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Progresses and Gaps on Monitoring of Snow and Its Components...
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Progresses and Gaps on Monitoring of Snow and Its Components at the Local, Regional to Global Scale and Its Applications

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 9622

Special Issue Editors

Dr. Ali Nadir Arslan

E-Mail Website
Guest Editor
Space and Earth Observation Centre, Finnish Meteorological Institute, FI-00101 Helsinki, Finland
Interests: remote sensing methods and applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carlo De Michele

E-Mail Website
Guest Editor
Department Civil and Environmental Engineering, Politecnico di Milano, 20133 Milano, Italy
Interests: snow hydrology; extremes in hydrologic variables
Special Issues, Collections and Topics in MDPI journals
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
Prof. Dr. Aynur Sensoy

E-Mail Website
Guest Editor
Department of Civil Engineering, Eskişehir Technical University, 26555 Eskişehir, Turkey
Interests: snow hydrology; hydrological modeling; remote sensing applications in hydrology; water resources management
Special Issues, Collections and Topics in MDPI journals
Dr. Leena Leppänen

E-Mail Website
Guest Editor
Finnish Meteorological Institute, 99600 Sodankylä, Finland
Interests: snow physics; snow microstructure; in-situ measurements; remote sensing

Special Issue Information

Dear Colleagues,

Snow plays an essential role in the climatic and environmental challenges of the 21st century. The snow cover represents a key source of global water resources and climate regulation, and has shown high sensitivity to the warming climate. The quantity and quality of collected snow information is also constantly increasing with the possible novel automated methods provided by recent technological development for cheaper and easier monitoring. During the last several decades, instrumentation and measurement techniques—especially remote sensing—have advanced quickly, providing a significant amount of new information about the extent and properties of snow (e.g., snow water equivalent (SWE), albedo, reflectance, microstructure, impurities). In addition, novel technologies such as unmanned aerial vehicles (UAVs) and webcams provide new opportunities and challenges. Optimization and agreement on sampling strategies are important in order to get spatially distributed data at different scales and ensure broad use of the acquired data. Data management has become an important issue after general open data policy, where data sets should be available and usable for other users. A large variety of NWP and hydrological models or operational applications routinely make use of snow data to improve their performance. The forecasting of snow-related hazards in Europe is mostly performed at the country or regional level, and heavily relies on the concurrent meteorological factors and snowpack properties, which are usually acquired from point measurements or physical models. A big challenge is bridging information from microstructural scales of the snowpack up to the grid resolution in models, and then to provide knowledge-based information on potential impacts to society, economy, and safety (e.g., hydro-power, water resources management , transportation, tourism, flooding, and avalanches).

This Special Issue invites and encourages researchers to submit recent developments and progresses on topics including but not limited to:

  • Harmonization towards snow data collection, curation, and management;
  • Novel techniques and sensors for both the remote sensing of snow and in-situ measurements of snow;
  • Snow models, snow retrievals and products, and data assimilation including improved modelling (snow, hydrology, NWP, climate, etc.) and prediction at different scales considering macro- and microscale snow properties;
  • Monitoring snow-related hazards and extreme events including latest reanalysis and satellite data sets and models to predict and forecast extreme events and snow-related natural hazards;
  • Climate change effect on snow dynamics including snow melting and rain-on-snow events;
  • Impact of snow monitoring and predictions on different economic sectors (energy, tourism, agriculture, transportation, etc.).

Dr. Ali Nadir Arslan
Prof. Dr. Carlo De Michele
Dr. Bartłomiej Luks
Prof. Dr. Aynur Sensoy
Dr. Leena Leppänen
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
  • snow observation
  • snow products
  • novel technologies
  • harmonization
  • NWP and hydrological models
  • snow data assimilation
  • snow-related hazards and extreme events
  • societal impacts

Published Papers (5 papers)

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Research

19 pages, 11820 KiB  
Article
Understanding the Snow Cover Climatology over Turkey from ERA5-Land Reanalysis Data and MODIS Snow Cover Frequency Product
by Zuhal Akyurek, Semih Kuter, Çağrı H. Karaman and Berkay Akpınar
Geosciences 2023, 13(10), 311; https://doi.org/10.3390/geosciences13100311 - 17 Oct 2023
Cited by 1 | Viewed by 1575
Abstract
Understanding the distribution, patterns, and characteristics of snowfall and snow cover within a given region over extended periods is important. Snow climatology provides valuable insights into the seasonal and long-term variations in snowfall, helping researchers and meteorologists understand the impacts of climate change [...] Read more.
Understanding the distribution, patterns, and characteristics of snowfall and snow cover within a given region over extended periods is important. Snow climatology provides valuable insights into the seasonal and long-term variations in snowfall, helping researchers and meteorologists understand the impacts of climate change on snow accumulation, melt rates, and snowmelt runoff. In this study, in order to understand the spatial and temporal variation in snow cover in Turkey, the temporal and spatial dynamics of snow cover in the country were analyzed during the latest and longest period from 1970 to 2022 using ERA5-Land reanalysis product. It is aimed (1) to show snow-covered area (SCA), snow duration, and snow depth trends over the country; (2) to examine the altitudinal difference of snow phenology response to climate change; and (3) to evaluate the Snow Cover Frequency Maps from MODIS Snow Cover Products with the reanalysis snow depth data. It is found that the “false snow” mapping problem still exists in the MOD10C1_CGF Snow Cover Frequency maps over Turkey, especially in the melting period. We found that an increasing trend of 0.4 °C/decade and snow duration have a decreasing trend due to the early melting between 1970 and 2022. This trend is even more noticeable at elevations below 2000 m. Another important finding is the decreasing trend in snow duration at altitudes below 500 m, indicating a shift from snow to rain for precipitation types. Full article
(This article belongs to the Special Issue Progresses and Gaps on Monitoring of Snow and Its Components at the Local, Regional to Global Scale and Its Applications)
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10 pages, 2026 KiB  
Article
Accuracy of Manual Snow Sampling, Depending on the Sampler’s Cross-Section—A Comparative Study
by Marko Kaasik, Outi Meinander, Leena Leppänen, Kati Anttila, Pavla Dagsson-Waldhauserova, Anders Ginnerup, Timo Hampinen, Yijing Liu, Andri Gunnarsson, Kirsty Langley and Ali Nadir Arslan
Geosciences 2023, 13(7), 205; https://doi.org/10.3390/geosciences13070205 - 07 Jul 2023
Cited by 1 | Viewed by 1257
Abstract
Snow sampling, either by inserting a tube through the entire snowpack or by taking samples from the vertical profile, is widely applied to measure the snow depth, density, and snow water equivalent (SWE). A comparative study of snow-sampling methods was carried out on [...] Read more.
Snow sampling, either by inserting a tube through the entire snowpack or by taking samples from the vertical profile, is widely applied to measure the snow depth, density, and snow water equivalent (SWE). A comparative study of snow-sampling methods was carried out on 24 March 2022 in Sodankylä, Finland. Six groups from five countries (Estonia, Finland, Greenland, Iceland, and Sweden) participated, using 12 different snow samplers, including 9 bulk tube samplers and 3 density cutters. The cross-sectional area of the SWE samplers varied from 11 to 100 cm2, while tube length varied from 30 cm to 100 cm. The cross-sectional area of the density profile cutters varied from 100 cm2 to 200 cm2 and the vertical sampling step varied from 5 cm to 10 cm. The samples were taken from snow pits in 55–65-centimeter-deep snow cover in a flat area with sparse pine trees, with the pits at a maximum distance of 10 m from each other. Each tube sampling series consisted of 3–10 vertical columns to ensure statistical validation. The snowpack was relatively soft, with two moderately hard crust layers. The density recorded in the tube sample measurements varied from 218 to 265 kgm−3. The measurement results of SWE, however, varied depending on the sampling equipment used, ranging from 148 to 180 kgm−2, with two outliers of 77 and 106 kgm−2, both with 11 cm2 samplers. Full article
(This article belongs to the Special Issue Progresses and Gaps on Monitoring of Snow and Its Components at the Local, Regional to Global Scale and Its Applications)
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11 pages, 1235 KiB  
Article
Use of Webcams in Support of Operational Snow Monitoring
by Cemal Melih Tanis, Elisa Lindgren, Anna Frey, Lasse Latva, Ali Nadir Arslan and Kari Luojus
Geosciences 2023, 13(3), 92; https://doi.org/10.3390/geosciences13030092 - 22 Mar 2023
Viewed by 1460
Abstract
Ultrasonic sensors are one of the most common automatic monitoring methods in operational snow depth monitoring with reliable results. On the other hand, there is significant uncertainty when measuring small snow depths (<2 cm), thus it cannot provide binary snow presence (on/off) information. [...] Read more.
Ultrasonic sensors are one of the most common automatic monitoring methods in operational snow depth monitoring with reliable results. On the other hand, there is significant uncertainty when measuring small snow depths (<2 cm), thus it cannot provide binary snow presence (on/off) information. The use of webcams in monitoring snow cover has proven to be successful in recent studies and applications. In this study, we applied an adaptive thresholding technique on images from webcams to obtain reliable snow on/off information to complement the ultrasonic snow depth measurements. Camera and ultrasonic sensor data from two weather stations in Finland were studied. The webcam data was processed using FMIPROT (Finnish Meteorological Institute Image Processing Tool) software, operating in a cloud computing environment, which can generate near real-time data. Our results indicate that webcam-derived data can be successfully used for quality control or as auxiliary data to support operational ultrasonic sensor measurements and provide a cost-effective improvement to operational monitoring capabilities. Webcam monitoring is especially useful during the melting season when the snow depth is below 15 mm, with accuracy values between 72% and 94%. Full article
(This article belongs to the Special Issue Progresses and Gaps on Monitoring of Snow and Its Components at the Local, Regional to Global Scale and Its Applications)
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20 pages, 5681 KiB  
Article
Comparison of Deterministic and Probabilistic Variational Data Assimilation Methods Using Snow and Streamflow Data Coupled in HBV Model for Upper Euphrates Basin
by Gökçen Uysal, Rodolfo Alvarado-Montero, Aynur Şensoy and Ali Arda Şorman
Geosciences 2023, 13(3), 89; https://doi.org/10.3390/geosciences13030089 - 19 Mar 2023
Viewed by 1550
Abstract
The operation of upstream reservoirs in mountainous regions fed by snowmelt is highly challenging. This is partly due to scarce information given harsh topographic conditions and a lack of monitoring stations. In this sense, snow observations from remote sensing provide additional and relevant [...] Read more.
The operation of upstream reservoirs in mountainous regions fed by snowmelt is highly challenging. This is partly due to scarce information given harsh topographic conditions and a lack of monitoring stations. In this sense, snow observations from remote sensing provide additional and relevant information about the current conditions of the basin. This information can be used to improve the model states of a forecast using data assimilation techniques, therefore enhancing the operation of reservoirs. Typical data assimilation techniques can effectively reduce the uncertainty of forecast initialization by merging simulations and observations. However, they do not take into account model, structural, or parametric uncertainty. The uncertainty intrinsic to the model simulations introduces complexity to the forecast and restricts the daily work of operators. The novel Multi-Parametric Variational Data Assimilation (MP-VarDA) uses different parameter sets to create a pool of models that quantify the uncertainty arising from model parametrization. This study focuses on the sensitivity of the parametric reduction techniques of MP-VarDA coupled in the HBV hydrological model to create model pools and the impact of the number of parameter sets on the performance of streamflow and Snow Cover Area (SCA) forecasts. The model pool is created using Monte Carlo simulation, combined with an Aggregated Distance (AD) Method, to create different model pool instances. The tests are conducted in the Karasu Basin, located at the uppermost part of the Euphrates River in Türkiye, where snowmelt is a significant portion of the yearly runoff. The analyses were conducted for different thresholds based on the observation exceedance probabilities. According to the results in comparison with deterministic VarDA, probabilistic MP-VarDA improves the m-CRPS gains of the streamflow forecasts from 57% to 67% and BSS forecast skill gains from 52% to 68% when streamflow and SCA are assimilated. This improvement rapidly increases for the first additional model parameter sets but reaches a maximum benefit after 5 parameter sets in the model pool. The improvement is notable for both methods in SCA forecasts, but the best m-CRPS gain is obtained for VarDA (31%), while the best forecast skill is detected in MP-VarDA (12%). Full article
(This article belongs to the Special Issue Progresses and Gaps on Monitoring of Snow and Its Components at the Local, Regional to Global Scale and Its Applications)
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18 pages, 3639 KiB  
Article
Intercomparison Experiment of Water-Insoluble Carbonaceous Particles in Snow in a High-Mountain Environment (1598 m a.s.l.)
by Outi Meinander, Anne Kasper-Giebl, Silvia Becagli, Minna Aurela, Daniela Kau, Giulia Calzolai and Wolfgang Schöner
Geosciences 2022, 12(5), 197; https://doi.org/10.3390/geosciences12050197 - 04 May 2022
Cited by 1 | Viewed by 1833
Abstract
The harmonization of sampling, sample preparation and laboratory analysis methods to detect carbon compounds in snow requires detailed documentation of those methods and their uncertainties. Moreover, intercomparison experiments are needed to reveal differences and quantify the uncertainties further. Here, we document our sampling, [...] Read more.
The harmonization of sampling, sample preparation and laboratory analysis methods to detect carbon compounds in snow requires detailed documentation of those methods and their uncertainties. Moreover, intercomparison experiments are needed to reveal differences and quantify the uncertainties further. Here, we document our sampling, filtering, and analysis protocols used in the intercomparison experiment from three laboratories to detect water-insoluble carbon in seasonal surface snow in the high-mountain environment at Kolm Saigurn (47.067842° N, 12.98394° E, alt 1598 m a.s.l.), Austria. The participating laboratories were TU Wien (Austria), the University of Florence (Italy), and the Finnish Meteorological Institute (Finland). For the carbon analysis, the NIOSH5040 and EUSAAR2 protocols of the OCEC thermal-optical method were used. The median of the measured concentrations of total carbon (TC) was 323 ppb, organic carbon (OC) 308 ppb, and elemental carbon (EC) 16 ppb. The methods and protocols used in this experiment did not reveal large differences between the laboratories, and the TC, OC, and EC values of four inter-comparison locations, five meters apart, did not show meter-scale horizontal variability in surface snow. The results suggest that the presented methods are applicable for future research and monitoring of carbonaceous particles in snow. Moreover, a recommendation on the key parameters that an intercomparison experiment participant should be asked for is presented to help future investigations on carbonaceous particles in snow. The work contributes to the harmonization of the methods for measuring the snow chemistry of seasonal snow deposited on the ground. Full article
(This article belongs to the Special Issue Progresses and Gaps on Monitoring of Snow and Its Components at the Local, Regional to Global Scale and Its Applications)
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