A snow-transport model for complex terrain

AbstractAs part of the winter environment in middle- and high-latitude regions, the interactions between wind, vegetation, topography and snowfall produce snow covers of non-uniform depth and snow water-equivalent distribution. A physically based numerical snow-transport model (SnowTran-3D) is developed and used to simulate this three-dimensional snow-depth evolution over topographically variable terrain. The mass-transport model includes processes related to vegetation snow-holding capacity, topographic modification of wind speeds, snow-cover shear strength, wind-induced surface-shear stress, snow transport resulting from saltation and suspension, snow accumulation and erosion, and sublimation of the blowing and drifting snow. The model simulates the cold-season evolution of snow-depth distribution when forced with inputs of vegetation type and topography, and atmospheric foreings of air temperature, humidity, wind speed and direction, and precipitation. Model outputs include the spatial and temporal evolution of snow depth resulting from variations in precipitation, saltation and suspension transport, and sublimation. Using 4 years of snow-depth distribution observations from the foothills north of the Brooks Range in Arctic Alaska, the model is found to simulate closely the observed snow-depth distribution patterns and the interannual variability.

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Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

Applied Sciences ◽

10.3390/app11188365 ◽

2021 ◽

Vol 11 (18) ◽

pp. 8365

Author(s):

Liming Gao ◽

Lele Zhang ◽

Yongping Shen ◽

Yaonan Zhang ◽

Minghao Ai ◽

...

Keyword(s):

Snow Cover ◽

River Basin ◽

Snow Depth ◽

Snow Water Equivalent ◽

Snow Accumulation ◽

Implicit Scheme ◽

Snow Water ◽

Irtysh River ◽

Simulation Results ◽

The Irtysh River

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

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Development of a Snow Load Alert System, “YukioroSignal” for Aiding Roof Snow Removal Decisions in Snowy Areas in Japan

Journal of Disaster Research ◽

10.20965/jdr.2020.p0688 ◽

2020 ◽

Vol 15 (6) ◽

pp. 688-697

Author(s):

Hiroyuki Hirashima ◽

Tsutomu Iyobe ◽

Katsuhisa Kawashima ◽

Hiroaki Sano ◽

◽

...

Keyword(s):

Snow Depth ◽

Load Distribution ◽

Snow Water Equivalent ◽

Snow Accumulation ◽

Risk Level ◽

Residential Areas ◽

Alert System ◽

Snow Removal ◽

Snow Load ◽

Snow Water

This study developed a snow load alert system, known as the “YukioroSignal”; this system aims to provide a widespread area for assessing snow load distribution and the information necessary for aiding house roof snow removal decisions in snowy areas of Japan. The system was released in January 2018 in Niigata Prefecture, Japan, and later, it was expanded to Yamagata and Toyama prefectures in January 2019. The YukioroSignal contains two elements: the “Quasi-Real-Time Snow Depth Monitoring System,” which collects snow depth data, and the numerical model known as SNOWPACK, which can calculate the snow water equivalent (SWE). The snow load per unit area is estimated to be equivalent to SWE. Based on the house damage risk level, snow load distribution was indicated by colors following the ISO 22324. The system can also calculate post-snow removal snow loads. The calculated snow load was validated by using the data collected through snow pillows. The simulated snow load had a root mean square error (RMSE) of 21.3%, which was relative to the observed snow load. With regard to residential areas during the snow accumulation period, the RMSE was 13.2%. YukioroSignal received more than 56,000 pageviews in the snowheavy 2018 period and 26,000 pageviews in the less snow-heavy 2019 period.

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A Distributed Snow-Evolution Modeling System (SnowModel)

Journal of Hydrometeorology ◽

10.1175/jhm548.1 ◽

2006 ◽

Vol 7 (6) ◽

pp. 1259-1276 ◽

Cited By ~ 287

Author(s):

Glen E. Liston ◽

Kelly Elder

Keyword(s):

Forest Canopy ◽

Snow Water Equivalent ◽

Vegetation Type ◽

Atmospheric Model ◽

Snow Accumulation ◽

Blowing Snow ◽

Canopy Interception ◽

Meteorological Forcing ◽

Spatially Distributed ◽

Evolution Modeling

Abstract SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. Since each of these submodels was originally developed and tested for nonforested conditions, details describing modifications made to the submodels for forested areas are provided. SnowModel was created to run on grid increments of 1 to 200 m and temporal increments of 10 min to 1 day. It can also be applied using much larger grid increments, if the inherent loss in high-resolution (subgrid) information is acceptable. Simulated processes include snow accumulation; blowing-snow redistribution and sublimation; forest canopy interception, unloading, and sublimation; snow-density evolution; and snowpack melt. Conceptually, SnowModel includes the first-order physics required to simulate snow evolution within each of the global snow classes (i.e., ice, tundra, taiga, alpine/mountain, prairie, maritime, and ephemeral). The required model inputs are 1) temporally varying fields of precipitation, wind speed and direction, air temperature, and relative humidity obtained from meteorological stations and/or an atmospheric model located within or near the simulation domain; and 2) spatially distributed fields of topography and vegetation type. SnowModel’s ability to simulate seasonal snow evolution was compared against observations in both forested and nonforested landscapes. The model closely reproduced observed snow-water-equivalent distribution, time evolution, and interannual variability patterns.

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Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach

The Cryosphere Discussions ◽

10.5194/tcd-7-4633-2013 ◽

2013 ◽

Vol 7 (5) ◽

pp. 4633-4680 ◽

Cited By ~ 1

Author(s):

J. Veitinger ◽

B. Sovilla ◽

R. S. Purves

Keyword(s):

Surface Roughness ◽

Snow Depth ◽

Depth Distribution ◽

Management Strategies ◽

Snow Accumulation ◽

Swiss Alps ◽

Transfer Energy ◽

Hazard Management ◽

Terrestrial Lidar ◽

Basin Scale

Abstract. In alpine terrain, the snow covered winter surface deviates from its underlying summer terrain due to the progressive smoothing caused by snow accumulation. Terrain smoothing is believed to be an important factor in avalanche formation, avalanche dynamics and affects surface heat transfer, energy balance as well as snow depth distribution. To characterize the effect of snow on terrain we use the concept of roughness. Roughness is calculated for several snow surfaces and its corresponding underlying terrain for three alpine basins in the Swiss Alps characterized by low medium and high terrain roughness. To this end, elevation models of winter and summer terrain are derived from high-resolution (1 m) measurements performed by airborne and terrestrial LIDAR. We showed that on basin scale terrain smoothing not only depends on mean snow depth in the basin but also on its variability. Terrain smoothing can be modelled in function of mean snow depth and its standard deviation using a power law. However, a relationship between terrain smoothing and snow depth does not exist on a pixel scale. Further we demonstrated the high persistence of snow surface roughness even in between winter seasons. Those persistent patterns might be very useful to improve the representation of a winter terrain without modelling of the snow cover distribution. This can potentially improve avalanche release area definition and in the long term natural hazard management strategies.

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Snow-Distribution Modelling in the Ammassalik Region, South East Greenland

Hydrology Research ◽

10.2166/nh.2003.0025 ◽

2003 ◽

Vol 34 (1-2) ◽

pp. 1-16 ◽

Cited By ~ 36

Author(s):

B. Hasholt ◽

G.E. Liston ◽

N.T. Knudsen

Keyword(s):

Snow Water Equivalent ◽

Transport Model ◽

Digital Terrain Model ◽

Climatic Conditions ◽

Blowing Snow ◽

Orographic Effects ◽

Terrain Model ◽

Snow Distribution ◽

Western Side ◽

Snow Transport

The Ammassalik region is characterized by a strong alpine relief, with altitudes up to 1,000 m. Glaciers are located mainly on the western side of ridges. The climate is low arctic, with annual precipitation amounts of more than 1,000 mm, which falls mainly as snow. Furthermore very strong storms occur frequently throughout the region. All together these factors support strong snow redistribution by wind, which likely explains the glacier locations, and also explains the observed regional runoff differences. The aim of this study is to apply the Liston & Sturm snow-transport model (SnowTran-3D) to elucidate the snow distribution according to the actual climatic conditions. A digital terrain model was used to determine the terrain forcing of the wind field. Precipitation data from the Ammassalik meteorological station were corrected for aerodynamic errors and orographic effects. Wind, temperature and humidity were obtained from a station located on a nunatak 515 m.a.s.l. at the equilibrium line on the Mittivakkat Glacier. The recorded winter accumulation (balance) of snow on the glacier was used for model calibration and testing. Significant snow transport from east-facing slopes to west-facing slopes was confirmed by the model. The drift accumulations were greatest at the head of the glacier, just on the lee side of the ridge east of the glacier. In some areas, as much as 10% of the precipitation was returned to the atmosphere by blowing-snow sublimation. An average snow water equivalent of 113 cm was obtained (not including some minor areas having snow depths as great as 4 m). These results compare well with glacier observations of 114 cm collected in May 1998 (during the field survey the 4 m areas are omitted because of crevasse hazards). Future work will use the model to test scenarios that include changes in wind regime. 1

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Influence of Strong Winds on Snow Distribution and Avalanche Activity

Annals of Glaciology ◽

10.3189/s0260305500007886 ◽

1989 ◽

Vol 13 ◽

pp. 195-201 ◽

Cited By ~ 4

Author(s):

R. Meister

Keyword(s):

Snow Depth ◽

Transport Model ◽

Snow Accumulation ◽

Drift Flux ◽

Zone Control ◽

Flux Measurements ◽

Snow Distribution ◽

Strong Winds ◽

Local Range ◽

Good Agreement

In a local range, crest winds were compared with winds at lower stations to make it possible to initiate a drift-transport model which would predict snow accumulation patterns on leeward slopes. Corrections to the model input were made after consideration of detailed drift-flux measurements in the lowest 2 m above snow surface. Good agreement was found between the total length of large avalanches in a path near the crest, the appropriate wind reading and the corrected snow-depth increments in the rupture zone. Control of medium-sized avalanches likely to cause injury to skiers can be improved with the proposed method.

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Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

The Cryosphere ◽

10.5194/tc-13-3413-2019 ◽

2019 ◽

Vol 13 (12) ◽

pp. 3413-3434 ◽

Cited By ~ 2

Author(s):

Rebecca Gugerli ◽

Nadine Salzmann ◽

Matthias Huss ◽

Darin Desilets

Keyword(s):

Snow Depth ◽

Snow Water Equivalent ◽

Cosmic Ray ◽

Snow Accumulation ◽

Scaling Factor ◽

High Temporal Resolution ◽

High Mountain ◽

Alpine Glacier ◽

Snow Drift ◽

Snow Water

Abstract. Snow water equivalent (SWE) measurements of seasonal snowpack are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be inferred from neutron counts. We present the analyses of temporally continuous SWE measurements by a CRS on an alpine glacier in Switzerland (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which differed markedly in the amount and timing of snow accumulation. By combining SWE with snow depth measurements, we calculate the daily mean density of the snowpack. Compared to manual field observations from snow pits, the autonomous measurements overestimate SWE by +2 % ± 13 %. Snow depth and the bulk snow density deviate from the manual measurements by ±6 % and ±9 %, respectively. The CRS measured with high reliability over two winter seasons and is thus considered a promising method to observe SWE at remote alpine sites. We use the daily observations to classify winter season days into those dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, snowmelt) or snow densification. For each of these process-dominated days the prevailing meteorological conditions are distinct. The continuous SWE measurements were also used to define a scaling factor for precipitation amounts from nearby meteorological stations. With this analysis, we show that a best-possible constant scaling factor results in cumulative precipitation amounts that differ by a mean absolute error of less than 80 mm w.e. from snow accumulation at this site.

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