Snow & Avalanche Study Estt

Snow cover duration in a seasonally snow covered mountain range (San Juan Mountains, USA) was found to be shortened by 18 to 35 days during ablation through surface shortwave radiative forcing by deposition of disturbed desert dust. Frequency of dust deposition and radiative forcing doubled when the Colorado Plateau, the dust source region, experienced intense drought (8 events and 39–59 Watts per square meter in 2006) versus a year with near normal precipitation (4 events and 17–34 Watts per square meter in 2005). It is likely that the current duration of snow cover and surface radiation budget represent a dramatic change from those before the widespread soil disturbance of the western US in the late 1800s that resulted in enhanced dust emission. Moreover, the projected increases in drought intensity and frequency and associated increases in dust emission from the desert southwest US may further reduce snow cover duration.

Abstract The high Himalayan mountains in the north of India are important sources for generating and maintaining the climate over the entire northern belt of the Indian subcontinent. They also influence extreme weather events, such as the western disturbances over the region during winter. The work presented here describes some current trends in weather and climate over the western Himalaya and suggests some possible explanations in the context of climate change. The work also shows how the special features of Indian orography in the western Himalaya affect climate change in the long term, changing the pattern of precipitation over the region. Data analysis of different ranges of the western Himalaya shows significant variations in temperature and snowfall trends in the past few decades. Possible explanations for the changing climate over the western Himalaya are proposed, in terms of variations in cloudiness. The possible effects of climate change on the number of snowfall days and the occurrences of western disturbances over the western Himalaya are also analysed.

Abstract. The identification of snow avalanche release areas is a very difficult task. The release mechanism of snow avalanches depends on many different terrain, meteorological, snowpack and triggering parameters and their interactions, which are very difficult to assess. In many alpine regions such as the Indian Himalaya, nearly no information on avalanche release areas exists mainly due to the very rough and poorly accessible terrain, the vast size of the region and the lack of avalanche records. However avalanche release information is urgently required for numerical simulation of avalanche events to plan mitigation measures, for hazard mapping and to secure important roads. The Rohtang tunnel access road near Manali, Himachal Pradesh, India, is such an example. By far the most reliable way to identify avalanche release areas is using historic avalanche records and field investigations accomplished by avalanche experts in the formation zones. But both methods are not feasible for this area due to the rough terrain, its vast extent and lack of time. Therefore, we develop an operational, easy-to-use automated potential release area (PRA) detection tool in Python/ArcGIS which uses high spatial resolution digital elevation models (DEMs) and forest cover information derived from airborne remote sensing instruments as input. Such instruments can acquire spatially continuous data even over inaccessible terrain and cover large areas. We validate our tool using a database of historic avalanches acquired over 56 yr in the neighborhood of Davos, Switzerland, and apply this method for the avalanche tracks along the Rohtang tunnel access road. This tool, used by avalanche experts, delivers valuable input to identify focus areas for more-detailed investigations on avalanche release areas in remote regions such as the Indian Himalaya and is a precondition for large-scale avalanche hazard mapping.

Seasonal snow cover is one of the important natural resources of the Himalayas. This is an important parameter in assessing the availability of water in the Himalayan Rivers, forecasting and assessing avalanches and numerous other applications. In the present paper, the normalized difference snow index (NDSI) technique is discussed to monitor snow cover using an AWiFS sensor on RESOURCESAT–1, a remote sensing satellite recently launched by India. AWiFS is a unique sensor, providing data of 56 m spatial resolution at 5‐day intervals. Satellite data and field investigations were carried out to assess the correct NDSI value representing snow. This technique is particularly useful for the Himalayan region as it can also be applied under mountain shadow conditions. This is possibly due to reflectance from diffuse radiation in shadow areas. An algorithm is developed to provide changes in the areal extent of snow at intervals of 5 and 10 days. In the 5‐daily product, snow extent will be generated scene‐wise. In this product, snow and cloud extent will be given. In the 10‐daily product, three scenes will be analysed and a basin‐wise estimate of the maximum snow extent will be available.

Dust deposition in the Rocky Mountains may be an important biogeochemical flux from upwind ecosystems. Seasonal (winter/spring) dust mass fluxes to the San Juan Mountains during the period from 2004 to 2008 ranged from 5 to 10 g m −2 , with individual deposition events reaching as high as 2 g m −2 . Dust deposited in the San Juan Mountains was primarily composed of silt‐ and clay‐sized particles, indicating a regional source area. The concentrations of most major and minor elements in this dust were similar to or less than average upper continental crustal concentrations, whereas trace element concentrations were often enriched. In particular, dust collected from the San Juan Mountain snowpack was characterized by enrichments of heavy metals including As, Cu, Cd, Mo, Pb, and Zn. The mineral composition of dust partially explained dust geochemistry; however, based on results of a sequential leaching procedure it appeared that trace element enrichments were associated with the organic‐, and not the mineral‐, fraction of dust. Our observations show that the dust‐derived fluxes of several nutrients and trace metals are substantial and, because many elements are deposited in a mobile form, could be important controls of vegetation, soil, or surface water chemistry. The flux measurements reported here are useful benchmarks for the characterization of ecosystem biogeochemical cycling in the Rocky Mountains.

This study discusses the observed long-term (1991-2015) and short-term (1991-2000 and 2001-2015) trends in winter temperature and precipitation over Northwestern Himalaya (NWH) along with its constituents, i.e. Lower Himalaya (LH), Greater Himalaya (GH) and Karakoram Himalaya (KH). An overall warming signature was observed over NWH since maximum, minimum and mean temperatures followed rising trends with a total increase of 0.9C, 0.19C and 0.65C respectively, in 25 years, the increase being statistically significant for maximum and mean temperatures. However, warming was not consistent over all zones of NWH with minimum temperature at LH showing anomalous cooling by 0.83C (statistically significant at = 0.05) during 25 years. The rise in mean temperature was observed highest at GH, i.e. 0.87C (1991-2015) followed by KH, i.e. 0.56C, which is in agreement with observations of comparatively higher rate of glacier retreat over GH than KH as reported in several studies. Total precipitation (rainfall + snowfall) was found to increase whereas snowfall was found to decrease with concurrent significant increase in rainfall at all zones of NWH. The spatiotemporal winter climatic variations over NWH support the impact on recently reported findings on the Himalayan snow cover and glacier variations at different durations.

The present study deals with spatio-temporal snow cover distribution in Northwest Himalaya (NWH) in a discourse on regional topography and prevalent climatology. Snow cover variation during 2001–2012 in NWH and eight major river basins was examined using MODIS data on board the Terra satellite. Slope match topographic correction was applied to eliminate the differential illumination effect on satellite imagery. The impact of cloud cover was removed by generating a 10-day maximum snow cover product. Annual and seasonal analysis shows a decreasing trend in snow cover area (SCA) over the entire NWH. Maximal SCA was observed in the windward river basins of the Lower and the Middle Himalayan zones and in the highly glaciated Shyok river basin of the Upper Himalaya. Monthly snow cover duration (SCD) maps revealed the effect of longitudinal variation as well as the strong influence of regional climatology and topography. The relationship of SCA with altitude and aspect was studied in all the river basins of NWH. The study shows a linear increment of SCA/D with increasing respect to elevation in all river basins. The maximum rate of SCA/D change with elevation was observed in the Jhelum river basin. In the Middle Himalayan Zone, an effect of basin orientation in regard to elevation was observed. Mean annual SCA at altitudes of up to 4500 m shows a decreasing trend. Seasonal analysis of aspect-wise snow cover shows that southern slopes have lower SCA during winter months. The difference in SCA between northern and southern slopes is even higher in summer and the monsoon period.

Abstract This study evaluated the performance of 07 gridded datasets viz. Asian Precipitation Highly-resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE), Climate Research Unit Time-Series (CRU-TS), University of Delaware (UDEL), Tropical rainfall Measurement Mission (TRMM)/ TMPA (TRMM Multi-Satellite Precipitation Analysis), Global Precipitation Climatology Centre (GPCC), Princeton Global Forcings Dataset (PGF), and European Reanalysis Interim (ERA-I) in capturing the amount, seasonality and trend of precipitation over different climatic zones of Northwestern Himalaya (NWH) i.e. Lower Himalaya (LH), Greater Himalaya (GH) and Karakoram Himalaya (KH). A similar comparison was also done for the temperature data but only with 05 datasets, viz. APHRODITE, CRU-TS, PGF, UDEL and ERA-I since TMPA and GPCC are precipitation datasets only. This study is a maiden attempt where in situ observation includes the data from elevations above 5000 m amsl (07 observatories) in NWH (Indian sub-region). Results reveal that for precipitation over NWH; ERA-I, GPCC, and TMPA/TRMM were found to be quite reliable datasets. For temperature, all datasets performed quite well but CRU-TS and ERA-I provided more reliable estimates. The mean absolute error ranged from 13.5 mm/month to 150.7 mm/month for precipitation and 0.75°C/month to 9.9°C/month for temperature. High values of the errors underpin the need for bias correction. On the basis of this analysis, monthly correction factors for wintertime temperature and precipitation have also been suggested for each dataset which when multiplied with corresponding datasets would result in closely approximated values for the area of interest. These results can serve as a guide for bias correction and selection of appropriate gridded datasets for use in studies pertaining to hydrological modeling over NWH.

A merged image of nadir viewing PAN and LISS III data of 2000 and PAN stereo data of 2000–2001 from Indian Remote Sensing satellite (IRS)‐1C covering Gangotri glacier was interpreted to identify its snout or terminus and to measure the retreat of this glacier with respect to the position of snout in a topographical map of 1962. Elevations from the map and DEM generated from stereo data were compared to determine the thickness of the glacier ice across the section of retreat prior to year 1962. The annual retreat of the glacier at the end of the ablation season during the year 2000–2001 was measured using PAN orthoimages.

Abstract A nonhydrostatic version of Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) is used to study the effects of the horizontal model resolution and orography while simulating an active western disturbance (WD) that affected northwest India from 21 to 25 January 1999. Two numerical experiments are conducted with six combinations of two factors: horizontal model resolution and topography. National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysed data are used for the initial and boundary conditions. Simulation results indicate that the distribution and the rate of simulated precipitation due to a WD over northwest India is highly sensitive to the horizontal model resolution and topography. The model with finer resolution (30 km) is better able to estimate effects of mesoscale forcing on precipitation over the selected domain. The amount of precipitation simulated over the coarse domain is much less than the observed precipitation owing to the model's unrealistic representation of orographic effects and mesoscale forcing. Simulated terrain, vertical velocity, wind and streamline at different horizontal model resolutions are presented. The detailed structure and distribution of wind speed are simulated in the finer domain. Simulated vertical velocity and precipitation are less in the second experiment when a flat topography is used across the domain, which indicates that topography plays a significant role in modulating the WD. Sensitivity of the horizontal model resolution for precipitation is assessed and it is found that the finer domain of the model simulation gives better results. Copyright © 2004 Royal Meteorological Society.

Avalanches are behind the majority of fatalities and heavy damage to property in snow-covered mountainous terrain like Himalaya. Recognizing avalanche susceptible areas and publication of avalanche susceptibility maps assist decision-makers and planners to execute suitable measures to reduce the avalanche risk. The present study is an attempt to prepare an avalanche susceptibility map of the Nubra valley region using multi-criteria decision analysis–analytical hierarchy process model in GIS environment. The most prominent avalanche occurrence factors used in this model are slope, aspect, curvature, elevation, terrain roughness and ground cover. ASTER GDEM V2 and Landsat 8 satellite imagery were used to generate considered factors. For validation of the results, prediction rate/accuracy is calculated using the avalanche inventory map of documented avalanche locations. To calculate the prediction accuracy, area under the ROC curve (ROC-AUC) method has been used. The prediction accuracy of the validation results using ROC-AUC shows 91%.

Monitoring acoustic emissions (AE) prior to imminent failure is considered a promising technique for assessing snow slope instability. Gaps in elastic wave propagation characteristics in snow hinder quantitative interpretation of AE signals. Our study focuses on characterizing the propagation of acoustic reference signals in the ultrasonic range across cylindrical snow samples with varying density (240–450 kg m− 3). We deduced the acoustic attenuation coefficient within snow by performing experiments with different column lengths to eliminate possible influences of the snow-sensor coupling. The attenuation coefficient was measured for the entire burst signal and for single frequency components in the range of 8 to 35 kHz. The acoustic wave propagation speed, calculated from the travel time of the acoustic signal, varied between 300 m s− 1 and 950 m s− 1, depending on the density and hardness of snow. From the sound speed we also estimated the Young's modulus of our snow samples; the values of the modulus ranged from 30 to 340 MPa for densities between 240 and 450 kg m− 3. In addition, we modeled the sound propagation for our experimental setup using Biot's model for wave propagation in a porous medium. The model results were in good agreement with our experimental results and suggest that our acoustic signals consisted of Biot's slow and fast waves. Our results can be used to improve the identification and localization of acoustic emission sources within snow in view of assessing snow slope instability.

In the present study, spectroradiometer (350–2500 nm) experiments are carried out in the field to understand the influence of snow grain size, contamination, moisture, ageing, snow depth, slope / aspect on spectral reflectance and to determine the sensitive wavelengths for mapping of snow and estimation of snow characteristics using satellite data. The observations suggest that, due to ageing and grain-size variation, the maximum variations in reflectance are observed in the near-infrared region, i.e. around 1040–1050 nm. For varying contamination and snow depth, the maximum variations are observed in the visible region, i.e. around 470 and 590 nm, respectively. For the moisture changes, the maximum variations are observed around 980 and 1160 nm. Based on the spectral signatures of seasonal snow, the normalized difference snow index (NDSI) is studied, and snow indexes, such as grain and contamination indexes, are proposed. The study also suggests that the NDSI increases with ageing, grain size and moisture content. The NDSI values remain constant with variations in slope and aspect. Attempts are made to estimate seasonal snow characteristics using multispectral Advanced Wide Field Sensor (AWiFS) Indian Remote Sensing (IRS-P6) and Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite data and validated with snow-meteorological observatory data of the study area.

The Himalayan region has a large concentration of glaciers and seasonal snow. Recent advances in remote sensing technologies have made it possible to study previously unexplored Himalayan cryosphere. These studies are essential to understand the water security of a large population living in Indo-Gangetic plains. We compile the work carried out by the scientific community to develop a state-of-art understanding of the Himalayan Cryosphere and discuss potential implications on water security. This paper covers the cryosphere's various aspects like snow and glacier extent, glacier stored water, loss in glacier area and length, long-term trend in glacier mass loss and potential future changes due to climate change. The changes in the cryospheric resources are likely to bring changes in the magnitude and seasonality of runoff components, especially in the basins with high meltwater contributions such as Indus. These changes in the basins' water resources need to be studied in context with the spatio-temporal heterogeneity in the climate change impacts, along with socio-economic dynamics for safeguarding water security and planning holistic water management adaptations.

Abstract. In the present study we describe the retrievals of snow grain size and spectral albedo (plane and spherical albedo) for western Himalayan snow cover using Hyperion sensor data. The asymptotic radiative transfer (ART) theory was explored for the snow retrievals. To make the methodology operational only five spectral bands (440, 500, 1050, 1240 and 1650 nm) of Hyperion were used for snow parameters retrieval. The bi-spectral method (440 nm in the visible and 1050/1240 nm in the NIR region) was used to retrieve snow grain size. Spectral albedos were retrieved using satellite reflectances and estimated grain size. A good agreement was observed between retrieved snow parameters and ground observed snow-meteorological conditions. The satellite retrieved grain sizes were compared with field spectroradiometer retrieved grain sizes and close results were found for lower Himalayan snow. The wavelength 1240 nm was found to be more suitable compared to 1050 nm for grain size retrieval along the steep slopes. The methodology was able to retrieve the spatial variations in snow parameters in different parts of western Himalaya which are due to snow climatic and terrain conditions of Himalaya. This methodology is of importance for operational snow cover and glacier monitoring in Himalayan region using space-borne and air-borne sensors.

Abstract The process of temperature gradient metamorphism in snow strongly affects the microstructure and associated mechanical properties of the snow. The purpose of this study was to: (1) examine the temporal variations in three-dimensional snow microstructure under the influence of a strong temperature gradient for 6 days using X-ray computed microtomography (μCT); and (2) numerically simulate the linear elastic properties of snow from microtomographic data using a voxel-based finite-element technique. The temporal changes in the snow structure were analyzed in terms of density, specific surface area (SSA), thickness distribution of ice matrix and pores, structure model index and mean intercept length (MIL) fabric tensor. The structural indices and orthotropic elastic compliance matrix were computed over several sub-volumes within the reconstructed volume to account for statistical uncertainties. The mean density increased by about 14% on day 1 and no significant trend was observed thereafter. The SSA decreased by 22%, whereas both the ice and pore thickness distributions widened with time. The computed Young’s moduli were 1.5–4 times larger than previously published dynamic measurements and found to be significantly correlated with ice volume fraction and MIL fabric measures. The increasing trend in computed moduli during the experiment is consistent with the observed development of thicker vertical ice structures. Multiple linear regression models of elastic compliances using fabric tensor formulation and ice volume fraction could explain 89.9–93.0% of the variance. Our results suggest a strong dependence of elastic properties on both density and microstructural fabric.

Abstract. This study compares different methods to retrieve the specific surface area (SSA) of snow from satellite radiance measurements in mountainous terrain. It aims at addressing the effect on the retrieval of topographic corrections of reflectance, namely slope and aspect of terrain, multiple reflections on neighbouring slopes and accounting (or not) for the anisotropy of snow reflectance. Using MODerate resolution Imaging Spectrometer (MODIS) data for six different clear sky scenes spanning a wide range of snow conditions during the winter season 2008–2009 over a domain of 46 × 50 km in the French Alps, we compared SSA retrievals with and without topographic correction, with a spherical or non-spherical snow reflectance model and, in spherical case, with or without anisotropy corrections. The retrieved SSA values were compared to field measurements and to the results of the detailed snowpack model Crocus, fed by driving data from the SAFRAN meteorological analysis. It was found that the difference in terms of surface SSA between retrieved values and SAFRAN-Crocus output was minimal when the topographic correction was taken into account, when using a retrieval method assuming disconnected spherical snow grains. In this case, the root mean square deviation was 9.4 m2 kg−1 and the mean difference was 0.1 m2 kg−1, based on 3170 pairs of observation and simulated values. The added-value of the anisotropy correction was not significant in our case, which may be explained by the presence of mixed pixels and surface roughness. MODIS retrieved data show SSA variations with elevation and aspect which are physically consistent and in good agreement with SAFRAN-Crocus outputs. The variability of the MODIS retrieved SSA within the topographic classes of the model was found to be relatively small (3.9 m2 kg−1). This indicates that semi-distributed snowpack simulations in mountainous terrain with a sufficiently large number of classes provides a representation of the snowpack variability consistent with the scale of MODIS 500 m pixels.

ABSTRACT Filling gaps in climate data concerning mountainous areas with high spatial variability is significantly important since gaps tend to decrease the accuracy of trend estimation. In this study, the performance of seven classical methods in estimating missing values of maximum temperature, minimum temperature and precipitation at different time scales, i.e. daily (with different cases of missing data), weekly, biweekly and monthly, over Karakoram Himalaya was evaluated. Four performance indicators, i.e. mean absolute error, root mean squared error, co‐efficient of efficiency and skill score, were used to evaluate the relative performance of the methods; the mean absolute error was preferred over the other three measures for selecting the best method. The results indicate that multiple linear regression using the least absolute deviation criterion is best suited for estimation of all variables at all temporal scales except monthly precipitation data. It was also found that, for any variable, the deviation from the observed values decreased with increasing time step, i.e. there was more deviation on a daily scale than monthly.

The Ku-band (13.5 GHz) based scatterometer is the main sensor onboard Scatterometer Satellite (SCATSAT-1) launched on 26th September 2016 by Indian Space Research Organization (ISRO). The SCATSAT-1 satellite sensor provides daily updates on the conditions of atmospheric, oceanographic, agriculture and cryospheric parameters. Moreover, it delivers data products (Level 1-4) in form of different parameters (Sigma-naught σ0, Gamma-naught γ0, brightness temperature BT, wind vectors and velocity) at two different polarization modes (HH and VV). Since launch, several studies have been carried out to explore the potential of SCATSAT1 satellite sensor for remote observation of the ocean as well as the land surface at the global level. Besides the conventional applications in weather and oceanic domains which are based on wind vector data, emerging applications over land use and land cover are also introduced. This paper aims to address the current status of SCATSAT-1 applications in different scientific domains such as oceanographic, cryospheric, agriculture and land hydrology. It is expected that such an extensive exploration of the applications of SCATSAT-1 satellite sensor will provide important insights for future utilization of scatterometer data.

Understanding snowfall variations in high‐elevation cold arid regions of the western Himalaya is important as snowmelt water is the main source of water to meet the scores of socioeconomic needs. The ground‐based observational data, though limited to the last two decades, show decreasing snowfall, raising the concern of looming water scarcity in the region. The tree‐ring data of Himalayan cedar from a network of six moisture‐stressed sites, where snowmelt water is the sole source of soil moisture for tree growth, were used to develop the November–April snow water equivalent (SWE) extending back to A.D. 1460. The reconstruction revealed persistent severe droughts in the 1780s followed by the 1480s and relatively lesser magnitude droughts in the 1540s–1560s, 1740s, and early twentieth century. The pluvial conditions observed in 1948–1958 and 1986–1996 stand out over any other period of such duration. The SWE reconstruction revealed large‐scale spatial coherence with the corresponding month's Palmer Drought Severity Index over the western Himalayan region. Significant relationship observed between SWE reconstruction and January–March Chenab River flow revealed its potential utility in understanding water resource availability in the long‐term perspective.