. These methods are designed to simulate continuous water equivalent of the snowpack (SWE) from daily HS only, with changes in SWE yielding daily HNW estimates. We compare both parametric methods against HNW predictions from a physics-based snow model (FSM2oshd) that integrates daily HS recordings using data assimilation. Our findings reveal that all methods exhibit similar performance, with relative biases of less than ∼3 % in replicating SWE observations commonly used for model evaluations. However, the ΔSNOW model tends to underestimate daily HNW by ∼17 %, whereas HS2SWE and FSM2oshd combined with a particle filter data assimilation scheme provide nearly unbiased estimates, with relative biases below ∼5 %. In contrast to the parsimonious parametric methods, we show that the physics-based approach can yield information about unobserved variables, such as total solid precipitation amounts, that may differ from HNW due to concurrent melt. Overall, our results underscore the potential of utilizing commonly available daily HS data in conjunction with appropriate modeling techniques to provide valuable insights into snow accumulation processes. Our study demonstrates that daily SWE observations or supplementary measurements like HNW are important for validating the day-to-day accuracy of simulations and should ideally already be incorporated during the calibration and development of models.

We present an hourly hydrometeorological and snow dataset with 100 m spatial resolution from the alpine Dischma watershed and its surroundings in eastern Switzerland, including station measurements of variables such as snow depth and catchment runoff. This dataset is particularly suited for different modelling experiments using distributed and process-based models, including physics-based snow and hydrological models. Additionally, the data are highly useful for testing various snow data assimilation schemes and for developing models representing snow–forest interactions. The dataset covers 7 water years from 1 October 2016 to 30 September 2023. The complete domain spans an area of 333 km² with altitudes ranging from 1250 to 3228 m. The Dischma Basin, with its outlet at 1671 m elevation, occupies 42.9 km². Included in the dataset are high-resolution (100 m) hourly meteorological data (air temperature, relative humidity, wind speed and direction, precipitation, and long- and shortwave radiation) from a numerical weather predication model and rain radar, land cover characteristics (primarily forest properties), and a digital elevation model. Notably, the dataset includes snow depth acquisitions obtained from airborne lidar and photogrammetry surveys, constituting the most extensive spatial snow depth dataset derived using such techniques in the European Alps. Along with these gridded datasets, we provide daily quality-controlled snow depth recordings from seven sites, biweekly snow water equivalent measurements from two locations, and hourly runoff and stream temperature observations for the Dischma watershed. The data compiled in this study will be useful to further develop our ability to forecast snow and hydrological conditions in high-alpine headwater catchments that are particularly sensitive to ongoing climate change. All data are available for download at https://doi.org/10.16904/envidat.568 (Magnusson et al., 2024).

The Mountain snowpack stores months of winter precipitation at high elevations, supplying snowmelt to lowland areas in drier seasons for agriculture and human consumption worldwide. Accurate seasonal predictions of the snowpack are thus of great importance, but such forecasts suffer from major challenges such as resolving interactions between forcing variables at high spatial resolutions. To test novel approaches to resolve these processes, seasonal snowpack simulations are run at different grid resolutions (50 m, 100 m, 250 m) and with variable forcing data for the water year 2016/2017. COSMO-1E data is either dynamically downscaled with the High-resolution Intermediate Complexity Atmospheric Research (HICAR) model or statistically downscaled to provide forcing data for snowpack simulations with the Flexible Snowpack Model (FSM2oshd). Simulations covering complex terrain in the Swiss Alps are carried out with the operational settings of the FSM2oshd model or with a model extension including wind- and gravitational-induced snow transport (FSM2trans). The simulated snow height is evaluated against observed snow height collected during LiDAR flights in spring 2017. Observed spatial snow accumulation patterns and snow height distribution are best matched with simulations using dynamically downscaled data and the FSM2trans model extension, indicating the importance of both accurate meteorological forcing data and snow transport schemes. This study demonstrates for the first time the effects of applying dynamical downscaling schemes to snowpack simulations at the seasonal and catchment scale.

In the boreal forest of eastern Canada, winter temperatures are projected to increase substantially by 2100. This region is also expected to receive less solid precipitation, resulting in a reduction in snow cover thickness and duration. These changes are likely to affect hydrological processes such as snowmelt, the soil thermal regime, and snow metamorphism. The exact impact of future changes is difficult to pinpoint in the boreal forest, due to its complex structure and the fact that snow dynamics under the canopy are very different from those in the gaps. In this study, we assess the influence of a low-snow and warm winter on snowmelt dynamics, soil freezing, snowpack properties, and spring streamflow in a humid and discontinuous boreal catchment of eastern Canada (47.29° N, 71.17° W; ≈ 850 m a.m.s.l.) based on observations and SNOWPACK simulations. We monitored the soil and snow thermal regimes and sampled physical properties of the snowpack under the canopy and in two forest gaps during an exceptionally low-snow and warm winter, projected to occur more frequently in the future, and during a winter with conditions close to normal. We observe that snowmelt was earlier but slower, top soil layers were cooler, and gradient metamorphism was enhanced during the low-snow and warm winter. However, we observe that snowmelt duration increased in forest gaps, that soil freezing was enhanced only under the canopy, and that snow permeability increased more strongly under the canopy than in either gap. Our results highlight that snow accumulation and melt dynamics are controlled by meteorological conditions, soil freezing is controlled by forest structure, and snow properties are controlled by both weather forcing and canopy discontinuity. Overall, observations and simulations suggest that the exceptionally low spring streamflow in the winter of 2020–2120 was mainly driven by low snow accumulation, slow snowmelt, and low precipitation in April and May rather than enhanced percolation through the snowpack and soil freezing.

Distributed energy and mass balance snowpack models at sub-kilometric scale have emerged as a tool for snow-hydrological forecasting over large areas. However, their development and evaluation often rely on a handful of well-observed sites on flat terrain with limited topographic representativeness. Validation of such models over large scales in rugged terrain is therefore necessary. Remote sensing of wet snow has always been motivated by its potential utility in snow hydrology. However, its concrete potential to enhance physically based operational snowpack models in real time remains unproven. Wet-snow maps could potentially help refine the temporal accuracy of simulated snowmelt onset, while the information content of remotely sensed snow cover fraction (SCF) pertains predominantly to the ablation season. In this work, wet-snow maps derived from Sentinel-1 and SCF retrieval from Sentinel-2 are compared against model results from a fully distributed energy balance snow model (FSM2oshd). The comparative analysis spans the winter seasons from 2017 to 2021, focusing on the geographic region of Switzerland. We use the concept of wet-snow line (WSL) to compare Sentinel-1 wet-snow maps with simulations. We show that while the match of the model with flat-field snow depth observation is excellent, the WSL reveals a delayed snowmelt in the southern aspects. Amending the albedo parametrization within FSM2oshd allowed for the achievement of earlier melt in such aspects preferentially, thereby reducing WSL biases. Biases with respect to Sentinel-2 snow-line (SL) observations were also substantially reduced. These results suggest that wet-snow maps contain valuable real-time information for snowpack models, complementing flat-field snow depth observations well, particularly in complex terrain and at higher elevations. The persisting correlation between wet-snow-line and snow-line biases provides insights into refined development, tuning, and data assimilation methodologies for operational snow-hydrological modelling.

The spatial and temporal variation of the seasonal snowpack in mountain regions is recognized as a clear knowledge gap for climate, ecology and water resources applications. Here, we identify three salient topics where recent developments in snow remote sensing and data assimilation can lead to significant progress: snow water equivalent, high resolution snow-covered area and long term snow cover observations including snow albedo. These topics can be addressed in the near future with institutional support.

Land surface processes, crucial for exchanging carbon, nitrogen, water, and energy between the atmosphere and terrestrial Earth, significantly impact the climate system. Many of these processes vary considerably at small spatial and temporal scales, in particular in mountainous terrain and complex topography. To examine the impact of spatial resolution and representativeness of input data on modelled land surface processes, we conducted simulations using the Community Land Model 5 (CLM5) at different resolutions and based on a range of input datasets over the spatial extent of Switzerland. Using high-resolution meteorological forcing and land use data, we found that increased resolution substantially improved the representation of snow cover in CLM5 (up to 52 % enhancement), allowing CLM5 to closely match performance of a dedicated snow model. However, a simple lapse-rate-based temperature downscaling provided large positive effects on model performance, even if simulations were based on coarse-resolution forcing datasets only. Results demonstrate the need for resolutions higher than 0.25° for accurate snow simulations in topographically complex terrain. These findings have profound implications for climate impact studies. As improvements were observed across the cascade of dependencies in the land surface model, high spatial resolution and high-quality forcing data become necessary for accurately capturing the effects of a declining snow cover and consequent shifts in the vegetation period, particularly in mountainous regions. This study further highlights the utility of multi-resolution modelling experiments when aiming to improve representation of variables in land surface models. By embracing high-resolution modelling, we can enhance our understanding of the land surface and its response to climate change.

Boreal and sub-alpine forests host seasonal snow for multiple months per year; however, snow regimes in these environments are rapidly changing due to rising temperatures and forest disturbances. Accurate prediction of forest snow dynamics, relevant for ecohydrology, biogeochemistry, cryosphere, and climate sciences, requires process-based models. While snow schemes that track the microstructure of individual snow layers have been proposed for avalanche research, so far, tree-scale processes resolving canopy representations only exist in a few snow-hydrological models. A framework that enables layer- and microstructure-resolving forest snow simulations at the meter scale is lacking to date. To fill this research gap, this study introduces the forest snow modeling framework FSMCRO, which combines two detailed, state-of-the art model components: the canopy representation from the Flexible Snow Model (FSM2) and the snowpack representation of the Crocus ensemble model system (ESCROC). We apply FSMCRO to discontinuous forests at boreal and sub-alpine sites to showcase how tree-scale forest snow processes affect layer-scale snowpack properties. Simulations at contrasting locations reveal marked differences in stratigraphy throughout the winter. These arise due to different prevailing processes at under-canopy versus gap locations and due to variability in snow metamorphism dictated by a spatially variable snowpack energy balance. Ensemble simulations allow us to assess the robustness and uncertainties of simulated stratigraphy. Spatially explicit simulations unravel the dependencies of snowpack properties on canopy structure at a previously unfeasible level of detail. Our findings thus demonstrate how hyper-resolution forest snow simulations can complement observational approaches to improve our understanding of forest snow dynamics, highlighting the potential of such models as research tools in interdisciplinary studies.

Snow plays a crucial role in regional climate systems worldwide. It is a key variable in the context of climate change because of its direct feedback to the climate system, while at the same time being very sensitive to climate change. Long-term spatial data on snow cover and snow water equivalent are scarce, due to the lack of satellite data or forcing data to run land surface models back in time. This study presents an R package, SnowQM, designed to correct for the bias in long-term spatial snow water equivalent data compared to a shorter-term and more accurate dataset, using the more accurate data to calibrate the correction. The bias-correction is based on the widely applied quantile mapping approach. A new method of spatial and temporal grouping of the data points is used to calculate the quantile distributions for each pixel. The main functions of the package are written in C⁺⁺ to achieve high performance. Parallel computing is implemented in the C⁺⁺ part of the code. In a case study over Switzerland, where a 60-year snow water equivalent climatology is produced at a resolution of 1 d and 1 km, SnowQM reduces the bias in snow water equivalent from -9 to -2 mm in winter and from -41 to -2 mm in spring. We show that the C⁺⁺ implementation notably outperforms simple R implementation. The limitations of the quantile mapping approach for snow, such as snow creation, are discussed. The proposed spatial data grouping improves the correction in homogeneous terrain, which opens the way for further use with other variables.

Snow plays a crucial role in the water balance of mountainous regions by affecting the timing and magnitude of runoff and, thus, water availability and flood hazards. However, estimating snow water equivalent (SWE) in mountainous regions is challenging due to its substantial spatial variability, the lack of accurate distributed measurements, and the uncertainties of snow models. Model uncertainties are primarily bound to uncertainties in the meteorological forcings. This study proposes an assimilation scheme to identify and correct spatiotemporal error patterns in the meteorological forcing data. Using a particle filter, we assimilated in situ snow depth observations from 444 stations across Switzerland into an ensemble simulated by the multi-layer, physics-based snow model FSM2OSHD. The ensemble is created by applying traceable, fixed perturbations to the energy input and the amount and phase of precipitation. This allows us to identify and correct errors in the meteorological forcing data for each station site and each 3-day assimilation window. Leveraging spatial correlation in these errors, we distribute the corrections across the entire model domain using a weighted three-dimensional spatial interpolation method. The refined meteorological data then serve as forcing for improved model runs, allowing unobserved grid points to benefit from the point assimilation. A leave-one-station-out cross-validation shows marked improvements in root-mean-squared error and bias for estimates of snow depth and SWE over the entire elevation range and multiple winter seasons. The proposed scheme is a promising step in developing comprehensive data assimilation solutions for large-scale, fully distributed, near real-time snow modeling applications, taking into account operational constraints and practical considerations.

Snow hydrological regimes in mountainous catchments are strongly influenced by snowpack heterogeneity resulting from wind- and gravity-induced redistribution processes, requiring them to be modelled at hectometre and finer resolutions. This study presents a novel modelling approach to address this issue, aiming at an intermediate-complexity solution to best represent these processes while maintaining operationally viable computational times. To this end, the physics-based snowpack model FSM2oshd was complemented by integrating the modules SnowTran-3D and SnowSlide to represent wind- and gravity-driven redistribution, respectively. This new modelling framework was further enhanced by implementing a density-dependent layering to account for erodible snow without the need to resolve microstructural properties. Seasonal simulations were performed over a 1180 km² mountain range in the Swiss Alps at 25, 50 and 100 m resolution, using appropriate downscaling and snow data assimilation techniques to provide accurate meteorological forcing. In particular, wind fields were dynamically downscaled using WindNinja to better reflect topographically induced flow patterns. The model results were assessed using snow depths from airborne lidar measurements. We found a remarkable improvement in the representation of snow accumulation and erosion areas, with major contributions from saltation and suspension as well as avalanches and with modest contributions from snowdrift sublimation. The aggregated snow depth distribution curve, key to snowmelt dynamics, significantly and consistently matched the measured distribution better than reference simulations from the peak of winter to the end of the melt season, with improvements at all spatial resolutions. This outcome is promising for a better representation of snow hydrological processes within an operational framework.

Mountain snowpack forecasting relies on accurate mass and energy input information in relation to the snowpack. For this reason, coupled snow–atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extents of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow–atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal timescales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial lidar data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model outputs suggesting that parameterizations of the process using surface wind fields may only be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model outputs during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain and a low bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and in studying the representation of snow accumulation and ablation processes.

Niederschlagsmessungen neigen dazu, den Schneefall zu unterschätzen, was als «Undercatch» bezeichnet wird. Dieser Effekt tritt auf, weil Messinstrumente das Windfeld verändern, und so nicht ausreichend Schneeflocken in das Messgerät gelangen. Aufgrund dieses Undercatch-Effekts kann die Messung des Schneefalls um etwa 40 Prozent zu niedrig ausfallen. Bei gegitterten Produkten wie etwa RhiresD kommt hinzu, dass es in höheren Lagen wenig Messgeräte gibt. In der Schweiz sind jedoch etwa 450 tägliche Schneehöhenbeobachtungen und -messungen mit Fokus auf hochgelegene Regionen verfügbar. Dieser Datensatz wurde verwendet, um den festen Niederschlagsanteil des weit verbreiteten Schweizer Niederschlagsgitters RhiresD zu korrigieren. Der feste Niederschlagsanteil von RhiresD wurde basierend auf stündlichen Lufttemperaturdaten bestimmt. Dazu wurden COSMO-Reanalysedaten genutzt, um tägliche Gitter von Niederschlags- und Lufttemperaturbeobachtungen in Stundenwerte umzurechnen. Dies begrenzte den Analysezeitraum auf sieben Jahre von September 2015 bis August 2022. Der Festniederschlag wurde zudem mit einem parametrischen Schneemodell ermittelt, das auf den Schneehöhenbeobachtungen basiert. Beide Datensätze wurden anschliessend kombiniert mit einer Datenassimilationsmethode namens Optimale Interpolation. Tagesgenaue Korrekturen für die vergangenen sieben Jahre, wie auch Statistiken für klimatologische Anwendungen konnten ermittelt und überprüft werden. Mit diesen Korrekturen kann insbesondere in hohen Lagen eine Verbesserung der Schneemengenschätzung erzielt werden.

Compte tenu des quantités de neige exceptionnelles dans l'ensemble de l'espace alpin, le risque de crues printanières généralisées a été évoqué très tôt au printemps 1999. Ces craintes se sont confirmées en mai, à la suite de deux épisodes de fortes précipitations à l'Ascension et à la Pentecôte. Les grands lacs subalpins - notamment le lac de Thoune et le lac de Constance - ont alors débordé, provoquant des dommages de l'ordre de 580 millions de CHF. La situation de crue tendue qui a duré plusieurs semaines reste dans les mémoires comme la dernière « crue printanière classique de grande ampleur en Suisse » et est considérée jusqu'à aujourd'hui comme l'exemple type d'une combinaison défavorable entre la fonte des neiges à grande échelle et de fortes précipitations. L'analyse globale des événements réalisée à l'époque par la Confédération a mis en évidence la nécessité d'un service hydrologique nivologique professionnalisé (permanent). Celui-ci a été mis en place quelques années plus tard au WSL Institut pour l'étude de la neige et des avalanches SLF et a depuis fait ses preuves.

Angesichts der ausserordentlichen Schneemengen im ganzen Alpenraum wurde bereits zu einem frühen Zeitpunkt im Frühjahr 1999 auf die Gefahr eines verbreiteten Frühlingshochwassers hingewiesen. Diese Befürchtungen bewahrheiteten sich im Mai als Folge von zwei Starkniederschlagsereignissen über Auffahrt und Pfingsten. Dabei traten insbesondere die grossen Alpenrandseen – namentlich der Thunersee und Bodensee – über die Ufer und verursachten Schäden in der Grössenordnung von rund 580 Mio. CHF. Die über mehrere Wochen andauernde angespannte Hochwasserlage bleibt als bisher letztes «klassisches, grossflächiges Frühlingshochwasser der Schweiz» in Erinnerung und gilt bis heute als Musterbeispiel einer ungünstigen Kombination von grossflächiger Schneeschmelze und Starkniederschlägen. Die damalige umfassende Ereignisanalyse des Bundes hat die Notwendigkeit eines professionalisierten (ständigen) schneehydrologischen Dienstes aufgezeigt. Dieser wurde ein paar Jahre später am WSL-Institut für Schnee und Lawinenforschung SLF aufgebaut und hat sich seither bestens bewährt.

Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate change impacts on biodiversity and ecosystem functioning. Here we combine a network of microclimate temperature measurements across different habitats and vertical heights with a novel radiative transfer model to map daily temperatures during the vegetation period at 10 m spatial resolution across Switzerland. Our results reveal strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 ^∘C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 ^∘C, respectively. We also found that, in open grasslands, maximum temperatures at 5 cm above ground are, on average, 3.4 ^∘C warmer than those of the macroclimate, suggesting that, in such habitats, heat exposure close to the ground is often underestimated when using macroclimatic data. Spatial interpolation was achieved by using a hybrid approach based on linear mixed-effect models with input from detailed radiation estimates from radiative transfer models that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography, and vegetation height. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by root mean squared errors ranging from 1.18 to 3.43 ^∘C, with minimum temperatures being predicted more accurately overall than maximum temperatures. The microclimate-mapping methodology presented here enables a biologically relevant perspective when analysing climate–species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change.

Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model and need many forcing variables at high temporal resolution. However, when trying to model snow depth (HS) on climatological timescales, which is often needed for winter tourism-related applications, these preconditions can cause barriers. Often, for these types of applications, empirical snow models are used. These can estimate snow accumulation and snowmelt based on daily precipitation and temperature data only. To convert the resultant snow water equivalent (SWE) time series into snow depth, we developed the empirical model SWE2HS. SWE2HS is a multilayer densification model which uses daily snow water equivalent as sole input. A constant new snow density is assumed and densification is calculated via exponential settling functions. The maximum snow density of a single layer changes over time due to overburden and SWE losses. SWE2HS has been calibrated on a data set derived from a network of manual snow stations in Switzerland. It has been validated against independent data derived from automatic weather stations (AWSs) in the European Alps (Austria, France, Germany, Switzerland) and against withheld data from the Swiss manual observer station data set which was not used for calibration. The model fits the calibration data with root mean squared error (RMSE) of 8.4 cm, coefficient of determination (R²) of 0.97, and bias of -0.3 cm; it is able to achieve RMSE of 20.5 cm, R² of 0.92, and bias of 2.5 cm on the validation data set from automatic weather stations and RMSE of 7.9 cm, R² of 0.97, and bias of -0.3 cm on the validation data set from manual stations. The temporal evolution of the bulk density can be reproduced reasonably well on all three data sets. Due to its simplicity, the model can be used as post-processing tool for output of any other snow model that provides daily snow water equivalent output. Owing to its empirical nature, SWE2HS should only be used in regions with a similar snow climatology as the European Alps or has to be recalibrated for other snow climatological conditions. The SWE2HS model is available as a Python package which can be easily installed and used.

The lateral transport of heat above abrupt (sub-)metre-scale steps in land surface temperature influences the local surface energy balance. We present a novel experimental method to investigate the stratification and dynamics of the near-surface atmospheric layer over a heterogeneous land surface. Using a high-resolution thermal infrared camera pointing at synthetic screens, a 30Hz sequence of frames is recorded. The screens are deployed upright and horizontally aligned with the prevailing wind direction. The screen’s surface temperature serves as a proxy for the local air temperature. We developed a method to estimate near-surface two-dimensional wind fields at centimetre resolution from tracking the air temperature pattern on the screens. Wind field estimations are validated with near-surface three-dimensional short-path ultrasonic data. To demonstrate the capabilities of the screen method, we present results from a comprehensive field campaign at an alpine research site during patchy snow cover conditions. The measurements reveal an extremely heterogeneous near-surface atmospheric layer. Vertical profiles of horizontal and vertical wind reflect multiple layers of different static stability within 2m above the surface. A dynamic, thin stable internal boundary layer (SIBL) develops above the leading edge of snow patches protecting the snow surface from warmer air above. During pronounced gusts, the warm air from aloft entrains into the SIBL and reaches down to the snow surface adding energy to the snow pack. Measured vertical turbulent sensible heat fluxes are shown to be consistent with air temperature and wind profiles obtained using the screen method and confirm its capabilities to investigate complex in situ near-surface heat exchange processes.

In mountain regions, forests that overlap with seasonal snow mostly reside in complex terrain. Due to persisting major observational challenges in these environments, the combined impact of forest structure and topography on seasonal snow cover dynamics is still poorly understood. Recent advances in forest snow process representation and increasing availability of detailed canopy structure datasets, however, now allow for hyper-resolution (<5m) snow model simulations capable of resolving tree-scale processes. These can shed light on the complex process interactions that govern forest snow dynamics. We present multi-year simulations at 2m resolution obtained with FSM2, a mass- and energy-balance-based forest snow model specifically developed and validated for metre-scale applications. We simulate an ∼3km2 model domain encompassing forested slopes of a sub-alpine valley in the eastern Swiss Alps and six snow seasons. Simulations thus span a wide range of canopy structures, terrain characteristics, and meteorological conditions. We analyse spatial and temporal variations in forest snow energy balance partitioning, aiming to quantify and understand the contribution of individual energy exchange processes at different locations and times. Our results suggest that snow cover evolution is equally affected by canopy structure, terrain characteristics, and meteorological conditions. We show that the interaction of these three factors can lead to snow accumulation and ablation patterns that vary between years. We further identify higher snow distribution variability and complexity in slopes that receive solar radiation early in winter. Our process-level insights corroborate and complement existing empirical findings that are largely based on snow distribution datasets only. Hyper-resolution simulations as presented here thus help to better understand how snowpacks and ecohydrological regimes in sub-alpine regions may evolve due to forest disturbances and a warming climate. They could further support the development of process-based sub-grid forest snow cover parameterizations or tiling approaches for coarse-resolution modelling applications.

The seasonal evolution of snow cover has significant impacts on the hydrological cycle and microclimate in mountainous regions. However, snow processes also play a crucial role in triggering alpine mass movements and flooding, posing risks to people and infrastructure. To mitigate these risks, many countries use operational forecast systems for snow distribution and melt. This paper presents the Swiss Operational Snow-hydrological (OSHD) model system, developed to provide daily analysis and forecasts on snow cover dynamics throughout Switzerland. The OSHD system is a sophisticated snow hydrological model designed specifically for the high-alpine terrain of the Swiss Alps. It leverages exceptional station data and high-resolution meteorological forcing data, as well as various reanalysis products to combine snow modeling with advanced data assimilation and meteorological downscaling methods. The system offers models of varying complexity, each tailored to specific modeling strategies and applications. For snowmelt runoff forecasting, monitoring snow water resources, and research-grade purposes, the OSHD system employs physics-based modeling chains. For snow climatological assessments, a conceptual model chain is available. We are pleased to present two comprehensive datasets from the conceptual and physics-based models that cover the entirety of Switzerland. The first dataset comprises a snow water equivalent climatology spanning 1998-2022, with a spatial resolution of 1 km. The second dataset includes snow distribution and snow melt data spanning 2016-2022 at a high spatial resolution of 250 m. To meet the needs of a multi-purpose snow hydrological model framework, the OSHD system employs various strategies for process representation and sub-grid parameterizations at the snow-canopy-atmosphere interface, particularly in complex terrain. Recent and ongoing model developments are aimed at accounting for complex forest snow processes, representing slope and ridge-scale precipitation and snow redistribution processes, as well as improving probabilistic snow forecasts and data assimilation procedures based on remote sensing products.

High-resolution (< 1 km) atmospheric modeling is increasingly used to study precipitation distributions in complex terrain and cryosphere–atmospheric processes. While this approach has yielded insightful results, studies over annual timescales or at the spatial extents of watersheds remain unrealistic due to the computational costs of running most atmospheric models. In this paper we introduce a high-resolution variant of the Intermediate Complexity Atmospheric Research (ICAR) model, HICAR. We detail the model development that enabled HICAR simulations at the hectometer scale, including changes to the advection scheme and the wind solver. The latter uses near-surface terrain parameters which allow HICAR to simulate complex topographic flow features. These model improvements clearly influence precipitation distributions at the ridge scale (50 m), suggesting that HICAR can approximate processes dependent on particle–flow interactions such as preferential deposition. A 250 m HICAR simulation over most of the Swiss Alps also shows monthly precipitation patterns similar to two different gridded precipitation products which assimilate available observations. Benchmarking runs show that HICAR uses 594 times fewer computational resources than the Weather Research and Forecasting (WRF) atmospheric model. This gain in efficiency makes dynamic downscaling accessible to ecohydrological research, where downscaled data are often required at hectometer resolution for whole basins at seasonal timescales. These results motivate further development of HICAR, including refinement of parameterizations used in the wind solver and coupling of the model with an intermediate-complexity snow model.