Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
2.7
Journals
Geosciences
Special Issues
Monitoring of the Seasonal Snow Cover
share announcement

Monitoring of the Seasonal Snow Cover

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Geophysics".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 11900

Special Issue Editors

Dr. Bartłomiej Luks

E-Mail Website
Guest Editor
Department of Polar and Marine Research, Institute of Geophysics Polish Academy of Sciences, 01-452 Warszawa, Poland
Interests: snow climatology; snow remote sensing; snow hydrology; glaciology
Special Issues, Collections and Topics in MDPI journals
Dr. Michał Laska

E-Mail Website
Guest Editor
Faculty of Natural Sciences, University of Silesia in Katowice, 40-007 Katowice, Poland
Interests: snow physics; melting dynamics; snow avalanche forecasting; glaciology; remote sensing
Mr. Esteban Alonso-González

E-Mail Website
Guest Editor
Pyrenean Institute of Ecology (IPE), Spanish National Research Council (CSIC), E-28006 Madrid, Spain
Interests: snow climatology; snow modelling; dynamical downscaling; glaciology

Special Issue Information

Dear Colleagues,

About one third of Earth’s landmass is seasonally covered by snow. In the higher latitudes and mountain regions, snow dominates the landscape for over half a year. It is one of the essential factors affecting Earth’s energy balance. Due to its retention capacity, it plays an important role in the water cycle, reshaping the hydrographs affecting the hydrological resources. Last but not least, seasonal snow cover is highly influenced by Climate Change. As a matter of exceptional sensitivity to prevailing meteorological and topographic conditions, snow cover undergoes rapid spatial and temporal dynamics, requiring a multiseasonal approach. Efforts to monitor snowpack properties (e.g., snow depth, snow density, snow water equivalent, snowpack distribution, snow extent and onset dates, among many others) are extremely important to our understanding of the water cycle and energy budget in the changing climate. Despite its importance, snowpack monitoring presents obvious challenges, causing a generalized lack of observations with scarce or too-short time series, making it difficult to study the snow cover at different spatio-temporal scales. Thus, snow monitoring initiatives are of crucial importance for the scientific community, to assess the impacts of the current Climate Change.

This Special Issue invites and encourages the submission of all manuscripts covering long- and short-term snow-monitoring activities, snow instrumentation/sensors, monitoring schemes and methodology, and applications where snow monitoring data are used, including, but not limited to:

  • In-situ measurements of snow parameters;
  • Short and long-range remote sensing of snowpack;
  • Spatial and temporal snowpack variability;
  • Snow hydrology;
  • Avalanches;
  • Novel techniques in snow monitoring (e.g., UAV, TLS, etc.).

Dr. Bartłomiej Luks
Dr. Michał Laska
Mr. Esteban Alonso-González
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Geosciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • snow cover monitoring programmes
  • snow depth
  • snow water equivalent
  • snow density
  • avalanches
  • snow mass and energy balance measurements
  • UAV and TLS snow measurements
  • snow remote sensing
  • spatial ant temporal variability of snow

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

24 pages, 14052 KiB  
Article
Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers
by Sebastian Rößler, Marius S. Witt, Jaakko Ikonen, Ian A. Brown and Andreas J. Dietz
Geosciences 2021, 11(3), 130; https://doi.org/10.3390/geosciences11030130 - 12 Mar 2021
Cited by 8 | Viewed by 3185
Abstract
The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to [...] Read more.
The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM). Full article
(This article belongs to the Special Issue Monitoring of the Seasonal Snow Cover)
►▼ Show Figures

Figure 1

15 pages, 3891 KiB  
Article
On the Seasonality of the Snow Optical Behaviour at Ny Ålesund (Svalbard Islands, Norway)
by Roberto Salzano, Christian Lanconelli, Giulio Esposito, Marco Giusto, Mauro Montagnoli and Rosamaria Salvatori
Geosciences 2021, 11(3), 112; https://doi.org/10.3390/geosciences11030112 - 02 Mar 2021
Cited by 7 | Viewed by 2311
Abstract
Polar areas are the most sensitive targets of climate change. From this perspective, the continuous monitoring of the cryosphere represents a critical need, which, now, we can only partially supply with specific satellite missions. The integration between remote-sensed multi-spectral images and field data [...] Read more.
Polar areas are the most sensitive targets of climate change. From this perspective, the continuous monitoring of the cryosphere represents a critical need, which, now, we can only partially supply with specific satellite missions. The integration between remote-sensed multi-spectral images and field data is crucial to validate retrieval algorithms and climatological models. The optical behavior of snow, at different wavelengths, provides significant information about the microphysical characteristics of the surface in addition to the spatial distribution of snow/ice covers. This work presents the unmanned apparatus installed at Ny Ålesund (Svalbard) that provides continuous spectral surface albedo. A narrow band device was compared to a full-range system, to remotely sensed data during the 2015 spring/summer period at the Amundsen-Nobile Climate Change Tower. The system was integrated with a camera aimed to acquire sky and ground images. The results confirmed the possibility of making continuous observations of the snow surface and highlighted the opportunity to monitor the spectral variations of snowed surfaces during the melting period. Full article
(This article belongs to the Special Issue Monitoring of the Seasonal Snow Cover)
►▼ Show Figures

Graphical abstract

21 pages, 5643 KiB  
Article
ENSO and Light-Absorbing Impurities and Their Impact on Snow Albedo in the Sierra Nevada de Santa Marta, Colombia
by Tomás R. Bolaño-Ortiz, Viverlys L. Diaz-Gutiérrez and Yiniva Camargo-Caicedo
Geosciences 2020, 10(11), 437; https://doi.org/10.3390/geosciences10110437 - 06 Nov 2020
Cited by 4 | Viewed by 3265
Abstract
Snow albedo is an important variable in the coupled atmosphere-earth system at the global level. Moreover, studying its behavior allows us to know the state of the cryosphere. The Sierra Nevada de Santa Marta (SNSM) is a glacier area and the northernmost tropical [...] Read more.
Snow albedo is an important variable in the coupled atmosphere-earth system at the global level. Moreover, studying its behavior allows us to know the state of the cryosphere. The Sierra Nevada de Santa Marta (SNSM) is a glacier area and the northernmost tropical (10.82° N, 73.75° W) region in South America. It has a height of up to 5775 m.a.sl., which is the second highest mountain in the world near the marine coast. We analyzed variations in snow albedo related to snow cover, snowfall, temperature, light-absorbing impurities such as blank carbon (BC), organic carbon (OC) and dust, and El Niño—Southern Oscillation (ENSO) phenomenon through 20 years (2000–2020). We mainly use daily data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua NASA satellites. Results showed through correlations that snow albedo has decreased due to Land Surface Temperature (55%, p < 0.001), a positive phase of ENSO (42%, p < 0.001) and dust (37%, p < 0.01) in the SNSM. Additionally, a dust negative effect was more evident on the southern side (up to 49%, p < 0.001) of the SNSM. Backward trajectories by the NOAA HYSPLIT model suggest that dust sources would be soil erosion in the surrounding region. Results can help recognize the influence of ENSO and dust in the glacier decrease of the SNSM. Full article
(This article belongs to the Special Issue Monitoring of the Seasonal Snow Cover)
►▼ Show Figures

Graphical abstract

Review

Jump to: Research

19 pages, 3810 KiB  
Review
A Call for More Snow Sampling
by Steven R. Fassnacht
Geosciences 2021, 11(11), 435; https://doi.org/10.3390/geosciences11110435 - 21 Oct 2021
Cited by 6 | Viewed by 2169
Abstract
The snowpack is important for water resources, tourism, ecology, and the global energy budget. Over the past century, we have gone from point measurements of snow water equivalent (SWE) to estimate spring and summer runoff volumes, to remote sensing of various snowpack properties [...] Read more.
The snowpack is important for water resources, tourism, ecology, and the global energy budget. Over the past century, we have gone from point measurements of snow water equivalent (SWE) to estimate spring and summer runoff volumes, to remote sensing of various snowpack properties at continuously finer spatial and temporal resolutions, to various complexities of snowpack and hydrological modeling, to the current fusion of field data with remote sensing and modeling, all to improve our estimates of the snowpack and the subsequent runoff. However, we are still limited by the uncertainty induced by scaling from point field measurements to the area represented by remote sensing and modeling. This paper uses several examples of fine-resolution sampling to issue a call to snow hydrologists and other earth scientists to collect more data, or at least to thoroughly evaluate their sampling strategy for collecting ground-truth measurements. Recommendations are provided for different approaches to have more representative sampling, when at all possible, to collect at least a few more samples or data points. Full article
(This article belongs to the Special Issue Monitoring of the Seasonal Snow Cover)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop