Snow Accumulation Model Research Articles

Predicting snow damage in conifer forests using a mechanistic snow damage model and high-resolution snow accumulation data

ABSTRACT Forest damage caused by heavy wet snow accumulation in the canopy is the second most important abiotic forest disturbance agent in Nordic conifer stands after wind. The extent and frequency of snow damage in the future climate in the Nordic region is a major uncertainty. Few mechanistic models of snow damage risk to trees exist that could support forest management scenario analysis and decision making. We propose a snow damage risk model consisting of a numerical weather prediction-based snow accumulation model for forest canopies and a mechanistic critical snow load model. Snow damage probability predictions were validated on snow breakage data from the winters of 2016 and 2018 covering 3.5 million individual trees in south-eastern Norway derived from pre- and post-damage aerial laser scanning campaigns. The proposed model demonstrated satisfactory damage and no-damage class separation with an AUC of 0.72 and 0.77 in Norway spruce and Scots pine, respectively, and an F1 score of 0.7 in conifers taller than 10 m that suffered moderate stem breakage. The model achieved a classification accuracy that is comparable to that of statistical models but is simpler and requires fewer inputs.

The Role of Basin Geometry in Mountain Snowpack Responses to Climate Change

Snowmelt contributions to streamflow in mid-latitude mountain basins typically dominate other runoff sources on annual and seasonal timescales. Future increases in temperature and changes in precipitation will affect both snow accumulation and seasonal runoff timing and magnitude, but the underlying and fundamental roles of mountain basin geometry and hypsometry on snowmelt sensitivity have received little attention. To investigate the role of basin geometry in snowmelt sensitivity, a linear snow accumulation model and the Cold Regions Hydrological Modeling (CRHM) platform driven are used to estimate how hypsometry affects basin-wide snow volumes and snowmelt runoff. Area-elevation distributions for fifty basins in western Canada were extracted, normalized according to their elevation statistics, and classified into three clusters that represent top-heavy, middle, and bottom-heavy basins. Prescribed changes in air temperature alter both the snow accumulation gradient and the total snowmelt energy, leading to snowpack volume reductions (10–40%), earlier melt onsets (1–4 weeks) and end of melt season (3 weeks), increases in early spring melt rates and reductions in seasonal areal melt rates (up to 50%). Basin hypsometry controls the magnitude of the basin response. The most sensitive basins are bottom-heavy, and have a greater proportion of their area at low elevations. The least sensitive basins are top-heavy, and have a greater proportion of their area at high elevations. Basins with similar proportional areas at high and low elevations fall in between the others in terms of sensitivity and other metrics. This work provides context for anticipating the impacts of ongoing hydrological change due to climate change, and provides guidance for both monitoring networks and distributed modeling efforts.

Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound

Abstract. Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of the freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice in McMurdo Sound with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season in 2011. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel captures the spatial snow distribution gradient in McMurdo Sound and falls within 2 cm snow water equivalent (s.w.e) of in situ measurements across the entire study area. However, it exhibits deviations of 5 cm s.w.e. from these measurements in the east where the effect of local topographic features has caused an overestimate of snow depth in the model. AMSR-E provides s.w.e. values half that of SnowModel for the majority of the sea ice growth season. The coarser-resolution ERA-Interim produces a very high mean s.w.e. value 20 cm higher than the in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on what interface the retracked CS-2 height is assumed to represent. Because of this ambiguity we vary the proportion of ice and snow that represents the freeboard – a mathematical alteration of the radar penetration into the snow cover – and assess this uncertainty in McMurdo Sound. The ranges in sea ice thickness uncertainty within these bounds, as means of the entire growth season, are 1.08, 4.94 and 1.03 m for SnowModel, ERA-Interim and AMSR-E respectively. Using an interpolated in situ snow dataset we find the best agreement between CS-2-derived and in situ thickness when this interface is assumed to be 0.07 m below the snow surface.

Projected decrease in wintertime bearing capacity on different forest and soil types in Finland under a warming climate

Abstract. Trafficability in forest terrain is controlled by ground-bearing capacity, which is crucial from the timber harvesting point of view. In winter, soil frost affects the most the bearing capacity, especially on peatland soils which have in general low bearing capacity. Ground frost similarly affects the bearing capacity of forest truck roads. A 20 cm thick layer of frozen soil or 40 cm thick layer of snow on the ground may already be sufficient for heavy forest harvesters. In this work, we studied the impacts of climate change on soil frost conditions and, consequently, on ground-bearing capacity from the timber harvesting point of view. The number of days with good wintertime bearing capacity was modelled by using a soil temperature model with a snow accumulation model and wide set of downscaled climate model data until the end of the 21st century. The model was calibrated for different forest and soil types. The results show that by the mid-21st century, the conditions with good bearing capacity will decrease in wintertime in Finland, most likely by about 1 month. The decrease in soil frost and wintertime bearing capacity will be more pronounced during the latter half of the century, when drained peatlands may virtually lack soil frost in most of winters in southern and western Finland. The projected decrease in the bearing capacity, accompanied with increasing demand for wood harvesting from drained peatlands, induces a clear need for the development of sustainable and resource-efficient logging practices for drained peatlands. This is also needed to avoid unnecessary harvesting damages, like rut formation on soils and damage to tree roots and stems.

Particle System Based Snow Simulating in Real Time

Abstract This paper deeply study in real-time simulation technology of realistic snow scenes, including snow real-time rendering techniques (real-time rendering of falling snowflakes), snow accumulation model, and wind field simulation (snow wind interaction model). The proposed innovations of this paper are:A snow particles cluster systems: which increase both the rendering efficiency and variety of the snowflakes; Wind snow interaction based on Boltztnann equation and D3Q15 model: this model simplify the calculation and offer a very true view, the algorithm can run in real time as well. These three solutions as above can notable increase the real characteristics in snow visualization and computation efficiency.

Snow accumulation distribution inferred from time‐lapse photography and simple modelling

AbstractThe spatial and temporal distribution of snow accumulation is complex and significantly influences the hydrological characteristics of mountain catchments. Many snow redistribution processes, such as avalanching, slushflow or wind drift, are controlled by topography, but their modelling remains challenging. In situ measurements of snow accumulation are laborious and generally have a coarse spatial or temporal resolution. In this respect, time‐lapse photography shows itself as a powerful tool for collecting information at relatively low cost and without the need for direct field access. In this paper, the snow accumulation distribution of an Alpine catchment is inferred by adjusting a simple snow accumulation model combined with a temperature index melt model to match the modelled melt‐out pattern evolution to the pattern monitored during an ablation season through terrestrial oblique photography. The comparison of the resulting end‐of‐winter snow water equivalent distribution with direct measurements shows that the achieved accuracy is comparable with that obtained with an inverse distance interpolation of the point measurements. On average over the ablation season, the observed melt‐out pattern can be reproduced correctly in 93% of the area visible from the fixed camera. The relations between inferred snow accumulation distribution and topographic variables indicate large scatter. However, a significant correlation with local slope is found and terrain curvature is detected as a factor limiting the maximal snow accumulation. Copyright © 2010 John Wiley & Sons, Ltd.

Impacts of climate change on the risk of snow-induced forest damage in Finland

This work studied the temporal and spatial variability of the risk of snow-induced forest damage in Finland under current and changing climatic conditions until the end of this century. The study was based on a snow accumulation model in which cumulative precipitation, air temperature and wind speed were used as input variables. The risk was analyzed in terms of the number of days per year when the accumulated amount of snow exceeded 20 kg m − 2. Based on the risk, the forest area and mean carbon stock of seedling, young thinning and advanced thinning stands at risk were calculated. Furthermore, the number of 5-day periods, when the accumulated amount of snow exceeded a risk limit, was calculated for the current and changing climatic conditions in order to study the frequency of damaging snowfalls. Compared to the baseline period 1961–1990, the risk of snow-induced forest damage and the amount of damaging snowfalls were predicted to decrease from the first 30-year period (1991–2020) onwards. Over the whole country, the mean annual number of risk days decreased by 11%, 23% and 56% in the first, second and third 30-year period, respectively, compared to the baseline period. In the most hazardous areas in north-western and north-eastern Finland, the number of risk days decreased from the baseline period of over 30 days to about 8 days per year at the end of the century. Correspondingly, the shares of the forest area at risk were 1.9%, 2.0% and 1.0% in the first, second and third 30-year period, respectively. The highest mean annual carbon stocks of young stands at risk were found in central, north-eastern and north-western Finland in the first and second 30-year period, varying between 0.6 and 1.2 Mg C ha − 1 year − 1, meaning at highest 3% of the mean carbon stock (Mg C stem wood ha − 1) of those areas. This study showed that although the risk of snow-induced forest damage was mainly affected by changes in critical weather events, the development of growing stock under the changing climatic conditions also had an effect on the risk assessment. However, timely management of forest stands in the areas with a high risk of snow-induced damage contributes to the trees’ increased resistance to the damage.