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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.

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 cover 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. Accurate long-term spatial data on snow cover 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, using more accurate data for calibrating the correction. The correction is based on the widely applied quantile mapping approach. A new method of spatial and temporal clustering of the data points is used to calculate the quantile distributions. The main functions of the package are written in C++ to achieve high performance and to allow parallel computing. In a case study over Switzerland, where a 60-year snow water equivalent climatology is produced at a resolution of 1 day and 1 km, SnowQM reduces the bias in snow water equivalent from −9 mm to −2 mm in winter and from −41 mm to −2 mm in spring. It is also significantly faster than pure R implementations. The limitations of the quantile mapping approach for snow, such as snow creation, are discussed. The proposed spatial clustering improves the correction in homogeneous terrain, which opens the way for further use with other variables.

A large portion of boreal and alpine forests of the Northern Hemisphere hosts seasonal snowpacks over multiple months of the year. Rising temperatures and forest disturbances are causing rapid change to these environments; therefore, accurate prediction of forest snow is relevant for a variety of disciplines such as biogeochemistry, ecohydrology, cryospheric, and climate sciences. Research in each of these fields relies on process-based models that are usually discipline-specific, e.g., snow hydrology and land surface models. These models are intended for a broad range of spatiotemporal scales and consequently include canopy and snowpack process representations of varying complexity. Detailed snow physics models that resolve the microstructure of individual snow layers, motivated by avalanche forecasting and snow remote sensing, have existed for years. More recent advances in forest snow process representation and increasing availability of high-resolution canopy structure datasets have led to the development of snow-hydrology models capable of resolving tree-scale processes.Here, we introduce a new model system that combines concepts from two such sophisticated models: the snowpack representation from Crocus, and the canopy representation from the Flexible Snow Model. We present multi-year simulations at 2-m resolution across sub-alpine and boreal forest landscapes. Spatially explicit simulations allow us to assess the spatio-temporal dynamics of snow properties, ground conditions and land surface states, and to unravel their distinct dependencies on canopy structure heterogeneities at a previously unfeasible level of detail. This work aims to inform and further promote the use of process-based modelling tools in interdisciplinary ecosystem research at the interface between snow and ecosystem science, and in support of environmental change impact studies, management practices and mitigation/adaptation strategies.

Background: In veterinary practice, most minor procedures such as radiographs, skin biopsies and wound treatments require sedation. The combination of butorphanol, ketamine and dexmedetomidine is commonly used, but the ideal dosages for this combination have not been reported. This randomized prospective clinical trial initially tested 8 clinically relevant combinations in a blinded procedure in 50 dogs. The quality of each combination was rated using a purposefully developed negative score (NS; 0-21.5, the lower the NS the better the quality of sedation), to judge the quality of sedation, the occurrence of side effects, and need for additional anaesthetics. Then the combinations were divided into "promising" and "unsatisfactory" subgroups and their centroids Pc and Uc were determined before a new combination (N) was calculated using the formula N=Pc+α*(Pc-Uc). The combination N was tested in 6 dogs and then it replaced the worst of the previous 8 combinations. The same procedure was repeated after each new combination until the NS did not improve any further. As a last step, the best combination was tested in 100 adult dogs undergoing diagnostic or therapeutic procedures. Results: The optimal combination found was dexmedetomidine 0.005 mg/kg, ketamine 1 mg kg-1, and butorphanol 0.3 mg kg-1 with a median NS of 1.5 (interquartile range 1.5-2.4). In all 112 dogs receiving this combination, the quality of sedation was satisfactory and no severe side effects were detected. Conclusions: The application of this optimization method allowed the calculation of an optimal drug combination for sedation in healthy dogs. This combination revealed safe after being tested in 100 animals. This combination can now be used in daily clinical practice for healthy adult dogs undergoing minor procedures.

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 time-scales 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 118x fewer computational resources than the WRF atmospheric model. This gain in efficiency makes dynamic downscaling accessible to ecohydrological research, where downscaled data is often required at hectometer resolution for whole basins at seasonal time scales. 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.

In Alpine regions, forests that overlap with seasonal snow mostly reside in complex terrain. Due to major observational challenges in these environments, the combined impact of forest structure and topography on seasonal snow cover dynamics is still poorly understood. However, recent advances in forest snow process representation and increasing availability of detailed canopy structure datasets now allow for hyper-resolution (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 meter-scale applications. Our 3km2 model domain encompasses forested slopes of a sub-alpine valley in the Eastern Swiss Alps. Simulations thus span a wide range of canopy structures, terrain characteristics, and meteorological conditions typical for the region. We analyze 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 fine-scale canopy structure, terrain characteristics and meteorological conditions. We show that the interaction of these three factors can lead to snow distribution and melt patterns that vary between years. Generally, we find 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 will thus help us to better understand how ecohydrological regimes sub-alpine regions may evolve as a result of forest disturbances and a warming climate.

Snow models rely on accurate meteorological input data at the spatial scales at which they operate. However, even the highest resolution operational atmospheric models often run at horizontal resolutions at least an order of magnitude coarser than most snow models. Different downscaling techniques can be employed to bridge this scale gap, typically being sorted into either statistical or dynamical techniques. Statistical techniques often rely on temporally invariant spatial patterns or simplistic conceptual relationships, making them computationally cheap but prone to errors. Dynamical downscaling generally offers a counterpoint to this tradeoff: stronger physical basis but more computational demand. Efforts have been made to optimize this tradeoff of dynamic downscaling, reducing computational demand while maintaining physical accuracy of predicted variables as well as the interdependency of downscaled variables such as winds and precipitation. The Intermediate Complexity Atmospheric Research (ICAR) model recently demonstrated an ability to match precipitation patterns from WRF, but with computational costs at least two orders of magnitude lower. While promising, these results from a 4km comparison did not translate to finer spatial resolutions often needed as input to snow models.Thus, we introduce the High-resolution Intermediate Complexity Atmospheric Research Model (HICAR), a new variant of the ICAR model developed for spatial resolutions as high as 50m. Relative to a traditional atmospheric model like WRF, HICAR maintains the orders-of-magnitude reduction in computational demand which ICAR displayed, while resolving terrain-induced effects on the wind field not seen in ICAR. This is achieved through a novel combination of adjustments to a background wind field based on terrain descriptors with a wind solver. The solver enforces a mass-conservation constraint on the 3D wind field. These modifications successfully mimic dynamic effects such as flow blocking, ridge-crest speed up, and lee-side recirculation to be captured in the resulting wind field. These features are of particular importance for resolving snow deposition patterns, where the snow particles are particularly susceptible to advection by the near-surface flow field. We validate the accuracy of HICAR’s flow features using a wind LiDAR deployed in complex terrain and show a comparison between flow fields from HICAR and WRF at a horizontal resolution of 50 m. These comparisons demonstrate HICAR’s ability to resolve terrain-induced modifications to the flow field which result in increased heterogeneity of ridge-scale snowfall patterns. To this point, preliminary comparisons of snow deposition patterns in complex terrain between the HICAR and WRF models are offered. With this new model, physically-based downscaling of precipitation and other atmospheric variables which preserves their interdependencies is made available for high-resolutions (100m) and large-spatial extents (10,000 km2) which are often demanded by operational land-surface models.

Late in the ablation season the snow cover gets patchy. The resulting surface temperature gradients and the lateral advection of heat over the partial snow cover engage different atmospheric processes such as the development of stable internal boundary layers (SIBL) or atmospheric decoupling close to the snow surface. Even though lateral advection of heat and the resulting atmospheric phenomena significantly influence the energy balance of the melting snow pack in spring, there is a lack of understanding and, thus, they are not explicitly taken into account in snow melt runoff models yet.To gain further understanding of those complex near-surface atmospheric processes at the meter to sub-meter scale, we conducted a comprehensive field campaign at an alpine research site. The field campaign included the measurement of meteorological parameters, snow ablation pattern, and turbulence using eddy-covariance sensors. Furthermore, we applied a novel experimental method. Two thin synthetic screens were vertically, in parallel to the prevailing wind direction, deployed across the transition from bare ground to snow covering a horizontal distance of 6m. The screens quickly adapt to ambient temperature and, thus, serve as a proxy for the local air temperature. Using a high resolution thermal infrared camera, a 30Hz sequence of infrared frames was recorded. The recorded air temperature fields capture the dynamics of turbulent eddies adjacent to the surface depending on different parameters such as wind speed or the snow coverage. A thin SIBL develops above the leading edge of snow patches possibly protecting the snow surface from warmer air above. However, sometimes the warm air entrains into the SIBL and reaches down to the snow surface adding further energy to the snow pack.In an attempt to quantify exchange processes from those dynamics, we developed a method to estimate high-resolution, near-surface 2D wind fields from tracking the air temperature pattern on the screens. A spatial correlation search yields the shift of an eddy or air parcel between two subsequent frames, using air temperature as a passive tracer. From this shift, the wind speed can be calculated at a very high spatial resolution. Vertical profiles of air temperature, horizontal, and vertical wind speeds across the transition from bare ground to snow can be evaluated with the advantage of a high spatial (0.01 m) and temporal (30 Hz) resolution.The screen measurements and wind speed estimation are validated with 3D short-path ultrasonic anemometer measurements close to the surface, which provide further insights into the turbulence characteristics close to the snow surface.With the high spatio-temporal resolution data we aim to better understand and quantify small scale energy transfer processes over patchy snow covers and their dependency on the atmospheric conditions. This will allow to improve parameterizations of these processes in coarser resolution snow melt models.

Seasonal snow is an essential water resource in many mountain regions. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) at regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval algorithms. We investigate the ability of the Sentinel-1 mission to monitor snow depth at sub-kilometer (100 m, 500 m, and 1 km) resolutions over the European Alps for 2017–2019. The Sentinel-1 backscatter observations, especially in cross-polarization, show a high correlation with regional model simulations of snow depth over Austria and Switzerland. The observed changes in radar backscatter with the accumulation or ablation of snow are used in an empirical change detection algorithm to retrieve snow depth. The algorithm includes the detection of dry and wet snow conditions. Compared to in situ measurements at 743 sites in the European Alps, dry snow depth retrievals at 500 m and 1 km resolution have a spatio-temporal correlation of 0.89. The mean absolute error equals 20 %–30 % of the measured values for snow depths between 1.5 and 3 m. The performance slightly degrades for retrievals at the finer 100 m spatial resolution as well as for retrievals of shallower and deeper snow. The results demonstrate the ability of Sentinel-1 to provide snow estimates in mountainous regions where satellite-based estimates of snow mass are currently lacking. The retrievals can improve our knowledge of seasonal snow mass in areas with complex topography and benefit a number of applications, such as water resource management, flood forecasting, and numerical weather prediction. However, future research is recommended to further investigate the physical basis of the sensitivity of Sentinel-1 backscatter observations to snow accumulation.

Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change., Remote sensing observations of mountain snow depth are still lacking for the Northern Hemisphere mountains. Here authors use Sentinel-1 satellite radar measurements to assess the snow depth in mountainous areas at 1 km² resolution and show that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences.

Climate projection studies of future changes in snow conditions and resulting rain-on-snow (ROS) flood events are subject to large uncertainties. Typically, emission scenario uncertainties and climate model uncertainties are included. This is the first study on this topic to also include quantification of natural climate variability, which is the dominant uncertainty for precipitation at local scales with large implications for runoff projections, for example. To quantify natural climate variability, a weather generator was applied to simulate inherently consistent climate variables for multiple realizations of current and future climates at 100 m spatial and hourly temporal resolution over a 12 x 12 km high-altitude study area in the Swiss Alps. The output of the weather generator was used as input for subsequent simulations with an energy balance snow model. The climate change signal for snow water resources stands out as early as mid-century from the noise originating from the three sources of uncertainty investigated, namely uncertainty in emission scenarios, uncertainty in climate models, and natural climate variability. For ROS events, a climate change signal toward more frequent and intense events was found for an RCP 8.5 scenario at high elevations at the end of the century, consistently with other studies. However, for ROS events with a substantial contribution of snowmelt to runoff (> 20 %), the climate change signal was largely masked by sources of uncertainty. Only those ROS events where snowmelt does not play an important role during the event will occur considerably more frequently in the future, while ROS events with substantial snowmelt contribution will mainly occur earlier in the year but not more frequently. There are two reasons for this: first, although it will rain more frequently in midwinter, the snowpack will typically still be too cold and dry and thus cannot contribute significantly to runoff; second, the very rapid decline in snowpack toward early summer, when conditions typically prevail for substantial contributions from snowmelt, will result in a large decrease in ROS events at that time of the year. Finally, natural climate variability is the primary source of uncertainty in projections of ROS metrics until the end of the century, contributing more than 70 % of the total uncertainty. These results imply that both the inclusion of natural climate variability and the use of a snow model, which includes a physically based process representation of water retention, are important for ROS projections at the local scale., The Cryosphere, 16 (9), ISSN:1994-0416, ISSN:1994-0424

Seasonal snow is an essential source of water, especially in mountain regions. However, accurate satellite observations of the amount of snow stored in mountains are still lacking. We provide estimates of snow depth at sub-kilometer resolution over the European Alps for 2017–2019 from Sentinel-1 observations. The retrievals are based on a change detection algorithm that includes the masking of wet snow. For dry snow conditions, 300-m Sentinel-1 retrievals have a spatiotemporal correlation of 0.82 and mean absolute error of 0.19m compared with in situ measurements from 743 sites across the Alps. The results show the potential of Sentinel-1 to provide unprecedented snow estimates in regions with complex topography, where satellite observations of snow mass are currently lacking.

To track peaks in resource abundance, temperate-zone animals use predictive environmental cues to rear their offspring when conditions are most favourable. However, climate change threatens the reliability of such cues when an animal and its resource respond differently to a changing environment. This is especially problematic in alpine environments, where climate warming exceeds the Holarctic trend and may thus lead to rapid asynchrony between peaks in resource abundance and periods of increased resource requirements such as reproductive period of high-alpine specialists. We therefore investigated interannual variation and long-term trends in the breeding phenology of a high-alpine specialist, the white-winged snowfinch, Montifringilla nivalis, using a 20-year dataset from Switzerland. We found that two thirds of broods hatched during snowmelt. Hatching dates positively correlated with April and May precipitation, but changes in mean hatching dates did not coincide with earlier snowmelt in recent years. Our results offer a potential explanation for recently observed population declines already recognisable at lower elevations. We discuss non-adaptive phenotypic plasticity as a potential cause for the asynchrony between changes in snowmelt and hatching dates of snowfinches, but the underlying causes are subject to further research.

Land Surface Albedo (LSA) of forested environments continues to be a source of uncertainty in land surface modeling, especially across seasonally snow covered domains. Assessment and improvement of global scale model performance has been hampered by the contrasting spatial scales of model resolution and in-situ LSA measurements. In this study, point-scale simulations of the Community Land Model 5.0 (CLM5) were evaluated across a large range of forest structures and solar angles at two climatically different locations. LSA measurements, using an uncrewed aerial vehicle with up and down-looking shortwave radiation sensors, showed canopy structural shading of the snow surface exerted a primary control on LSA. Diurnal patterns of measured LSA revealed strong effects of both azimuth and zenith angles, neither of which were adequately represented in simulations. In sparse forest environments, LSA were overestimated by up to 66%. Further analysis revealed a lack of correlation between Plant Area Index (PAI), the primary canopy descriptor in CLM5, and measured LSA. Instead, measured LSA showed considerable correlation with the fraction of snow visible in the sensor's field of view, a correlation which increased further when only considering the sunlit fraction of visible snow. The use of effective PAI values as a simple first-order correction for the discrepancy between measured and simulated LSA in sparse forest environments substantially improved model results (64%–76% RMSE reduction). However, the large biases suggest the need for a more generic solution, for example, by introducing a canopy metric that represents canopy gap fraction rather than assuming a spatially homogeneous canopy.

Processes shaping forest snow cover evolution often vary at small spatial scales, which are not resolved by most model applications. Representing this variability at larger scales and coarser model resolutions constitutes a major challenge for model developers. In this study, we use a well‐validated hyper‐resolution forest snow model that explicitly resolves the spatial variability of canopy‐snow interactions at the meter scale to explore adequate representation of forest‐snow processes at coarser resolutions (50 m). For this purpose, we assess coarser‐resolution runs against spatially averaged results from corresponding hyper‐resolution simulations over a 150,000 m2 model domain. For the coarser‐resolution simulations, we tested alternative upscaling strategies. Our results reveal considerable discrepancies between strategies that utilize generalized canopy metrics versus strategies that apply a more detailed set of process‐specific canopy descriptors. Particularly, the inclusion of canopy descriptors that represent the various scales and perspectives relevant to the individual processes leads to accurate simulation of forest snow cover dynamics at coarse resolutions. Our results further demonstrate that a realistic representation of snow‐covered fraction in snowmelt calculations is important even for relatively small (∼50 m) grid cells. Ultimately, this work provides recommendations for modeling forest‐snow processes in large‐scale applications, which allow coarse resolution simulations to approximate spatially averaged results of corresponding hyper‐resolution simulations.

Light is a key driver of forest biodiversity and functioning. Light regimes beneath tree canopies are mainly driven by the solar angle, topography, and vegetation structure, whose three‐dimensional complexity creates heterogeneous light conditions that are challenging to quantify, especially across large areas. Remotely sensed canopy structure data from airborne laser scanning (ALS) provide outstanding opportunities for advancement in this respect. We used ALS point clouds and a digital terrain model to produce hemispherical photographs from which we derived indices of nondirectional diffuse skylight and direct sunlight reaching the understory. We validated our approach by comparing the performance of these indices, as well as canopy closure (CCl) and canopy cover (CCo), for explaining the light conditions experienced by forest plant communities, as indicated by the Landolt indicator values for light (L light) from 43 vegetation surveys along an elevational gradient. We applied variation partitioning to analyze how the independent and joint statistical effects of light, macroclimate, and soil on the spatial variation in plant species composition (i.e., turnover, Simpson dissimilarity, β SIM) depend on light approximation methodology. Diffuse light explained L light best, followed by direct light, CCl and CCo (R2 = .31, .23, .22, and .22, respectively). The combination of diffuse and direct light improved the model performance for β SIM compared with CCl and CCo (R2 = .30, .27 and .24, respectively). The independent effect of macroclimate on β SIM dropped from an R 2 of .15 to .10 when diffuse light and direct light were included. The ALS methods presented here outperform conventional approximations of below‐canopy light conditions, which can now efficiently be quantified along entire horizontal and vertical forest gradients, even in topographically complex environments such as mountains. The effect of macroclimate on forest plant communities is prone to be overestimated if local light regimes and associated microclimates are not accurately accounted for.

Small-scale variations in radiative transfer through forest canopies are strongly linked to canopy structural heterogeneity. To date, upscaling of radiative transfer parameterizations developed at the point scale is hampered by (i) poor representation of canopy structure variability and (ii) limited spatially explicit subcanopy irradiance data to assess the performance of radiative transfer schemes at typical resolutions of land surface models. This study presents a novel approach for efficient in situ characterization of canopy structure and subcanopy irradiance over large spatial extents. The method involves a handheld radiometer assembly mounted on a motorized gimbal developed for nonstationary continuous measurements of shortwave and longwave radiation along forest transects. In combination with radiation and temperature data from a stationary reference station, spatially resolved estimates of sky-view fraction, canopy transmissivity, and longwave enhancement could be obtained. Under favorable meteorological conditions, validation against sky-view fraction data from hemispherical photographs yielded an RMSE of 0.03 (i.e., 3%). Irradiance measurements under heterogeneous canopy cover revealed strong spatial coherence between longwave radiation enhancement, shortwave radiation attenuation, and sky-view fraction on overcast days. Under clear-sky conditions, however, sun flecks caused highly variable shortwave radiation transmissivity patterns. This study demonstrates the potential of handheld radiometer surveys to deliver valuable spatially distributed datasets of collocated canopy structure and subcanopy irradiance which can be used (i) as reference data for alternative approaches to derive canopy structure parameters, (ii) to improve modeling of subcanopy radiation across a wide range of canopy distributions, and (iii) to support respective model upscaling efforts.

Snow deposition patterns in complex terrain are heavily dependent on the underlying topography. This topography affects precipitating clouds at the kilometer-scale and causes changes to the wind field at the sub-kilometer scale, resulting in altered advection of falling hydrometeors. Snow particles are particularly sensitive to changes in the near-surface flow field due to their low density. Atmospheric models which run at the kilometer scale cannot resolve the actual heterogeneity of the underlying terrain, resulting in precipitation maps which do not capture terrain-affected precipitation patterns. Thus, snow-atmosphere interactions such as preferential deposition are often not resolved in precipitation data used as input to snow models. To bridge this spatial gap and resolve snow-atmosphere interactions at the sub-kilometer scale, we couple an intermediate complexity atmospheric model (ICAR) to the COSMO NWP model. Applying this model to sub-kilometer terrain (horizontal resolution of 50 and 250 m) required changes to ICAR’s computational grid, atmospheric dynamics, and boundary layer flow. As a result, the near-surface flow now accounts for surface roughness and topographically induced speed up. This has been achieved by using terrain descriptors calculated once at initialization which consider a point’s exposure or sheltering relative to surrounding terrain. In particular, the use of a 3-dimensional Sx parameter allows us to simulate areas of stagnation and recirculation on the lee of terrain features. Our approach maintains the accurate large-scale precipitation patterns from COSMO but resolves the dynamics induced by terrain at the sub-kilometer scale without adding additional computational burden. We find that solid precipitation patterns at the ridge scale, such as preferential deposition of snow, are better resolved in the high-resolution version of ICAR than the current ICAR or COSMO models. This updated version of ICAR presents a new tool to dynamically downscale NWP output for snow models and enables future studies of snow-atmosphere interactions at domain scales of 100’s of kilometers.

Forest snow cover dynamics affect hydrological regimes, ecosystem processes, and climate feedbacks, and thus need to be captured by model applications that operate across a wide range of spatial scales. At large scales and coarse model resolutions, high spatial variability of the processes shaping forest snow cover evolution creates a major modelling challenge. Variability of canopy-snow interactions is determined by heterogeneous canopy structure and can only be explicitly resolved with hyper-resolution models (Here, we address this challenge with model upscaling experiments with the forest snow model FSM2, using hyper-resolution simulations as intermediary between experimental data and coarse-resolution simulations. When run at 2-m resolution, FSM2 is shown to capture the spatial variability of forest snow dynamics with a high level of detail: Its accurate performance is verified at the level of individual energy balance components based on extensive, spatially distributed sub-canopy measurements of micrometeorological and snow variables, obtained with mobile multi-sensor platforms. Results from hyper-resolution simulations over a 150,000 m2 domain are then compared to spatially lumped, coarse-resolution runs, where 50m x 50m grid cells are represented by one model run only. For the spatially lumped simulations, we evaluate alternative upscaling strategies, aiming to explore the representation of forest snow processes at model resolutions coarser than the spatial scales at which these processes vary and interact.Different upscaling strategies exhibited large discrepancies in simulated (1) distribution of snow water equivalent at peak of winter, and (2) timing of snow disappearance. Our results indicate that detailed canopy structure metrics, as included in hyper-resolution runs, are necessary to capture the spatial variability of forest snow processes even at coarser resolutions. They further demonstrate the relevance of accounting for unresolved sub-grid variability in snowmelt calculations even at relatively small spatial aggregation scales. By identifying important model features, which allow coarse-resolution simulations to approximate spatially averaged results of corresponding hyper-resolution simulations, this work provides recommendations for modelling forest snow processes in medium- to large-scale applications.

Snowmelt runoff predictions in alpine catchments are challenging because of the high spatial variability of the snow cover driven by various snow accumulation and ablation processes. In spring, the coexistence of bare and snow-covered ground engages a number of processes such as the enhanced lateral advection of heat over partial snow cover, the development of internal boundary layers, and atmospheric decoupling effects due to increasing stability at the snow cover. The interdependency of atmospheric conditions, topographic settings and snow coverage remains a challenge to accurately account for these processes in snow melt models.In this experimental study, we used an Infrared Camera (VarioCam) pointing at thin synthetic projection screens with negligible heat capacity. Using the surface temperature of the screen as a proxy for the air temperature, we obtained a two-dimensional instantaneous measurement. Screens were installed across the transition between snow-free and snow-covered areas. With IR-measurements taken at 10Hz, we capture the dynamics of turbulent temperature fluctuations over the patchy snow cover at high spatial and temporal resolution. From this data we were able to obtain high-frequency, two-dimensional windfield estimations adjacent to the surface.Preliminary results show the formation of a stable internal boundary layer (SIBL), which was temporally highly variable. Our data suggest that the SIBL height is very shallow and strongly sensitive to the mean near-surface wind speed. Only strong gusts were capable of penetrating through this SIBL leading to an enhanced energy input to the snow surface.With these type of results from our experiments and further measurements this spring we aim to better understand small scale energy transfer processes over patch snow cover and it’s dependency on the atmospheric conditions, enabling to improve parameterizations of these processes in coarser-resolution snow melt models.

In mountainous terrain, wind-driven transport of deposited snow affects the overall distribution of snow, and can have a significant effect on snowmelt patterns even at coarser resolution. In an operational modelling perspective, a compromise must be found to represent this complex small-scale process with enough accuracy while mitigating the computational costs of snow cover simulations over large domains. To achieve this compromise, we implemented the SNOWTRAN-3D snow transport module within the FSM intermediate complexity snow cover model. We included a new layering scheme and a historical variable of past snow wetting, but without resolving the snow microstructure. Simulations are run and evaluated over a small mountain range in the Swiss Alps at 25 to 100 m resolution. Being implemented in the model framework of the SLF operational snow hydrology service (OSHD), simulations further benefit from snow data assimilation techniques to provide improved estimates of solid precipitation fields. As complex wind patterns in mountains are the key processes driving snow transport, we tested statistical and dynamical methods to downscale 1 km resolution COSMO winds to better reflect topographically-induced flow patterns. These simulations are a first step working towards the integration of wind transport processes over large domains in an intermediate-complexity and -resolution operational modelling framework.

When formulating a hydrologic model, scientists rely on parameterizations of multiple processes based on field data, but literature review suggests that more frequently people select parameterizations that were included in pre-existing models rather than re-evaluating the underlying field experiments. Problems arise when limited field data exist, when “trusted” approaches do not get reevaluated, and when processes fundamentally change in different environments. The physics and dynamics of snow interception by conifers, including both loading and unloading of snow, is just such a case. The most commonly used interception parameterization is based on data from four trees from one site, but field study results are not directly transferable between environments. The process varies dramatically between locations with relatively warmer versus colder winters. Here, we combine a comprehensive literature review with a model to demonstrate essential improvements to model representations of snow interception. We recommend that, as a first and essential step, all models include increased loading due to increased adhesion and cohesion when temperatures rise from -3 and 0°C. The commonly used parameters of a fixed maximum value for loading and an e-folding time for unloading are not supported by observations or physical understanding and are not necessary to reproduce observations. In addition to unloading based on physical processes, such as wind or canopy warming, all models must represent melting of in-canopy snow so that it can be unloaded in liquid form. As a second step, we propose field experiments across climates and forest types to investigate: a) a representation of the force balance between adhesion and cohesion versus gravity for both interception efficiency and rates of unloading, b) wind effects during and between storms, and c) lubrication when snow melts. For greatest impact, this framework requires dedicated field measurements. These processes are essential for models to accurately represent the impacts of dynamically changing forest cover and snow cover on both global albedo and water supplies.

The complex dynamics of snow accumulation and melt processes under forest canopies entail major observational and modelling challenges, as they vary strongly in space and time. In this study, we present novel datasets acquired with mobile multi‐sensor platforms in sub‐alpine and boreal forest stands. These datasets include spatially and temporally resolved measurements of short‐ and longwave irradiance, air and snow surface temperatures, wind speed, and snow depth, all co‐registered to canopy structure information. We then apply the energy balance snow model FSM2 to obtain concurrent, distributed simulations of the forest snowpack at very high (‘hyper’) resolution (2 m). Our datasets allow us to assess the performance of alternative canopy representation strategies within FSM2 at the level of individual snow energy balance components and in a spatially explicit manner. We demonstrate the benefit of accounting for detailed spatial patterns of short‐ and longwave radiation transfer through the canopy, and show the importance of describing wind attenuation by the canopy using stand‐scale metrics. With the proposed canopy representation, snowmelt dynamics in discontinuous forest stands were successfully reproduced. Hyper‐resolution simulations resolving these effects provide an optimal basis for assessing the snow‐hydrological impacts of forest disturbances, and for validating and improving the representation of forest snow processes in land surface models intended for coarser‐scale applications.

The spatial distribution of snow in the mountains is significantly influenced through interactions of topography with wind, precipitation, shortwave and longwave radiation, and avalanches that may relocate the accumulated snow. One of the most crucial model parameters for various applications such as weather forecasts, climate predictions and hydrological modeling is the fraction of the ground surface that is covered by snow, also called fractional snow-covered area (fSCA). While previous subgrid parameterizations for the spatial snow depth distribution and fSCA work well, performances were scale-dependent. Here, we were able to confirm a previously established empirical relationship of peak of winter parameterization for the standard deviation of snow depth σHS by evaluating it with 11 spatial snow depth data sets from 7 different geographic regions and snow climates with resolutions ranging from 0.1 to 3 m. An enhanced performance (mean percentage errors, MPE, decreased by 25 %) across all spatial scales ≥ 200 m was achieved by recalibrating and introducing a scale-dependency in the dominant scaling variables. Scale-dependent MPEs vary between −7 % and 3 % for σHS and between 0 % and 1 % for fSCA. We performed a scale- and region-dependent evaluation of the parameterizations to assess the potential performances with independent data sets. This evaluation revealed that for the majority of the regions, the MPEs mostly lie between ±10 % for σHS and between −1 % and 1.5 % for fSCA. This suggests that the new parameterizations perform similarly well in most geographical regions.