Triggering Mechanisms of Gayari Avalanche, Pakistan

A massive snow avalanche occurred on April, 2012 at Gayari, located in NE part of Pakistan, close to India and China Border. The catastrophic avalanche killed nearly 148 people, majority of which were Pakistan army personnel destroying army base camp. To mitigate its future hazard, different triggering mechanisms have been investigated in this study. We contemplate that the avalanche was triggered due to snow pack existence on favorable slope in combination with different meteorological conditions and anomalous ground vibration. The avalanche occurrence clock was advanced by two earthquakes: M4.1 at a distance ∼ 125 km that occurred about 21 hours before and another comparatively larger (M5.6) earthquake that occurred comparatively at larger distance (∼ 370 km) and longer time (∼ 25 days) before which have significantly changed the loading conditions. The latter event (M 5.6) has imparted maximum peak dynamic stress and cumulative seismic moment a month before the avalanche. Interestingly the avalanche occurred within the seismic coda of M2.8 earthquake from Hindu Kush region, located at 560 km distance. Although the size and its expected impact on avalanche might be minor but its role in instantaneous triggering cannot be ruled out. Even smaller events at larger distance have been reported to cause snow avalanches in same environments. The presence of cracks within the avalanche, were further weaken by persistence of extremely low temperature (lowest in the past decade), causing high precipitation rate along with altering the mechanical properties of the weak layer within the snow pack. Robust wind pressure pattern highest and lowest in March and April, 2012 respectively might be responsible for abrupt changes in loading conditions.

Performance analysis of beam divergence propagation through rainwater and snow pack in free space optical communication

In the present work the future communication requirements need to fulfill with high data rate, FSO (free space optic) with it is tremendous potential is the solution. This research observed the effectiveness analysis of FSO systems by modifying one of the most important FSO parameters beam divergence, under the most affected weather attenuating condition Rainwater and snow pack. The simulation is obtained and analyzed under single channels CSRZ-FSO (carrier-suppressed return-to-zero/free space optical) systems having capacity of 40 Gbps between two transceivers with variable distance. The connection is presently under 5 meteorological turbulences (light rain, medium rain, wet snow, heavy rain and dry snow). The results show the heavy rain and dry snow have a very high attenuation carried out in terms of Q-factor. this result led us to conclude that small divergence offers significant performance improvement for FSO link and this performance decrease every time the beam divergence increase, Therefore, to build inexpensive and reliable transmission media, we go with new method that still in the experiment area called hybrid RF/FSO (radio frequency/free space optical) that compatible with atmospherically status.

Induced glide-snow avalanches with low friction geotextiles

<p>Gliding snow avalanches are of growing concern for the management of ski areas, transport corridors and spatial planning. With a warming climate there appear to be increasing reports of gliding snow hazards in alpine regions. The management of gliding snow avalanches can be achieved through either stabilization or artificially triggering a slide. Triggering sliding is attractive because it has the potential to remove the hazard entirely. In this research, we investigate the potential of managing gliding snow avalanches through the early release of snow accumulations using low friction geotextiles.</p><p>A series of geotextiles have been installed on slopes between 25 and 35° during the autumn months and the behavior of snow accumulations observed during the winter. Initial findings indicate that reducing the basal friction can be effective in inducing early release of gliding snow avalanches. However, the interaction of the flanking snow pack and stauchwall appear dominant in the behavior of the system. This contribution reports on the initial findings of these experiments and discusses the potential applications to managing gliding snow avalanches.  </p>

Mapping the potential distribution of frozen ground in Tucarroya (Monte Perdido Massif, the Pyrenees)

This paper shows the creation of a map of frozen ground potential for the Tucarroya valley in Ordesa National Park. To create this map, it was necessary to combine the identified landforms associated to the presence of frozen ground by fieldwork, ground temperature data continuously recorded during two years by automatic loggers, a Basal Temperature of Snow (BTS) survey, and predictor variables derived from a high resolution Digital Elevation Model (DEM). Four environments have been differentiated: unfrozen ground, seasonal frozen ground, possible permafrost and probable permafrost. The map confirms a very limited variety and extension of permafrost, above 2700 m a.s.l. on gentle and shadowed slopes. Seasonal frozen ground is the most common thermal regime, as it can be developed above 2500 m a.s.l. Snow-pack duration and thickness tightly control the duration of frozen ground and the freezing-thawing cycles. Frost activity and unfrozen ground is restricted from 2570 to 2750 m a.s.l.

Identifying vegetation-geomorphology relationships in permafrost with airborne LiDAR, electrical resistivity tomography, seasonal thaw depth measurements, and machine learning

<p>Mean annual temperatures in interior Alaska, currently -1°C, are projected to increase as much as 5°C by 2100. An increase in mean annual temperatures is expected to degrade permafrost and alter hydrogeology, soils, vegetation, and microbial communities. Ice and carbon rich “yedoma type” permafrost in the area is ecosystem protected against thaw by a cover of thick organic soils and mosses. As such, interactions between vegetation, permafrost ice content, the snow pack, and the soil thermal regime are critical in maintaining permafrost. We studied how and where vegetation and soil surface characteristics can be used to identify subsurface permafrost composition. Of particular interest were potential relationships between permafrost ice content, the soil thermal regime, and vegetation cover. We worked along 400-500 m transects at sites that represent the variety of ecotypes common in interior Alaska. Airborne LiDAR imagery was collected from May 9-11, 2014 with a spatial resolution of 0.25 m. During the winters from 2013-2019 snow pack depths have been made at roughly 1 m intervals along site transects using a snow depth datalogger coupled with a GPS. In late summer from 2013-2019 maximum seasonal thaw depths have been measured at 4 m intervals along each transect. Electrical resistivity tomography measurements were collected across the site transects. A variety of machine learning geospatial analysis approaches were also used to identify relationships between ecosystem characteristics, seasonal thaw, and permafrost soil and ice composition. Wintertime measurements show a clear relationship between vegetation cover and snow depth. Interception (and shallow snow) was evident in the birch and white spruce forests and where dense shrubs are present while the open tussock and intermittent shrub regions yield the greatest snow depths. Results from repeat seasonal thaw depth measurements also show a strong relationship with vegetation where mixed birch and spruce forest is associated with the deepest seasonal thaw. The tussock/shrub and spruce forest zones consistently exhibited the shallowest seasonal thaw. Roughly 60% of the seasonal thaw along the transects occurred by mid-July and downward movement of the thaw front had mostly ceased by late August with little additional thaw between August 20 and early October. Summer precipitation shows a relationship with seasonal thaw depth with the wettest summers associated with the deepest thaw. Results from this study identify clear relationships between ecotype, permafrost composition, and seasonal thaw dynamics that can help identify how and where permafrost degradation can be expected in a warmer future arctic.</p>

Residence time of nitrogen deposit in a nival subalpine catchment using the hyper-resolution ParFlow-CLM-EcoSLIM critical zone model.

<p>Mountain regions represent a particular challenge for critical zone modelling as snowpack interacts with soils, vegetation, surface water and atmosphere and plays a primary role on the water transfers but also on the carbon and nitrogen cycles. Indeed, in these environments ecosystems are adapted to a snow regime under change due to the rise in the 0°C isotherm. In addition, atmospheric nitrogen deposition, a product of industrial activity carried by valley winds and mesoscale atmospheric circulation, already impacts some high-altitude ecosystems by modifying nutrient flows (nitrogen and carbon in particular). These combined forcings could lead to major ecosystem changes (distribution of water, carbon and nitrogen flows, growth rates, species, etc.). Anticipating this evolution, and the associated flows (CO2, nitrogen, water) under this double constraint, remains problematic due to the lack of adapted models.</p><p>In this study, we use the Parflow/CLM/Ecoslim model on a small (17ha) nival subalpine catchment close to Lautaret Pass (French Alps) where meteorological and hydrological parameters are measured together with snowpack survey and chemical concentrations measurements in the air, the rivers, the snowpack the vegetation and the ground. Simulations are constrained by a spatially distributed forcing and evaluated from snow pack dynamic and ET measurements. The simulations allow us to estimate the Nitrogen quantities that can be processed by vegetation and those drained in river flows. The estimation of the residence times is then calculated from the velocity field in the catchment. The wide snow cover time distribution leads to wide distribution resident time for any particle deposit. This can impact nitrogen chemical history and any other chemical compounds in the snow pack and the ground even for such small scales.</p>

Links between the moisture origin and isotopic signature in water vapour, snowfall and snow pack at Finse Alpine Research Center (1222m) in Southern Noway

<p>Seasonal snow cover is a crucial resource for hydropower in Norway. Understanding water sources and processes related to inter-annual snow cover variability is therefore of fundamental societal relevance. The stable water isotope composition of precipitation provides a natural, integrated tracer of the condensation history during atmospheric water transport. The main parameters dD and d18O along with the secondary quantity d-excess give information about the origin and transport history of moisture from its source to its sink. When snow falls and deposits on the ground as a sediment, it creates a record in the form of the seasonal snow pack.</p><p>Here we utilize data acquired during a field campaign in the winter season of 2018-2019 at the Finse Alpine Research Station Center (1222m, 60.6N, 7.5E) in Norway, in order to investigate the transfer of the isotopic signal of source and transport conditions from vapour to snowfall, and to the snow pack.</p><p>Over a main period of two months, snowfall was sampled daily, while the water vapour was continuously measured from ambient air guided through a heated inlet to a Picarro L2130i infrared spectrometer, with daily calibration runs. During five periods with intense snowfall, we carried out higher frequency sampling down to 15 minute intervals. Covering the entire winter season, five snowpits were sampled for isotopic analysis as well as detailed stratigraphy. In total more than 400 snow samples where taken and analysed for their isotopic composition, accompanied by routine meteorological observations over the winter season at the site. In addition, we compare the variations in the observed isotope signal at Finse with one derived from moisture source analysis using the Lagrangian diagnostic WaterSip, based on the FLEXPART model and ERA Interim reanalysis data.</p><p>To investigate to what degree moisture source information is archived in the snow pack, and how it evolves during the season, we compare snow observations at different time resolution (daily and high frequency snowfall samples) with the record of the snow pack, aided by the snow model CROCUS. The meteorological observations supply context for understanding the snow formation conditions. In particular, deviations from isotopic equilibrium between vapour and precipitation at ambient temperature conditions provide insight into the dominant condensation regime during different intense observation periods.</p>

Cosmic-ray neutron sensing based monitoring of snowpack dynamics: A comparison of four conversion methods

<p>Snow monitoring instruments like snow pillows are influenced by disturbances such as energy transport into the snowpack, influences from wind fields or varying snow properties within the snowpack (e.g. ice layers). The intensity of epithermal neutrons that are produced in the soil by cosmic radiation and measured above the ground surface is sensitive to soil moisture in the upper decimetres of the ground within a radius of hectometres. Recently, it has been shown that aboveground cosmic ray neutron sensors (CRNS) are also a promising technique to monitor snow pack development thanks to the larger support that they provide and to the lower need for maintenance compared to conventional sensor systems. The basic principle is that snow water moderates neutron intensity in the footprint of the CRNS probe. The epithermal neutrons originating from the soil become increasingly attenuated with increasing depth of the snow cover, so that the neutron intensity measured by the CRN probe above the snow cover is directly related to the snow water equivalent.</p><p>In this paper, we use long-term CRNS measurements in the Pinios Hydrologic Observatory, Greece, to test different methods for the conversion from neutron count rates to snow pack characteristics, namely: i) linear regression, ii) the standard N<sub>0</sub>-calibration function, iii) a physically-based calibration approach and iv) the thermal to epithermal neutron ratio. The latter was also tested for its reliability in determining the start and end of snowpack development, respectively. The CRNS-derived snow pack dynamics are compared with snow depth measurements by a sonic sensor located near the CRNS probe. In the presentation, we will discuss the accuracy of the four conversion methods and provide recommendations for the application of CRNS-based snow pack measurements.</p>