An estimate of ground heat flux for a seasonal snow cover

An estimation of ground heat flux for two locations has been done using temperature gradient method. Effective media approach has been adopted for predicting the effective thermal conductivity of ground. For comparison, in situ measurement of effective thermal conductivity of ground has also been done by thermal probe method. The measured values of thermal conductivity are in agreement with the calculated values. The estimated values of ground heat flux have been used to evaluate the melt rate at ground-snow interface.

Shift in seasonal snowpack melt-out date due to light-absorbing particles at a high-altitude site in Central Himalaya

Snow darkening by deposited light-absorbing particles (LAP) has the potential to accelerate snowmelt and shift the snow melt-out date. Here we investigate the sensitivity of the seasonal snow cover duration to changes in LAP at a high altitude valley site in the Central Himalayas, India. First, the variation of the albedo of the seasonal snow was emulated using two seasons of automatic weather station (AWS) data and applying a constant, but realistic deposition of LAP to the snow. Then, the number of days with snowmelt were evaluated based on the estimated net energy budget of the seasonal snow cover and the derived surface temperature. The impact on the energy budget by LAP combined with the melt-day analysis resulted in very simple relations to determine the contribution of LAP to the number of days with snowmelt of the seasonal snow in Himalaya. Above a concentration of 1 ng g-1 (Elemental Carbon equivalent, ECeq, which in this study includes EC and the absorption equivalent EC contribution by other light absorbing particles, such as mineral dust) in new snow, the number of days with snowmelt can be estimated by; days=0.0109(log⁡(〖EC〗_eq )+1)PP±0.0033(log⁡(〖EC〗_eq )+1)PP, where PP is the seasonal precipitation in mm snow water equivalent. A change in ECeq by a factor of two corresponds to about 1/3 of a day per 100 mm precipitation. Although the change in the number of days with melt caused by the changes in ECeq is small, the estimated total change in the snow melt-out date by LAP can be significant. For our realistic base case scenario for the Sunderdhunga Valley, Central Himalayas, India, of ECeq=100 ng g-1 and PP=400 mm, this yields in an advancement of the melt-out date of about 13 days.

UAV-Based Estimate of Snow Cover Dynamics: Optimizing Semi-Arid Forest Structure for Snow Persistence

Seasonal snow cover in the dry forests of the American West provides essential water resources to both human and natural systems. The structure of trees and their arrangement across the landscape are important drivers of snow cover distribution across these forests, varying widely in both space and time. We used unmanned aerial vehicle (UAV) multispectral imagery and Structure-from-Motion (SfM) models to quantify rapidly melting snow cover dynamics and examine the effects of forest structure shading on persistent snow cover in a recently thinned ponderosa pine forest. Using repeat UAV multispectral imagery (n = 11 dates) across the 76 ha forest, we first developed a rapid and effective method for identifying persistent snow cover with 90.2% overall accuracy. The SfM model correctly identified 98% (n = 1280) of the trees, when compared with terrestrial laser scanner validation data. Using the SfM-derived forest structure variables, we then found that canopy shading associated with the vertical and horizontal metrics was a significant driver of persistent snow cover patches (R2 = 0.70). The results indicate that UAV image-derived forest structure metrics can be used to accurately predict snow patch size and persistence. Our results provide insight into the importance of forest structure, specifically canopy shading, in the amount and distribution of persistent seasonal snow cover in a typical dry forest environment. An operational understanding of forest structure effects on snow cover will help drive forest management that can target snow cover dynamics in addition to forest health.

Long-term spatio-temporal seasonal snow cover variability in the Hindu Kush Himalaya

<p>The Hindu Kush Himalaya (HKH), the worlds ‘water tower’, contains the largest volume of snow and ice outside of the polar ice sheets and is the headwater area of Asia’s largest rivers. Due to the complex topography and its great spatial extent the HKH is characterised by variable temperature and precipitation pattern and thus exhibits large heterogeneity in the presence of seasonal snow cover (SSC). Previous studies usually focused on regional studies of snow cover area percentage or the influence of snow melt on the local hydrological system. Here we present a systematic overview of spatio-temporal SSC variability of the entire HKH region on a climate relevant time scale (four decades).</p><p>Our results are based on Advanced Very High Resolution (AVHRR) data, collected onboard the polar orbiting satellites NOAA-7 to -19, providing daily, global imagery at a spatial resolution of 5 km since 1982 up to today. This unique dataset is exceptionally valuable to derive pixel-based SSC information using a Normalised Difference Snow Cover (NDSI) approach including additional thresholds related to topography and land cover, and developed in the frame of ESA CCI+ snow.  Calibrated and geocoded reflectance data and a consistent cloud mask, derived in the ESA CCI cloud project, are used. A temporal gap-filling was applied to mitigate the influence of clouds. Reference snow maps from high-resolution optical satellite data as well as in-situ station data were used to validate the time series.</p><p>The dataset allows analysis of the state and trends of SSC at regional and sub-regional level. We thus investigated spatio-temporal evolution and long-term variability of SSC for the entire HKH as well as for 14 hydrological basins. We find large spatial difference in the amount of SSC depending on the regional elevation and precipitation characteristics. Furthermore, we investigate SSC phenology, which is directly linked to climate change and thus of high relevance for seasonal water storage and mountain streamflow. Our analysis indicates a significant decline in snow cover area percentage (SCA %) during warm and dry summer month and a decreasing tendency from high winter through spring to early summer. At the hydrological basin level, no significant long-term trend was detected, however, both western and central basins indicate a decrease in SCA % and generally the latest years are strongly negative. Moreover, we examine SCA % anomalies at the highest available temporal frequency (daily information) and reveal an overall shortening of the SSC occurrence and a general decrease of SSC extent in the HKH region.</p>

A new ice core from the Eastern Summit of Mt. Elbrus, Caucasus, Russia

<p>Meteorological regime and glacier surface heat balance, GPR measurements of the ice thickness and seasonal snow cover were investigated in the crater of the Eastern Summit of Mt. Elbrus In the period from 18 to 30 August 2020 at 560 m a.s.l. On the base of preliminary data analysis, the predominance of fluctuations in the synoptic scale over the diurnal ones was revealed; high values of the average and maximum wind speed associated with the influence of jet currents and with the effects of leeward storms were identified; extremely high temporal variability of relative humidity and its very high deficit in cloudless conditions, which contributes to intensive evaporation and sublimation from the snow surface, were explored. The maximum ice thickness in the crater reaches 100 m, with an average of 45 m. A new 96.01 m ice core from glacier surface to bedrock has been recovered. The drilling speed varied from 11 to 1 m / h, decreasing on average with depth from 4.5 to 4.0 m / h. The thickness of the snow-firn strata is about 20 m, which is three times less than on the Western Plateau. The borehole temperature was measured. The temperature on the glacier bedrock was -0.6 °С. The calculated heat flux was 0.39 W/m2. Air sampling was carried out in the crater of the Eastern Summit of Elbrus and on the Garabashi glacier. The repeated measurement of the soil temperature in the fumarole field on the Elbrus Eastern Summit outer crater rim suggests that the temperature regime is stable.</p><p>The research was carried out on the territory of the Elbrus National Park with the financial support of the Russian Science Foundation (project 17-17-01270).</p>

Monitoring snowpack evolution with meteorological reanalysis data in the Atlas Mountains

<p>The seasonal snow cover in the Altas mountains of Morocco is an important resource, mostly because it provides melt-water runoff for irrigation during the crop growing season. However, the knowledge on physical properties of the snowpack (e.g., snow water equivalent (SWE) and snowmelt) is still very limited due to the scarcity or the lack of ground measurements in the elevated area. In this study we suggest that the recent progresses of meteorological reanalysis data (e.g., MERRA-2 and ERA-5) open new perspectives to overcome this issue. We fed a distributed snowpack evolution model (SnowModel) with downscaled ERA-5 and MERRA-2 reanalyses and evaluate their performance to simulate snow cover. The modeling covers the period 1981 to 2019 (37 water years). SnowModel simulations were assessed using observations of river discharge, snow height and snow cover area derived from MODIS.</p><p>For most of hydrological years, the results show a good performance for both MERRA-2 and ERA-5 with a slight superiority of ERA-5, to reproduce the snowpack state.</p><p><strong>Key words</strong>: snow, snow water equivalent, reanalysis , MERRA-2, ERA-5</p>

Consequences of the 2019 Greenland Ice Sheet Melt Episode on Albedo

In mid-June 2019, the Greenland ice sheet (GrIS) experienced an extreme early-season melt event. This, coupled with an earlier-than-average melt onset and low prior winter snowfall over western Greenland, led to a rapid decrease in surface albedo and greater solar energy absorption over the melt season. The 2019 melt season resulted in significantly more melt than other recent years, even compared to exceptional melt years previously identified in the moderate-resolution imaging spectroradiometer (MODIS) record. The increased solar radiation absorbance in 2019 warmed the surface and increased the rate of meltwater production. We use two decades of satellite-derived albedo from the MODIS MCD43 record to show a significant and extended decrease in albedo in Greenland during 2019. This decrease, early in the melt season and continuing during peak summer insolation, caused increased radiative forcing of the ice sheet of 2.33 Wm−2 for 2019. Radiative forcing is strongly influenced by the dramatic seasonal differences in surface albedo experienced by any location experiencing persistent and seasonal snow-cover. We also illustrate the utility of the newly developed Landsat-8 albedo product for better capturing the detailed spatial heterogeneity of the landscape, leading to a more refined representation of the surface energy budget. While the MCD43 data accurately capture the albedo for a given 500 m pixel, the higher spatial resolution 30 m Landsat-8 albedos more fully represent the detailed landscape variations.