To identify the regional characteristics of mountain snowpacks related to avalanches in the central mountains of Japan, snow-pit observations were carried out over a period of between two and five winters at six study sites in three major mountain ranges: the north, central and south alps. The relationship between snowpack characteristics and meteorological properties was then examined to suggest indicators that could determine the snowpack characteristics in the region using meteorological data. On the basis of the dominant type of persistent weakness found within snowpacks, the study sites were divided into three regions: (1) the mountains on the Sea of Japan side - the persistent weakness was caused mainly by the presence of melt-freeze crust (MFcr) layers; (2) the inland mountains - the persistent weakness was caused mainly by the presence of weak layers consisting of near-surface faceted crystals (FCsf); and (3) the mountains on the Pacific Ocean side - the persistent weakness was caused mainly by the formation of thick weak layers consisting of depth hoar (DH) near the ground. The results also suggest that certain meteorological elements are useful indicators of the tendency of a persistent weakness to form within the snowpack: (1) DH layers: TG (°C m−1) = |average air temperature |/average snow depth (notable weakness in DH layers is found in areas where the TG exceeds 10°C m−1, but in such areas rainfall prevents the development of DH layers); (2) FCsf layers: FCsf index (CC m) = average diurnal range of air temperature × snow depth (notable weakness in FCsf layers is found in areas where the FCsf index is ∼10°C m); (3) MFcr layers: MFcr index (day m) = number of days that exceed 0°C × snow depth (notable weakness in MFcr layers is found in areas where the MFcr index exceeds 40 day m).

Dynamic ice models use stress tensor to describe the forces arising from internal ice friction. The model stress values are typically one to two magnitudes smaller than values measured by stressmeters deployed on ice floes. The synthesis of the pack-ice stress state from the measurements has been complicated by the peaky character of stress records, and the means to connect them with spatial stress distribution of the floe system have been lacking. Here a reanalysis of Arctic Sea Ice Mechanics Initiative (SIMI) data is made in terms of extreme value statistics. The basic quantity is the maximum stress observed during a time period. The records exhibit self-affine scaling. The statistics are then determined by two parameters, the Hurst exponent H and a reference stress level. Similar analysis is possible for the kinematic data. This establishes the comparability of stress records with each other and with kinematic records. The results suggest that the exponent is related to the stress state of the regional floe system, while the stress level is determined by local floe characteristics. Based on this a characterization of spatial distribution of pack-ice stresses is given.

The aim of this study is to investigate spatial and temporal variations of meteorological variables known to control glacier mass-balance processes, and to demonstrate their relevance in High Asia. The study uses the NCEP/NCAR reanalysis database (US National Centers for Environmental Prediction and US National Center for Atmospheric Research), which is global and continuous since 1 January 1948. A positive degree-day (PDD) model of annual glacier-wide summer surface balance, Bs, uses upper-air temperatures at NCEP/NCAR gridpoints near each of six glaciers between 40 and 45° N and between 71 and 87°E and two others near 50° N, 87° E. Model error ranged between 0.19 and 0.41 mw.e., comparable with the observational accuracy of Bs. Sensitivity to temperature change, dBs=dT, ranges between –0.5 and –0.2 m w.e. a–1 °C–1 for these glaciers.

The major river systems of India, i.e. the Indus, Ganga and Brahmaputra river systems originating in the Himalayan region, are considered the lifeline of the Indian subcontinent. The main sources maintaining the flow of the Himalayan rivers are snow/glacial melt runoff, rainfall runoff and base flow. The Beas River originates from Beas Kund Glacier in the Himalayan region and flows down to join the Sutlej River, which is a tributary of the Indus River system. In the present study two approaches, namely hydrologic modelling and isotope analysis, have been applied to estimate the contribution of snow and glacier melt. Samples of streamflow, rainfall and snow for isotopic analysis were collected daily from April to September and weekly from October to March during 2010 and 2011. The isotope analysis of samples reveals that the snow/glacier melt contribution to the Beas River at Manali is 50% of the total flow during these 2 years. Snowmelt runoff modelling has been carried out using the SNOWMOD model, and the snow/glacier melt runoff contribution is calculated to be 52% of the total flow during the same period. These findings indicate that the results obtained from the two approaches are similar.

A good estimate of the spatial probability density function (PDF) of snow water equivalent (SWE) provides the mean of the snow reservoir, but also enables modelling of the changes in snow-covered area (SCA), which is crucial for the runoff dynamics in spring. The spatial PDF of accumulated SWE is here modelled as a sum of correlated gamma-distributed variables, called units. The spatial variance of accumulated SWE is evaluated by the covariance matrix of the units. For accumulation events, there are only positive elements in the covariance matrix, whereas for melting events there are both positive and negative elements. The negative elements dictate that the correlation between melt and SWE is negative. After accumulation and melting events, the changes in the spatial moments are weighted by changes in SCA. Results from the model are in good agreement with observed spatial moments of SWE and SCA and found to provide better estimates of the spatial variability than the current model for snow distribution used in the Norwegian version of the Swedish rainfall–runoff model HBV. The parameters in the distribution model are estimated from observed historical precipitation, so no calibration parameters are introduced.