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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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.
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Related subject area
Discipline: Snow | Subject: Remote Sensing
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Snow water equivalent retrieval over Idaho – Part 1: Using Sentinel-1 repeat-pass interferometry
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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.
James Dillon, Christopher Donahue, Evan Schehrer, Karl Birkeland, and Kevin Hammonds
EGUsphere, https://doi.org/10.5194/egusphere-2023-3133, https://doi.org/10.5194/egusphere-2023-3133, 2024
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Surface hoar crystals are snow grains that form when vapor deposits on the 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 accuracy upwards of 95 %.
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.
Steven J. Pestana, C. Chris Chickadel, and Jessica D. Lundquist
EGUsphere, https://doi.org/10.5194/egusphere-2023-1784, https://doi.org/10.5194/egusphere-2023-1784, 2023
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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’ve learned that the position of the sun and surface features such as trees that can cast shadows impact GOES-R infrared images.
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
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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.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
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Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Julien Meloche, Alexandre Langlois, Nick Rutter, Alain Royer, Josh King, Branden Walker, Philip Marsh, and Evan J. Wilcox
The Cryosphere, 16, 87–101, https://doi.org/10.5194/tc-16-87-2022, https://doi.org/10.5194/tc-16-87-2022, 2022
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To estimate snow water equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.
Christopher Donahue, S. McKenzie Skiles, and Kevin Hammonds
The Cryosphere, 16, 43–59, https://doi.org/10.5194/tc-16-43-2022, https://doi.org/10.5194/tc-16-43-2022, 2022
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The amount of water within a snowpack is important information for predicting snowmelt and wet-snow avalanches. From within a controlled laboratory, the optimal method for measuring liquid water content (LWC) at the snow surface or along a snow pit profile using near-infrared imagery was determined. As snow samples melted, multiple models to represent wet-snow reflectance were assessed against a more established LWC instrument. The best model represents snow as separate spheres of ice and water.
Zacharie Barrou Dumont, Simon Gascoin, Olivier Hagolle, Michaël Ablain, Rémi Jugier, Germain Salgues, Florence Marti, Aurore Dupuis, Marie Dumont, and Samuel Morin
The Cryosphere, 15, 4975–4980, https://doi.org/10.5194/tc-15-4975-2021, https://doi.org/10.5194/tc-15-4975-2021, 2021
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Since 2020, the Copernicus High Resolution Snow & Ice Monitoring Service has distributed snow cover maps at 20 m resolution over Europe in near-real time. These products are derived from the Sentinel-2 Earth observation mission, with a revisit time of 5 d or less (cloud-permitting). Here we show the good accuracy of the snow detection over a wide range of regions in Europe, except in dense forest regions where the snow cover is hidden by the trees.
Xiaodan Wu, Kathrin Naegeli, Valentina Premier, Carlo Marin, Dujuan Ma, Jingping Wang, and Stefan Wunderle
The Cryosphere, 15, 4261–4279, https://doi.org/10.5194/tc-15-4261-2021, https://doi.org/10.5194/tc-15-4261-2021, 2021
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We performed a comprehensive accuracy assessment of an Advanced Very High Resolution Radiometer global area coverage snow-cover extent time series dataset for the Hindu Kush Himalayan (HKH) region. The sensor-to-sensor consistency, the accuracy related to snow depth, elevations, land-cover types, slope, and aspects, and topographical variability were also explored. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006.
Pia Nielsen-Englyst, Jacob L. Høyer, Kristine S. Madsen, Rasmus T. Tonboe, Gorm Dybkjær, and Sotirios Skarpalezos
The Cryosphere, 15, 3035–3057, https://doi.org/10.5194/tc-15-3035-2021, https://doi.org/10.5194/tc-15-3035-2021, 2021
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The Arctic region is responding heavily to climate change, and yet, the air temperature of Arctic ice-covered areas is heavily under-sampled when it comes to in situ measurements. This paper presents a method for estimating daily mean 2 m air temperatures (T2m) in the Arctic from satellite observations of skin temperature, providing spatially detailed observations of the Arctic. The satellite-derived T2m product covers clear-sky snow and ice surfaces in the Arctic for the period 2000–2009.
Pinja Venäläinen, Kari Luojus, Juha Lemmetyinen, Jouni Pulliainen, Mikko Moisander, and Matias Takala
The Cryosphere, 15, 2969–2981, https://doi.org/10.5194/tc-15-2969-2021, https://doi.org/10.5194/tc-15-2969-2021, 2021
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Information about snow water equivalent (SWE) is needed in many applications, including climate model evaluation and forecasting fresh water availability. Space-borne radiometer observations combined with ground snow depth measurements can be used to make global estimates of SWE. In this study, we investigate the possibility of using sparse snow density measurement in satellite-based SWE retrieval and show that using the snow density information in post-processing improves SWE estimations.
Linlu Mei, Vladimir Rozanov, Christine Pohl, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2757–2780, https://doi.org/10.5194/tc-15-2757-2021, https://doi.org/10.5194/tc-15-2757-2021, 2021
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This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 1 of two companion papers and shows the method description and sensitivity study. The paper investigates the major factors, including the assumptions of snow optical properties, snow particle distribution and atmospheric conditions (cloud and aerosol), impacting snow property retrievals from satellite observation.
Linlu Mei, Vladimir Rozanov, Evelyn Jäkel, Xiao Cheng, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2781–2802, https://doi.org/10.5194/tc-15-2781-2021, https://doi.org/10.5194/tc-15-2781-2021, 2021
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This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 2 of two companion papers and shows the results and validation. The paper performs the new retrieval algorithm on the Sea and Land
Surface Temperature Radiometer (SLSTR) instrument and compares the retrieved snow properties with ground-based measurements, aircraft measurements and other satellite products.
Ahmad Hojatimalekshah, Zachary Uhlmann, Nancy F. Glenn, Christopher A. Hiemstra, Christopher J. Tennant, Jake D. Graham, Lucas Spaete, Arthur Gelvin, Hans-Peter Marshall, James P. McNamara, and Josh Enterkine
The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, https://doi.org/10.5194/tc-15-2187-2021, 2021
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We describe the relationships between snow depth, vegetation canopy, and local-scale processes during the snow accumulation period using terrestrial laser scanning (TLS). In addition to topography and wind, our findings suggest the importance of fine-scale tree structure, species type, and distributions on snow depth. Snow depth increases from the canopy edge toward the open areas, but wind and topographic controls may affect this trend. TLS data are complementary to wide-area lidar surveys.
Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, Elizabeth A. Burakowski, Christina Herrick, and Eunsang Cho
The Cryosphere, 15, 1485–1500, https://doi.org/10.5194/tc-15-1485-2021, https://doi.org/10.5194/tc-15-1485-2021, 2021
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This pilot study describes a proof of concept for using lidar on an unpiloted aerial vehicle to map shallow snowpack (< 20 cm) depth in open terrain and forests. The 1 m2 resolution snow depth map, generated by subtracting snow-off from snow-on lidar-derived digital terrain models, consistently had 0.5 to 1 cm precision in the field, with a considerable reduction in accuracy in the forest. Performance depends on the point cloud density and the ground surface variability and vegetation.
Elisabeth D. Hafner, Frank Techel, Silvan Leinss, and Yves Bühler
The Cryosphere, 15, 983–1004, https://doi.org/10.5194/tc-15-983-2021, https://doi.org/10.5194/tc-15-983-2021, 2021
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Satellites prove to be very valuable for documentation of large-scale avalanche periods. To test reliability and completeness, which has not been satisfactorily verified before, we attempt a full validation of avalanches mapped from two optical sensors and one radar sensor. Our results demonstrate the reliability of high-spatial-resolution optical data for avalanche mapping, the suitability of radar for mapping of larger avalanches and the unsuitability of medium-spatial-resolution optical data.
Xiongxin Xiao, Shunlin Liang, Tao He, Daiqiang Wu, Congyuan Pei, and Jianya Gong
The Cryosphere, 15, 835–861, https://doi.org/10.5194/tc-15-835-2021, https://doi.org/10.5194/tc-15-835-2021, 2021
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Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. Due to the fact that observations from optical satellite sensors are affected by clouds, this study attempts to capture dynamic characteristics of snow cover at a fine spatiotemporal resolution (daily; 6.25 km) accurately by using passive microwave data. We demonstrate the potential to use the passive microwave and the MODIS data to map the fractional snow cover area.
Marco Bongio, Ali Nadir Arslan, Cemal Melih Tanis, and Carlo De Michele
The Cryosphere, 15, 369–387, https://doi.org/10.5194/tc-15-369-2021, https://doi.org/10.5194/tc-15-369-2021, 2021
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The capability of time-lapse photography to retrieve snow depth time series was tested. We demonstrated that this method can be efficiently used in three different case studies: two in the Italian Alps and one in a forested region of Finland, with an accuracy comparable to the most common methods such as ultrasonic sensors or manual measurements. We hope that this simple method based only on a camera and a graduated stake can enable snow depth measurements in dangerous and inaccessible sites.
Lucie A. Eberhard, Pascal Sirguey, Aubrey Miller, Mauro Marty, Konrad Schindler, Andreas Stoffel, and Yves Bühler
The Cryosphere, 15, 69–94, https://doi.org/10.5194/tc-15-69-2021, https://doi.org/10.5194/tc-15-69-2021, 2021
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In spring 2018 in the alpine Dischma valley (Switzerland), we tested different industrial photogrammetric platforms for snow depth mapping. These platforms were high-resolution satellites, an airplane, unmanned aerial systems and a terrestrial system. Therefore, this study gives a general overview of the accuracy and precision of the different photogrammetric platforms available in space and on earth and their use for snow depth mapping.
Maxim Lamare, Marie Dumont, Ghislain Picard, Fanny Larue, François Tuzet, Clément Delcourt, and Laurent Arnaud
The Cryosphere, 14, 3995–4020, https://doi.org/10.5194/tc-14-3995-2020, https://doi.org/10.5194/tc-14-3995-2020, 2020
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Terrain features found in mountainous regions introduce large errors into the calculation of the physical properties of snow using optical satellite images. We present a new model performing rapid calculations of solar radiation over snow-covered rugged terrain that we tested over a site in the French Alps. The results of the study show that all the interactions between sunlight and the terrain should be accounted for over snow-covered surfaces to correctly estimate snow properties from space.
Tim Carlsen, Gerit Birnbaum, André Ehrlich, Veit Helm, Evelyn Jäkel, Michael Schäfer, and Manfred Wendisch
The Cryosphere, 14, 3959–3978, https://doi.org/10.5194/tc-14-3959-2020, https://doi.org/10.5194/tc-14-3959-2020, 2020
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The angular reflection of solar radiation by snow surfaces is particularly anisotropic and highly variable. We measured the angular reflection from an aircraft using a digital camera in Antarctica in 2013/14 and studied its variability: the anisotropy increases with a lower Sun but decreases for rougher surfaces and larger snow grains. The applied methodology allows for a direct comparison with satellite observations, which generally underestimated the anisotropy measured within this study.
César Deschamps-Berger, Simon Gascoin, Etienne Berthier, Jeffrey Deems, Ethan Gutmann, Amaury Dehecq, David Shean, and Marie Dumont
The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, https://doi.org/10.5194/tc-14-2925-2020, 2020
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We evaluate a recent method to map snow depth based on satellite photogrammetry. We compare it with accurate airborne laser-scanning measurements in the Sierra Nevada, USA. We find that satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountains.
Phillip Harder, John W. Pomeroy, and Warren D. Helgason
The Cryosphere, 14, 1919–1935, https://doi.org/10.5194/tc-14-1919-2020, https://doi.org/10.5194/tc-14-1919-2020, 2020
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Unmanned-aerial-vehicle-based (UAV) structure-from-motion (SfM) techniques have the ability to map snow depths in open areas. Here UAV lidar and SfM are compared to map sub-canopy snowpacks. Snow depth accuracy was assessed with data from sites in western Canada collected in 2019. It is demonstrated that UAV lidar can measure the sub-canopy snow depth at a high accuracy, while UAV-SfM cannot. UAV lidar promises to quantify snow–vegetation interactions at unprecedented accuracy and resolution.
Carlo Marin, Giacomo Bertoldi, Valentina Premier, Mattia Callegari, Christian Brida, Kerstin Hürkamp, Jochen Tschiersch, Marc Zebisch, and Claudia Notarnicola
The Cryosphere, 14, 935–956, https://doi.org/10.5194/tc-14-935-2020, https://doi.org/10.5194/tc-14-935-2020, 2020
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In this paper, we use for the first time the synthetic aperture radar (SAR) time series acquired by Sentinel-1 to monitor snowmelt dynamics in alpine regions. We found that the multitemporal SAR allows the identification of the three phases that characterize the melting process, i.e., moistening, ripening and runoff, in a spatial distributed way. We believe that the presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.
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...
