Articles | Volume 17, issue 10
https://doi.org/10.5194/tc-17-4363-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-4363-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Oct 2023
Research article |
| 17 Oct 2023
| 17 Oct 2023
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions
Leo-Juhani Meriö
CORRESPONDING AUTHOR
Water, Energy and Environmental Engineering, Faculty of Technology, University of Oulu, Oulu, 90014, Finland
Water Resources, Finnish Environment Institute (Syke), 90014, Oulu, Finland
Anssi Rauhala
Civil Engineering, Faculty of Technology, University of Oulu, Oulu, 90014, Finland
Pertti Ala-aho
Water, Energy and Environmental Engineering, Faculty of Technology, University of Oulu, Oulu, 90014, Finland
Anton Kuzmin
Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu, 80101, Finland
Pasi Korpelainen
Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu, 80101, Finland
Timo Kumpula
Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu, 80101, Finland
Bjørn Kløve
Water, Energy and Environmental Engineering, Faculty of Technology, University of Oulu, Oulu, 90014, Finland
Hannu Marttila
Water, Energy and Environmental Engineering, Faculty of Technology, University of Oulu, Oulu, 90014, Finland
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The "Global SnowPack" product of the German Aerospace Center (DLR) contains binary information about the presence or absence of snow on a global scale since the year 2000. Now incorporating new input datasets, it was possible to increase the spatial resolution to 370 m. The detailed accuracy assessment proves the feasibility of the applied methods to remove data gaps caused by clouds and polar darkness.
Sonia Dupuis, Frank-Michael Göttsche, and Stefan Wunderle
The Cryosphere, 18, 6027–6059, https://doi.org/10.5194/tc-18-6027-2024, https://doi.org/10.5194/tc-18-6027-2024, 2024
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The Arctic has experienced pronounced warming the last few decades. This warming threatens ecosystems, vegetation dynamics, snow cover duration, and permafrost. Traditional monitoring methods like stations and climate models lack the detail needed. Land surface temperature (LST) data derived from satellites offer high spatial and temporal coverage, perfect for studying changes in the Arctic. In particular, LST information from AVHRR provides a 40-year record, valuable for analysing trends.
Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall
The Cryosphere, 18, 5407–5430, https://doi.org/10.5194/tc-18-5407-2024, https://doi.org/10.5194/tc-18-5407-2024, 2024
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This study uses radar imagery from the Sentinel-1 satellite to derive snow depth from increases in the returning energy. These retrieved depths are then compared to nine lidar-derived snow depths across the western United State to assess the ability of this technique to be used to monitor global snow distributions. We also qualitatively compare the changes in underlying Sentinel-1 amplitudes against both the total lidar snow depths and nine automated snow monitoring stations.
Alexander Störmer, Timo Kumpula, Miguel Villoslada, Pasi Korpelainen, Henning Schumacher, and Benjamin Burkhard
EGUsphere, https://doi.org/10.5194/egusphere-2024-2862, https://doi.org/10.5194/egusphere-2024-2862, 2024
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Snow has a major impact on palsa development, yet understanding its distribution at small scale remains limited. We used LiDAR UAS and ground truth data in combination with machine learning to model snow distribution at three palsa sites. We identified extremes in snow depth corresponding to palsa topography, providing insights into the influence of snow distribution on their formation. The results demonstrate the applicability of machine learning for modeling snow distribution at a small scale.
Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn
The Cryosphere, 18, 5015–5029, https://doi.org/10.5194/tc-18-5015-2024, https://doi.org/10.5194/tc-18-5015-2024, 2024
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Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
Filippo Emilio Scarsi, Alessandro Battaglia, Maximilian Maahn, and Stef Lhermitte
EGUsphere, https://doi.org/10.5194/egusphere-2024-1917, https://doi.org/10.5194/egusphere-2024-1917, 2024
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Snowfall measurements at high latitudes are crucial for estimating ice sheet mass balance. Spaceborne radar and radiometer missions help estimate snowfall but face uncertainties. This work evaluates uncertainties in snowfall estimates from a fixed near-nadir radar (CloudSat-like) and a conically scanning radar (WIVERN-like), determining that WIVERN will provide much better estimates than CloudSat, and at much smaller spatial and temporal scales.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
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Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 2024
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This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
Randall Bonnell, Daniel McGrath, Jack Tarricone, Hans-Peter Marshall, Ella Bump, Caroline Duncan, Stephanie Kampf, Yunling Lou, Alex Olsen-Mikitowicz, Megan Sears, Keith Williams, Lucas Zeller, and Yang Zheng
The Cryosphere, 18, 3765–3785, https://doi.org/10.5194/tc-18-3765-2024, https://doi.org/10.5194/tc-18-3765-2024, 2024
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Snow provides water for billions of people, but the amount of snow is difficult to detect remotely. During the 2020 and 2021 winters, a radar was flown over mountains in Colorado, USA, to measure the amount of snow on the ground, while our team collected ground observations to test the radar technique’s capabilities. The technique yielded accurate measurements of the snowpack that had good correlation with ground measurements, making it a promising application for the upcoming NISAR satellite.
Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small
The Cryosphere, 18, 3495–3512, https://doi.org/10.5194/tc-18-3495-2024, https://doi.org/10.5194/tc-18-3495-2024, 2024
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Automated stations measure snow properties at a single point but are frequently used to validate data that represent much larger areas. We use lidar snow depth data to see how often the mean snow depth surrounding a snow station is within 10 cm of the snow station depth at different scales. We found snow stations overrepresent the area-mean snow depth in ~ 50 % of cases, but the direction of bias at a site is temporally consistent, suggesting a site could be calibrated to the surrounding area.
Orie Sasaki, Evan Stewart Miles, Francesca Pellicciotti, Akiko Sakai, and Koji Fujita
EGUsphere, https://doi.org/10.5194/egusphere-2024-2026, https://doi.org/10.5194/egusphere-2024-2026, 2024
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This study proposes a new method to detect snowline altitude (SLA) using the Google Earth Engine platform with high-resolution satellite imagery, applicable anywhere in the world. Applying this method to five glaciated watersheds in the Himalayas reveals regional consistencies and differences in snow dynamics. We also investigate the primary controls of these dynamics by analyzing climatic factors and topographic characteristics.
Isis Brangers, Hans-Peter Marshall, Gabrielle De Lannoy, Devon Dunmire, Christian Mätzler, and Hans Lievens
The Cryosphere, 18, 3177–3193, https://doi.org/10.5194/tc-18-3177-2024, https://doi.org/10.5194/tc-18-3177-2024, 2024
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To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
James Dillon, Christopher Donahue, Evan Schehrer, Karl Birkeland, and Kevin Hammonds
The Cryosphere, 18, 2557–2582, https://doi.org/10.5194/tc-18-2557-2024, https://doi.org/10.5194/tc-18-2557-2024, 2024
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Surface hoar crystals are snow grains that form when vapor deposits on a snow surface. They create a weak layer in the snowpack that can cause large avalanches to occur. Thus, determining when and where surface hoar forms is a lifesaving matter. Here, we developed a means of mapping surface hoar using remote-sensing technologies. We found that surface hoar displayed heightened texture, hence the variability of brightness. Using this, we created surface hoar maps with an accuracy upwards of 95 %.
Adriano Lemos and Aku Riihelä
EGUsphere, https://doi.org/10.5194/egusphere-2024-869, https://doi.org/10.5194/egusphere-2024-869, 2024
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Here we used satellite imagery to measure snow depth in northern Finland and compared to on-site weather stations from 2019–2022. We correlated snow depths and vegetation coverage, and found thicker snow over non-vegetated areas and frozen water bodies due to the satellite's sensitivity. Our estimates showed underestimated results of snow depth and need further investigation, but they highlight the potential in monitoring seasonal snow changes, particularly where direct measurements are lacking.
Steven J. Pestana, C. Chris Chickadel, and Jessica D. Lundquist
The Cryosphere, 18, 2257–2276, https://doi.org/10.5194/tc-18-2257-2024, https://doi.org/10.5194/tc-18-2257-2024, 2024
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We compared infrared images taken by GOES-R satellites of an area with snow and forests against surface temperature measurements taken on the ground, from an aircraft, and by another satellite. We found that GOES-R measured warmer temperatures than the other measurements, especially in areas with more forest and when the Sun was behind the satellite. From this work, we learned that the position of the Sun and surface features such as trees that can cast shadows impact GOES-R infrared images.
Jiahui Xu, Yao Tang, Linxin Dong, Shujie Wang, Bailang Yu, Jianping Wu, Zhaojun Zheng, and Yan Huang
The Cryosphere, 18, 1817–1834, https://doi.org/10.5194/tc-18-1817-2024, https://doi.org/10.5194/tc-18-1817-2024, 2024
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Understanding snow phenology (SP) and its possible feedback are important. We reveal spatiotemporal heterogeneous SP on the Tibetan Plateau (TP) and the mediating effects from meteorological, topographic, and environmental factors on it. The direct effects of meteorology on SP are much greater than the indirect effects. Topography indirectly effects SP, while vegetation directly effects SP. This study contributes to understanding past global warming and predicting future trends on the TP.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
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We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
Siddharth Singh, Michael Durand, Edward Kim, and Ana P. Barros
The Cryosphere, 18, 747–773, https://doi.org/10.5194/tc-18-747-2024, https://doi.org/10.5194/tc-18-747-2024, 2024
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Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is measured by the snow water equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical–statistical framework to estimate SWE from airborne X- and Ku-band synthetic aperture radar backscatter measurements constrained by physical snow hydrology and radar models. We explored spatial resolutions and vertical structures that agree well with ground observations.
Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
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The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
Dhiraj Kumar Singh, Srinivasarao Tanniru, Kamal Kant Singh, Harendra Singh Negi, and RAAJ Ramsankaran
The Cryosphere, 18, 451–474, https://doi.org/10.5194/tc-18-451-2024, https://doi.org/10.5194/tc-18-451-2024, 2024
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In situ techniques for snow depth (SD) measurement are not adequate to represent the spatiotemporal variability in SD in the Western Himalayan region. Therefore, this study focuses on the high-resolution mapping of daily snow depth in the Indian Western Himalayan region using passive microwave remote-sensing-based algorithms. Overall, the proposed multifactor SD models demonstrated substantial improvement compared to the operational products. However, there is a scope for further improvement.
Michael Durand, Joel T. Johnson, Jack Dechow, Leung Tsang, Firoz Borah, and Edward J. Kim
The Cryosphere, 18, 139–152, https://doi.org/10.5194/tc-18-139-2024, https://doi.org/10.5194/tc-18-139-2024, 2024
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Seasonal snow accumulates each winter, storing water to release later in the year and modulating both water and energy cycles, but the amount of seasonal snow is one of the most poorly measured components of the global water cycle. Satellite concepts to monitor snow accumulation have been proposed but not selected. This paper shows that snow accumulation can be measured using radar, and that (contrary to previous studies) does not require highly accurate information about snow microstructure.
Jennika Hammar, Inge Grünberg, Steven V. Kokelj, Jurjen van der Sluijs, and Julia Boike
The Cryosphere, 17, 5357–5372, https://doi.org/10.5194/tc-17-5357-2023, https://doi.org/10.5194/tc-17-5357-2023, 2023
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Roads on permafrost have significant environmental effects. This study assessed the Inuvik to Tuktoyaktuk Highway (ITH) in Canada and its impact on snow accumulation, albedo and snowmelt timing. Our findings revealed that snow accumulation increased by up to 36 m from the road, 12-day earlier snowmelt within 100 m due to reduced albedo, and altered snowmelt patterns in seemingly undisturbed areas. Remote sensing aids in understanding road impacts on permafrost.
Anssi Rauhala, Leo-Juhani Meriö, Anton Kuzmin, Pasi Korpelainen, Pertti Ala-aho, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4343–4362, https://doi.org/10.5194/tc-17-4343-2023, https://doi.org/10.5194/tc-17-4343-2023, 2023
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Snow conditions in the Northern Hemisphere are rapidly changing, and information on snow depth is important for decision-making. We present snow depth measurements using different drones throughout the winter at a subarctic site. Generally, all drones produced good estimates of snow depth in open areas. However, differences were observed in the accuracies produced by the different drones, and a reduction in accuracy was observed when moving from an open mire area to forest-covered areas.
Kirsty Wivell, Stuart Fox, Melody Sandells, Chawn Harlow, Richard Essery, and Nick Rutter
The Cryosphere, 17, 4325–4341, https://doi.org/10.5194/tc-17-4325-2023, https://doi.org/10.5194/tc-17-4325-2023, 2023
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Satellite microwave observations improve weather forecasts, but to use these observations in the Arctic, snow emission must be known. This study uses airborne and in situ snow observations to validate emissivity simulations for two- and three-layer snowpacks at key frequencies for weather prediction. We assess the impact of thickness, grain size and density in key snow layers, which will help inform development of physical snow models that provide snow profile input to emissivity simulations.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
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As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
César Deschamps-Berger, Simon Gascoin, David Shean, Hannah Besso, Ambroise Guiot, and Juan Ignacio López-Moreno
The Cryosphere, 17, 2779–2792, https://doi.org/10.5194/tc-17-2779-2023, https://doi.org/10.5194/tc-17-2779-2023, 2023
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The estimation of the snow depth in mountains is hard, despite the importance of the snowpack for human societies and ecosystems. We measured the snow depth in mountains by comparing the elevation of points measured with snow from the high-precision altimetric satellite ICESat-2 to the elevation without snow from various sources. Snow depths derived only from ICESat-2 were too sparse, but using external airborne/satellite products results in spatially richer and sufficiently precise snow depths.
Edward H. Bair, Jeff Dozier, Karl Rittger, Timbo Stillinger, William Kleiber, and Robert E. Davis
The Cryosphere, 17, 2629–2643, https://doi.org/10.5194/tc-17-2629-2023, https://doi.org/10.5194/tc-17-2629-2023, 2023
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To test the title question, three snow cover products were used in a snow model. Contrary to previous work, higher-spatial-resolution snow cover products only improved the model accuracy marginally. Conclusions are as follows: (1) snow cover and albedo from moderate-resolution sensors continue to provide accurate forcings and (2) finer spatial and temporal resolutions are the future for Earth observations, but existing moderate-resolution sensors still offer value.
Valentina Premier, Carlo Marin, Giacomo Bertoldi, Riccardo Barella, Claudia Notarnicola, and Lorenzo Bruzzone
The Cryosphere, 17, 2387–2407, https://doi.org/10.5194/tc-17-2387-2023, https://doi.org/10.5194/tc-17-2387-2023, 2023
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The large amount of information regularly acquired by satellites can provide important information about SWE. We explore the use of multi-source satellite data, in situ observations, and a degree-day model to reconstruct daily SWE at 25 m. The results show spatial patterns that are consistent with the topographical features as well as with a reference product. Being able to also reproduce interannual variability, the method has great potential for hydrological and ecological applications.
Jack Tarricone, Ryan W. Webb, Hans-Peter Marshall, Anne W. Nolin, and Franz J. Meyer
The Cryosphere, 17, 1997–2019, https://doi.org/10.5194/tc-17-1997-2023, https://doi.org/10.5194/tc-17-1997-2023, 2023
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Mountain snowmelt provides water for billions of people across the globe. Despite its importance, we cannot currently measure the amount of water in mountain snowpacks from satellites. In this research, we test the ability of an experimental snow remote sensing technique from an airplane in preparation for the same sensor being launched on a future NASA satellite. We found that the method worked better than expected for estimating important snowpack properties.
Sara E. Darychuk, Joseph M. Shea, Brian Menounos, Anna Chesnokova, Georg Jost, and Frank Weber
The Cryosphere, 17, 1457–1473, https://doi.org/10.5194/tc-17-1457-2023, https://doi.org/10.5194/tc-17-1457-2023, 2023
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We use synthetic-aperture radar (SAR) and optical observations to map snowmelt timing and duration on the watershed scale. We found that Sentinel-1 SAR time series can be used to approximate snowmelt onset over diverse terrain and land cover types, and we present a low-cost workflow for SAR processing over large, mountainous regions. Our approach provides spatially distributed observations of the snowpack necessary for model calibration and can be used to monitor snowmelt in ungauged basins.
Vasana Dharmadasa, Christophe Kinnard, and Michel Baraër
The Cryosphere, 17, 1225–1246, https://doi.org/10.5194/tc-17-1225-2023, https://doi.org/10.5194/tc-17-1225-2023, 2023
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This study highlights the successful usage of UAV lidar to monitor small-scale snow depth distribution. Our results show that underlying topography and wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. This emphasizes the importance of including and better representing these processes in physically based models for accurate snowpack estimates.
Ruben Urraca and Nadine Gobron
The Cryosphere, 17, 1023–1052, https://doi.org/10.5194/tc-17-1023-2023, https://doi.org/10.5194/tc-17-1023-2023, 2023
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We evaluate the fitness of some of the longest satellite (NOAA CDR, 1966–2020) and reanalysis (ERA5, 1950–2020; ERA5-Land, 1950–2020) products currently available to monitor the Northern Hemisphere snow cover trends using 527 stations as the reference. We found different artificial trends and stepwise discontinuities in all the products that hinder the accurate monitoring of snow trends, at least without bias correction. The study also provides updates on the snow cover trends during 1950–2020.
Annett Bartsch, Helena Bergstedt, Georg Pointner, Xaver Muri, Kimmo Rautiainen, Leena Leppänen, Kyle Joly, Aleksandr Sokolov, Pavel Orekhov, Dorothee Ehrich, and Eeva Mariatta Soininen
The Cryosphere, 17, 889–915, https://doi.org/10.5194/tc-17-889-2023, https://doi.org/10.5194/tc-17-889-2023, 2023
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Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. In extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record. Retrieval is most robust in the tundra biome, where records can be used to identify extremes and the results can be applied to impact studies at regional scale.
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
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Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
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We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Timbo Stillinger, Karl Rittger, Mark S. Raleigh, Alex Michell, Robert E. Davis, and Edward H. Bair
The Cryosphere, 17, 567–590, https://doi.org/10.5194/tc-17-567-2023, https://doi.org/10.5194/tc-17-567-2023, 2023
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Understanding global snow cover is critical for comprehending climate change and its impacts on the lives of billions of people. Satellites are the best way to monitor global snow cover, yet snow varies at a finer spatial resolution than most satellite images. We assessed subpixel snow mapping methods across a spectrum of conditions using airborne lidar. Spectral-unmixing methods outperformed older operational methods and are ready to to advance snow cover mapping at the global scale.
Jilu Li, Fernando Rodriguez-Morales, Xavier Fettweis, Oluwanisola Ibikunle, Carl Leuschen, John Paden, Daniel Gomez-Garcia, and Emily Arnold
The Cryosphere, 17, 175–193, https://doi.org/10.5194/tc-17-175-2023, https://doi.org/10.5194/tc-17-175-2023, 2023
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Alaskan glaciers' loss of ice mass contributes significantly to ocean surface rise. It is important to know how deeply and how much snow accumulates on these glaciers to comprehend and analyze the glacial mass loss process. We reported the observed seasonal snow depth distribution from our radar data taken in Alaska in 2018 and 2021, developed a method to estimate the annual snow accumulation rate at Mt. Wrangell caldera, and identified transition zones from wet-snow zones to ablation zones.
Ghislain Picard, Henning Löwe, and Christian Mätzler
The Cryosphere, 16, 3861–3866, https://doi.org/10.5194/tc-16-3861-2022, https://doi.org/10.5194/tc-16-3861-2022, 2022
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Microwave satellite observations used to monitor the cryosphere require radiative transfer models for their interpretation. These models represent how microwaves are scattered by snow and ice. However no existing theory is suitable for all types of snow and ice found on Earth. We adapted a recently published generic scattering theory to snow and show how it may improve the representation of snows with intermediate densities (~500 kg/m3) and/or with coarse grains at high microwave frequencies.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
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Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Elisabeth D. Hafner, Patrick Barton, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 16, 3517–3530, https://doi.org/10.5194/tc-16-3517-2022, https://doi.org/10.5194/tc-16-3517-2022, 2022
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Knowing where avalanches occur is very important information for several disciplines, for example avalanche warning, hazard zonation and risk management. Satellite imagery can provide such data systematically over large regions. In our work we propose a machine learning model to automate the time-consuming manual mapping. Additionally, we investigate expert agreement for manual avalanche mapping, showing that our network is equally as good as the experts in identifying avalanches.
Joëlle Voglimacci-Stephanopoli, Anna Wendleder, Hugues Lantuit, Alexandre Langlois, Samuel Stettner, Andreas Schmitt, Jean-Pierre Dedieu, Achim Roth, and Alain Royer
The Cryosphere, 16, 2163–2181, https://doi.org/10.5194/tc-16-2163-2022, https://doi.org/10.5194/tc-16-2163-2022, 2022
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Changes in the state of the snowpack in the context of observed global warming must be considered to improve our understanding of the processes within the cryosphere. This study aims to characterize an arctic snowpack using the TerraSAR-X satellite. Using a high-spatial-resolution vegetation classification, we were able to quantify the variability in snow depth, as well as the topographic soil wetness index, which provided a better understanding of the electromagnetic wave–ground interaction.
Jayson Eppler, Bernhard Rabus, and Peter Morse
The Cryosphere, 16, 1497–1521, https://doi.org/10.5194/tc-16-1497-2022, https://doi.org/10.5194/tc-16-1497-2022, 2022
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We introduce a new method for mapping changes in the snow water equivalent (SWE) of dry snow based on differences between time-repeated synthetic aperture radar (SAR) images. It correlates phase differences with variations in the topographic slope which allows the method to work without any "reference" targets within the imaged area and without having to numerically unwrap the spatial phase maps. This overcomes the key challenges faced in using SAR interferometry for SWE change mapping.
Sebastian Buchelt, Kirstine Skov, Kerstin Krøier Rasmussen, and Tobias Ullmann
The Cryosphere, 16, 625–646, https://doi.org/10.5194/tc-16-625-2022, https://doi.org/10.5194/tc-16-625-2022, 2022
Short summary
Short summary
In this paper, we present a threshold and a derivative approach using Sentinel-1 synthetic aperture radar time series to capture the small-scale heterogeneity of snow cover (SC) and snowmelt. Thereby, we can identify start of runoff and end of SC as well as perennial snow and SC extent during melt with high spatiotemporal resolution. Hence, our approach could support monitoring of distribution patterns and hydrological cascading effects of SC from the catchment scale to pan-Arctic observations.
Cited articles
Adams, M. S., Bühler, Y., and Fromm, R.: Multitemporal accuracy and precision assessment of unmanned aerial system photogrammetry for slope-scale snow depth maps in Alpine terrain, Pure Appl. Geophys., 175, 3303–3324, https://doi.org/10.1007/s00024-017-1748-y, 2018.
Agisoft: AgiSoft Metashape Professional (Version 1.6.0), AgiSoft [software], https://www.agisoft.com/downloads/installer/ (last access: 8 September 2019), 2019.
Agisoft: Agisoft Metashape User Manual: Professional Edition, Version 2.0. Agisoft LLC, https://www.agisoft.com/pdf/metashape-pro_2_0_en.pdf (last access: 25 May 2023), 2023.
Ala-aho, P., Tetzlaff, D., McNamara, J. P., Laudon, H., Kormos, P., and Soulsby, C.: Modeling the isotopic evolution of snowpack and snowmelt: Testing a spatially distributed parsimonious approach, Water Resour. Res., 53, 5813–5830, https://doi.org/10.1002/2017WR020650, 2017.
Blume-Werry, G., Kreyling, J., Laudon, H., and Milbau, A.: Short-term climate change manipulation effects do not scale up to long-term legacies: Effects of an absent snow cover on boreal forest plants, J. Ecol., 104, 1638–1648, https://doi.org/10.1111/1365-2745.12636, 2016.
Brown, R. D. and Robinson, D. A.: Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty, The Cryosphere, 5, 219–229, https://doi.org/10.5194/tc-5-219-2011, 2011.
Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075–1088, https://doi.org/10.5194/tc-10-1075-2016, 2016.
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B., Cullen, N. J., Kerr, T., Örn Hreinsson, E., and Woods, R. A.: Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review, Water Resour. Res., 47, W07539, https://doi.org/10.1029/2011WR010745, 2011.
Deems, J. S. and Painter, T. H.: Lidar measurement of snow depth: accuracy and error sources, Proceedings of the 2006 International Snow Science Workshop, Telluride, Colorado, USA, International Snow Science Workshop, 330–338, 1–6 October 2006.
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow depth: a review, J. Glaciol., 59, 467–479, https://doi.org/10.3189/2013JoG12J154, 2013.
De Michele, C., Avanzi, F., Passoni, D., Barzaghi, R., Pinto, L., Dosso, P., Ghezzi, A., Gianatti, R., and Della Vedova, G.: Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation, The Cryosphere, 10, 511–522, https://doi.org/10.5194/tc-10-511-2016, 2016.
Dietz, A. J., Kuenzer, C., Gessner, U., and Dech, S.: Remote sensing of snow – a review of available methods, Int. J. Remote Sens., 33, 4094–4134, https://doi.org/10.1080/01431161.2011.640964, 2012.
Dunn, O. J.: Multiple comparisons among means, J. Am. Stat. Assoc., 56, 52–64, 1961.
Dunn, O. J.: Multiple comparisons using rank sums, Technometrics, 6, 241–252, https://doi.org/10.2307/1266041, 1964.
Earman, S., Campbell, A. R., Phillips, F. M., and Newman, B. D.: Isotopic exchange between snow and atmospheric water vapor: Estimation of the snowmelt component of groundwater recharge in the southwestern United States, J. Geophys. Res.-Atmos., 111, D09302, https://doi.org/10.1029/2005JD006470, 2006.
EEA: European Union, Copernicus land monitoring service 2018, European environment agency, 2018.
Esri: ArcGIS Desktop (Version 10.6) (Software), https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview (last access: 6 August 2019), 2019.
Essery, R., Bunting, P., Rowlands, A., Rutter, N., Hardy, J., Melloh, R., Link, T., Marks, D., and Pomeroy, J.: Radiative transfer modeling of a coniferous canopy characterized by airborne remote sensing, J. Hydrometeorol., 9, 228–241, https://doi.org/10.1175/2007JHM870.1, 2008.
Faria, D. A., Pomeroy, J. W., and Essery, R. L. H.: Effect of covariance between ablation and snow water equivalent on depletion of snow-covered area in a forest, Hydrol. Process., 14, 2683–2695, https://doi.org/10.1002/1099-1085(20001030)14:15<2683::AID-HYP86>3.0.CO;2-N, 2000.
Feiccabrino, J. and Lundberg, A.: Precipitation phase discrimination in Sweden, 65th Eastern Snow Conference, Fairlee (Lake Morey), Vermont, USA, Abstract No. 18, 28–30 May 2008, 2008.
Fernandes, R., Prevost, C., Canisius, F., Leblanc, S. G., Maloley, M., Oakes, S., Holman, K., and Knudby, A.: Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos, The Cryosphere, 12, 3535–3550, https://doi.org/10.5194/tc-12-3535-2018, 2018.
Gary, H. L.: Snow accumulation and snowmelt as influenced by a small clearing in a lodgepole pine forest, Water Resour. Res., 10, 348–353, https://doi.org/10.1029/WR010i002p00348, 1974.
Gelfan, A. N., Pomeroy, J. W., and Kuchment, L. S.: Modeling forest cover influences on snow accumulation, sublimation, and melt, J. Hydrometeorol., 5, 785–803, https://doi.org/10.1175/1525-7541(2004)005<0785:MFCIOS>2.0.CO;2, 2004.
Godsey, S. E., Kirchner, J. W., and Tague, C. L.: Effects of changes in winter snowpacks on summer low flows: case studies in the Sierra Nevada, California, USA, Hydrol. Process., 28, 5048–5064, https://doi.org/10.1002/hyp.9943, 2014.
Golding, D. L. and Swanson, R. H.: Snow accumulation and melt in small forest openings in Alberta, Can. J. Forest Res., 8, 380–388, https://doi.org/10.1139/x78-057, 1978.
Harder, P., Pomeroy, J. W., and Helgason, W. D.: Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques, The Cryosphere, 14, 1919–1935, https://doi.org/10.5194/tc-14-1919-2020, 2020.
Hardy, J. P., Davis, R. E., Jordan, R., Li, X., Woodcock, C., Ni, W., and McKenzie, J. C.: Snow ablation modeling at the stand scale in a boreal jack pine forest, J. Geophys. Res.-Atmos., 102, 29397–29405, https://doi.org/10.1029/96JD03096, 1997.
Hewer, M. J. and Gough, W. A.: Thirty years of assessing the impacts of climate change on outdoor recreation and tourism in Canada, Tourism Management Perspectives, 26, 179–192, https://doi.org/10.1016/j.tmp.2017.07.003, 2018.
Hiemstra, C. A., Liston, G. E., and Reiners, W. A.: Snow redistribution by wind and interactions with vegetation at upper treeline in the Medicine Bow Mountains, Wyoming, USA, Arct. Antarct. Alp. Res., 34, 262–273, https://doi.org/10.1080/15230430.2002.12003493, 2002.
Jenicek, M., Seibert, J., Zappa, M., Staudinger, M., and Jonas, T.: Importance of maximum snow accumulation for summer low flows in humid catchments, Hydrol. Earth Syst. Sci., 20, 859–874, https://doi.org/10.5194/hess-20-859-2016, 2016.
Jost, G., Weiler, M., Gluns, D. R., and Alila, Y.: The influence of forest and topography on snow accumulation and melt at the watershed-scale, J. Hydrol., 347, 101–115, https://doi.org/10.1016/j.jhydrol.2007.09.006, 2007.
Ketcheson, S. J., Whittington, P. N., and Price, J. S.: The effect of peatland harvesting on snow accumulation, ablation and snow surface energy balance, Hydrol. Process., 26, 2592–2600, https://doi.org/10.1002/hyp.9325, 2012.
Kinar, N. J. and Pomeroy, J. W.: Measurement of the physical properties of the snowpack, Rev. Geophys., 53, 481–544, https://doi.org/10.1002/2015RG000481, 2015.
Lendzioch, T., Langhammer, J., and Jenicek, M.: Tracking forest and open area effects on snow accumulation by unmanned aerial vehicle photogrammetry, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 917–923, https://doi.org/10.5194/isprs-archives-XLI-B1-917-2016, 2016.
Lievens, H., Demuzere, M., Marshall, H. P., Reichle, R. H., Brucker, L., Brangers, I., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W., Jonas, T., Kim, E. J., Koch, I., Marty, C., Saloranta, T., Schöber, J. and De Lannoy, G. J. M.: Snow depth variability in the Northern Hemisphere mountains observed from space, Nat. Commun., 10, 1–12, https://doi.org/10.1038/s41467-019-12566-y, 2019.
Lin, M., Lucas Jr, H. C., and Shmueli, G.: Research commentary–too big to fail: large samples and the p-value problem, Inf. Syst. Res., 24, 906–917, https://doi.org/10.1287/isre.2013.0480, 2013.
Liston, G. E.: Interrelationships among snow distribution, snowmelt, and snow cover depletion: Implications for atmospheric, hydrologic, and ecologic modelling, J. Appl. Meteorol., 38, 1474–1487, https://doi.org/10.1175/1520-0450(1999)038<1474:IASDSA>2.0.CO;2, 1999.
Liston, G. E., Mcfadden, J. P., Sturm, M., and Pielke, R. A.: Modelled changes in arctic tundra snow, energy and moisture fluxes due to increased shrubs, Glob. Change Biol., 8, 17–32, https://doi.org/10.1046/j.1354-1013.2001.00416.x, 2002.
López-Moreno, J. I., Revuelto, J., Fassnacht, S. R., Azorín-Molina, C., Vicente-Serrano, S. M., Morán-Tejeda, E., and Sexstone, G. A.: Snowpack variability across various spatio-temporal resolutions, Hydrol. Process., 29, 1213–1224, https://doi.org/10.1002/hyp.10245, 2015.
Lundberg, A. and Koivusalo, H.: Estimating winter evaporation in boreal forests with operational snow course data, Hydrol. Process., 17, 1479–1493, https://doi.org/10.1002/hyp.1179, 2003.
Lundquist, J. D. and Lott, F.: Using inexpensive temperature sensors to monitor the duration and heterogeneity of snow-covered areas, Water Resour. Res., 44, W00D16, https://doi.org/10.1029/2008WR007035, 2008.
Lundquist, J. D., Dickerson-Lange, S. E., Lutz, J. A., and Cristea, N. C.: Lower forest density enhances snow retention in regions with warmer winters: A global framework developed from plot-scale observations and modelling, Water Resour. Res., 49, 6356–6370, https://doi.org/10.1002/wrcr.20504, 2013.
Luomaranta, A., Aalto, J., and Jylhä, K.: Snow cover trends in Finland over 1961–2014 based on gridded snow depth observations, Int. J. Climatol., 39, 3147–3159, https://doi.org/10.1002/joc.6007, 2019.
Mankin, J. S., Viviroli, D., Singh, D., Hoekstra, A. Y., and Diffenbaugh, N. S.: The potential for snow to supply human water demand in the present and future, Environ. Res. Lett., 10, 114016, https://doi.org/10.1088/1748-9326/10/11/114016, 2015.
Marttila, H., Lohila, A., Ala-aho, P., Noor, K., Welker, J.M., Croghan, D., Mustonen, K., Meriö, L.J., Autio, A., Muhic, F., Bailey, H., Aurela, M., Vuorenmaa, J., Penttilä, T., Hyöky, V., Klein, E., Kuzmin, A., Korpelainen, P., Kumpula, T., Rauhala, A., and Kløve, B.: Subarctic catchment water storage and carbon cycling – Leading the way for future studies using integrated datasets at Pallas, Finland, Hydrol. Process., 35, e14350, https://doi.org/10.1002/hyp.14350, 2021.
McKay, G. A. and Gray, D. M.: The distribution of snow cover, in: Handbook of Snow: Principles, Process, Management and Use, Illustrated edition, edited by: Gray, D. M. and Male, D. H., Blackburn Press, 153–190, ISBN 9781932846065, 2004.
Meriö, L. J., Marttila, H., Ala-aho, P., Hänninen, P., Okkonen, J., Sutinen, R., and Kløve, B.: Snow profile temperature measurements in spatiotemporal analysis of snowmelt in a subarctic forest-mire hillslope, Cold Reg. Sci. Technol., 151, 119–132, https://doi.org/10.1016/j.coldregions.2018.03.013, 2018.
Mudryk, L., Santolaria-Otín, M., Krinner, G., Ménégoz, M., Derksen, C., Brutel-Vuilmet, C., Brady, M., and Essery, R.: Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble, The Cryosphere, 14, 2495–2514, https://doi.org/10.5194/tc-14-2495-2020, 2020.
Musselman, K. N., Molotch, N. P., and Brooks, P. D.: Effects of vegetation on snow accumulation and ablation in a mid-latitude sub-alpine forest, Hydrol. Process., 22, 2767–2776, https://doi.org/10.1002/hyp.7050, 2008.
Musselman, K. N., Clark, M. P., Liu, C., Ikeda, K., and Rasmussen, R.: Slower snowmelt in a warmer world, Nat. Clim. Change, 7, 214–219, https://doi.org/10.1038/nclimate3225, 2017.
Neumann, N.N., Derksen, C., Smith, C., and Goodison, B.: Characterizing local scale snow cover using point measurements during the winter season, Atmos. Ocean, 44, 257–269, https://doi.org/10.3137/ao.440304, 2006.
Neuvonen, M., Sievänen, T., Fronzek, S., Lahtinen, I., Veijalainen, N., and Carter, T. R.: Vulnerability of cross-country skiing to climate change in Finland–An interactive mapping tool, J. Outdoor Recreat. Tour., 11, 64–79, https://doi.org/10.1016/j.jort.2015.06.010, 2015.
Niedzielski, T., Szymanowski, M., Miziński, B., Spallek, W., Witek-Kasprzak, M., Ślopek, J., Kasprzak, M., Błaś, M., Sobik, M., Jancewicz, K., and Borowicz, D.: Estimating snow water equivalent using unmanned aerial vehicles for determining snow-melt runoff, J. Hydrol., 578, 124046, https://doi.org/10.1016/j.jhydrol.2019.124046, 2019.
Pirazzini, R., Leppänen, L., Picard, G., Lopez-Moreno, J. I., Marty, C., Macelloni, G., Kontu, A., Von Lerber, A., Tanis, C. M., Schneebeli, M., and De Rosnay, P.: European in-situ snow measurements: Practices and purposes, Sensors, 18, 2016, https://doi.org/10.3390/s18072016, 2018.
Pomeroy, J. W. and Brun, E.: Physical properties of snow, in: Snow ecology: An interdisciplinary examination of snow-covered ecosystems, edited by: Jones, H. G., Pomeroy, J. W., Walker, D. A., and Homan, R. W., Cambridge University Press, Cambridge, 45–126, ISBN 9780521584838, 2001.
Pomeroy, J. W. and Goodison, B. E.: Winter and snow, in: The Surface Climates of Canada, edited by: Bailey, W. G., Oke, T. R., and Rouse, W. R., McGill-Queen's Press, 68–100, ISBN 9780773516724, 1997.
Pomeroy, J. W., Gray, D. M., Hedstrom, N. R., and Janowicz, J. R.: Prediction of seasonal snow accumulation in cold climate forests, Hydrol. Process., 16, 3543–3558, https://doi.org/10.1002/hyp.1228, 2002.
Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., and Norberg, J.: Patterns and trends of northern hemisphere snow mass from 1980 to 2018, Nature, 581, 294–298, https://doi.org/10.1038/s41586-020-2258-0, 2020.
Rauhala, A., Meriö, L. J., Korpelainen, P., and Kuzmin, A.: Unmanned aircraft system (UAS) snow depth mapping at the Pallas Atmosphere-Ecosystem Supersite, Fairdata [data set], https://doi.org/10.23729/43d37797-e8cf-4190-80f1-ff567ec62836, 2022.
Rauhala, A., Meriö, L.-J., Kuzmin, A., Korpelainen, P., Ala-aho, P., Kumpula, T., Kløve, B., and Marttila, H.: Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 1: Measurements, processing, and accuracy assessment, The Cryosphere, 17, 4343–4362, https://doi.org/10.5194/tc-17-4343-2023, 2023.
Redpath, T. A. N., Sirguey, P., and Cullen, N. J.: Repeat mapping of snow depth across an alpine catchment with RPAS photogrammetry, The Cryosphere, 12, 3477–3497, https://doi.org/10.5194/tc-12-3477-2018, 2018.
Schirmer, M. and Pomeroy, J. W.: Processes governing snow ablation in alpine terrain – detailed measurements from the Canadian Rockies, Hydrol. Earth Syst. Sci., 24, 143–157, https://doi.org/10.5194/hess-24-143-2020, 2020.
Scott, D., Dawson, J., and Jones, B.: Climate change vulnerability of the US Northeast winter recreation–tourism sector, Mitig. Adapt. Strat. Gl., 13, 577–596, https://doi.org/10.1007/s11027-007-9136-z, 2008.
Sturm, M.: White water: Fifty years of snow research in WRR and the outlook for the future, Water Resour. Res., 51, 4948–4965, https://doi.org/10.1002/2015WR017242, 2015.
Sturm, M. and Holmgren, J.: An automatic snow depth probe for field validation campaigns, Water Resour. Res., 54, 9695–9701, https://doi.org/10.1029/2018WR023559, 2018.
Sutinen, R., Närhi, P., Middleton, M., Hänninen, P., Timonen, M. and Sutinen, M.L.: Advance of Norway spruce (Picea abies) onto mafic Lommoltunturi fell in Finnish Lapland during the last 200 years, Boreas, 41, 367–378, https://doi.org/10.1111/j.1502-3885.2011.00238.x, 2012.
SYKE: Corine Land Cover 2018, Finnish Environmental Institute [data set], http://data.europa.eu/88u/dataset/-0b4b2fac-adf1-43a1-a829-70f02bf0c0e5-?locale=en (last access: 14 October 2019), 2019.
Vander Jagt, B., Lucieer, A., Wallace, L., Turner, D., and Durand, M.: Snow depth retrieval with UAS using photogrammetric techniques, Geosciences, 5, 264–285, https://doi.org/10.3390/geosciences5030264, 2015.
Varhola, A., Coops, N. C., Weiler, M., and Moore, R. D.: Forest canopy effects on snow accumulation and ablation: An integrative review of empirical results, J. Hydrol., 392, 219–233, https://doi.org/10.1016/j.jhydrol.2010.08.009, 2010.
Webster, C., Rutter, N., Zahner, F., and Jonas, T.: Modeling subcanopy incoming longwave radiation to seasonal snow using air and tree trunk temperatures, J. Geophys. Res.-Atmos., 121, 1220–1235, https://doi.org/10.1002/2015JD024099, 2016.
Zhang, Z., Glaser, S., Bales, R., Conklin, M., Rice, R., and Marks, D.: Insights into mountain precipitation and snowpack from a basin-scale wireless-sensor network, Water Resour. Res., 53, 6626–6641, https://doi.org/10.1002/2016WR018825, 2017.
Short summary
Information on seasonal snow cover is essential in understanding snow processes and operational forecasting. We study the spatiotemporal variability in snow depth and snow processes in a subarctic, boreal landscape using drones. We identified multiple theoretically known snow processes and interactions between snow and vegetation. The results highlight the applicability of the drones to be used for a detailed study of snow depth in multiple land cover types and snow–vegetation interactions.
Information on seasonal snow cover is essential in understanding snow processes and operational...
