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

2021 ◽  
Author(s):  
Johan Ström ◽  
Jonas Svensson ◽  
Henri Honkanen ◽  
Eija Asmi ◽  
Nathaniel B. Dkhar ◽  
...  

Abstract. 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.

2008 ◽  
Vol 117 (5) ◽  
pp. 567-573 ◽  
Author(s):  
Prem Datt ◽  
P. K. Srivastava ◽  
P. S. Negi ◽  
P. K. Satyawali

1993 ◽  
Vol 18 ◽  
pp. 179-184
Author(s):  
Tsutomu Nakamura ◽  
Osamu Abe

The average amounts of seasonal snow cover and snowfall in Japan were calculated as 7.9 × 1013kg and 1.2 × 1014kg, respectively. The mass of seasonal snow cover of a heavy-snowfall winter, 1980–81 (56-Gosetsu), was calculated as 1.3 × 1014kg. The amount of 7.9 × 1013kg was converted to water equivalent of 230 mm on the whole snow-covered area, including snow-prone area. A mean of 370 mm in snow water equivalent was calculated for the snow area where mean snow depth on the ground was more than 10 cm.


1993 ◽  
Vol 18 ◽  
pp. 179-184
Author(s):  
Tsutomu Nakamura ◽  
Osamu Abe

The average amounts of seasonal snow cover and snowfall in Japan were calculated as 7.9 × 1013kg and 1.2 × 1014kg, respectively. The mass of seasonal snow cover of a heavy-snowfall winter, 1980–81 (56-Gosetsu), was calculated as 1.3 × 1014kg. The amount of 7.9 × 1013kg was converted to water equivalent of 230 mm on the whole snow-covered area, including snow-prone area. A mean of 370 mm in snow water equivalent was calculated for the snow area where mean snow depth on the ground was more than 10 cm.


2021 ◽  
Author(s):  
Wassim Mohamed Baba ◽  
Abdelghani Boudhar ◽  
Simon Gascoin ◽  
Lahoucine Hanich ◽  
Ahmed Marchane ◽  
...  

<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>


1987 ◽  
Vol 9 ◽  
pp. 166-169 ◽  
Author(s):  
J. Martinec ◽  
A. Rango

Areal snow-cover data provided by remote sensing enable the areal water equivalent at the start of the snow melt season to be evaluated. To this end, the time scale in the graphical representation of the snow coverage curves is replaced by the totalized computed daily melt depths. These refer to the seasonal snow cover at the starting date and disregard subsequent snowfalls.


1987 ◽  
Vol 9 ◽  
pp. 166-169 ◽  
Author(s):  
J. Martinec ◽  
A. Rango

Areal snow-cover data provided by remote sensing enable the areal water equivalent at the start of the snow melt season to be evaluated. To this end, the time scale in the graphical representation of the snow coverage curves is replaced by the totalized computed daily melt depths. These refer to the seasonal snow cover at the starting date and disregard subsequent snowfalls.


2013 ◽  
Vol 37 (4) ◽  
pp. 296-305 ◽  
Author(s):  
Qi-Qian WU ◽  
Fu-Zhong WU ◽  
Wan-Qin YANG ◽  
Zhen-Feng XU ◽  
Wei HE ◽  
...  

2014 ◽  
Vol 60 (1) ◽  
pp. 51-64 ◽  
Author(s):  
Snehmani ◽  
Anshuman Bhardwaj ◽  
Mritunjay Kumar Singh ◽  
R.D. Gupta ◽  
Pawan Kumar Joshi ◽  
...  

Hydrology ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 32
Author(s):  
Benjamin J. Hatchett

Snowpack seasonality in the conterminous United States (U.S.) is examined using a recently-released daily, 4 km spatial resolution gridded snow water equivalent and snow depth product developed by assimilating station-based observations and gridded temperature and precipitation estimates from PRISM. Seasonal snowpacks for the period spanning water years 1982–2017 were calculated using two established methods: (1) the classic Sturm approach that requires 60 days of snow cover with a peak depth >50 cm and (2) the snow seasonality metric (SSM) that only requires 60 days of continuous snow cover to define seasonal snow. The latter approach yields continuous values from −1 to +1, where −1 (+1) indicates an ephemeral (seasonal) snowpack. The SSM approach is novel in its ability to identify both seasonal and ephemeral snowpacks. Both approaches identify seasonal snowpacks in western U.S. mountains and the northern central and eastern U.S. The SSM approach identifies greater areas of seasonal snowpacks compared to the Sturm method, particularly in the Upper Midwest, New England, and the Intermountain West. This is a result of the relaxed depth constraint compared to the Sturm approach. Ephemeral snowpacks exist throughout lower elevation regions of the western U.S. and across a broad longitudinal swath centered near 35° N spanning the lee of the Rocky Mountains to the Atlantic coast. Because it lacks a depth constraint, the SSM approach may inform the location of shallow but long-duration snowpacks at risk of transitioning to ephemeral snowpacks with climatic change. A case study in Oregon during an extreme snow drought year (2014/2015) highlights seasonal to ephemeral snowpack transitions. Aggregating seasonal and ephemeral snowpacks to the HUC-8 watershed level in the western U.S. demonstrates the majority of watersheds are at risk of losing seasonal snow.


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