Articles | Volume 12, issue 5
https://doi.org/10.5194/tc-12-1579-2018
© Author(s) 2018. 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-12-1579-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 May 2018
Research article |
| 03 May 2018
| 03 May 2018
Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan
Earth Research Institute, University of California, Santa Barbara, CA 93106-3060, USA
Andre Abreu Calfa
Department of Computer Science, University of California, Santa Barbara, CA 93106-5110, USA
now at: Arista Networks, Santa Clara CA 95054, USA
Karl Rittger
National Snow and Ice Data Center, University of Colorado, Boulder, CO 80309-0449, USA
Jeff Dozier
Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106-5131, USA
Related authors
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
Short summary
Short summary
Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Edward H. Bair, Jeff Dozier, Charles Stern, Adam LeWinter, Karl Rittger, Alexandria Savagian, Timbo Stillinger, and Robert E. Davis
The Cryosphere, 16, 1765–1778, https://doi.org/10.5194/tc-16-1765-2022, https://doi.org/10.5194/tc-16-1765-2022, 2022
Short summary
Short summary
Understanding how snow and ice reflect solar radiation (albedo) is important for global climate. Using high-resolution topography, darkening from surface roughness (apparent albedo) is separated from darkening by the composition of the snow (intrinsic albedo). Intrinsic albedo is usually greater than apparent albedo, especially during melt. Such high-resolution topography is often not available; thus the use of a shade component when modeling mixtures is advised.
Edward H. Bair, Karl Rittger, Jawairia A. Ahmad, and Doug Chabot
The Cryosphere, 14, 331–347, https://doi.org/10.5194/tc-14-331-2020, https://doi.org/10.5194/tc-14-331-2020, 2020
Short summary
Short summary
Ice and snowmelt feed the Indus River and Amu Darya, but validation of estimates from satellite sensors has been a problem until recently, when we were given daily snow depth measurements from these basins. Using these measurements, estimates of snow on the ground were created and compared with models. Estimates of water equivalent in the snowpack were mostly in agreement. Stratigraphy was also modeled and showed 1 year with a relatively stable snowpack but another with multiple weak layers.
Edward H. Bair, Robert E. Davis, and Jeff Dozier
Earth Syst. Sci. Data, 10, 549–563, https://doi.org/10.5194/essd-10-549-2018, https://doi.org/10.5194/essd-10-549-2018, 2018
Short summary
Short summary
The mass and energy balance of the snowpack govern its evolution. Here, we present a fully filtered and model-ready dataset containing a continuous hourly record of selected measurements from three sites on Mammoth Mountain, CA USA. These measurements can be used to run a variety of snow models and complement a previously published dataset. In addition to the hand-weighed snow water equivalent, novel measurements include hourly snow albedo corrected for terrain and other measurement biases.
E. H. Bair, R. Simenhois, A. van Herwijnen, and K. Birkeland
The Cryosphere, 8, 1407–1418, https://doi.org/10.5194/tc-8-1407-2014, https://doi.org/10.5194/tc-8-1407-2014, 2014
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
Short summary
Short summary
Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Edward H. Bair, Jeff Dozier, Charles Stern, Adam LeWinter, Karl Rittger, Alexandria Savagian, Timbo Stillinger, and Robert E. Davis
The Cryosphere, 16, 1765–1778, https://doi.org/10.5194/tc-16-1765-2022, https://doi.org/10.5194/tc-16-1765-2022, 2022
Short summary
Short summary
Understanding how snow and ice reflect solar radiation (albedo) is important for global climate. Using high-resolution topography, darkening from surface roughness (apparent albedo) is separated from darkening by the composition of the snow (intrinsic albedo). Intrinsic albedo is usually greater than apparent albedo, especially during melt. Such high-resolution topography is often not available; thus the use of a shade component when modeling mixtures is advised.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Edward H. Bair, Karl Rittger, Jawairia A. Ahmad, and Doug Chabot
The Cryosphere, 14, 331–347, https://doi.org/10.5194/tc-14-331-2020, https://doi.org/10.5194/tc-14-331-2020, 2020
Short summary
Short summary
Ice and snowmelt feed the Indus River and Amu Darya, but validation of estimates from satellite sensors has been a problem until recently, when we were given daily snow depth measurements from these basins. Using these measurements, estimates of snow on the ground were created and compared with models. Estimates of water equivalent in the snowpack were mostly in agreement. Stratigraphy was also modeled and showed 1 year with a relatively stable snowpack but another with multiple weak layers.
Chandan Sarangi, Yun Qian, Karl Rittger, Kathryn J. Bormann, Ying Liu, Hailong Wang, Hui Wan, Guangxing Lin, and Thomas H. Painter
Atmos. Chem. Phys., 19, 7105–7128, https://doi.org/10.5194/acp-19-7105-2019, https://doi.org/10.5194/acp-19-7105-2019, 2019
Short summary
Short summary
Radiative forcing induced by deposition of light-absorbing particles (LAPs) on snow is an important surface forcing. Here, we have used high-resolution WRF-Chem (coupled with online snow–LAP–radiation model) simulations for 2013–2014 to estimate the spatial variation in LAP-induced snow albedo darkening effect in high-mountain Asia. Significant improvement in simulated LAP–snow properties with use of a higher spatial resolution for the same model configuration is illustrated over this region.
Edward H. Bair, Robert E. Davis, and Jeff Dozier
Earth Syst. Sci. Data, 10, 549–563, https://doi.org/10.5194/essd-10-549-2018, https://doi.org/10.5194/essd-10-549-2018, 2018
Short summary
Short summary
The mass and energy balance of the snowpack govern its evolution. Here, we present a fully filtered and model-ready dataset containing a continuous hourly record of selected measurements from three sites on Mammoth Mountain, CA USA. These measurements can be used to run a variety of snow models and complement a previously published dataset. In addition to the hand-weighed snow water equivalent, novel measurements include hourly snow albedo corrected for terrain and other measurement biases.
E. H. Bair, R. Simenhois, A. van Herwijnen, and K. Birkeland
The Cryosphere, 8, 1407–1418, https://doi.org/10.5194/tc-8-1407-2014, https://doi.org/10.5194/tc-8-1407-2014, 2014
K. M. Sterle, J. R. McConnell, J. Dozier, R. Edwards, and M. G. Flanner
The Cryosphere, 7, 365–374, https://doi.org/10.5194/tc-7-365-2013, https://doi.org/10.5194/tc-7-365-2013, 2013
Related subject area
Discipline: Snow | Subject: Numerical Modelling
Regime shifts in Arctic terrestrial hydrology manifested from impacts of climate warming
Snow cover prediction in the Italian central Apennines using weather forecast and land surface numerical models
A data exploration tool for averaging and accessing large data sets of snow stratigraphy profiles useful for avalanche forecasting
Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX flagship pilot study Land Use and Climate Across Scales (LUCAS) models – Part 1: Evaluation of the snow-albedo effect
Elements of future snowpack modeling – Part 1: A physical instability arising from the nonlinear coupling of transport and phase changes
Elements of future snowpack modeling – Part 2: A modular and extendable Eulerian–Lagrangian numerical scheme for coupled transport, phase changes and settling processes
Assessment of neutrons from secondary cosmic rays at mountain altitudes – Geant4 simulations of environmental parameters including soil moisture and snow cover
A seasonal algorithm of the snow-covered area fraction for mountainous terrain
Snow cover duration trends observed at sites and predicted by multiple models
Deep ice layer formation in an alpine snowpack: monitoring and modeling
Multi-physics ensemble snow modelling in the western Himalaya
Micromechanical modeling of snow failure
Changing characteristics of runoff and freshwater export from watersheds draining northern Alaska
Winter tourism under climate change in the Pyrenees and the French Alps: relevance of snowmaking as a technical adaptation
A simulation of a large-scale drifting snowstorm in the turbulent boundary layer
Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain
Michael A. Rawlins and Ambarish V. Karmalkar
The Cryosphere, 18, 1033–1052, https://doi.org/10.5194/tc-18-1033-2024, https://doi.org/10.5194/tc-18-1033-2024, 2024
Short summary
Short summary
Flows of water, carbon, and materials by Arctic rivers are being altered by climate warming. We used simulations from a permafrost hydrology model to investigate future changes in quantities influencing river exports. By 2100 Arctic rivers will receive more runoff from the far north where abundant soil carbon can leach in. More water will enter them via subsurface pathways particularly in summer and autumn. An enhanced water cycle and permafrost thaw are changing river flows to coastal areas.
Edoardo Raparelli, Paolo Tuccella, Valentina Colaiuda, and Frank S. Marzano
The Cryosphere, 17, 519–538, https://doi.org/10.5194/tc-17-519-2023, https://doi.org/10.5194/tc-17-519-2023, 2023
Short summary
Short summary
We evaluate the skills of a single-layer (Noah) and a multi-layer (Alpine3D) snow model, forced with the Weather Research and Forecasting model, to reproduce snowpack properties observed in the Italian central Apennines. We found that Alpine3D reproduces the observed snow height and snow water equivalent better than Noah, while no particular model differences emerge on snow cover extent. Finally, we observed that snow settlement is mainly due to densification in Alpine3D and to melting in Noah.
Florian Herla, Pascal Haegeli, and Patrick Mair
The Cryosphere, 16, 3149–3162, https://doi.org/10.5194/tc-16-3149-2022, https://doi.org/10.5194/tc-16-3149-2022, 2022
Short summary
Short summary
We present an averaging algorithm for multidimensional snow stratigraphy profiles that elicits the predominant snow layering among large numbers of profiles and allows for compiling of informative summary statistics and distributions of snowpack layer properties. This creates new opportunities for presenting and analyzing operational snowpack simulations in support of avalanche forecasting and may inspire new ways of processing profiles and time series in other geophysical contexts.
Anne Sophie Daloz, Clemens Schwingshackl, Priscilla Mooney, Susanna Strada, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Michal Belda, Tomas Halenka, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 2403–2419, https://doi.org/10.5194/tc-16-2403-2022, https://doi.org/10.5194/tc-16-2403-2022, 2022
Short summary
Short summary
Snow plays a major role in the regulation of the Earth's surface temperature. Together with climate change, rising temperatures are already altering snow in many ways. In this context, it is crucial to better understand the ability of climate models to represent snow and snow processes. This work focuses on Europe and shows that the melting season in spring still represents a challenge for climate models and that more work is needed to accurately simulate snow–atmosphere interactions.
Konstantin Schürholt, Julia Kowalski, and Henning Löwe
The Cryosphere, 16, 903–923, https://doi.org/10.5194/tc-16-903-2022, https://doi.org/10.5194/tc-16-903-2022, 2022
Short summary
Short summary
This companion paper deals with numerical particularities of partial differential equations underlying 1D snow models. In this first part we neglect mechanical settling and demonstrate that the nonlinear coupling between diffusive transport (heat and vapor), phase changes and ice mass conservation contains a wave instability that may be relevant for weak layer formation. Numerical requirements are discussed in view of the underlying homogenization scheme.
Anna Simson, Henning Löwe, and Julia Kowalski
The Cryosphere, 15, 5423–5445, https://doi.org/10.5194/tc-15-5423-2021, https://doi.org/10.5194/tc-15-5423-2021, 2021
Short summary
Short summary
This companion paper deals with numerical particularities of partial differential equations underlying one-dimensional snow models. In this second part we include mechanical settling and develop a new hybrid (Eulerian–Lagrangian) method for solving the advection-dominated ice mass conservation on a moving mesh alongside Eulerian diffusion (heat and vapor) and phase changes. The scheme facilitates a modular and extendable solver strategy while retaining controls on numerical accuracy.
Thomas Brall, Vladimir Mares, Rolf Bütikofer, and Werner Rühm
The Cryosphere, 15, 4769–4780, https://doi.org/10.5194/tc-15-4769-2021, https://doi.org/10.5194/tc-15-4769-2021, 2021
Short summary
Short summary
Neutrons from secondary cosmic rays, measured at 2660 m a.s.l. at Zugspitze, Germany, are highly affected by the environment, in particular by snow, soil moisture, and mountain shielding. To quantify these effects, computer simulations were carried out, including a sensitivity analysis on snow depth and soil moisture. This provides a possibility for snow depth estimation based on the measured number of secondary neutrons. This method was applied at Zugspitze in 2018.
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mäder, Alec van Herwijnen, Louis Quéno, Yves Bühler, Jeff S. Deems, and Simon Gascoin
The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, https://doi.org/10.5194/tc-15-4607-2021, 2021
Short summary
Short summary
The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
Short summary
Short summary
Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Louis Quéno, Charles Fierz, Alec van Herwijnen, Dylan Longridge, and Nander Wever
The Cryosphere, 14, 3449–3464, https://doi.org/10.5194/tc-14-3449-2020, https://doi.org/10.5194/tc-14-3449-2020, 2020
Short summary
Short summary
Deep ice layers may form in the snowpack due to preferential water flow with impacts on the snowpack mechanical, hydrological and thermodynamical properties. We studied their formation and evolution at a high-altitude alpine site, combining a comprehensive observation dataset at a daily frequency (with traditional snowpack observations, penetration resistance and radar measurements) and detailed snowpack modeling, including a new parameterization of ice formation in the 1-D SNOWPACK model.
David M. W. Pritchard, Nathan Forsythe, Greg O'Donnell, Hayley J. Fowler, and Nick Rutter
The Cryosphere, 14, 1225–1244, https://doi.org/10.5194/tc-14-1225-2020, https://doi.org/10.5194/tc-14-1225-2020, 2020
Short summary
Short summary
This study compares different snowpack model configurations applied in the western Himalaya. The results show how even sparse local observations can help to delineate climate input errors from model structure errors, which provides insights into model performance variation. The results also show how interactions between processes affect sensitivities to climate variability in different model configurations, with implications for model selection in climate change projections.
Grégoire Bobillier, Bastian Bergfeld, Achille Capelli, Jürg Dual, Johan Gaume, Alec van Herwijnen, and Jürg Schweizer
The Cryosphere, 14, 39–49, https://doi.org/10.5194/tc-14-39-2020, https://doi.org/10.5194/tc-14-39-2020, 2020
Michael A. Rawlins, Lei Cai, Svetlana L. Stuefer, and Dmitry Nicolsky
The Cryosphere, 13, 3337–3352, https://doi.org/10.5194/tc-13-3337-2019, https://doi.org/10.5194/tc-13-3337-2019, 2019
Short summary
Short summary
We investigate the changing character of runoff, river discharge and other hydrological elements across watershed draining the North Slope of Alaska over the period 1981–2010. Our synthesis of observations and modeling reveals significant increases in the proportion of subsurface runoff and cold season discharge. These and other changes we describe are consistent with warming and thawing permafrost, and have implications for water, carbon and nutrient cycling in coastal environments.
Pierre Spandre, Hugues François, Deborah Verfaillie, Marc Pons, Matthieu Vernay, Matthieu Lafaysse, Emmanuelle George, and Samuel Morin
The Cryosphere, 13, 1325–1347, https://doi.org/10.5194/tc-13-1325-2019, https://doi.org/10.5194/tc-13-1325-2019, 2019
Short summary
Short summary
This study investigates the snow reliability of 175 ski resorts in the Pyrenees (France, Spain and Andorra) and the French Alps under past and future conditions (1950–2100) using state-of-the-art climate projections and snowpack modelling accounting for snow management, i.e. grooming and snowmaking. The snow reliability of ski resorts shows strong elevation and regional differences, and our study quantifies changes in snow reliability induced by snowmaking under various climate scenarios.
Zhengshi Wang and Shuming Jia
The Cryosphere, 12, 3841–3851, https://doi.org/10.5194/tc-12-3841-2018, https://doi.org/10.5194/tc-12-3841-2018, 2018
Short summary
Short summary
Drifting snowstorms that are hundreds of meters in depth are reproduced using a large-eddy simulation model combined with a Lagrangian particle tracking method, which also exhibits obvious spatial structures following large-scale turbulent vortexes. The horizontal snow transport flux at high altitude, previously not observed, actually occupies a significant proportion of the total flux. Thus, previous models may largely underestimate the total mass flux and consequently snow sublimation.
Franziska Gerber, Nikola Besic, Varun Sharma, Rebecca Mott, Megan Daniels, Marco Gabella, Alexis Berne, Urs Germann, and Michael Lehning
The Cryosphere, 12, 3137–3160, https://doi.org/10.5194/tc-12-3137-2018, https://doi.org/10.5194/tc-12-3137-2018, 2018
Short summary
Short summary
A comparison of winter precipitation variability in operational radar measurements and high-resolution simulations reveals that large-scale variability is well captured by the model, depending on the event. Precipitation variability is driven by topography and wind. A good portion of small-scale variability is captured at the highest resolution. This is essential to address small-scale precipitation processes forming the alpine snow seasonal snow cover – an important source of water.
Cited articles
Adam, J. C., Clark, E. A., Lettenmaier, D. P., and Wood, E. F.: Correction of global precipitation products for orographic effects, J. Climate, 19, 15–38, https://doi.org/10.1175/JCLI3604.1, 2006.
Bair, E. H., Dozier, J., Davis, R. E., Colee, M. T., and Claffey, K. J.: CUES – A study site for measuring snowpack energy balance in the Sierra Nevada, Front. Earth Sci., 3, 58, https://doi.org/10.3389/feart.2015.00058, 2015.
Bair, E. H., Rittger, K., Davis, R. E., Painter, T. H., and Dozier, J.: Validating reconstruction of snow water equivalent in California's Sierra Nevada using measurements from the NASA Airborne Snow Observatory, Water Resour. Res., 52, 8437–8460, https://doi.org/10.1002/2016WR018704, 2016.
Bair, E. H., Rittger, K., and Dozier, J.: Reconstructed SWE for MODIS tile h23v05, calendar years 2003–2012, data available at: ftp://ftp.snow.ucsb.edu/pub/org/snow/products/reconstruction/h23v05, last access: 1 June 2016.
Barrett, A.: National Operational Hydrologic Remote Sensing Center SNOw Data Assimiliation System (SNODAS) products at NSIDC, National Snow and Ice Data Center, Boulder, Special Report 11, 19, 2003.
Beaudoing, Hiroko and M. Rodell, NASA/GSFC/HSL: GLDAS Noah Land Surface Model L4 3 hourly 0.25×0.25 degree V2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), available at: https://doi.org/10.5067/E7TYRXPJKWOQ (last access: 1 August 2016), 2016.
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140, https://doi.org/10.1023/A:1018054314350, 1996.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J.: Classification and Regression Trees, Chapman and Hall, New York, 368 pp., 1984.
Brodzik, M. J. and Long, D.: Calibrated Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR (CETB), National Snow and Ice Data Center, Boulder, CETB ATBD rev 0.11, 91, 2016.
Brodzik, M. J., Long, D. G., Hardman M. A., Paget, A., and Armstrong., R.: MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR, Version 1, various resolutions, Cylindrical Equal Area Projection, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center, available at: https://doi.org/10.5067/MEASURES/CRYOSPHERE/NSIDC-0630.001 (last access: 4 August 2017), 2016.
Burt, P. and Adelson, E.: The Laplacian pyramid as a compact image code, IEEE T. Commun., 31, 532–540, https://doi.org/10.1109/TCOM.1983.1095851, 1983.
Chabot, D. and Kaba, A.: Avalanche forecasting in the central Asian countries of Afghanistan, Pakistan and Tajikistan, Proc. 2016 Intl. Snow Sci. Wksp., Breckenridge, CO, available at: http://arc.lib.montana.edu/snow-science/item/2310 (last access: 23 April 2018), 2016.
Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F., Schaake, J. C., Robock, A., Marshall, C., Sheffield, J., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T., Tarpley, J. D., and Meng, J.: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project, J. Geophys. Res.-Atmos., 108, 8842, https://doi.org/10.1029/2002JD003118, 2003.
Daly, S. F., Vuyovich, C. M., Deeb, E. J., Newman, S. D., Baldwin, T. B., and Gagnon, J. J.: Assessment of the snow conditions in the major watersheds of Afghanistan using multispectral and passive microwave remote sensing, Hydrol. Process., 26, 2631–2642, https://doi.org/10.1002/hyp.9367, 2012.
Davis, R. E., Elder, K., Howlett, D., and Bouzaglou, E.: Relating storm and weather factors to dry slab avalanche activity at Alta, Utah, and Mammoth Mountain, California, using classification and regression trees, Cold Reg. Sci. Technol., 30, 79–89, https://doi.org/10.1016/S0165-232X(99)00032-4, 1999.
Derksen, C., Walker, A., and Goodison, B.: Evaluation of passive microwave snow water equivalent retrievals across the boreal forest/tundra transition of western Canada, Remote Sens. Environ., 96, 315–327, https://doi.org/10.1016/j.rse.2005.02.014, 2005.
Doelling, D.: CERES SYN 1 deg 3-hour edition 3A, surface fluxes, available at: 10.5067/TERRA+AQUA/CERES/SYN1DEG3HOUR_L3.003A, last access: 17 October 2017.
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., Münkemüller, T., McClean, C., Osborne, P. E., Reineking, B., Schröder, B., Skidmore, A. K., Zurell, D., and Lautenbach, S.: Collinearity: A review of methods to deal with it and a simulation study evaluating their performance, Ecography, 36, 27–46, https://doi.org/10.1111/j.1600-0587.2012.07348.x, 2013.
Dozier, J.: Mountain hydrology, snow color, and the fourth paradigm, Eos, Transactions American Geophysical Union, 92, 373–375, https://doi.org/10.1029/2011EO430001, 2011.
Dozier, J. and Frew, J.: Rapid calculation of terrain parameters for radiation modeling from digital elevation data, IEEE Geosci. Remote S., 28, 963–969, https://doi.org/10.1109/36.58986, 1990.
Dozier, J., Painter, T. H., Rittger, K., and Frew, J. E.: Time-space continuity of daily maps of fractional snow cover and albedo from MODIS, Adv. Water Resour., 31, 1515–1526, https://doi.org/10.1016/j.advwatres.2008.08.011, 2008.
Dozier, J., Bair, E. H., and Davis, R. E.: Estimating the spatial distribution of snow water equivalent in the world's mountains, WIREs Water, 3, 461–474, https://doi.org/10.1002/wat2.1140, 2016.
Durand, M., Kim, E. J., Margulis, S. A., and Molotch, N. P.: A first-order characterization of errors from neglecting stratigraphy in forward and inverse passive microwave modeling of snow, IEEE Geosci. Remote S., 8, 730–734, https://doi.org/10.1109/LGRS.2011.2105243, 2011.
Fassnacht, S. R., Dressler, K. A., and Bales, R. C.: Snow water equivalent interpolation for the Colorado River Basin from snow telemetry (SNOTEL) data, Water Resour. Res., 39, 1208, https://doi.org/10.1029/2002WR001512, 2003.
Fassnacht, S. R., Dressler, K. A., Hultstrand, D. M., Bales, R. C., and Patterson, G.: Temporal inconsistencies in coarse-scale snow water equivalent patterns: Colorado River Basin snow telemetry-topography regressions, Pirineos: Revista de Ecología de Montaña, 167, 165–185, https://doi.org/10.3989/Pirineos.2012.167008, 2012.
Friedman, J. H. and Meulman, J. J.: Multiple additive regression trees with application in epidemiology, Stat. Med., 22, 1365–1381, https://doi.org/10.1002/sim.1501, 2003.
Girotto, M., Margulis, S. A., and Durand, M.: Probabilistic SWE reanalysis as a generalization of deterministic SWE reconstruction techniques, Hydrol. Process., 28, 3875–3895, https://doi.org/10.1002/hyp.9887, 2014.
Guan, B., Molotch, N. P., Waliser, D. E., Jepsen, S. M., Painter, T. H., and Dozier, J.: Snow water equivalent in the Sierra Nevada: Blending snow sensor observations with snowmelt model simulations, Water Resour. Res., 49, 5029–5046, https://doi.org/10.1002/wrcr.20387, 2013.
Hagan, M. T., Demuth, H. B., Beale, M. H., and De Jesús, O.: Neural Network Design, 2nd Edn., edited by: Hagan, M., Stillwater, OK, 800 pp., 2014.
Hampel, F. R.: The influence curve and its role in robust estimation, J. Am. Stat. Assoc., 69, 383–393, https://doi.org/10.1080/01621459.1974.10482962, 1974.
Hancock, S., Baxter, R., Evans, J., and Huntley, B.: Evaluating global snow water equivalent products for testing land surface models, Remote Sens. Environ., 128, 107–117, https://doi.org/10.1016/j.rse.2012.10.004, 2013.
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edn., Springer, Berlin, 745 pp., 2009.
Hinkelman, L. M., Lapo, K. E., Cristea, N. C., and Lundquist, J. D.: Using CERES SYN surface irradiance data as forcing for snowmelt simulation in complex terrain, J. Hydrometeorol., 16, 2133–2152, https://doi.org/10.1175/JHM-D-14-0179.1, 2015.
Jepsen, S. M., Molotch, N. P., Williams, M. W., Rittger, K. E., and Sickman, J. O.: Interannual variability of snowmelt in the Sierra Nevada and Rocky Mountains, United States: Examples from two alpine watersheds, Water Resour. Res., 48, W02529, https://doi.org/10.1029/2011WR011006, 2012.
Kattelmann, R.: Snowmelt lysimeters in the evaluation of snowmelt models, Ann. Glaciol., 31, 406–410, https://doi.org/10.3189/172756400781820048, 2000.
Kelly, R.: The AMSR-E snow depth algorithm: Description and initial results, Journal of the Remote Sensing Society of Japan, 29, 307–317, https://doi.org/10.11440/rssj.29.307, 2009.
Kelly, R.: Descriptions of GCOM-W1 AMSR2 Level 1R and Level 2 Algorithms, Earth Observation Research Center, Japan Aerospace Exploration Agency, Report NDX-120015A, 17, 2013.
Kelly, R. E., Chang, A. T. C., Tsang, L., and Foster, J. L.: A prototype AMSR-E global snow area and snow depth algorithm, IEEE Geosci. Remote S., 41, 230–242, https://doi.org/10.1109/TGRS.2003.809118, 2003.
Lapo, K. E., Hinkelman, L. M., Sumargo, E., Hughes, M., and Lundquist, J. D.: A critical evaluation of modeled solar irradiance over California for hydrologic and land surface modeling, J. Geophys. Res.-Atmos., 122, 299–317, https://doi.org/10.1002/2016JD025527, 2017.
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., and Wood, E. F.: Inroads of remote sensing into hydrologic science during the WRR era, Water Resour. Res., 51, 7309–7342, https://doi.org/10.1002/2015WR017616, 2015.
Li, Z.-X.: Modelling the passive microwave remote sensing of wet snow, Prog. Electromagn. Res., 62, 143–164, https://doi.org/10.2528/PIER05102402, 2006.
Liston, G. E. and Elder, K.: A meteorological distribution system for high-resolution terrestrial modeling (MicroMet), J. Hydrometeorol., 7, 217–234, https://doi.org/10.1175/JHM486.1, 2006.
Liston, G. E., Haehnel, R. B., Sturm, M., Hiemstra, C. A., Berezovskaya, S., and Tabler, R. D.: Simulating complex snow distributions in windy environments using SnowTran-3D, J. Glaciol., 53, 241–256, https://doi.org/10.3189/172756507782202865, 2007.
Malik, M. J., van der Velde, R., Vekerdy, Z., and Su, Z.: Improving modeled snow albedo estimates during the spring melt season, J. Geophys. Res.-Atmos., 119, 7311–7331, https://doi.org/10.1002/2013JD021344, 2014.
Margulis, S. A., Girotto, M., Cortés, G., and Durand, M.: A particle batch smoother approach to snow water equivalent estimation, J. Hydrometeorol., 16, 1752–1772, https://doi.org/10.1175/JHM-D-14-0177.1, 2015.
Marks, D. and Dozier, J.: Climate and energy exchange at the snow surface in the alpine region of the Sierra Nevada, 2, Snow cover energy balance, Water Resour. Res., 28, 3043–3054, https://doi.org/10.1029/92WR01483, 1992.
Markstrom, S. L., Regan, R. S., Hay, L. E., Viger, R. J., Webb, R. M. T., Payn, R. A., and LaFontaine, J. H.: PRMS-IV, the precipitation-runoff modeling system, version 4: U.S. Geol. Surv. Techniques and Methods, 6, B7, 158 pp., 2015.
Markus, T., Powell, D. C., and Wang, J. R.: Sensitivity of passive microwave snow depth retrievals to weather effects and snow evolution, IEEE Geosci. Remote S., 44, 68–77, https://doi.org/10.1109/TGRS.2005.860208, 2006.
Martinec, J. and Rango, A.: Areal distribution of snow water equivalent evaluated by snow cover monitoring, Water Resour. Res., 17, 1480–1488, https://doi.org/10.1029/WR017i005p01480, 1981.
Milly, P. C. D. and Dunne, K. A.: Macroscale water fluxes 1. Quantifying errors in the estimation of basin mean precipitation, Water Resour. Res., 38, 1205, https://doi.org/10.1029/2001WR000759, 2002.
Molotch, N. P.: Reconstructing snow water equivalent in the Rio Grande headwaters using remotely sensed snow cover data and a spatially distributed snowmelt model, Hydrol. Process., 23, 1076–1089, https://doi.org/10.1002/hyp.7206, 2009.
Molotch, N. P. and Bales, R. C.: Scaling snow observations from the point to the grid element: Implications for observation network design, Water Resour. Res., 41, W11421, https://doi.org/10.1029/2005WR004229, 2005.
Molotch, N. P. and Bales, R. C.: Comparison of ground-based and airborne snow surface albedo parameterizations in an alpine watershed: Impact on snowpack mass balance, Water Resour. Res., 42, W05410, https://doi.org/10.1029/2005WR004522, 2006.
Molotch, N. P. and Margulis, S. A.: Estimating the distribution of snow water equivalent using remotely sensed snow cover data and a spatially distributed snowmelt model: A multi-resolution, multi-sensor comparison, Adv. Water Resour., 31, 1503–1514, https://doi.org/10.1016/j.advwatres.2008.07.017, 2008.
Molotch, N. P., Margulis, S. A., and Jepsen, S. M.: Response to comment by A.G. Slater, M.P. Clark, and A.P. Barrett on “Estimating the distribution of snow water equivalent using remotely sensed snow cover data and a spatially distributed snowmelt model: A multi-resolution, multi-sensor comparison” [[Adv. Water Resour., 31, 1503–1514, 2008]. Adv. Water. Resour., 32, 1680–1684, 2009], Adv. Water Resour., 33, 231–239, https://doi.org/10.1016/j.advwatres.2009.11.008, 2010.
NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team: ASTER Global Digital Elevation Model, various tiles, NASA EOSDIS Land Processes DAAC, available at: https://doi.org/10.5067/ASTER/ASTGTM.002 (last access: 4 December 2014), 2009.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nolin, A. W.: Recent advances in remote sensing of seasonal snow, J. Glaciol., 56, 1141–1150, https://doi.org/10.3189/002214311796406077, 2010.
Painter, T. H., Rittger, K., McKenzie, C., Slaughter, P., Davis, R. E., and Dozier, J.: Retrieval of subpixel snow-covered area, grain size, and albedo from MODIS, Remote Sens. Environ., 113, 868–879, https://doi.org/10.1016/j.rse.2009.01.001, data available at: https://snow.jpl.nasa.gov (last access: 23 March 2017), 2009.
Painter, T. H., Bryant, A. C., and Skiles, S. M.: Radiative forcing by light absorbing impurities in snow from MODIS surface reflectance data, Geophys. Res. Lett., 39, L17502, https://doi.org/10.1029/2012GL052457, 2012a.
Painter, T. H., Brodzik, M. J., Racoviteanu, A., and Armstrong, R.: Automated mapping of Earth's annual minimum exposed snow and ice with MODIS, Geophys. Res. Lett., 39, L20501, https://doi.org/10.1029/2012GL053340, 2012b.
Peitzsch, E. H., Hendrikx, J., Fagre, D. B., and Reardon, B.: Examining spring wet slab and glide avalanche occurrence along the Going-to-the-Sun Road corridor, Glacier National Park, Montana, USA, Cold Reg. Sci. Technol., 78, 73–81, https://doi.org/10.1016/j.coldregions.2012.01.012, 2012.
Raleigh, M. S., Rittger, K., Moore, C. E., Henn, B., Lutz, J. A., and Lundquist, J. D.: Ground-based testing of MODIS fractional snow cover in subalpine meadows and forests of the Sierra Nevada, Remote Sens. Environ., 128, 44–57, https://doi.org/10.1016/j.rse.2012.09.016, 2013.
Rittger, K., Painter, T. H., and Dozier, J.: Assessment of methods for mapping snow cover from MODIS, Adv. Water Resour., 51, 367–380, https://doi.org/10.1016/j.advwatres.2012.03.002, 2013.
Rittger, K., Bair, E. H., Kahl, A., and Dozier, J.: Spatial estimates of snow water equivalent from reconstruction, Adv. Water Resour., 94, 345–363, https://doi.org/10.1016/j.advwatres.2016.05.015, 2016.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, B. Am. Meteor. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004.
Rosenthal, W. and Dozier, J.: Automated mapping of montane snow cover at subpixel resolution from the Landsat Thematic Mapper, Water Resour. Res., 32, 115–130, https://doi.org/10.1029/95WR02718, 1996.
Rutan, D. A., Kato, S., Doelling, D. R., Rose, F. G., Nguyen, L. T., Caldwell, T. E., and Loeb, N. G.: CERES synoptic product: Methodology and validation of surface radiant flux, J. Atmos. Ocean. Technol., 32, 1121–1143, https://doi.org/10.1175/JTECH-D-14-00165.1, 2015.
Schneider, D. and Molotch, N. P.: Real-time estimation of snow water equivalent in the Upper Colorado River Basin using MODIS-based SWE reconstructions and SNOTEL data, Water Resour. Res., 52, 7892–7910, https://doi.org/10.1002/2016WR019067, 2016.
Schulz, O. and de Jong, C.: Snowmelt and sublimation: field experiments and modelling in the High Atlas Mountains of Morocco, Hydrol. Earth Syst. Sci., 8, 1076–1089, https://doi.org/10.5194/hess-8-1076-2004, 2004.
Slater, A. G., Barrett, A. P., Clark, M. P., Lundquist, J. D., and Raleigh, M. S.: Uncertainty in seasonal snow reconstruction: Relative impacts of model forcing and image availability, Adv. Water Resour., 55, 165–177, https://doi.org/10.1016/j.advwatres.2012.07.006, 2013.
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., and Zeileis, A.: Conditional variable importance for random forests, BMC Bioinformatics, 9, 307, https://doi.org/10.1186/1471-2105-9-307, 2008.
Sturm, M., Holmgren, J., and Liston, G. E.: A seasonal snow cover classification system for local to global applications, J. Climate, 8, 1261–1283, https://doi.org/10.1175/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2, 1995.
Sturm, M., Taras, B., Liston, G. E., Derksen, C., Jonas, T., and Lea, J.: Estimating snow water equivalent using snow depth data and climate classes, J. Hydrometeorol., 11, 1380–1394, https://doi.org/10.1175/2010jhm1202.1, 2010.
Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Kärnä, J.-P., Koskinen, J., and Bojkov, B.: Estimating Northern Hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements, Remote Sens. Environ., 115, 3517–3529, https://doi.org/10.1016/j.rse.2011.08.014, 2011.
Tan, B., Woodcock, C. E., Hu, J., Zhang, P., Ozdogan, M., Huang, D., Yang, W., Knyazikhin, Y., and Myneni, R. B.: The impact of gridding artifacts on the local spatial properties of MODIS data: Implications for validation, compositing, and band-to-band registration across resolutions, Remote Sens. Environ., 105, 98–114, https://doi.org/10.1016/j.rse.2006.06.008, 2006.
Tedesco, M. and Narvekar, P. S.: Assessment of the NASA AMSR-E SWE product, IEEE J. Sel. Top. Appl., 3, 141–159, https://doi.org/10.1109/JSTARS.2010.2040462, 2010.
United Nations: UNEP in Afghanistan: Laying the foundations for sustainable development, United Nations Environment Programme, Geneva, 36, 2009.
USAID: Afghanistan Food Security Update, FEWS Net, Washington, DC, 4, 2008.
USGS: Global Land Survey, available at: http://glcfapp.glcf.umd.edu/data/gls/ (last access: 1 September 2017), 2009.
US Army Corps of Engineers: Snow Hydrology: Summary Report of the Snow Investigations, North Pacific Division, Corps of Engineers, Portland, OR, 462 pp., 1956.
Vander Jagt, B. J., Durand, M. T., Margulis, S. A., Kim, E. J., and Molotch, N. P.: The effect of spatial variability on the sensitivity of passive microwave measurements to snow water equivalent, Remote Sens. Environ., 136, 163–179, https://doi.org/10.1016/j.rse.2013.05.002, 2013.
Vuyovich, C. and Jacobs, J. M.: Snowpack and runoff generation using AMSR-E passive microwave observations in the Upper Helmand Watershed, Afghanistan, Remote Sens. Environ., 115, 3313–3321, https://doi.org/10.1016/j.rse.2011.07.014, 2011.
Warren, S. and Wiscombe, W.: A model for the spectral albedo of snow, II, Snow containing atmospheric aerosols, J. Atmos. Sci., 37, 2734–2745, https://doi.org/10.1175/1520-0469(1980)037<2734:AMFTSA>2.0.CO;2, 1980.
Wever, N., Fierz, C., Mitterer, C., Hirashima, H., and Lehning, M.: Solving Richards Equation for snow improves snowpack meltwater runoff estimations in detailed multi-layer snowpack model, The Cryosphere, 8, 257–274, https://doi.org/10.5194/tc-8-257-2014, 2014.
World Meteorlogical Organization: 1961–1990 Global climate normals, availabe at: ftp://ftp.atdd.noaa.gov/pub/GCOS/WMO-Normals/RA-II/AH (last access: 28 August 2017), 1998.
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V., Duan, Q., Mo, K., Fan, Y., and Mocko, D.: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products, J. Geophys. Res.-Atmos., 117, D03109, https://doi.org/10.1029/2011JD016048, 2012.
Xiaoxiong, X., Nianzeng, C., and Barnes, W.: Terra MODIS on-orbit spatial characterization and performance, IEEE Geosci. Remote S., 43, 355–365, https://doi.org/10.1109/TGRS.2004.840643, 2005.
Short summary
In Afghanistan, almost no snow measurements exist. Operational estimates use measurements from satellites, but all have limitations. We have developed a satellite-based technique called reconstruction that accurately estimates the snowpack retrospectively. To solve the problem of estimating today's snowpack, we used machine learning, trained on our reconstructed snow estimates, using predictors that are available today. Our results show low errors, demonstrating the utility of this approach.
In Afghanistan, almost no snow measurements exist. Operational estimates use measurements from...
