Skip to main content Accessibility help
×
Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-24T04:24:10.408Z Has data issue: false hasContentIssue false

5 - Data Assimilation of Seasonal Snow

from Part II - ‘Fluid’ Earth Applications: From the Surface to the Space

Published online by Cambridge University Press:  20 June 2023

Alik Ismail-Zadeh
Affiliation:
Karlsruhe Institute of Technology, Germany
Fabio Castelli
Affiliation:
Università degli Studi, Florence
Dylan Jones
Affiliation:
University of Toronto
Sabrina Sanchez
Affiliation:
Max Planck Institute for Solar System Research, Germany
Get access

Summary

Abstract: There is a fundamental need to understand and improve the errors and uncertainties associated with estimates of seasonal snow analysis and prediction. Over the past few decades, snow cover remote sensing techniques have increased in accuracy, but the retrieval of spatially and temporally continuous estimates of snow depth or snow water equivalent remains challenging tasks. Model-based snow estimates often bear significant uncertainties due to model structure and error-prone forcing data and parameter estimates. A potential method to overcome model and observational shortcomings is data assimilation. Data assimilation leverages the information content in both observations and models while minimising inherent limitations that result from uncertainty. This chapter reviews current snow models, snow remote sensing methods, and data assimilation techniques that can reduce uncertainties in the characterisation of seasonal snow.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Albers, D. J., Levine, M., Gluckman, B. et al. (2017). Personalized glucose forecasting for type 2 diabetes using data assimilation. PLoS Computational Biology, 13(4), e1005232.CrossRefGoogle ScholarPubMed
Anderson, M. G., and Burt, T. P., eds. (1985). Hydrological forecasting. Chichester: Wiley.Google Scholar
Andreadis, K. M., and Lettenmaier, D. P. (2006). Assimilating remotely sensed snow observations into a macroscale hydrology model. Advances in Water Resources, 29(6), 872–86.Google Scholar
Aoki, T., Motoyoshi, H., Kodama, Y., Yasunari, T. J., and Sugiura, K. (2007). Variations of the snow physical parameters and their effects on albedo in Sapporo, Japan. Annals of Glaciology, 46, 375–81.Google Scholar
Arsenault, K. R., Houser, P. R., De Lannoy, G. J., and Dirmeyer, P. A. (2013). Impacts of snow cover fraction data assimilation on modeled energy and moisture budgets. Journal of Geophysical Research: Atmospheres, 118(14), 7489–504.Google Scholar
Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–88.Google Scholar
Barrett, A. P. (2003). National Operational Hydrologic Remote Sensing Center SNOw Data Assimilation System (SNODAS) Products at NSIDC. NSIDC Special Report 11. Boulder, CO: National Snow and Ice Data Center. https://nsidc.org/sites/default/files/nsidc_special_report_11.pdf.Google Scholar
Bavay, M., Lehning, M., Jonas, T., and Löwe, H. (2009). Simulations of future snow cover and discharge in Alpine headwater catchments. Hydrological Processes: An International Journal, 23(1), 95108.CrossRefGoogle Scholar
Berghuijs, W. R., Woods, R. A., Hutton, C. J., and Sivapalan, M. (2016). Dominant flood generating mechanisms across the United States. Geophysical Research Letters, 43(9), 4382–90.CrossRefGoogle Scholar
Bernier, M., Fortin, J.-P., Gauthier, Y. et al. (1999). Determination of snow water equivalent using RADARSAT SAR data in eastern Canada. Hydrological Processes, 13(18), 3041–51.Google Scholar
Bonan, G. B. (1998). The land surface climatology of the NCAR Land Surface Model coupled to the NCAR Community Climate Model. Journal of Climate, 11(6), 1307–26.2.0.CO;2>CrossRefGoogle Scholar
Brown, R. D., and Brasnett, B. (2010). Canadian Meteorological Centre (CMC) daily snow depth analysis data. Version 1 [Data Set]. Boulder, CO: National Snow and Ice Data Center. https://doi.org/10.5067/W9FOYWH0EQZ3.Google Scholar
Brown, R. D., Brasnett, B., and Robinson, D. (2003). Gridded North American monthly snow depth and snow water equivalent for GCM evaluation. Atmosphere-Ocean, 41(1), 114.Google Scholar
Brun, E., David, P., Sudul, M., and Brunot, G. (1992). A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting. Journal of Glaciology, 38(128), 1322.CrossRefGoogle Scholar
Cantet, P., Boucher, M. A., Lachance-Coutier, S., Turcotte, R., and Fortin, V. (2019). Using a particle filter to estimate the spatial distribution of the snowpack water equivalent. Journal of Hydrometeorology, 20(4), 577–94.Google Scholar
Carroll, S. S., and Carroll, T. R. (1989). Effect of uneven snow cover on airborne snow water equivalent estimates obtained by measuring terrestrial gamma radiation. Water Resources Research, 25(7), 1505–10.CrossRefGoogle Scholar
Carroll, T. (1987). Operational airborne measurements of snow water equivalent and soil moisture using terrestrial gamma radiation in the United States. IAHS-AISH Publication, 166, 213–23.Google Scholar
Carroll, T. (2001). Airborne gamma radiation snow survey program: A user’s guide, version 5.0. National Operational Hydrologic Remote Sensing Center (NOHRSC), Chanhassen, 14.Google Scholar
Chang, A. T., Foster, J. L., and Hall, D. K. (1987). Nimbus-7 SMMR derived global snow cover parameters. Annals of Glaciology, 9, 3944.Google Scholar
Cho, E., Jacobs, J. M., and Vuyovich, C. M. (2020). The value of long‐term (40 years) airborne gamma radiation SWE record for evaluating three observation‐based gridded SWE data sets by seasonal snow and land cover classifications. Water Resources Research, 56(1). https://doi.org/10.1029/2019WR025813Google Scholar
Church, J. E. (1933). Snow surveying: Its principles and possibilities. Geographical Review, 23(4), 529–63.Google Scholar
Clark, M. P., Slater, A. G., Barrett, A. P. et al. (2006). Assimilation of snow covered area information into hydrologic and land-surface models. Advances in Water Resources, 29(8), 1209–21.CrossRefGoogle Scholar
Clifford, D. (2010). Global estimates of snow water equivalent from passive microwave instruments: History, challenges and future developments. International Journal of Remote Sensing, 31(14), 3707–26.Google Scholar
Cline, D. W., Bales, R. C., and Dozier, J. (1998). Estimating the spatial distribution of snow in mountain basins using remote sensing and energy balance modeling. Water Resources Research, 34(5), 1275–85.CrossRefGoogle Scholar
Conde, V., Nico, G., Mateus, P. et al. (2019). On the estimation of temporal changes of snow water equivalent by spaceborne SAR interferometry: A new application for the Sentinel-1 mission. Journal of Hydrology and Hydromechanics, 67(1), 93100.CrossRefGoogle Scholar
Cortés, G., Girotto, M., and Margulis, S. A. (2014). Analysis of sub-pixel snow and ice extent over the extratropical Andes using spectral unmixing of historical Landsat imagery. Remote Sensing of Environment, 141, 6478.CrossRefGoogle Scholar
Croce, P., Formichi, P., Landi, F. et al. (2018). The snow load in Europe and the climate change. Climate Risk Management, 20, 138–54.Google Scholar
De Lannoy, G. J., Reichle, R. H., Arsenault, K. R. et al. (2012). Multiscale assimilation of Advanced Microwave Scanning Radiometer–EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado. Water Resources Research, 48(1). https://doi.org/10.1029/2011WR010588.Google Scholar
De Lannoy, G. J., Reichle, R. H., Houser, P. R. et al. (2010). Satellite-scale snow water equivalent assimilation into a high-resolution land surface model. Journal of Hydrometeorology, 11(2), 352–69.CrossRefGoogle Scholar
Dechant, C., and Moradkhani, H. (2011). Radiance data assimilation for operational snow and streamflow forecasting. Advances in Water Resources, 34(3), 351–64.CrossRefGoogle Scholar
Deems, J. S., Painter, T. H., and Finnegan, D. C. (2013). Lidar measurement of snow depth: A review. Journal of Glaciology, 59(215), 467–79.Google Scholar
Derksen, C. (2008). The contribution of AMSR-E 18.7 and 10.7 GHz measurements to improved boreal forest snow water equivalent retrievals. Remote Sensing of Environment, 112(5), 2701–10.CrossRefGoogle Scholar
Descamps, S., Aars, J., Fuglei, E. et al. (2017). Climate change impacts on wildlife in a High Arctic archipelago – Svalbard, Norway. Global Change Biology, 23(2), 490502.Google Scholar
Deschamps-Berger, C., Gascoin, S., Berthier, E. et al. (2020). Snow depth mapping from stereo satellite imagery in mountainous terrain: Evaluation using airborne laser-scanning data. The Cryosphere, 14(9), 2925–40.Google Scholar
Dietz, A. J., Kuenzer, C., Gessner, U., and Dech, S. (2012). Remote sensing of snow: A review of available methods. International Journal of Remote Sensing, 33(13), 4094–134.CrossRefGoogle Scholar
Domine, F., Salvatori, R., Legagneux, L. et al. (2006). Correlation between the specific surface area and the short wave infrared (SWIR) reflectance of snow. Cold Regions Science and Technology, 46(1), 60–8.CrossRefGoogle Scholar
Dozier, J. (1989). Spectral signature of alpine snow cover from the Landsat Thematic Mapper. Remote Sensing of Environment, 28, 922.CrossRefGoogle Scholar
Durand, M., and Margulis, S. A. (2006). Feasibility test of multifrequency radiometric data assimilation to estimate snow water equivalent. Journal of Hydrometeorology, 7(3), 443–57.Google Scholar
Durand, M., and Margulis, S. A. (2007). Correcting first‐order errors in snow water equivalent estimates using a multifrequency, multiscale radiometric data assimilation scheme. Journal of Geophysical Research: Atmospheres, 112(D13). https://doi.org/10.1029/2006JD008067.CrossRefGoogle Scholar
Durand, M., Molotch, N. P., and Margulis, S. A. (2008). A Bayesian approach to snow water equivalent reconstruction. Journal of Geophysical Research: Atmospheres, 113(D20). https://doi.org/10.1029/2008JD009894.CrossRefGoogle Scholar
Dziubanski, D. J., and Franz, K. J. (2016). Assimilation of AMSR-E snow water equivalent data in a spatially-lumped snow model. Journal of Hydrology, 540, 2639.Google Scholar
Essery, R., Martin, E., Douville, H., Fernandez, A., and Brun, E. (1999). A comparison of four snow models using observations from an alpine site. Climate Dynamics, 15(8), 583–93.Google Scholar
Evensen, G., and Van Leeuwen, P. J. (2000). An ensemble Kalman smoother for nonlinear dynamics. Monthly Weather Review, 128(6), 1852–67.Google Scholar
Forman, B. A., and Reichle, R. H. (2014). Using a support vector machine and a land surface model to estimate large-scale passive microwave brightness temperatures over snow-covered land in North America. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(9), 4431–41.Google Scholar
Forman, B. A., Reichle, R. H., and Rodell, M. (2012). Assimilation of terrestrial water storage from GRACE in a snow‐dominated basin. Water Resources Research, 48(1).CrossRefGoogle Scholar
Foster, J. L., Hall, D. K., Chang, A. T. C., and Rango, A. (1984). An overview of passive microwave snow research and results. Reviews of Geophysics, 22(2), 195208.Google Scholar
Foster, J. L., Hall, D. K., Chang, A. T. et al. (1999). Effects of snow crystal shape on the scattering of passive microwave radiation. IEEE Transactions on Geoscience and Remote Sensing, 37(2), 1165–8.CrossRefGoogle Scholar
Foster, J. L., Sun, C., Walker, J. P. et al. (2005). Quantifying the uncertainty in passive microwave snow water equivalent observations. Remote Sensing of Environment, 94(2), 187203.Google Scholar
Frei, A., Tedesco, M., Lee, S. et al. (2012). A review of global satellite-derived snow products. Advances in Space Research, 50(8), 1007–29.CrossRefGoogle Scholar
Gascoin, S., Grizonnet, M., Bouchet, M., Salgues, G., and Hagolle, O. (2019). Theia Snow collection: High-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data. Earth System Science Data, 11(2), 493514.Google Scholar
Girotto, M., Cortés, G., Margulis, S. A., and Durand, M. (2014a). Examining spatial and temporal variability in snow water equivalent using a 27 year reanalysis: Kern River watershed, Sierra Nevada. Water Resources Research, 50(8), 6713–34.CrossRefGoogle Scholar
Girotto, M., Margulis, S. A., and Durand, M. (2014b). Probabilistic SWE reanalysis as a generalization of deterministic SWE reconstruction techniques. Hydrological Processes, 28(12), 3875–95.Google Scholar
Girotto, M., Musselman, K. N., and Essery, R. L. (2020). Data assimilation improves estimates of climate-sensitive seasonal snow. Current Climate Change Reports, 6, 8194.Google Scholar
Golding, D. L., and Swanson, R. H. (1978). Snow accumulation and melt in small forest openings in Alberta. Canadian Journal of Forest Research, 8(4), 380–8.Google Scholar
Günther, D., Marke, T., Essery, R., and Strasser, U. (2019). Uncertainties in snowpack simulations: Assessing the impact of model structure, parameter choice, and forcing data error on point‐scale energy balance snow model performance. Water Resources Research, 55(4), 2779–800.Google Scholar
Hagopian, J., Bolcar, M., Chambers, J. et al. (2016). Advanced topographic laser altimeter system (ATLAS) receiver telescope assembly (RTA) and transmitter alignment and test. Proc. SPIE 9972, Earth Observing Systems, XXI, 997207. https://doi.org/10.1117/12.2240241.Google Scholar
Hall, D. K., Box, J. E., Casey, K. A. et al. (2008). Comparison of satellite-derived and in-situ observations of ice and snow surface temperatures over Greenland. Remote Sensing of Environment, 112(10), 3739–49.CrossRefGoogle Scholar
Hall, D. K., and Martinec, J. (1985). Remote Sensing of Ice and Snow. London: Chapman & Hall.CrossRefGoogle Scholar
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr, K. J. (2002). MODIS snow-cover products. Remote Sensing of Environment, 83(1–2), 181–94.CrossRefGoogle Scholar
Hammond, J. C., Saavedra, F. A., and Kampf, S. K. (2018). Global snow zone maps and trends in snow persistence 2001–2016. International Journal of Climatology, 38(12), 4369–83.Google Scholar
Han, P., Long, D., Li, X. et al. (2021). A dual state-parameter updating scheme using the particle filter and high-spatial-resolution remotely sensed snow depths to improve snow simulation. Journal of Hydrology, 594, 125979.CrossRefGoogle Scholar
Hedrick, A. R., Marks, D., Havens, S. et al. (2018). Direct insertion of NASA Airborne Snow Observatory‐derived snow depth time series into the iSnobal energy balance snow model. Water Resources Research, 54(10), 8045–63.Google Scholar
Helmert, J., Şensoy Şorman, A., Alvarado Montero, R. et al. (2018). Review of snow data assimilation methods for hydrological, land surface, meteorological and climate models: Results from a COST HarmoSnow survey. Geosciences, 8(12), 489.Google Scholar
Houser, P. R. (2013). Improved disaster management using data assimilation. In Tiefenbacher, J., ed., Approaches to Disaster Management: Examining the Implications of Hazards, Emergencies and Disasters. London: IntechOpen, 83103. https://doi.org/10.5772/3355.Google Scholar
Houser, P. R., Shuttleworth, W. J., Famiglietti, J. S. et al. (1998). Integration of soil moisture remote sensing and hydrologic modeling using data assimilation. Water Resources Research, 34(12), 3405–20.Google Scholar
Huang, C., Newman, A. J., Clark, M. P., Wood, A. W., and Zheng, X. (2017). Evaluation of snow data assimilation using the ensemble Kalman filter for seasonal streamflow prediction in the western United States. Hydrology and Earth System Sciences, 21(1), 635–50.CrossRefGoogle Scholar
Hüsler, F., Jonas, T., Riffler, M., Musial, J. P., and Wunderle, S. (2014). A satellite-based snow cover climatology (1985–2011) for the European Alps derived from AVHRR data. The Cryosphere, 8(1), 7390.CrossRefGoogle Scholar
Jepsen, S. M., Harmon, T. C., Ficklin, D. L., Molotch, N. P., and Guan, B. (2018). Evapotranspiration sensitivity to air temperature across a snow-influenced watershed: Space-for-time substitution versus integrated watershed modeling. Journal of Hydrology, 556, 645–59.Google Scholar
Jordan, R. E. (1991). A One-Dimensional Temperature Model for a Snow Cover: Technical Documentation for SNTHERM. 89. (No. CRREL-SR-91-16). Hanover, NH: Cold Regions Research and Engineering Lab,Google Scholar
Jost, G., Weiler, M., Gluns, D. R., and Alila, Y. (2007). The influence of forest and topography on snow accumulation and melt at the watershed-scale. Journal of Hydrology, 347(1–2), 101–15.Google Scholar
Kelly, R. (2009). The AMSR-E snow depth algorithm: Description and initial results. Journal of the Remote Sensing Society of Japan, 29(1), 307–17.Google Scholar
Kendra, J. R., Sarabandi, K., and Ulaby, F. T. (1998). Radar measurements of snow: Experiment and analysis. IEEE Transactions on Geoscience and Remote Sensing, 36(3), 864–79.Google Scholar
Kim, E., Gatebe, C., Hall, D., et al. (2017). NASA’s SnowEx campaign: Observing seasonal snow in a forested environment. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, July 23–28, 2017, pp. 1388–90, https://doi.org/10.1109/IGARSS.2017.8127222.Google Scholar
Kinar, N. J., and Pomeroy, J. W. (2015). Measurement of the physical properties of the snowpack. Reviews of Geophysics, 53(2), 481544.Google Scholar
Krinner, G., Derksen, C., Essery, R. et al. (2018). ESM-SnowMIP: Assessing snow models and quantifying snow-related climate feedbacks. Geoscientific Model Development, 11(12), 5027–49.Google Scholar
Kumar, M., Marks, D., Dozier, J., Reba, M., and Winstral, A. (2013). Evaluation of distributed hydrologic impacts of temperature-index and energy-based snow models. Advances in Water Resources, 56, 7789.Google Scholar
Lafaysse, M., Cluzet, B., Dumont, M. et al. (2017). A multiphysical ensemble system of numerical snow modelling. The Cryosphere, 11(3), 1173–98.Google Scholar
Largeron, C., Dumont, M., Morin, S. et al. (2020). Toward snow cover estimation in mountainous areas using modern data assimilation methods: A review. Frontiers in Earth Science, 8, 325. https://doi.org/10.3389/feart.2020.00325.Google Scholar
Lehning, M., Bartelt, P., Brown, B. et al. (1999). SNOWPACK model calculations for avalanche warning based upon a new network of weather and snow stations. Cold Regions Science and Technology, 30(1–3), 145–57.Google Scholar
Leisenring, M., and Moradkhani, H. (2011). Snow water equivalent prediction using Bayesian data assimilation methods. Stochastic Environmental Research and Risk Assessment, 25(2), 253–70.Google Scholar
Lemmetyinen, J., Derksen, C., Rott, H. et al. (2018). Retrieval of effective correlation length and snow water equivalent from radar and passive microwave measurements. Remote Sensing, 10(2), 170. https://doi.org/10.3390/rs10020170.Google Scholar
Li, D., Durand, M., and Margulis, S. A. (2012). Potential for hydrologic characterization of deep mountain snowpack via passive microwave remote sensing in the Kern River basin, Sierra Nevada, USA. Remote Sensing of Environment, 125, 3448.Google Scholar
Li, D., Lettenmaier, D. P., Margulis, S. A., and Andreadis, K. (2019). The value of accurate high-resolution and spatially continuous snow information to streamflow forecasts. Journal of Hydrometeorology, 20(4), 731–49.Google Scholar
Lievens, H., Demuzere, M., Marshall, H.-P. et al. (2019). Snow depth variability in the Northern Hemisphere mountains observed from space. Nature Communications, 10(1), 112.Google Scholar
Liu, Y., Fang, Y., and Margulis, S. A. (2021). Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat-MODIS era snow reanalysis dataset. The Cryosphere, 15, 5261–80.Google Scholar
Liu, Y., Peters-Lidard, C. D., Kumar, S. et al. (2013). Assimilating satellite-based snow depth and snow cover products for improving snow predictions in Alaska. Advances in Water Resources, 54, 208–27.Google Scholar
Lundquist, J. D., Chickadel, C., Cristea, N. et al. (2018). Separating snow and forest temperatures with thermal infrared remote sensing. Remote Sensing of Environment, 209, 764–79.Google Scholar
Lundquist, J., Hughes, M., Gutmann, E., and Kapnick, S. (2019). Our skill in modeling mountain rain and snow is bypassing the skill of our observational networks. Bulletin of the American Meteorological Society, 100(12), 2473–90.Google Scholar
Luojus, K., Pulliainen, J., Takala, M. et al. (2021). GlobSnow v3. 0 Northern Hemisphere snow water equivalent dataset. Scientific Data, 8(1), 116.Google Scholar
Lv, Z., and Pomeroy, J. W. (2020). Assimilating snow observations to snow interception process simulations. Hydrological Processes, 34(10), 2229–46.Google Scholar
Magnusson, J., Gustafsson, D., Hüsler, F., and Jonas, T. (2014). Assimilation of point SWE data into a distributed snow cover model comparing two contrasting methods. Water Resources Research, 50(10), 7816–35.Google Scholar
Margulis, S. A., Cortés, G., Girotto, M., and Durand, M. (2016). A Landsat-era Sierra Nevada snow reanalysis (1985–2015). Journal of Hydrometeorology, 17(4), 1203–21.Google Scholar
Margulis, S. A., Fang, Y., Li, D., Lettenmaier, D. P., and Andreadis, K. (2019). The utility of infrequent snow depth images for deriving continuous space‐time estimates of seasonal snow water equivalent. Geophysical Research Letters, 46(10), 5331–40.Google Scholar
Margulis, S. A., Girotto, M., Cortés, G., and Durand, M. (2015). A particle batch smoother approach to snow water equivalent estimation. Journal of Hydrometeorology, 16(4), 1752–72.Google Scholar
Meromy, L., Molotch, N. P., Link, T. E., Fassnacht, S. R., and Rice, R. (2013). Subgrid variability of snow water equivalent at operational snow stations in the western USA. Hydrological Processes, 27(17), 2383–400.Google Scholar
Miller, S. D., Lee, T. F., and Fennimore, R. L. (2005). Satellite-based imagery techniques for daytime cloud/snow delineation from MODIS. Journal of Applied Meteorology, 44(7), 987–97.Google Scholar
Molotch, N. P., and Margulis, S. A. (2008). 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. Advances in Water Resources, 31(11), 1503–14.CrossRefGoogle Scholar
Musselman, K. N., Addor, N., Vano, J. A., and Molotch, N. P. (2021). Winter melt trends portend widespread declines in snow water resources. Nature Climate Change, 11(5), 418–24.Google Scholar
Musselman, K. N., Lehner, F., Ikeda, K. et al. (2018). Projected increases and shifts in rain-on-snow flood risk over western North America. Nature Climate Change, 8(9), 808–12.Google Scholar
Musselman, K. N., Pomeroy, J. W., Essery, R. L., and Leroux, N. (2015). Impact of windflow calculations on simulations of alpine snow accumulation, redistribution and ablation. Hydrological Processes, 29(18), 3983–99.Google Scholar
Nadim, F., Kjekstad, O., Peduzzi, P., Herold, C., and Jaedicke, C. (2006). Global landslide and avalanche hotspots. Landslides, 3(2), 159–73.Google Scholar
Nagler, T., Rott, H., Ripper, E., Bippus, G., and Hetzenecker, M. (2016). Advancements for snowmelt monitoring by means of Sentinel-1 SAR. Remote Sensing, 8(4), 348. https://doi.org/10.3390/rs8040348.Google Scholar
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E. et al. (2011). The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. Journal of Geophysical Research: Atmospheres, 116(D12).Google Scholar
Nolan, M., Larsen, C., and Sturm, M. (2015). Mapping snow depth from manned aircraft on landscape scales at centimeter resolution using structure-from-motion photogrammetry. The Cryosphere, 9(4), 1445–63.Google Scholar
Oaida, C. M., Reager, J. T., Andreadis, K. M. et al. (2019). A high-resolution data assimilation framework for snow water equivalent estimation across the western United States and validation with the airborne snow observatory. Journal of Hydrometeorology, 20(3), 357–78.Google Scholar
Ohmura, A. (2001). Physical basis for the temperature-based melt-index method. Journal of Applied Meteorology, 40(4), 753–61.Google Scholar
Painter, T. H., Berisford, D. F., Boardman, J. W. et al. (2016). The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo. Remote Sensing of Environment, 184, 139–52.Google Scholar
Painter, T. H., Bryant, A. C., and Skiles, S. M. (2012). Radiative forcing by light absorbing impurities in snow from MODIS surface reflectance data. Geophysical Research Letters, 39(17).Google Scholar
Painter, T. H., Rittger, K., McKenzie, C. et al. (2009). Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sensing of Environment, 113(4), 868–79.Google Scholar
Picard, G., Sandells, M., and Löwe, H. (2018). SMRT: An active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1. 0). Geoscientific Model Development, 11(7), 2763–88.Google Scholar
Pulliainen, J., Luojus, K., Derksen, C. et al. (2020). Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018. Nature, 581(7808), 294–8.Google Scholar
Raleigh, M. S., Livneh, B., Lapo, K., and Lundquist, J. D. (2016). How does availability of meteorological forcing data impact physically based snowpack simulations? Journal of Hydrometeorology, 17(1), 99120.Google Scholar
Rango, A. (1996). Spaceborne remote sensing for snow hydrology applications. Hydrological Sciences Journal, 41(4), 477–94.CrossRefGoogle Scholar
Rice, R., Bales, R. C., Painter, T. H., and Dozier, J. (2011). Snow water equivalent along elevation gradients in the Merced and Tuolumne River basins of the Sierra Nevada. Water Resources Research, 47(8).Google Scholar
Riggs, G. A., Hall, D. K., and Román, M. O. (2017). Overview of NASA’s MODIS and visible infrared imaging radiometer suite (VIIRS) snow-cover earth system data records. Earth System Science Data, 9(2), 765–77.Google Scholar
Rodell, M., and Houser, P. R. (2004). Updating a land surface model with MODIS-derived snow cover. Journal of Hydrometeorology, 5(6), 1064–75.Google Scholar
Rosenthal, W., and Dozier, J. (1996). Automated mapping of montane snow cover at subpixel resolution from the Landsat Thematic Mapper. Water Resources Research, 32(1), 115–30.Google Scholar
Santi, E., Brogioni, M., Leduc-Leballeur, M. et al. (2022). Exploiting the ANN Potential in Estimating Snow Depth and Snow Water Equivalent From the Airborne SnowSAR Data at X-and Ku-Bands. IEEE Transactions on Geoscience and Remote Sensing, 60, 116. https://do.org/ 10.1109/TGRS.2021.3086893.Google Scholar
Schlosser, C. A., Robock, A., Vinnikov, K. Y., Speranskaya, N. A., and Xue, Y. (1997). 18-year land-surface hydrology model simulations for a midlatitude grassland catchment in Valdai, Russia. Monthly Weather Review, 125(12), 3279–96.Google Scholar
Schmidt, R. A. (1982). Properties of blowing snow. Reviews of Geophysics, 20(1), 3944.Google Scholar
Schmugge, T. J., Kustas, W. P., Ritchie, J. C., Jackson, T. J., and Rango, A. (2002). Remote sensing in hydrology. Advances in Water Resources, 25(8–12), 1367–85.Google Scholar
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty, R. S. (1999). Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data. Water Resources Research, 35(7), 2145–60.Google Scholar
Shi, J., and Dozier, J. (2000). Estimation of snow water equivalence using SIR-C/X-SAR. I. Inferring snow density and subsurface properties. IEEE Transactions on Geoscience and Remote Sensing, 38(6), 2465–74.Google Scholar
Shufen, S., and Yongkang, X. (2001). Implementing a new snow scheme in simplified simple biosphere model. Advances in Atmospheric Sciences, 18(3), 335–54.Google Scholar
Skiles, S. M., Flanner, M., Cook, J. M., Dumont, M., and Painter, T. H. (2018). Radiative forcing by light-absorbing particles in snow. Nature Climate Change, 8(11), 964–71.CrossRefGoogle Scholar
Skiles, S. M., and Painter, T. H. (2019). Toward understanding direct absorption and grain size feedbacks by dust radiative forcing in snow with coupled snow physical and radiative transfer modeling. Water Resources Research, 55(8), 7362–78.Google Scholar
Slater, A. G., Barrett, A. P., Clark, M. P., Lundquist, J. D., and Raleigh, M. S. (2013). Uncertainty in seasonal snow reconstruction: Relative impacts of model forcing and image availability. Advances in Water Resources, 55, 165–77.Google Scholar
Slater, A. G., and Clark, M. P. (2006). Snow data assimilation via an ensemble Kalman filter. Journal of Hydrometeorology, 7(3), 478–93.Google Scholar
Smith, C. D., Kontu, A., Laffin, R., and Pomeroy, J. W. (2017). An assessment of two automated snow water equivalent instruments during the WMO Solid Precipitation Intercomparison Experiment. The Cryosphere, 11(1), 101–16.Google Scholar
Smyth, E. J., Raleigh, M. S., and Small, E. E. (2019). Particle filter data assimilation of monthly snow depth observations improves estimation of snow density and SWE. Water Resources Research, 55(2), 1296–311.Google Scholar
Stigter, E. E., Wanders, N., Saloranta, T. M. et al. (2017). Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment. The Cryosphere, 11(4), 1647–64.Google Scholar
Strozzi, T., and Matzler, C. (1998). Backscattering measurements of alpine snowcovers at 5.3 and 35 GHz. IEEE Transactions on Geoscience and Remote Sensing, 36(3), 838–48.Google Scholar
Su, H., Yang, Z.-L., Niu, G.-Y., and Dickinson, R. E. (2008). Enhancing the estimation of continental‐scale snow water equivalent by assimilating MODIS snow cover with the ensemble Kalman filter. Journal of Geophysical Research: Atmospheres, 113(D8).Google Scholar
Tachiiri, K., Shinoda, M., Klinkenberg, B., and Morinaga, Y. (2008). Assessing Mongolian snow disaster risk using livestock and satellite data. Journal of Arid Environments, 72(12), 2251–63.Google Scholar
Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., and Watkins, M. M. (2004). GRACE measurements of mass variability in the Earth system. Science, 305(5683), 503–5.Google Scholar
Tedesco, M. (2014). Remote Sensing of the Cryosphere. Chichester: John Wiley & Sons.Google Scholar
Tedesco, M., Pulliainen, J., Takala, M., Hallikainen, M., and Pampaloni, P. (2004). Artificial neural network-based techniques for the retrieval of SWE and snow depth from SSM/I data. Remote Sensing of Environment, 90(1), 7685.Google Scholar
Thirel, G., Salamon, P., Burek, P., and Kalas, M. (2013). Assimilation of MODIS snow cover area data in a distributed hydrological model using the particle filter. Remote Sensing, 5(11), 5825–50.Google Scholar
Todt, M., Rutter, N., Fletcher, C. G. et al. (2018). Simulation of longwave enhancement in boreal and montane forests. Journal of Geophysical Research: Atmospheres, 123(24), 13731–47.Google Scholar
Toure, A. M., Reichle, R. H., Forman, B. A., Getirana, A., and De Lannoy, G. J. (2018). Assimilation of MODIS snow cover fraction observations into the NASA catchment land surface model. Remote Sensing, 10(2), 316.Google Scholar
Van Leeuwen, P. J. (2009). Particle filtering in geophysical systems. Monthly Weather Review, 137(12), 4089–114.Google Scholar
Veyssière, G., Karbou, F., Morin, S., Lafaysse, M., and Vionnet, V. (2019). Evaluation of sub-kilometric numerical simulations of c-band radar backscatter over the French alps against sentinel-1 observations. Remote Sensing, 11(1), 8. https://doi.org/10.3390/rs11010008.Google Scholar
Viallon-Galinier, L., Hagenmuller, P., and Lafaysse, M. (2020). Forcing and evaluating detailed snow cover models with stratigraphy observations. Cold Regions Science and Technology, 180, 103163.Google Scholar
Viviroli, D., Dürr, H. H., Messerli, B., Meybeck, M., and Weingartner, R. (2007). Mountains of the world, water towers for humanity: Typology, mapping, and global significance. Water Resources Research, 43(7).Google Scholar
Wang, J., Forman, B. A., Girotto, M., and Reichle, R. H. (2021). Estimating terrestrial snow mass via multi‐sensor assimilation of synthetic AMSR‐E brightness temperature spectral differences and synthetic GRACE terrestrial water storage retrievals. Water Resources Research, 57(9), e2021WR029880.CrossRefGoogle Scholar
Wiesmann, A., and Mätzler, C. (1999). Microwave emission model of layered snowpacks. Remote Sensing of Environment, 70(3), 307316.Google Scholar
Winstral, A., Magnusson, J., Schirmer, M., and Jonas, T. (2019). The bias‐detecting ensemble: A new and efficient technique for dynamically incorporating observations into physics‐based, multilayer snow models. Water Resources Research, 55(1), 613–31.Google Scholar
Xue, Y., and Forman, B. A. (2017). Integration of satellite-based passive microwave brightness temperature observations and an ensemble-based land data assimilation framework to improve snow estimation in forested regions. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 311–14.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×