Experimental Study of Interference Effects of a High-Rise Building on the Snow Load on a Low-Rise Building with a Flat Roof

To explore the interference effects of a high-rise building on the snow load on a low-rise building with a flat roof, a series of wind tunnel tests were carried out with fine silica sand as a substitute for snow particles. The effects of the height of the interfering building and the distance between buildings on the snow distribution of the target building under three different wind directions were studied. The snow depth on the target building roof and the mass of particles blown off from the target building were measured during the wind tunnel tests, and the results showed that the snow distribution of the target building roof tends to be uniform when the interfering building is located upstream of the target building due to the shelter effect. When the interfering building is on the side of the target building, the snow distribution of the target building tends to be more uneven, because the interfering building increases the friction velocity on the target building roof near the interfering building. However, when the interfering building is located downstream of the target building, there will be an amplification effect of snow accumulation, and the snow distribution on the target building roof is nearly the same as that of the isolated condition. Under each wind direction, the interference effect of the snow load increases with the increase of the building height and the decrease of the building spacing. Therefore, the influence of the surrounding buildings on the snow distribution of the building roof cannot be ignored and should be considered in the structure design.

Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic

The spatial distribution of snow plays a vital role in Arctic climate, hydrology, and ecology due to its fundamental influence on the water balance, thermal regimes, vegetation, and carbon flux. However, for earth system modelling, the spatial distribution of snow is not well understood, and therefore, it is not well modeled, which can lead to substantial uncertainties in snow cover representations. To capture key hydro-ecological controls on snow spatial distribution, we carried out intensive field studies over multiple years for two small (2017–2019, ~2.5 km2) sub-Arctic study sites located on the Seward Peninsula of Alaska. Using an intensive suite of field observations (> 22,000 data points), we developed simple models of spatial distribution of snow water equivalent (SWE) using factors such as topographic characteristics, vegetation characteristics based on greenness (normalized different vegetation index, NDVI), and a simple metric for approximating winds. The most successful model was the random forest using both study sites and all years, which was able to accurately capture the complexity and variability of snow characteristics across the sites. Approximately 86 % of the SWE distribution could be accounted for, on average, by the random forest model at the study sites. Factors that impacted year-to-year snow distribution included NDVI, elevation, and a metric to represent coarse microtopography (topographic position index, or TPI), while slope, wind, and fine microtopography factors were less important. The models were used to predict SWE at the locations through the study area and for all years. The characterization of the SWE spatial distribution patterns and the statistical relationships developed between SWE and its impacting factors will be used for the improvement of snow distribution modelling in the Department of Energy’s earth system model, and to improve understanding of hydrology, topography, and vegetation dynamics in the Arctic and sub-Arctic regions of the globe.

Assessment of a snow storage gradient across a maritime mountain environment: a ground penetrating radar investigation

<p>The Southern Alps of New Zealand experience some of the highest precipitation rates globally, and dramatic west to east climatic gradients. Our current knowledge of this precipitation distribution is based on weather station data and river discharge measurements, but there is a clear data gap in the high elevation, central Southern Alps. Here, estimates of precipitation strongly diverge. This problem exists because of the difficulties of quantifying the depth and distribution of snow in a remote, high-altitude mountainous region. In order to improve our knowledge of snow distribution within this data-poor region, snow depths of (< 10m) were assessed parallel to the prevailing westerly wind direction at five locations across the mountain range, between the névé of Franz Josef Glacier, Waiho catchment, to the west and Jollie Valley, Pukaki catchment, in the east. The geophysical method of Ground Penetrating Radar (GPR) was used because of its ability to image the deep snow packs experienced in the study region. Comparison of measurement techniques over the (< 3km) surveyed transects showed that ground-based GPR gave the best sample size (41000 samples) and accuracy due to the high spatial resolution. Airborne GPR (8571 samples) overestimated snow depth by 8 % in low-gradient homogenous terrain, and 24% in steep heterogeneous terrain. The difference is ascribed to the larger view area of the GPR in the airborne survey. Direct probing of snow depth also performed poorly in comparison to ground-based GPR when generalising snow distribution over an area. Across-mountain precipitation peaked ~5 km west of the main divide, between 1700 and 2000 m a.s.l, providing the first empirical support to existing estimates of the location of peak precipitation. Results show decreasing precipitation from 12 ma-1 at Franz Josef Glacier, in the Waiho catchment, to 1.8 ma-1 at Jollie River valley, in the Lake Pukaki catchment, 25 km to the south-east. Internal reflection horizons in snow-pack radargrams allowed snowfall events to be tracked, and a relationship lowland and mountain precipitation to be established. Snowfall accumulation 'factors' were derived for different atmospheric circulation indices, and these will enable improved accuracy in modelling of snow accumulation processes. Further research is required to refine the relationship between synoptic-classed accumulation rates and inter-annual variations in climatic circulation. These refinements of measurement techniques and quantification of and snow distribution and depth allow for better estimation of river discharge and timing estimates for, hydroelectric power generation, and glacier mass balance.</p>

Assessment of a snow storage gradient across a maritime mountain environment: a ground penetrating radar investigation

<p>The Southern Alps of New Zealand experience some of the highest precipitation rates globally, and dramatic west to east climatic gradients. Our current knowledge of this precipitation distribution is based on weather station data and river discharge measurements, but there is a clear data gap in the high elevation, central Southern Alps. Here, estimates of precipitation strongly diverge. This problem exists because of the difficulties of quantifying the depth and distribution of snow in a remote, high-altitude mountainous region. In order to improve our knowledge of snow distribution within this data-poor region, snow depths of (< 10m) were assessed parallel to the prevailing westerly wind direction at five locations across the mountain range, between the névé of Franz Josef Glacier, Waiho catchment, to the west and Jollie Valley, Pukaki catchment, in the east. The geophysical method of Ground Penetrating Radar (GPR) was used because of its ability to image the deep snow packs experienced in the study region. Comparison of measurement techniques over the (< 3km) surveyed transects showed that ground-based GPR gave the best sample size (41000 samples) and accuracy due to the high spatial resolution. Airborne GPR (8571 samples) overestimated snow depth by 8 % in low-gradient homogenous terrain, and 24% in steep heterogeneous terrain. The difference is ascribed to the larger view area of the GPR in the airborne survey. Direct probing of snow depth also performed poorly in comparison to ground-based GPR when generalising snow distribution over an area. Across-mountain precipitation peaked ~5 km west of the main divide, between 1700 and 2000 m a.s.l, providing the first empirical support to existing estimates of the location of peak precipitation. Results show decreasing precipitation from 12 ma-1 at Franz Josef Glacier, in the Waiho catchment, to 1.8 ma-1 at Jollie River valley, in the Lake Pukaki catchment, 25 km to the south-east. Internal reflection horizons in snow-pack radargrams allowed snowfall events to be tracked, and a relationship lowland and mountain precipitation to be established. Snowfall accumulation 'factors' were derived for different atmospheric circulation indices, and these will enable improved accuracy in modelling of snow accumulation processes. Further research is required to refine the relationship between synoptic-classed accumulation rates and inter-annual variations in climatic circulation. These refinements of measurement techniques and quantification of and snow distribution and depth allow for better estimation of river discharge and timing estimates for, hydroelectric power generation, and glacier mass balance.</p>

Spatiotemporal dynamics of encroaching tall vegetation in timberline ecotone of the Polar Urals region, Russia

Previous studies discovered a spatially heterogeneous expansion of Siberian larch into the tundra of the Polar Urals (Russia). This study reveals that the spatial pattern of encroachment of tree stands is related to environmental factors including topography and snow cover. Structural and allometric characteristics of trees, along with terrain elevation and snow depth were collected along a transect 860 m long and 80 m wide. Terrain curvature indices, as representative properties, were derived across a range of scales in order to characterize microtopography. A density-based clustering method was used here to analyze the spatial and temporal patterns of tree stems distribution. Results of the topographic analysis suggest that trees tend to cluster in areas with convex surface. The clustering analysis also indicates that the patterns of tree locations are linked to snow distribution. Records from the earliest campaign in 1960 show that trees lived mainly at the middle and bottom of the transect across the areas of high snow depth. As trees expanded uphill with a warming climate in recent decades, the high snow depth areas also shifted upward creating favorable conditions for recent trees growth at locations that were previously covered with heavy snow. The identified landscape signatures of increasing above-ground Arctic biomass in terms of tall vegetation can facilitate scaling to larger area regions.

Modelling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.

The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these regions, the wind has a substantial effect and redistributes a large part of the snow, which complicates precipitation estimates. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove these uncertainties. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open source fluid dynamics software OpenFOAM. We combine the numerical simulations with terrestrial lidar observations of surface dynamics to simulate snow deposition on a piece of MOSAiC sea ice with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against terrestrial laser scans. However, the approximations imposed by the numerical framework together with potential measurement errors (precipitation) give rise to quantitative inaccuracies. The modelling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic.

Mapping snow depth and volume at the alpine watershed scale from aerial imagery using Structure from Motion

Time series mapping of water held as snow in the mountains at global scales is an unsolved challenge to date. In a few locations, lidar-based airborne campaigns have been used to provide valuable data sets that capture snow distribution in near real-time over multiple seasons. Here, an alternative method is presented to map snow depth and quantify snow volume using aerial images and Structure from Motion (SfM) photogrammetry over an alpine watershed (300 km2). The results were compared at multiple resolutions to the lidar-derived snow depth measurements from the Airborne Snow Observatory (ASO), collected simultaneously. Where snow was mapped by both ASO and SfM, the depths compared well, with a mean difference between −0.02 m and 0.03 m, NMAD of 0.22 m, and close snow volume agreement (+/−5 %). ASO mapped a larger snow area relative to SfM, with SfM missing ~14 % of total snow volume as a result. Analyzing the differences shows that challenges for SfM photogrammetry remain in vegetated areas, over shallow snow (< 1 m), and slope angles over 50 degrees. Our results indicate that capturing large scale snow depth and volume with airborne images and photogrammetry could be an additional viable resource for understanding and monitoring snow water resources in certain environments.

AUTOMATED AND PERMANENT LONG-RANGE TERRESTRIAL LASER SCANNING IN A HIGH MOUNTAIN ENVIRONMENT: SETUP AND FIRST RESULTS

A terrestrial laser scanner (TLS) of the type RIEGL VZ-6000 has been permanently installed and automated at Hintereisferner glacier located in the Ötztal Alps, Austria, to identify snow (re)distribution from surface height changes. A first case study is presented that shows and discusses detected snow distribution at the glacier after a snowfall event, together with concurrent snow erosion and deposition caused by avalanches. The paper shows the potential of a TLS system in a high mountain environment, which is also applicable to other environmental mapping applications. It introduces the setup of the TLS system, its automation procedure, and a first and preliminary uncertainty analysis. TLS data are generally influenced by four uncertainty sources: atmospheric conditions, scanning geometry, mechanical properties, and surface reflectance properties. The first three sources have significant influence on the TLS data at Hintereisferner, whereby the total accuracy of the TLS system is estimated to be in a range of a few decimetres, subject to ongoing more detailed data analysis.

Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS

Given the importance of snow on different land and atmospheric processes, accurate representation of seasonal snow evolution including distribution and melt volume, is highly imperative to any water resources development trajectories. The limitation of reliable snow-melt estimation in these regions is however, further exacerbated with data scarcity. This study attempts to develop relatively simpler extended degree-day snow-models driven by freely available snow cover images in snow-dominated regions. This approach offers relative simplicity and plausible alternative to data intensive models as well as in-situ measurements and have a wide scale applicability, allowing immediate verification with point measurements. The methodology employs readily available MODIS composite images to calibrate the snow-melt models on snow-distribution in contrast to the traditional snow-water equivalent based calibration. The spatial distribution of snow cover is simulated using different extended degree-day models calibrated against MODIS snow-cover images for cloud-free days or a set of images representing a period within the snow season. The study was carried out in Baden-Württemberg in Germany, and in Switzerland. The simulated snow cover show very good agreement with MODIS snow cover distribution and the calibrated parameters exhibit relative stability across the time domain. The snow-melt from these calibrated models were further used as standalone inputs to a “truncated” HBV without the snow component in Reuss (Switzerland), and Horb and Neckar (Baden-Wuerttemberg) catchments, to assess the performance of the melt outputs in comparison to a calibrated standard HBV model. The results show slight increase in overall NSE performance and a better NSE performance during the winter. Furthermore, 3–15 % decrease in mean squared error was observed for the catchments in comparison to the results from standard HBV. The increased NSE performance, albeit less, can be attributed to the added reliability of snow-distribution coming from the MODIS calibrated outputs. This paper highlights that the calibration using readily available images used in this method allows a flexible regional calibration of snow cover distribution in mountainous areas across a wide geographical extent with reasonably accurate precipitation and temperature data. Likewise, the study concludes that simpler specific alterations to processes contributing to snow-melt can contribute to identifying the snow-distribution and to some extent the flows in snow-dominated regimes.