Articles | Volume 9, issue 4
https://doi.org/10.5194/tc-9-1445-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-9-1445-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| Highlight paper
| 
05 Aug 2015
Research article | Highlight paper |
| 05 Aug 2015
Mapping snow depth from manned aircraft on landscape scales at centimeter resolution using structure-from-motion photogrammetry
M. Nolan
CORRESPONDING AUTHOR
Institute of Northern Engineering, University of Alaska Fairbanks, 306 Tanana Loop, Fairbanks, AK 99775, USA
C. Larsen
Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775, USA
M. Sturm
Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775, USA
Related authors
Ellie Broadman, Lorna L. Thurston, Erik Schiefer, Nicholas P. McKay, David Fortin, Jason Geck, Michael G. Loso, Matt Nolan, Stéphanie H. Arcusa, Christopher W. Benson, Rebecca A. Ellerbroek, Michael P. Erb, Cody C. Routson, Charlotte Wiman, A. Jade Wong, and Darrell S. Kaufman
Earth Syst. Sci. Data, 11, 1957–1970, https://doi.org/10.5194/essd-11-1957-2019, https://doi.org/10.5194/essd-11-1957-2019, 2019
Short summary
Short summary
Rapid climate warming is impacting physical processes in Arctic environments. Glacier–fed lakes are influenced by many of these processes, and they are impacted by the changing behavior of weather, glaciers, and rivers. We present data from weather stations, river gauging stations, lake moorings, and more, following 4 years of environmental monitoring in the watershed of Lake Peters, a glacier–fed lake in Arctic Alaska. These data can help us study the changing dynamics of this remote setting.
Matt Nolan and Kit DesLauriers
The Cryosphere, 10, 1245–1257, https://doi.org/10.5194/tc-10-1245-2016, https://doi.org/10.5194/tc-10-1245-2016, 2016
Short summary
Short summary
We measured the heights of the five tallest peaks in the US Arctic using fodar, a new airborne photogrammetric technique using structure-from-motion software. The highest peaks are Mt Isto (2735.6 m), Mt. Hubley (2717.6 m), Mt. Chamberlin (2712.3 m), Mt. Michelson (2698.1 m), and an unnamed peak (2694.9 m). We found fodar suitable for topographic change detection on the centimeter scale in steep mountain terrain, such as for measuring snow depths.
J.-F. Lamarque, F. Dentener, J. McConnell, C.-U. Ro, M. Shaw, R. Vet, D. Bergmann, P. Cameron-Smith, S. Dalsoren, R. Doherty, G. Faluvegi, S. J. Ghan, B. Josse, Y. H. Lee, I. A. MacKenzie, D. Plummer, D. T. Shindell, R. B. Skeie, D. S. Stevenson, S. Strode, G. Zeng, M. Curran, D. Dahl-Jensen, S. Das, D. Fritzsche, and M. Nolan
Atmos. Chem. Phys., 13, 7997–8018, https://doi.org/10.5194/acp-13-7997-2013, https://doi.org/10.5194/acp-13-7997-2013, 2013
Ellie Broadman, Lorna L. Thurston, Erik Schiefer, Nicholas P. McKay, David Fortin, Jason Geck, Michael G. Loso, Matt Nolan, Stéphanie H. Arcusa, Christopher W. Benson, Rebecca A. Ellerbroek, Michael P. Erb, Cody C. Routson, Charlotte Wiman, A. Jade Wong, and Darrell S. Kaufman
Earth Syst. Sci. Data, 11, 1957–1970, https://doi.org/10.5194/essd-11-1957-2019, https://doi.org/10.5194/essd-11-1957-2019, 2019
Short summary
Short summary
Rapid climate warming is impacting physical processes in Arctic environments. Glacier–fed lakes are influenced by many of these processes, and they are impacted by the changing behavior of weather, glaciers, and rivers. We present data from weather stations, river gauging stations, lake moorings, and more, following 4 years of environmental monitoring in the watershed of Lake Peters, a glacier–fed lake in Arctic Alaska. These data can help us study the changing dynamics of this remote setting.
Matt Nolan and Kit DesLauriers
The Cryosphere, 10, 1245–1257, https://doi.org/10.5194/tc-10-1245-2016, https://doi.org/10.5194/tc-10-1245-2016, 2016
Short summary
Short summary
We measured the heights of the five tallest peaks in the US Arctic using fodar, a new airborne photogrammetric technique using structure-from-motion software. The highest peaks are Mt Isto (2735.6 m), Mt. Hubley (2717.6 m), Mt. Chamberlin (2712.3 m), Mt. Michelson (2698.1 m), and an unnamed peak (2694.9 m). We found fodar suitable for topographic change detection on the centimeter scale in steep mountain terrain, such as for measuring snow depths.
J.-F. Lamarque, F. Dentener, J. McConnell, C.-U. Ro, M. Shaw, R. Vet, D. Bergmann, P. Cameron-Smith, S. Dalsoren, R. Doherty, G. Faluvegi, S. J. Ghan, B. Josse, Y. H. Lee, I. A. MacKenzie, D. Plummer, D. T. Shindell, R. B. Skeie, D. S. Stevenson, S. Strode, G. Zeng, M. Curran, D. Dahl-Jensen, S. Das, D. Fritzsche, and M. Nolan
Atmos. Chem. Phys., 13, 7997–8018, https://doi.org/10.5194/acp-13-7997-2013, https://doi.org/10.5194/acp-13-7997-2013, 2013
Related subject area
Seasonal Snow
An examination of changes in autumn Eurasian snow cover and its relationship with the winter Arctic Oscillation using 20th Century Reanalysis version 3
Historical snow measurements in the central and southern Apennine Mountains: climatology, variability, and trend
Benchmarking of snow water equivalent (SWE) products based on outcomes of the SnowPEx+ Intercomparison Project
Snow depth sensitivity to mean temperature, precipitation, and elevation in the Austrian and Swiss Alps
Use of multiple reference data sources to cross-validate gridded snow water equivalent products over North America
Trends in the annual snow melt-out day over the French Alps and the Pyrenees from 38 years of high resolution satellite data (1986–2023)
Characterization of non-Gaussianity in the snow distributions of various landscapes
A simple snow temperature index model exposes discrepancies between reanalysis snow water equivalent products
Which global reanalysis dataset has better representativeness in snow cover on the Tibetan Plateau?
From snow accumulation to snow depth distributions by quantifying meteoric ice fractions in the Weddell Sea
Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?
Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry
Variability and drivers of winter near-surface temperatures over boreal and tundra landscapes
Spatiotemporal snow water storage uncertainty in the midlatitude American Cordillera
Evaluation of snow cover properties in ERA5 and ERA5-Land with several satellite-based datasets in the Northern Hemisphere in spring 1982–2018
Sublimation Measurements of Tundra and Taiga Snowpack in Alaska
Multi-decadal analysis of past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets
Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas
Change in the potential snowfall phenology: past, present, and future in the Chinese Tianshan mountainous region, Central Asia
The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland
Assessing the seasonal evolution of snow depth spatial variability and scaling in complex mountain terrain
Impact of measured and simulated tundra snowpack properties on heat transfer
Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods
Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network
Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982–2014
Past changes in natural and managed snow reliability of French Alps ski resorts from 1961 to 2019
Multilayer observation and estimation of the snowpack cold content in a humid boreal coniferous forest of eastern Canada
Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset
Recent changes in pan-Arctic sea ice, lake ice, and snow-on/off timing
Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements
Observed snow depth trends in the European Alps: 1971 to 2019
Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling
Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps)
Local-scale variability of snow density on Arctic sea ice
Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach
Evaluation of long-term Northern Hemisphere snow water equivalent products
Towards a webcam-based snow cover monitoring network: methodology and evaluation
Simulated single-layer forest canopies delay Northern Hemisphere snowmelt
Spatiotemporal variability and decadal trends of snowmelt processes on Antarctic sea ice observed by satellite scatterometers
Converting snow depth to snow water equivalent using climatological variables
Avalanches and micrometeorology driving mass and energy balance of the lowest perennial ice field of the Alps: a case study
The optical characteristics and sources of chromophoric dissolved organic matter (CDOM) in seasonal snow of northwestern China
Brief Communication: Early season snowpack loss and implications for oversnow vehicle recreation travel planning
Multi-component ensembles of future meteorological and natural snow conditions for 1500 m altitude in the Chartreuse mountain range, Northern French Alps
Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system
Canadian snow and sea ice: historical trends and projections
Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models
Black carbon and mineral dust in snow cover on the Tibetan Plateau
Snow farming: conserving snow over the summer season
Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites
Gareth J. Marshall
The Cryosphere, 19, 663–683, https://doi.org/10.5194/tc-19-663-2025, https://doi.org/10.5194/tc-19-663-2025, 2025
Short summary
Short summary
Eurasian autumn snow cover (SC) can influence Northern Hemisphere weather in the following winter by affecting the Arctic Oscillation (AO) mode of atmospheric variability. Using data back to 1836, we show that there have been significant decreases in October and November SC. For the first time, we describe a robust relationship between September SC in northeastern Eurasia and the AO. In addition, the longer dataset reveals the temporal variability in previously identified SC–AO relationships.
Vincenzo Capozzi, Francesco Serrapica, Armando Rocco, Clizia Annella, and Giorgio Budillon
The Cryosphere, 19, 565–595, https://doi.org/10.5194/tc-19-565-2025, https://doi.org/10.5194/tc-19-565-2025, 2025
Short summary
Short summary
This “journey through time” discovers historical information about snow precipitation in the Italian Apennines. In this area, in the second half of the past century, a gradual decline in snow persistence on the ground, as well as in the frequency of occurrence of snowfall events, has been observed, especially in sites located above 1000 m above sea level. The old data rescued in this study strongly enhance our knowledge about past snowfall variability and climate in the Mediterranean area.
Lawrence Mudryk, Colleen Mortimer, Chris Derksen, Aleksandra Elias Chereque, and Paul Kushner
The Cryosphere, 19, 201–218, https://doi.org/10.5194/tc-19-201-2025, https://doi.org/10.5194/tc-19-201-2025, 2025
Short summary
Short summary
We evaluate and rank 23 different datasets on their ability to accurately estimate historical snow amounts. The evaluation uses new a set of surface snow measurements with improved spatial coverage, enabling evaluation across both mountainous and nonmountainous regions. Performance measures vary tremendously across the products: while most perform reasonably in nonmountainous regions, accurate representation of snow amounts in mountainous regions and of historical trends is much more variable.
Matthew Switanek, Gernot Resch, Andreas Gobiet, Daniel Günther, Christoph Marty, and Wolfgang Schöner
The Cryosphere, 18, 6005–6026, https://doi.org/10.5194/tc-18-6005-2024, https://doi.org/10.5194/tc-18-6005-2024, 2024
Short summary
Short summary
Snow depth plays an important role in water resources, mountain tourism, and hazard management across the European Alps. Our study uses station-based historical observations to quantify how changes in temperature and precipitation affect average seasonal snow depth. We find that the relationship between these variables has been surprisingly robust over the last 120 years. This allows us to more accurately estimate how future climate will affect seasonal snow depth in different elevation zones.
Colleen Mortimer, Lawrence Mudryk, Eunsang Cho, Chris Derksen, Mike Brady, and Carrie Vuyovich
The Cryosphere, 18, 5619–5639, https://doi.org/10.5194/tc-18-5619-2024, https://doi.org/10.5194/tc-18-5619-2024, 2024
Short summary
Short summary
Ground measurements of snow water equivalent (SWE) are vital for understanding the accuracy of large-scale estimates from satellites and climate models. We compare two types of measurements – snow courses and airborne gamma SWE estimates – and analyze how measurement type impacts the accuracy assessment of gridded SWE products. We use this analysis to produce a combined reference SWE dataset for North America, applicable for future gridded SWE product evaluations and other applications.
Zacharie Barrou Dumont, Simon Gascoin, Jordi Inglada, Andreas Dietz, Jonas Köhler, Matthieu Lafaysse, Diego Monteiro, Carlo Carmagnola, Arthur Bayle, Jean-Pierre Dedieu, Olivier Hagolle, and Philippe Choler
EGUsphere, https://doi.org/10.5194/egusphere-2024-3505, https://doi.org/10.5194/egusphere-2024-3505, 2024
Short summary
Short summary
We generated annual maps of snow melt-out day at 20 m resolution over a period of 38 years from ten different satellites. This study fills a knowledge gap on the evolution of mountain snow in Europe by covering a much longer period and by characterizing trends at much higher resolution than previous studies. We found a trend for earlier melt-out with an average reduction of 5.51 days per decade over the French Alps and of 4.04 day per decade over the Pyrenees over the period 1986–2023.
Noriaki Ohara, Andrew D. Parsekian, Benjamin M. Jones, Rodrigo C. Rangel, Kenneth M. Hinkel, and Rui A. P. Perdigão
The Cryosphere, 18, 5139–5152, https://doi.org/10.5194/tc-18-5139-2024, https://doi.org/10.5194/tc-18-5139-2024, 2024
Short summary
Short summary
Snow distribution characterization is essential for accurate snow water estimation for water resource prediction from existing in situ observations and remote-sensing data at a finite spatial resolution. Four different observed snow distribution datasets were analyzed for Gaussianity. We found that non-Gaussianity of snow distribution is a signature of the wind redistribution effect. Generally, seasonal snowpack can be approximated well by a Gaussian distribution for a fully snow-covered area.
Aleksandra Elias Chereque, Paul J. Kushner, Lawrence Mudryk, Chris Derksen, and Colleen Mortimer
The Cryosphere, 18, 4955–4969, https://doi.org/10.5194/tc-18-4955-2024, https://doi.org/10.5194/tc-18-4955-2024, 2024
Short summary
Short summary
We look at three commonly used snow depth datasets that are produced through a combination of snow modelling and historical measurements (reanalysis). When compared with each other, these datasets have differences that arise for various reasons. We show that a simple snow model can be used to examine these inconsistencies and highlight issues. This method indicates that one of the complex datasets should be excluded from further studies.
Shirui Yan, Yang Chen, Yaliang Hou, Kexin Liu, Xuejing Li, Yuxuan Xing, Dongyou Wu, Jiecan Cui, Yue Zhou, Wei Pu, and Xin Wang
The Cryosphere, 18, 4089–4109, https://doi.org/10.5194/tc-18-4089-2024, https://doi.org/10.5194/tc-18-4089-2024, 2024
Short summary
Short summary
The snow cover over the Tibetan Plateau (TP) plays a role in climate and hydrological systems, yet there are uncertainties in snow cover fraction (SCF) estimations within reanalysis datasets. This study utilized the Snow Property Inversion from Remote Sensing (SPIReS) SCF data to assess the accuracy of eight widely used reanalysis SCF datasets over the TP. Factors contributing to uncertainties were analyzed, and a combined averaging method was employed to provide optimized SCF simulations.
Stefanie Arndt, Nina Maaß, Leonard Rossmann, and Marcel Nicolaus
The Cryosphere, 18, 2001–2015, https://doi.org/10.5194/tc-18-2001-2024, https://doi.org/10.5194/tc-18-2001-2024, 2024
Short summary
Short summary
Antarctic sea ice maintains year-round snow cover, crucial for its energy and mass budgets. Despite its significance, snow depth remains poorly understood. Over the last decades, Snow Buoys have been deployed extensively on the sea ice to measure snow accumulation but not actual depth due to snow transformation into meteoric ice. Therefore, in this study we utilize sea ice and snow models to estimate meteoric ice fractions in order to calculate actual snow depth in the Weddell Sea.
Benjamin Poschlod and Anne Sophie Daloz
The Cryosphere, 18, 1959–1981, https://doi.org/10.5194/tc-18-1959-2024, https://doi.org/10.5194/tc-18-1959-2024, 2024
Short summary
Short summary
Information about snow depth is important within climate research but also many other sectors, such as tourism, mobility, civil engineering, and ecology. Climate models often feature a spatial resolution which is too coarse to investigate snow depth. Here, we analyse high-resolution simulations and identify added value compared to a coarser-resolution state-of-the-art product. Also, daily snow depth extremes are well reproduced by two models.
Zachary Hoppinen, Shadi Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, and Carrie Vuyovich
The Cryosphere, 18, 575–592, https://doi.org/10.5194/tc-18-575-2024, https://doi.org/10.5194/tc-18-575-2024, 2024
Short summary
Short summary
We used changes in radar echo travel time from multiple airborne flights to estimate changes in snow depths across Idaho for two winters. We compared our radar-derived retrievals to snow pits, weather stations, and a 100 m resolution numerical snow model. We had a strong Pearson correlation and root mean squared error of 10 cm relative to in situ measurements. Our retrievals also correlated well with our model, especially in regions of dry snow and low tree coverage.
Vilna Tyystjärvi, Pekka Niittynen, Julia Kemppinen, Miska Luoto, Tuuli Rissanen, and Juha Aalto
The Cryosphere, 18, 403–423, https://doi.org/10.5194/tc-18-403-2024, https://doi.org/10.5194/tc-18-403-2024, 2024
Short summary
Short summary
At high latitudes, winter ground surface temperatures are strongly controlled by seasonal snow cover and its spatial variation. Here, we measured surface temperatures and snow cover duration in 441 study sites in tundra and boreal regions. Our results show large variations in how much surface temperatures in winter vary depending on the landscape and its impact on snow cover. These results emphasise the importance of understanding microclimates and their drivers under changing winter conditions.
Yiwen Fang, Yufei Liu, Dongyue Li, Haorui Sun, and Steven A. Margulis
The Cryosphere, 17, 5175–5195, https://doi.org/10.5194/tc-17-5175-2023, https://doi.org/10.5194/tc-17-5175-2023, 2023
Short summary
Short summary
Using newly developed snow reanalysis datasets as references, snow water storage is at high uncertainty among commonly used global products in the Andes and low-resolution products in the western United States, where snow is the key element of water resources. In addition to precipitation, elevation differences and model mechanism variances drive snow uncertainty. This work provides insights for research applying these products and generating future products in areas with limited in situ data.
Kerttu Kouki, Kari Luojus, and Aku Riihelä
The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023, https://doi.org/10.5194/tc-17-5007-2023, 2023
Short summary
Short summary
We evaluated snow cover properties in state-of-the-art reanalyses (ERA5 and ERA5-Land) with satellite-based datasets. Both ERA5 and ERA5-Land overestimate snow mass, whereas albedo estimates are more consistent between the datasets. Snow cover extent (SCE) is accurately described in ERA5-Land, while ERA5 shows larger SCE than the satellite-based datasets. The trends in snow mass, SCE, and albedo are mostly negative in 1982–2018, and the negative trends become more apparent when spring advances.
Kelsey Spehlmann, Eugénie Euskirchen, and Svetlana Stuefer
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-153, https://doi.org/10.5194/tc-2023-153, 2023
Revised manuscript accepted for TC
Short summary
Short summary
Sublimation is the hidden portion of the water cycle where snow changes phase directly to water vapor, skipping the liquid state. Though sublimation is difficult to measure, especially in remote regions such as arctic and subarctic Alaska where this study took place, our measurements confirm that sublimation is a substantial component of the annual water cycle. Results from this research contribute to knowledge of how site conditions affect sublimation rates and the winter hydrologic cycle.
Diego Monteiro and Samuel Morin
The Cryosphere, 17, 3617–3660, https://doi.org/10.5194/tc-17-3617-2023, https://doi.org/10.5194/tc-17-3617-2023, 2023
Short summary
Short summary
Beyond directly using in situ observations, often sparsely available in mountain regions, climate model simulations and so-called reanalyses are increasingly used for climate change impact studies. Here we evaluate such datasets in the European Alps from 1950 to 2020, with a focus on snow cover information and its main drivers: air temperature and precipitation. In terms of variability and trends, we identify several limitations and provide recommendations for future use of these datasets.
Leon J. Bührle, Mauro Marty, Lucie A. Eberhard, Andreas Stoffel, Elisabeth D. Hafner, and Yves Bühler
The Cryosphere, 17, 3383–3408, https://doi.org/10.5194/tc-17-3383-2023, https://doi.org/10.5194/tc-17-3383-2023, 2023
Short summary
Short summary
Information on the snow depth distribution is crucial for numerous applications in high-mountain regions. However, only specific measurements can accurately map the present variability of snow depths within complex terrain. In this study, we show the reliable processing of images from aeroplane to large (> 100 km2) detailed and accurate snow depth maps around Davos (CH). We use these maps to describe the existing snow depth distribution, other special features and potential applications.
Xuemei Li, Xinyu Liu, Kaixin Zhao, Xu Zhang, and Lanhai Li
The Cryosphere, 17, 2437–2453, https://doi.org/10.5194/tc-17-2437-2023, https://doi.org/10.5194/tc-17-2437-2023, 2023
Short summary
Short summary
Quantifying change in the potential snowfall phenology (PSP) is an important area of research for understanding regional climate change past, present, and future. However, few studies have focused on the PSP and its change in alpine mountainous regions. We proposed three innovative indicators to characterize the PSP and its spatial–temporal variation. Our study provides a novel approach to understanding PSP in alpine mountainous regions and can be easily extended to other snow-dominated regions.
Moritz Buchmann, Gernot Resch, Michael Begert, Stefan Brönnimann, Barbara Chimani, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 17, 653–671, https://doi.org/10.5194/tc-17-653-2023, https://doi.org/10.5194/tc-17-653-2023, 2023
Short summary
Short summary
Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are susceptible to inhomogeneities that can affect the trends and even change the sign. To assess the relevance of homogenisation for daily snow depths, we investigated its impact on trends and changes in extreme values of snow indices between 1961 and 2021 in the Swiss observation network.
Zachary S. Miller, Erich H. Peitzsch, Eric A. Sproles, Karl W. Birkeland, and Ross T. Palomaki
The Cryosphere, 16, 4907–4930, https://doi.org/10.5194/tc-16-4907-2022, https://doi.org/10.5194/tc-16-4907-2022, 2022
Short summary
Short summary
Snow depth varies across steep, complex mountain landscapes due to interactions between dynamic natural processes. Our study of a winter time series of high-resolution snow depth maps found that spatial resolutions greater than 0.5 m do not capture the complete patterns of snow depth spatial variability at a couloir study site in the Bridger Range of Montana, USA. The results of this research have the potential to reduce uncertainty associated with snowpack and snow water resource analysis.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike
The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, https://doi.org/10.5194/tc-16-4201-2022, 2022
Short summary
Short summary
Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.
Moritz Buchmann, John Coll, Johannes Aschauer, Michael Begert, Stefan Brönnimann, Barbara Chimani, Gernot Resch, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 16, 2147–2161, https://doi.org/10.5194/tc-16-2147-2022, https://doi.org/10.5194/tc-16-2147-2022, 2022
Short summary
Short summary
Knowledge about inhomogeneities in a data set is important for any subsequent climatological analysis. We ran three well-established homogenization methods and compared the identified break points. By only treating breaks as valid when detected by at least two out of three methods, we enhanced the robustness of our results. We found 45 breaks within 42 of 184 investigated series; of these 70 % could be explained by events recorded in the station history.
Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont
The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, https://doi.org/10.5194/tc-16-1281-2022, 2022
Short summary
Short summary
The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover models suffer from large errors, while snowpack observations are sparse. Data assimilation combines them into a better estimate of the snow cover. A major challenge is to propagate information from observed into unobserved areas. This paper presents a spatialized version of the particle filter, in which information from in situ snow depth observations is successfully used to constrain nearby simulations.
Kerttu Kouki, Petri Räisänen, Kari Luojus, Anna Luomaranta, and Aku Riihelä
The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, https://doi.org/10.5194/tc-16-1007-2022, 2022
Short summary
Short summary
We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.
Lucas Berard-Chenu, Hugues François, Emmanuelle George, and Samuel Morin
The Cryosphere, 16, 863–881, https://doi.org/10.5194/tc-16-863-2022, https://doi.org/10.5194/tc-16-863-2022, 2022
Short summary
Short summary
This study investigates the past snow reliability (1961–2019) of 16 ski resorts in the French Alps using state-of-the-art snowpack modelling. We used snowmaking investment figures to infer the evolution of snowmaking coverage at the individual ski resort level. Snowmaking improved snow reliability for the core of the winter season for the highest-elevation ski resorts. However it did not counterbalance the decreasing trend in snow cover reliability for lower-elevation ski resorts and in spring.
Achut Parajuli, Daniel F. Nadeau, François Anctil, and Marco Alves
The Cryosphere, 15, 5371–5386, https://doi.org/10.5194/tc-15-5371-2021, https://doi.org/10.5194/tc-15-5371-2021, 2021
Short summary
Short summary
Cold content is the energy required to attain an isothermal (0 °C) state and resulting in the snow surface melt. This study focuses on determining the multi-layer cold content (30 min time steps) relying on field measurements, snow temperature profile, and empirical formulation in four distinct forest sites of Montmorency Forest, eastern Canada. We present novel research where the effect of forest structure, local topography, and meteorological conditions on cold content variability is explored.
Yufei Liu, Yiwen Fang, and Steven A. Margulis
The Cryosphere, 15, 5261–5280, https://doi.org/10.5194/tc-15-5261-2021, https://doi.org/10.5194/tc-15-5261-2021, 2021
Short summary
Short summary
We examined the spatiotemporal distribution of stored water in the seasonal snowpack over High Mountain Asia, based on a new snow reanalysis dataset. The dataset was derived utilizing satellite-observed snow information, which spans across 18 water years, at a high spatial (~ 500 m) and temporal (daily) resolution. Snow mass and snow storage distribution over space and time are analyzed in this paper, which brings new insights into understanding the snowpack variability over this region.
Alicia A. Dauginis and Laura C. Brown
The Cryosphere, 15, 4781–4805, https://doi.org/10.5194/tc-15-4781-2021, https://doi.org/10.5194/tc-15-4781-2021, 2021
Short summary
Short summary
This work examines changes in the timing (on/off dates) of Arctic snow, lake ice, and sea ice to investigate how they have responded to recent climate change and determine if they are responding similarly. We looked at pan-Arctic trends since 1997 and regional trends since 2004 using (mainly) satellite data. Strong regional variability was shown in the snow and ice trends, which highlights the need for a detailed understanding of the regional response to ongoing changes in the Arctic climate.
Moritz Buchmann, Michael Begert, Stefan Brönnimann, and Christoph Marty
The Cryosphere, 15, 4625–4636, https://doi.org/10.5194/tc-15-4625-2021, https://doi.org/10.5194/tc-15-4625-2021, 2021
Short summary
Short summary
We investigated the impacts of local-scale variations by analysing snow climate indicators derived from parallel snow measurements. We found the largest relative inter-pair differences for all indicators in spring and the smallest in winter. The findings serve as an important basis for our understanding of uncertainties of commonly used snow indicators and provide, in combination with break-detection methods, the groundwork in view of any homogenization efforts regarding snow time series.
Michael Matiu, Alice Crespi, Giacomo Bertoldi, Carlo Maria Carmagnola, Christoph Marty, Samuel Morin, Wolfgang Schöner, Daniele Cat Berro, Gabriele Chiogna, Ludovica De Gregorio, Sven Kotlarski, Bruno Majone, Gernot Resch, Silvia Terzago, Mauro Valt, Walter Beozzo, Paola Cianfarra, Isabelle Gouttevin, Giorgia Marcolini, Claudia Notarnicola, Marcello Petitta, Simon C. Scherrer, Ulrich Strasser, Michael Winkler, Marc Zebisch, Andrea Cicogna, Roberto Cremonini, Andrea Debernardi, Mattia Faletto, Mauro Gaddo, Lorenzo Giovannini, Luca Mercalli, Jean-Michel Soubeyroux, Andrea Sušnik, Alberto Trenti, Stefano Urbani, and Viktor Weilguni
The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, https://doi.org/10.5194/tc-15-1343-2021, 2021
Short summary
Short summary
The first Alpine-wide assessment of station snow depth has been enabled by a collaborative effort of the research community which involves more than 30 partners, 6 countries, and more than 2000 stations. It shows how snow in the European Alps matches the climatic zones and gives a robust estimate of observed changes: stronger decreases in the snow season at low elevations and in spring at all elevations, however, with considerable regional differences.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
Short summary
Short summary
High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
François Tuzet, Marie Dumont, Ghislain Picard, Maxim Lamare, Didier Voisin, Pierre Nabat, Mathieu Lafaysse, Fanny Larue, Jesus Revuelto, and Laurent Arnaud
The Cryosphere, 14, 4553–4579, https://doi.org/10.5194/tc-14-4553-2020, https://doi.org/10.5194/tc-14-4553-2020, 2020
Short summary
Short summary
This study presents a field dataset collected over 30 d from two snow seasons at a Col du Lautaret site (French Alps). The dataset compares different measurements or estimates of light-absorbing particle (LAP) concentrations in snow, highlighting a gap in the current understanding of the measurement of these quantities. An ensemble snowpack model is then evaluated for this dataset estimating that LAPs shorten each snow season by around 10 d despite contrasting meteorological conditions.
Joshua King, Stephen Howell, Mike Brady, Peter Toose, Chris Derksen, Christian Haas, and Justin Beckers
The Cryosphere, 14, 4323–4339, https://doi.org/10.5194/tc-14-4323-2020, https://doi.org/10.5194/tc-14-4323-2020, 2020
Short summary
Short summary
Physical measurements of snow on sea ice are sparse, making it difficulty to evaluate satellite estimates or model representations. Here, we introduce new measurements of snow properties on sea ice to better understand variability at distances less than 200 m. Our work shows that similarities in the snow structure are found at longer distances on younger ice than older ice.
Jianwei Yang, Lingmei Jiang, Kari Luojus, Jinmei Pan, Juha Lemmetyinen, Matias Takala, and Shengli Wu
The Cryosphere, 14, 1763–1778, https://doi.org/10.5194/tc-14-1763-2020, https://doi.org/10.5194/tc-14-1763-2020, 2020
Short summary
Short summary
There are many challenges for accurate snow depth estimation using passive microwave data. Machine learning (ML) techniques are deemed to be powerful tools for establishing nonlinear relations between independent variables and a given target variable. In this study, we investigate the potential capability of the random forest (RF) model on snow depth estimation at temporal and spatial scales. The result indicates that the fitted RF algorithms perform better on temporal than spatial scales.
Colleen Mortimer, Lawrence Mudryk, Chris Derksen, Kari Luojus, Ross Brown, Richard Kelly, and Marco Tedesco
The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, https://doi.org/10.5194/tc-14-1579-2020, 2020
Short summary
Short summary
Existing stand-alone passive microwave SWE products have markedly different climatological SWE patterns compared to reanalysis-based datasets. The AMSR-E SWE has low spatial and temporal correlations with the four reanalysis-based products evaluated and GlobSnow and perform poorly in comparisons with snow transect data from Finland, Russia, and Canada. There is better agreement with in situ data when multiple SWE products, excluding the stand-alone passive microwave SWE products, are combined.
Céline Portenier, Fabia Hüsler, Stefan Härer, and Stefan Wunderle
The Cryosphere, 14, 1409–1423, https://doi.org/10.5194/tc-14-1409-2020, https://doi.org/10.5194/tc-14-1409-2020, 2020
Short summary
Short summary
We present a method to derive snow cover maps from freely available webcam images in the Swiss Alps. With marginal manual user input, we can transform a webcam image into a georeferenced map and therewith perform snow cover analyses with a high spatiotemporal resolution over a large area. Our evaluation has shown that webcams could not only serve as a reference for improved validation of satellite-based approaches, but also complement satellite-based snow cover retrieval.
Markus Todt, Nick Rutter, Christopher G. Fletcher, and Leanne M. Wake
The Cryosphere, 13, 3077–3091, https://doi.org/10.5194/tc-13-3077-2019, https://doi.org/10.5194/tc-13-3077-2019, 2019
Short summary
Short summary
Vegetation is often represented by a single layer in global land models. Studies have found deficient simulation of thermal radiation beneath forest canopies when represented by single-layer vegetation. This study corrects thermal radiation in forests for a global land model using single-layer vegetation in order to assess the effect of deficient thermal radiation on snow cover and snowmelt. Results indicate that single-layer vegetation causes snow in forests to be too cold and melt too late.
Stefanie Arndt and Christian Haas
The Cryosphere, 13, 1943–1958, https://doi.org/10.5194/tc-13-1943-2019, https://doi.org/10.5194/tc-13-1943-2019, 2019
David F. Hill, Elizabeth A. Burakowski, Ryan L. Crumley, Julia Keon, J. Michelle Hu, Anthony A. Arendt, Katreen Wikstrom Jones, and Gabriel J. Wolken
The Cryosphere, 13, 1767–1784, https://doi.org/10.5194/tc-13-1767-2019, https://doi.org/10.5194/tc-13-1767-2019, 2019
Short summary
Short summary
We present a new statistical model for converting snow depths to water equivalent. The only variables required are snow depth, day of year, and location. We use the location to look up climatological parameters such as mean winter precipitation and mean temperature difference (difference between hottest month and coldest month). The model is simple by design so that it can be applied to depth measurements anywhere, anytime. The model is shown to perform better than other widely used approaches.
Rebecca Mott, Andreas Wolf, Maximilian Kehl, Harald Kunstmann, Michael Warscher, and Thomas Grünewald
The Cryosphere, 13, 1247–1265, https://doi.org/10.5194/tc-13-1247-2019, https://doi.org/10.5194/tc-13-1247-2019, 2019
Short summary
Short summary
The mass balance of very small glaciers is often governed by anomalous snow accumulation, winter precipitation being multiplied by snow redistribution processes, or by suppressed snow ablation driven by micrometeorological effects lowering net radiation and turbulent heat exchange. In this study we discuss the relative contribution of snow accumulation (avalanches) versus micrometeorology (katabatic flow) on the mass balance of the lowest perennial ice field of the Alps, the Ice Chapel.
Yue Zhou, Hui Wen, Jun Liu, Wei Pu, Qingcai Chen, and Xin Wang
The Cryosphere, 13, 157–175, https://doi.org/10.5194/tc-13-157-2019, https://doi.org/10.5194/tc-13-157-2019, 2019
Short summary
Short summary
We first investigated the optical characteristics and potential sources of chromophoric dissolved organic matter (CDOM) in seasonal snow over northwestern China. The abundance of CDOM showed regional variation. At some sites strongly influenced by local soil, the absorption of CDOM cannot be neglected compared to black carbon. We found two humic-like and one protein-like fluorophores in snow. The major sources of snow CDOM were soil, biomass burning, and anthropogenic pollution.
Benjamin J. Hatchett and Hilary G. Eisen
The Cryosphere, 13, 21–28, https://doi.org/10.5194/tc-13-21-2019, https://doi.org/10.5194/tc-13-21-2019, 2019
Short summary
Short summary
We examine the timing of early season snowpack relevant to oversnow vehicle (OSV) recreation over the past 3 decades in the Lake Tahoe region (USA). Data from two independent data sources suggest that the timing of achieving sufficient snowpack has shifted later by 2 weeks. Increasing rainfall and more dry days play a role in the later onset. Adaptation strategies are provided for winter travel management planning to address negative impacts of loss of early season snowpack for OSV usage.
Deborah Verfaillie, Matthieu Lafaysse, Michel Déqué, Nicolas Eckert, Yves Lejeune, and Samuel Morin
The Cryosphere, 12, 1249–1271, https://doi.org/10.5194/tc-12-1249-2018, https://doi.org/10.5194/tc-12-1249-2018, 2018
Short summary
Short summary
This article addresses local changes of seasonal snow and its meteorological drivers, at 1500 m altitude in the Chartreuse mountain range in the Northern French Alps, for the period 1960–2100. We use an ensemble of adjusted RCM outputs consistent with IPCC AR5 GCM outputs (RCPs 2.6, 4.5 and 8.5) and the snowpack model Crocus. Beyond scenario-based approach, global temperature levels on the order of 1.5 °C and 2 °C above preindustrial levels correspond to 25 and 32% reduction of mean snow depth.
Paul J. Kushner, Lawrence R. Mudryk, William Merryfield, Jaison T. Ambadan, Aaron Berg, Adéline Bichet, Ross Brown, Chris Derksen, Stephen J. Déry, Arlan Dirkson, Greg Flato, Christopher G. Fletcher, John C. Fyfe, Nathan Gillett, Christian Haas, Stephen Howell, Frédéric Laliberté, Kelly McCusker, Michael Sigmond, Reinel Sospedra-Alfonso, Neil F. Tandon, Chad Thackeray, Bruno Tremblay, and Francis W. Zwiers
The Cryosphere, 12, 1137–1156, https://doi.org/10.5194/tc-12-1137-2018, https://doi.org/10.5194/tc-12-1137-2018, 2018
Short summary
Short summary
Here, the Canadian research network CanSISE uses state-of-the-art observations of snow and sea ice to assess how Canada's climate model and climate prediction systems capture variability in snow, sea ice, and related climate parameters. We find that the system performs well, accounting for observational uncertainty (especially for snow), model uncertainty, and chaotic climate variability. Even for variables like sea ice, where improvement is needed, useful prediction tools can be developed.
Lawrence R. Mudryk, Chris Derksen, Stephen Howell, Fred Laliberté, Chad Thackeray, Reinel Sospedra-Alfonso, Vincent Vionnet, Paul J. Kushner, and Ross Brown
The Cryosphere, 12, 1157–1176, https://doi.org/10.5194/tc-12-1157-2018, https://doi.org/10.5194/tc-12-1157-2018, 2018
Short summary
Short summary
This paper presents changes in both snow and sea ice that have occurred over Canada during the recent past and shows climate model estimates for future changes expected to occur by the year 2050. The historical changes of snow and sea ice are generally coherent and consistent with the regional history of temperature and precipitation changes. It is expected that snow and sea ice will continue to decrease in the future, declining by an additional 15–30 % from present day values by the year 2050.
Andrew M. Snauffer, William W. Hsieh, Alex J. Cannon, and Markus A. Schnorbus
The Cryosphere, 12, 891–905, https://doi.org/10.5194/tc-12-891-2018, https://doi.org/10.5194/tc-12-891-2018, 2018
Short summary
Short summary
Estimating winter snowpack throughout British Columbia is challenging due to the complex terrain, thick forests, and high snow accumulations present. This paper describes a way to make better snow estimates by combining publicly available data using machine learning, a branch of artificial intelligence research. These improved estimates will help water resources managers better plan for changes in rivers and lakes fed by spring snowmelt and will aid other research that supports such planning.
Yulan Zhang, Shichang Kang, Michael Sprenger, Zhiyuan Cong, Tanguang Gao, Chaoliu Li, Shu Tao, Xiaofei Li, Xinyue Zhong, Min Xu, Wenjun Meng, Bigyan Neupane, Xiang Qin, and Mika Sillanpää
The Cryosphere, 12, 413–431, https://doi.org/10.5194/tc-12-413-2018, https://doi.org/10.5194/tc-12-413-2018, 2018
Short summary
Short summary
Light-absorbing impurities deposited on snow can reduce surface albedo and contribute to the near-worldwide melting of snowpack and ice. This study focused on the black carbon and mineral dust in snow cover on the Tibetan Plateau. We discussed their concentrations, distributions, possible sources, and albedo reduction and radiative forcing. Findings indicated that the impacts of black carbon and mineral dust need to be properly accounted for in future regional climate projections.
Thomas Grünewald, Fabian Wolfsperger, and Michael Lehning
The Cryosphere, 12, 385–400, https://doi.org/10.5194/tc-12-385-2018, https://doi.org/10.5194/tc-12-385-2018, 2018
Short summary
Short summary
Snow farming is the conservation of snow during summer. Large snow piles are covered with a sawdust insulation layer, reducing melt and guaranteeing a specific amount of available snow in autumn, independent of the weather conditions. Snow volume changes in two heaps were monitored, showing that about a third of the snow was lost. Model simulations confirmed the large effect of the insulation on energy balance and melt. The model can now be used as a tool to examine future snow-farming projects.
Kristoffer Aalstad, Sebastian Westermann, Thomas Vikhamar Schuler, Julia Boike, and Laurent Bertino
The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, https://doi.org/10.5194/tc-12-247-2018, 2018
Short summary
Short summary
We demonstrate how snow cover data from satellites can be used to constrain estimates of snow distributions at sites in the Arctic. In this effort, we make use of data assimilation to combine the information contained in the snow cover data with a simple snow model. By comparing our snow distribution estimates to independent observations, we find that this method performs favorably. Being modular, this method could be applied to other areas as a component of a larger reanalysis system.
Cited articles
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, 2005.
Bauder, A., Funk, M., and Huss, M.: Ice-volume changes of selected glaciers in the Swiss Alps since the end of the 19th century, Ann. Glaciol., 46, 145–149, 2007.
Berthier, E., Vadon, H., Baratoux, D., Arnaud, Y., Vincent, C., Feigl, K., Remy, F., and Legresy, B.: Surface motion of mountain glaciers derived from satellite optical imagery, Remote Sens. Environ., 95, 14–28, 2005.
Bitelli, G., Dubbini, M., and Zanutta, A.: Terrestrial laser scanning and digital photogrammetry techniques to monitor landslide bodies, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 35, 246–251, 2004.
Brandenberger, A. J.: Map of the McCall Glacier, Brooks Range, Alaska, American Geographical Society, New York, AGS Report, 11 pp., 1959.
Bühler, Y., Marty, M., Egli, L., Veitinger, J., Jonas, T., Thee, P., and Ginzler, C.: Snow depth mapping in high-alpine catchments using digital photogrammetry, The Cryosphere, 9, 229–243, https://doi.org/10.5194/tc-9-229-2015, 2015.
Buhler, Y., Meier, L., and Ginzler, C.: Potential of Operational High Spatial Resolution Near-Infrared Remote Sensing Instruments for Snow Surface Type Mapping, IEEE Geosci. Remote S., 12, 821–825, 2015.
Castebrunet, H., Eckert, N., Giraud, G., Durand, Y., and Morin, S.: Projected changes of snow conditions and avalanche activity in a warming climate: the French Alps over the 2020–2050 and 2070–2100 periods, The Cryosphere, 8, 1673–1697, https://doi.org/10.5194/tc-8-1673-2014, 2014.
Clifford, D.: Global estimates of snow water equivalent from passive microwave instruments: history, challenges and future developments, Int. J. Remote Sens., 31, 3707–3726, 2010.
Cline, D. W.: Digital photogrammetric determination of alpine snowpack distribution for hydrological modeling, Proceedings of the Western Snow Conference, 115–123, 1994.
Conway, H. and Abrahamson, J.: Snow stability index, J. Glaciol., 30, 321–327, 1984.
Cox, L. and March, R. S.: Comparison of geodetic and glaciological mass balance techniques, Gulkana Glacier, Alaska, USA, J. Glaciol., 50, 363–370, 2004.
d'Oleire-Oltmanns, S., Marzolff, I., Peter, K. D., and Ries, J. B.: Unmanned aerial vehicle (UAV) for monitoring soil erosion in Morocco, Remote Sens., 4, 3390–3416, 2012.
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow depth: a review, J. Glaciol., 59, 467–479, 2013.
Delacourt, C., Allemand, P., Berthier, E., Raucoules, D., Casson, B., Grandjean, P., Pambrun, C., and Varel, E.: Remote-sensing techniques for analysing landslide kinematics: a review, B. Soc. Geol. Fr., 178, 89–100, 2007.
Déry, S. J. and Brown, R. D.: Recent Northern Hemisphere snow cover extent trends and implications for the snow-albedo feedback, Geophys. Res. Lett., 34, https://doi.org/10.1029/2007GL031474, 2007.
Eisenbeiß, H.: UAV photogrammetry, 2009, Dipl.-Ing., University of Technology Dresden, Zürich, Eidgenössischen Technischen Hochschule, ETH, Zürich, 237 pp., 2009.
Esch, D. C.: Long-term evaluations of insulated roads and airfields in Alaska, Transp. Res. Record, 1481, 56–62, 1995.
Fassnacht, S. and Deems, J.: Measurement sampling and scaling for deep montane snow depth data, Hydrol. Process., 20, 829–838, 2006.
Ferretti, A., Prati, C., and Rocca, F.: Permanent scatterers in SAR interferometry, IEEE T. Geosci. Remote, 39, 8–20, 2001.
Fladeland, M., Sumich, M., Lobitz, B., Kolyer, R., Herlth, D., Berthold, R., McKinnon, D., Monforton, L., Brass, J., and Bland, G.: The NASA SIERRA science demonstration programme and the role of small-medium unmanned aircraft for earth science investigations, Geocarto International, 26, 157–163, 2011.
Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L., and Carbonneau, P. E.: Topographic structure from motion: a new development in photogrammetric measurement, Earth Surf. Proc. Land., 38, 421–430, 2013.
Gao, Y. and Shen, X.: A new method for carrier-phase-based precise point positioning, Navigation, 49, 109–116, 2002.
Gauthier, D., Conlan, M., and Jamieson, B.: Photogrammetry of fracture lines and avalanche terrain: Potential applications to research and hazard mitigation projects, International Snow Science Workshop 29 September 3 October 2014, Banff, Canada, 109–115, 2014.
Goodrich, L.: The influence of snow cover on the ground thermal regime, Can. Geotech. J., 19, 421–432, 1982.
Hamilton, T. D.: Comparative glacier photographs from northern Alaska, J. Glaciol., 5, 479–487, 1965.
Hitchcock, C. B. and Miller, O. M.: Nine glacier maps, northwestern North America, American Geographical Society, New York, AGS Special Publication, 34 pp., 1960.
Hopkinson, C., Sitar, M., Chasmer, L., and Treitz, P.: Mapping snowpack depth beneath forest canopies using airborne lidar, Photogramm. Eng. Rem. S., 70, 323–330, 2004.
Huang, L. and Li, Z.: Comparison of SAR and optical data in deriving glacier velocity with feature tracking, Int. J. Remote Sens., 32, 2681–2698, 2011.
Hugenholtz, C. H., Whitehead, K., Brown, O. W., Barchyn, T. E., Moorman, B. J., LeClair, A., Riddell, K., and Hamilton, T.: Geomorphological mapping with a small unmanned aircraft system (sUAS): Feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model, Geomorphology, 194, 16–24, 2013.
Irschara, A., Kaufmann, V., Klopschitz, M., Bischof, H., and Leberl, F.: Towards fully automatic photogrammetric reconstruction using digital images taken from UAVs, http://www.researchgate.net/profile/Franz_Leberl/publication/266468862_TOWARDS_FULLY_AUTOMATIC_PHOTOGRAMMETRIC_RECONSTRUCTION_USING_DIGITAL_IMAGES_TAKEN_FROM_UAVS/links/54de1eb30cf22a26721eceae.pdf (last access: 2 August 2015), 2010.
James, L. A., Hodgson, M. E., Ghoshal, S., and Latiolais, M. M.: Geomorphic change detection using historic maps and DEM differencing: The temporal dimension of geospatial analysis, Geomorphology, 137, 181–198, 2012.
Jamieson, B. and Stethem, C.: Snow avalanche hazards and management in Canada: challenges and progress, Nat. Hazards, 26, 35–53, 2002.
Johnson, A. J., Larsen, C. F., Murphy, N., Arendt, A. A., and Zirnheld, S. L.: Mass balance in the Glacier Bay area of Alaska, USA, and British Columbia, Canada, 1995–2011, using airborne laser altimetry, J. Glaciol., 59, 632–648, 2013.
Koenderink, J. J. and Van Doorn, A. J.: Affine structure from motion, JOSA A, 8, 377–385, 1991.
König, M. and Sturm, M.: Mapping snow distribution in the Alaskan Arctic using aerial photography and topographic relationships, Water Resour. Res., 34, 3471–3483, 1998.
Krimmel, R. M.: Mass balance and volume of South Cascade Glacier, Washington 1958–1985, in: Glacier fluctuations and climatic change, Springer, the Netherlands, 193–206, 1989.
Lee, C., Jones, S., Bellman, C., and Buxton, L.: DEM creation of a snow covered surface using digital aerial photography, Remote Sensing and Spatial Information Sciences, Beijing, 831–836, 2008.
Lemke, P., Ren, J., Alley, R. B., Allison, I., Carrasco, J., Flato, G., Fujii, Y., Kaser, G., Mote, P., and Thomas, R. H.: Observations: Changes in snow, ice and frozen ground, Part of the Working Group I contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 337–383, 2007.
Liston, G. E., Haehnel, R. B., Sturm, M., Hiemstra, C. A., Berezovskaya, S., and Tabler, R. D.: Instruments and methods simulating complex snow distributions in windy environments using SnowTran-3D, J. Glaciol., 53, 241–256, 2007.
Liston, G. E. and Sturm, M.: Winter precipitation patterns in arctic Alaska determined from a blowing-snow model and snow-depth observations, J. Hydrometeorol., 3, 646–659, 2002.
Lucieer, A., de Jong, S., and Turner, D.: Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography, Prog. Phys. Geog., 38, 97–116, https://doi.org/10.1177/0309133313515293, 2013.
Maune, D.: Digital elevation model technologies and applications: the DEM users manual, Asprs Publications, Bethesda, Md, 2001.
McCurdy, P., Woodward, L., Davidson, J., Wilson, R., and Ask, R.: Manual of photogrammetry. American Society of Photogrammetry, Pitman Publishing Corporation, 1944.
McKay, G. A.: Problems of measuring and evaluating snow cover, Proceedings Workshop Seminar of Snow Hydrology, Secretariat Canadian National Committee for the IHD, Ottawa, 49–62, 1968.
Ménard, C. B., Essery, R., Pomeroy, J., Marsh, P., and Clark, D. B.: A shrub bending model to calculate the albedo of shrub tundra, Hydrol. Process., 28, 341–351, 2014.
Miller, P. E., Kunz, M., Mills, J. P., King, M. A., Murray, T., James, T. D., and Marsh, S. H.: Assessment of glacier volume change using ASTER-based surface matching of historical photography, IEEE T. Geosci. Remote, 47, 1971–1979, 2009.
Najibi, N. and Arabsheibani, R.: Snow-covered surface variability and DEM generation using aerial photogrammetry in Mount Odin, Canada, Geodesy and Cartography, 39, 113–120, 2013.
Nex, F. and Remondino, F.: UAV for 3D mapping applications: a review, Applied Geomatics, 6, 1–15, 2014.
Nolan, M. and Fatland, D. R.: Penetration depth as a DInSAR observable and proxy for soil moisture, IEEE T. Geosci. Remote, 41, 532–537, 2003.
Nolan, M., Arendt, A., and Rabus, B.: Volume change of McCall Glacier, Arctic Alaska, from 1956 to 2003, Ann. Glaciol., 42, 409–416, 2005.
Nolan, M., Churchwell, R., Adams, J., McClellands, J., Tape, K., Kendall, S., Powell, A., Dunton, K., Payer, D., and Martin, P.: Predicting the impact of glacier loss on fish, birds, floodplains, and estuaries in the Arctic National Wildlife Refuge, Proceedings of the Fourth Interagency Conference on Research in the Watersheds: Observing, Studying and Managing for Change, US Geological Survey Scientific Investigations Report, 49–54, 2011.
Nuth, C. and Kääb, A.: Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change, The Cryosphere, 5, 271–290, https://doi.org/10.5194/tc-5-271-2011, 2011.
Offenbacher, E. L. and Colbeck, S. C.: Remote Sensing of Snow Covers Using the Gamma-Ray Technique, No. CRREL-91-9, Cold Regions Research and Engineering Lab Hanover NH, 1991.
Otake, K.: Snow survey by aerial photographs, GeoJournal, 4, 367–369, 1980.
Pauli, J. N., Zuckerberg, B., Whiteman, J. P., and Porter, W.: The subnivium: a deteriorating seasonal refugium, Front. Ecol. Environ., 11, 260–267, 2013.
Post, A.: Distribution of surging glaciers in western North America, J. of Glaciol., 8, 229–240, 1969.
Post, A.: Annual aerial photography of glaciers in northwest North America: How it all began and its golden age, Phys. Geogr., 16, 15–26, 1995.
Prokop, A.: Assessing the applicability of terrestrial laser scanning for spatial snow depth measurements, Cold Reg. Sci. Technol., 54, 155–163, 2008.
Pruitt, W. O.: Snow as a factor in the winter ecology of the barren ground caribou (Rangifer arcticus), Arctic, 158–179, 1959.
Rawls, W., Jackson, T., and Zuzel, J.: Comparison of areal snow storage sampling procedures for rangeland watersheds, Nord. Hydrol., 11, 71–82, 1980.
Rinaudo, F., Chiabrando, F., Lingua, A. M., and Spanò, A. T.: Archaeological site monitoring: UAV photogrammetry can be an answer, The International archives of the photogrammetry, Remote Sensing and Spatial Information Sciences, 39, 583–588, 2012.
Rittger, K., Painter, T. H., and Dozier, J.: Assessment of methods for mapping snow cover from MODIS, Adv. Water Resour., 51, 367–380, 2013.
Robinson, D. A., Dewey, K. F., and Heim Jr., R. R.: Global snow cover monitoring: An update, B. Am. Meteorol. Soc., 74, 1689–1696, 1993.
Rott, H., Cline, D., Duguay, C., Essery, R., Haas, C., Macelloni, G., Malnes, E., Pulliainen, J., Rebhan, H., and Yueh, S.: CoReH2O A Ku-and X-Band SAR Mission for Snow and Ice Monitoring, in: Synthetic Aperture Radar (EUSAR), 7th European Conference on VDE, 1–4, 2008.
Russell, D. E., Martell, A. M., and Nixon, W. A.: Range ecology of the Porcupine caribou herd in Canada, Rangifer, 13, 1–168, 1993.
Ryan, J. C., Hubbard, A. L., Box, J. E., Todd, J., Christoffersen, P., Carr, J. R., Holt, T. O., and Snooke, N.: UAV photogrammetry and structure from motion to assess calving dynamics at Store Glacier, a large outlet draining the Greenland ice sheet, The Cryosphere, 9, 1–11, https://doi.org/10.5194/tc-9-1-2015, 2015.
Scambos, T. A., Dutkiewicz, M. J., Wilson, J. C., and Bindschadler, R. A.: Application of image cross-correlation to the measurement of glacier velocity using satellite image data, Remote Sens. Environ., 42, 177–186, 1992.
Schreiber, B. T., Lyon, D. R., Martin, E. L., and Confer, H. A.: Impact of prior flight experience on learning Predator UAV operator skills, Air Force Research Lab, Mesa, AZ, Human Effectiveness Directorate, 2002.
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty, R. S.: Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data, Water Resour. Res., 35, 2145–2160, 1999.
Slater, A. G. and Clark, M. P.: Snow data assimilation via an ensemble Kalman filter, J. Hydrometeorol., 7, 478–493, 2006.
Snay, R. A. and Soler, T.: Continuously operating reference station (CORS): history, applications, and future enhancements, J. Surv. Eng., 134, 95–104, 2008.
Sturm, M.: Field techniques for Snow Observations on Sea Ice, in Field Techniques for Sea Ice Research, in: Field Techniques for Sea Ice Research, U. Alaska Press, edited by: Eicken, H. and Salganek, M., Fairbanks, Chapter 3.1, 25–47, 2009.
Sturm, M. and Benson, C.: Scales of spatial heterogeneity for perennial and seasonal snow layers, Ann. Glaciol., 38, 253–260, 2004.
Sturm, M. and Holmgren, J. A.: Self-recording snow depth probe, U.S. Patent No. 5.864.059, 26 January, 1999.
Sturm, M., Holmgren, J., and Liston, G. E.: A seasonal snow cover classification system for local to global applications, J. Climate, 8, 1261–1283, 1995.
Sturm, M., Douglas, T., Racine, C., and Liston, G. E.: Changing snow and shrub conditions affect albedo with global implications, J. Geophys. Res.-Biogeo., 110, https://doi.org/10.1029/2005JG000013, 2005.
Sturm, M., Hellig, H., Urban, F., and Liston, G.: The snow cover of the Arctic National Wildlife Refuge, Arctic, in preparation, 2015.
Taber, S.: Frost heaving, J. Geol., 37, 428–461, 1929.
Weller, G., Nolan, M., Wendler, G., Benson, C., Echelmeyer, K., and Untersteiner, N.: Fifty years of McCall Glacier research: from the International Geophysical Year, 1957–1958, to the International Polar Year, 2007–2008, Arctic, 60, 101–110, 2007.
Warren, S. G.: Optical properties of snow, Rev. Geophys., 20, 67–89, 1982.
Westoby, M., Brasington, J., Glasser, N., Hambrey, M., and Reynolds, J.: Structure-from-Motion'photogrammetry: A low-cost, effective tool for geoscience applications, Geomorphology, 179, 300–314, 2012.
Wheaton, J. M., Brasington, J., Darby, S. E., and Sear, D. A.: Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets, Earth Surf. Proc. Land., 35, 136–156, 2010.
Whitehead, K., Moorman, B. J., and Hugenholtz, C. H.: Brief Communication: Low-cost, on-demand aerial photogrammetry for glaciological measurement, The Cryosphere, 7, 1879–1884, https://doi.org/10.5194/tc-7-1879-2013, 2013.
Whitlock, C.: When drones fall from the sky, http://www.washingtonpost.com/sf/investigative/2014/06/20/when-drones-fall-from-the-sky/ (last access: 2 August 2015), 2014.
Woodget, A., Carbonneau, P., Visser, F., and Maddock, I.: Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry, Earth Surf. Proc. Land., 40, 47–64, 2014.
Yan, K. and Cheng, T.: Close Shot Photogrammetry for Measuring Wind-Drifted Snow Distribution on Stepped Flat Roofs, Computing, Communication, Control, and Management, ISECS International Colloquium, Vol. 1, 332–335, 2008.
Short summary
This paper presents a photogrammetric method for measuring topography from manned aircraft with an accuracy of 30 cm and repeatability of 8 cm, at significantly lower cost than other methods. Here we created difference maps to demonstrate that we could measure snow depth with an accuracy of 10 cm compared to over 6000 snow-probe measurements on the ground, but do so over entire watersheds at 10-20 cm spatial resolution rather than just a few transects.
This paper presents a photogrammetric method for measuring topography from manned aircraft with...
