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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.

Soil moisture plays a key role in soil nutrient and carbon cycling; plant productivity; and energy, water, and greenhouse gas exchanges between the land and the atmosphere. The knowledge on drivers of spatiotemporal soil moisture dynamics in subarctic landscapes is limited. In this study, we used the Spatial Forest Hydrology (SpaFHy) model, in situ soil moisture data, and Sentinel-1 synthetic aperture radar (SAR)-based soil moisture estimates to explore spatiotemporal controls of soil moisture in a subarctic headwater catchment in northwestern Finland. The role of groundwater dynamics and lateral flow in soil moisture was studied through three groundwater model conceptualizations: (i) omission of groundwater storage and lateral flow, (ii) conceptual TOPMODEL approach based on topographic wetness index, and (iii) explicit 2D lateral groundwater flow. The model simulations were compared against continuous point soil moisture measurements, distributed manual measurements, and novel SAR-based soil moisture estimates available at high spatial and temporal resolutions. Based on model scenarios and model–data comparisons, we assessed when and where the lateral groundwater flow shapes shallow soil moisture and under which conditions soil moisture variability is driven more by local ecohydrology, i.e., the balance of infiltration, drainage, and evapotranspiration. The choice of groundwater flow model was shown to have a strong impact on modeled soil moisture dynamics within the catchment. All model conceptualizations captured the observed soil moisture dynamics in the upland forests, but accounting for the lateral groundwater flow was necessary to reproduce the saturated conditions common in the peatlands and occasionally in lowland forest grid cells. We further highlight the potential of integrating multi-scale observations with land surface and hydrological models. The results have implications for ecohydrological and biogeochemical processes, as well as for modeling hydrology and Earth system feedbacks in subarctic and boreal environments.

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.

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.

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.

The snowpack has a major influence on the land surface energy budget. Accurate simulation of the snowpack energy and radiation budget is challenging due to, e.g., effects of vegetation and topography, as well as limitations in the theoretical understanding of turbulent transfer in the stable boundary layer. Studies that evaluate snow, hydrology and land surface models against detailed observations of all surface energy balance components at high latitudes are scarce. In this study, we compared different configurations of the SURFEX land surface model against surface energy flux, snow depth and soil temperature observations from four eddy-covariance stations in Finland. The sites cover two different climate and snow conditions, representing the southern and northern subarctic zones, as well as the contrasting forest and peatland ecosystems typical for the boreal landscape. We tested different turbulent flux parameterizations implemented in the Crocus snowpack model. In addition, we examined common alternative approaches to conceptualize soil and vegetation, and we assessed their performance in simulating surface energy fluxes, snow conditions and soil thermal regime. Our results show that a stability correction function that increases the turbulent exchange under stable atmospheric conditions is imperative to simulate sensible heat fluxes over the peatland snowpacks and that realistic peat soil texture (soil organic content) parameterization greatly improves the soil temperature simulations. For accurate simulations of surface energy fluxes, snow and soil conditions in forests, an explicit vegetation representation is necessary. Moreover, we demonstrate the high sensitivity of surface fluxes to a poorly documented parameter involved in snow cover fraction computation. Although we focused on models within the SURFEX platform, the results have broader implications for choosing suitable turbulent flux parameterization and model structures depending on the potential use cases for high-latitude land surface modeling.

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 (<5 m) 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 2 m resolution obtained with FSM2, a mass- and energy-balance-based forest snow model specifically developed and validated for metre-scale applications. We simulate an ∼3 km2 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.

Snow depth mapping in Alpine forests is of high importance for hydrogeology, ecology, tourism, and natural hazards prevention. Different remote sensing approaches have been employed for the precise mapping of snow depth within forests. However, optical sensors cannot provide below-canopy information. While Airborne Laser Scanning (ALS) systems have been used successfully in this context and allow obtaining data below canopies, the costs of acquisitions are very high, not allowing frequent data acquisitions. UAV-based Lidar technology potentially can provide the critical below-canopy information at lower cost and allows for frequent acquisitions.First attempts to employ a UAV-based Lidar system in forests have proven promising, but they are limited to flat forests and to grid-level snow depth calculations. In this study, we present UAV-based Lidar data of both flat and steep forests. Different Lidar processing workflows are analyzed and compared, and snow depth algorithms are used both at the point and the grid level. Whereas the UAV-Lidar system proved capable of mapping snow in both environments, the steep forests' data processing comes with greater challenges, especially for the 3D registration, ground classification, and point-to-point snow depth calculations.

Aim of this study was to investigate the distribution of versican proteoglycan within the human dentine organic matrix by means of a correlative immunohistochemical analysis with field emission in-lens scanning electron microscope (FEI-SEM), transmission electron microscope (TEM), fluorescence microscope (FM) and biochemical assay. Specimens containing dentine and predentine were obtained from non carious human teeth and divided in three groups: 1) FEI-SEM group: sections were exposed to a pre-embedding immunohistochemical procedure; 2) TEM group: specimens were fixed, demineralised, embedded and submitted to a post-embedding immunohistochemical procedure; 3) FM group: sections mineralised and submitted to a pre-embedding immunohistochemical procedure with fluorescence labelling. Specimens were exposed to two different antibodies to assay distribution of versican fragments and whole versican molecule.Western Blotting analysis of dentine and pulp extracts was also performed. The correlative FEI-SEM,TEM and FM analysis revealed positive immunoreaction for versican fragments both in predentine and dentine, while few gold particles identifying the whole versican molecule were found in predentine only under TEM. No labelling of versican whole molecule was detected by FEI-SEM and FM analysis. The immunoblotting analysis confirmed the morphological findings. This study suggests that in fully developed human teeth versican fragments are significant constituents of the human dentine and predentine organic matrix, while versican whole molecule can be visualised in scarce amount within predentine only. The role of versican fragments within human dentine organic matrix should be further elucidated.

Aim of this study was to investigate the distribution of versican proteoglycan within the human dentine organic matrix by means of a correlative immunohistochemical analysis with field emission in-lens scanning electron microscope (FEI-SEM), transmission electron microscope (TEM), fluorescence microscope (FM) and biochemical assay. Specimens containing dentine and predentine were obtained from non carious human teeth and divided in three groups: 1) FEI-SEM group: sections were exposed to a pre-embedding immunohistochemical procedure; 2) TEM group: specimens were fixed, demineralised, embedded and submitted to a post-embedding immunohistochemical procedure; 3) FM group: sections mineralised and submitted to a pre-embedding immunohistochemical procedure with fluorescence labelling. Specimens were exposed to two different antibodies to assay distribution of versican fragments and whole versican molecule.Western Blotting analysis of dentine and pulp extracts was also performed. The correlative FEI-SEM,TEM and FM analysis revealed positive immunoreaction for versican fragments both in predentine and dentine, while few gold particles identifying the whole versican molecule were found in predentine only under TEM. No labelling of versican whole molecule was detected by FEI-SEM and FM analysis. The immunoblotting analysis confirmed the morphological findings. This study suggests that in fully developed human teeth versican fragments are significant constituents of the human dentine and predentine organic matrix, while versican whole molecule can be visualised in scarce amount within predentine only. The role of versican fragments within human dentine organic matrix should be further elucidated.