Articles | Volume 27, issue 1
https://doi.org/10.5194/hess-27-21-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-27-21-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating spatiotemporally continuous snow water equivalent from intermittent satellite observations: an evaluation using synthetic data
Xiaoyu Ma
Department of Geography, University of California, 90095 Los Angeles, United States
Dongyue Li
CORRESPONDING AUTHOR
Department of Geography, University of California, 90095 Los Angeles, United States
Department of Civil and Environmental Engineering, University of
California, 90095 Los Angeles, United States
Yiwen Fang
Department of Civil and Environmental Engineering, University of
California, 90095 Los Angeles, United States
Steven A. Margulis
Department of Civil and Environmental Engineering, University of
California, 90095 Los Angeles, United States
Dennis P. Lettenmaier
Department of Geography, University of California, 90095 Los Angeles, United States
Department of Civil and Environmental Engineering, University of
California, 90095 Los Angeles, United States
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The Cryosphere, 19, 3309–3327, https://doi.org/10.5194/tc-19-3309-2025, https://doi.org/10.5194/tc-19-3309-2025, 2025
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Accurate snow water equivalent (SWE) estimates are crucial for water management in snowmelt-dependent regions, but bias and uncertainty in precipitation data make this challenging. Here, we leverage insights from a historical SWE data product to correct these biases and yield more accurate SWE estimates and streamflow predictions. Incorporating snow depth observations further boosts accuracy. This study demonstrates an effective method to downscale and bias-correct global mountain precipitation.
Haorui Sun, Yiwen Fang, Steven A. Margulis, Colleen Mortimer, Lawrence Mudryk, and Chris Derksen
The Cryosphere, 19, 2017–2036, https://doi.org/10.5194/tc-19-2017-2025, https://doi.org/10.5194/tc-19-2017-2025, 2025
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The European Space Agency's Snow Climate Change Initiative (Snow CCI) developed a high-quality snow cover extent and snow water equivalent (SWE) climate data record. However, gaps exist in complex terrain due to challenges in using passive microwave sensing and in situ measurements. This study presents a methodology to fill the mountain SWE gap using Snow CCI snow cover fraction within a Bayesian SWE reanalysis framework, with potential applications in untested regions and with other sensors.
Lu Su, Dennis P. Lettenmaier, Ming Pan, and Benjamin Bass
Hydrol. Earth Syst. Sci., 28, 3079–3097, https://doi.org/10.5194/hess-28-3079-2024, https://doi.org/10.5194/hess-28-3079-2024, 2024
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We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river basins in the Western US. We developed transfer relationships to similar basins and extended the fine-tuned parameters to ungauged basins. Both models performed best in humid areas, and the skills improved post-calibration. VIC outperforms Noah-MP in all but interior dry basins following regionalization. VIC simulates annual mean streamflow and high flow well, while Noah-MP performs better for low flows.
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
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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.
Justin M. Pflug, Yiwen Fang, Steven A. Margulis, and Ben Livneh
Hydrol. Earth Syst. Sci., 27, 2747–2762, https://doi.org/10.5194/hess-27-2747-2023, https://doi.org/10.5194/hess-27-2747-2023, 2023
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Wolverine denning habitat inferred using a snow threshold differed for three different spatial representations of snow. These differences were based on the annual volume of snow and the elevation of the snow line. While denning habitat was most influenced by winter meteorological conditions, our results show that studies applying thresholds to environmental datasets should report uncertainties stemming from different spatial resolutions and uncertainties introduced by the thresholds themselves.
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
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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.
Cited articles
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., and
Arshad, H.: State-of-the-art in artificial neural network applications, A
survey, Heliyon, 4, 3–6, https://doi.org/10.1016/j.heliyon.2018.e00938, 2018.
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, https://doi.org/10.1038/nature04141, 2005.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Chakraborty, D., Başağaoğlu, H., Gutierrez, L., and Mirchi, A.:
Explainable AI reveals new hydroclimatic insights for ecosystem-centric
groundwater management, Environ. Res. Lett., 16, 114024,
https://doi.org/10.1088/1748-9326/ac2fde, 2021.
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B.,
Cullen, N. J., Kerr, T., Örn Hreinsson, E., and Woods, R. A.:
Representing spatial variability of snow water equivalent in hydrologic and
land-surface models: A review, Water Resour. Res., 47, 17–18,
https://doi.org/10.1029/2011WR010745, 2011.
Cline, D. W., Bales, R. C., and Dozier, J.: Estimating the spatial
distribution of snow in mountain basins using remote sensing and energy
balance modeling, Water Resour. Res., 34, 1275–1285,
https://doi.org/10.1029/97WR03755, 1998.
Costa, M. A., de Pádua Braga, A., and de Menezes, B. R.: Improving
generalization of MLPs with sliding mode control and the
Levenberg–Marquardt algorithm, Neurocomputing, 70, 1342–1347,
https://doi.org/10.1016/j.neucom.2006.09.003, 2007.
Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.
Dikshit, A. and Pradhan, B.: Explainable AI in drought forecasting, Machine
Learning with Applications, 6, 100192,
https://doi.org/10.1016/j.mlwa.2021.100192, 2021a.
Dikshit, A. and Pradhan, B.: Interpretable and explainable AI (XAI) model
for spatial drought prediction, Sci. Total. Environ., 801, 149797,
https://doi.org/10.1016/j.scitotenv.2021.149797, 2021b.
Dong, C.: Remote sensing, hydrological modeling and in situ observations in
snow cover research: A review, J. Hydrol., 561, 573–583,
https://doi.org/10.1016/j.jhydrol.2018.04.027, 2018.
Dozier, J.: Mountain hydrology, snow color, and the fourth paradigm,
Transactions American Geophysical Union, EOS, 92, 373–374,
https://doi.org/10.1029/2011EO430001, 2011.
Fang, Y., Liu, Y., and Margulis, S. A.: A western United States snow
reanalysis dataset over the Landsat era from water years 1985 to 2021, Sci.
Data, 9, 677, https://doi.org/10.1038/s41597-022-01768-7, 2022.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,
D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004,
https://doi.org/10.1029/2005RG000183, 2007.
Foster, J. L., Sun, C., Walker, J. P., Kelly, R., Chang, A., Dong, J., and
Powell, H.: Quantifying the uncertainty in passive microwave snow water
equivalent observations, Remote. Sens. Environ., 94, 187–203,
https://doi.org/10.1016/j.rse.2004.09.012, 2005.
Gardner, M. W. and Dorling, S. R.: Artificial neural networks (the multilayer perceptron) – a review of applications in the atmospheric sciences, Atmos. Environ., 32, 2627–2636, https://doi.org/10.1016/S1352-2310(97)00447-0, 1998.
Garrison, J. L., Piepmeier, J., Shah, R., Vega, M. A., Spencer, D. A.,
Banting, R., Firman, C. M., Nold, B., Larsen, K., and Bindlish, R.: SNOOPI:
A Technology Validation Mission for P-band Reflectometry using Signals of
Opportunity, in: IGARSS 2019, IEEE Int. Geosci. Remote. Se. Symposium,
5082–5085, https://doi.org/10.1109/IGARSS.2019.8900351, 2019.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan,
K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate.,
30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
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.
Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S., and Klambauer, G.:
NeuralHydrology – Interpreting LSTMs in Hydrology, in: Explainable AI:
Interpreting, Explaining and Visualizing Deep Learning, edited by: Samek,
W., Montavon, G., Vedaldi, A., Hansen, L. K., and Müller, K.-R.,
Springer International Publishing, Cham, 347–362,
https://doi.org/10.1007/978-3-030-28954-6_19, 2019.
Kuter, S.: Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression, Remote Sens. Environ., 255, 112294, https://doi.org/10.1016/j.rse.2021.112294, 2021.
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, D., Wrzesien, M. L., Durand, M., Adam, J., and Lettenmaier, D. P.: How
much runoff originates as snow in the western United States, and how will
that change in the future?, Geophys. Res. Lett., 44, 6163–6172,
https://doi.org/10.1002/2017GL073551, 2017a.
Li, D., Durand, M., and Margulis, S. A.: Estimating snow water equivalent in
a Sierra Nevada watershed via spaceborne radiance data assimilation, Water
Resour. Res., 53, 647–671, https://doi.org/10.1002/2016WR018878, 2017b.
Lievens, H., Brangers, I., Marshall, H.-P., Jonas, T., Olefs, M., and De Lannoy, G.: Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps, The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, 2022.
Liston, G. E.: Representing Subgrid Snow Cover Heterogeneities in Regional
and Global Models, J. Climate., 17, 1381–1397,
https://doi.org/10.1175/1520-0442(2004)017<1381:RSSCHI>2.0.CO;2, 2004.
Liu, C., Huang, X., Li, X., and Liang, T.: MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area, Remote Sens.-Basel., 12, 962, https://doi.org/10.3390/rs12060962, 2020.
Luce, C. H., Lopez-Burgos, V., and Holden, Z.: Sensitivity of snowpack
storage to precipitation and temperature using spatial and temporal analog
models, Water. Resour. Res., 50, 9447–9462,
https://doi.org/10.1002/2013WR014844, 2014.
Magnusson, J., Gustafsson, D., Hüsler, F., and Jonas, T.: Assimilation
of point SWE data into a distributed snow cover model comparing two
contrasting methods, Water. Resour. Res., 50, 7816–7835,
https://doi.org/10.1002/2014WR015302, 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.
Ma, X., Li, D., Fang, Y., Margulis, S. A., and Lettenmaier, D. P.: Datasets of estimating spatiotemporally continuous snow water equivalent from intermittent satellite track observations using machine learning methods, figshare [data set], https://doi.org/10.6084/m9.figshare.20044424.v1, 2022.
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, 1–2, https://doi.org/10.1029/2005WR004229, 2005.
Molotch, N. P. and Bales, R. C.: SNOTEL representativeness in the Rio Grande
headwaters on the basis of physiographics and remotely sensed snow cover
persistence, Hydrol. Process., 20, 723–739,
https://doi.org/10.1002/hyp.6128, 2006.
Nolin, A. W.: Recent advances in remote sensing of seasonal snow, J.
Glaciol., 56, 1141–1150, https://doi.org/10.3189/002214311796406077, 2010.
Pflug, J. M. and Lundquist, J. D.: Inferring Distributed Snow Depth by
Leveraging Snow Pattern Repeatability: Investigation Using 47 Lidar
Observations in the Tuolumne Watershed, Sierra Nevada, California, Water
Resour. Res., 56, e2020WR027243, https://doi.org/10.1029/2020WR027243, 2020.
Raleigh, M. S. and Lundquist, J. D.: Comparing and combining SWE estimates
from the SNOW-17 model using PRISM and SWE reconstruction, Water Resour.
Res., 48, p. 13, https://doi.org/10.1029/2011WR010542, 2012.
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.
Segal, M. R.: Machine Learning Benchmarks and Random Forest Regression, UCSF: Center for Bioinformatics and Molecular Biostatistics, https://escholarship.org/uc/item/35x3v9t4 (last access: 23 December 2022), 2004.
Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C.,
Kim, D.-H., Collins, K. M., Channan, S., DiMiceli, C., and Townshend, J. R.:
Global, 30 m resolution continuous fields of tree cover: Landsat-based
rescaling of MODIS vegetation continuous fields with lidar-based estimates
of error, Int. J. Digit. Earth, 6, 427–448,
https://doi.org/10.1080/17538947.2013.786146, 2013.
Shah, R., Yueh, S., Xu, X., Elder, K., Huang, H., and Tsang, L.:
Experimental Results of Snow Measurement Using P-Band Signals of
Opportunity, in: IGARSS 2018–2018 IEEE International Geoscience and Remote
Sensing Symposium, IGARSS 2018–2018, IEEE Geosci. Remote Se. Symposium,
6280–6283, https://doi.org/10.1109/IGARSS.2018.8517749, 2018.
Sicart, J. E., Pomeroy, J. W., Essery, R. L. H., and Bewley, D.: Incoming
longwave radiation to melting snow: observations, sensitivity and estimation
in Northern environments, Hydrol. Process., 20, 3697–3708,
https://doi.org/10.1002/hyp.6383, 2006.
Sun, S. and Xue, Y.: Implementing a new snow scheme in Simplified Simple
Biosphere Model, Adv. Atmos. Sci., 18, 335–354,
https://doi.org/10.1007/BF02919314, 2001.
Tanaka, M. and Okutomi, M.: A novel inference of a restricted boltzmann
machine, IEEE, 2014 22nd Int. C. Patt. Recog., 1526–1531,
https://doi.org/10.1109/ICPR.2014.271, 2014.
Trujillo, E., Molotch, N. P., Goulden, M. L., Kelly, A. E., and Bales, R.
C.: Elevation-dependent influence of snow accumulation on forest greening,
Nat. Geosci., 5, 705–709, https://doi.org/10.1038/ngeo1571, 2012.
Vapnik, V. N.: Estimation of Dependences Based on Empirical Data, Addendum 1, New York: Springer-Verlag, 1982.
Vapnik, V.: The Support Vector Method of Function Estimation, in: Nonlinear Modeling: Advanced Black-Box Techniques, edited by: Suykens, J. A. K. and Vandewalle, J., Springer US, Boston, MA, 55–85, https://doi.org/10.1007/978-1-4615-5703-6_3, 1998.
Walker, A. E. and Goodison, B. E.: Discrimination of a wet snow cover using
passive microwave satellite data, Ann. Glaciol., 17, 307–311,
https://doi.org/10.3189/S026030550001301X, 1993.
Xue, Y., Sun, S., Kahan, D. S., and Jiao, Y.: Impact of parameterizations in
snow physics and interface processes on the simulation of snow cover and
runoff at several cold region sites, J. Geophys. Res-Atmos., 108, 8859,
https://doi.org/10.1029/2002JD003174, 2003.
Yueh, S. H., Shah, R., Xu, X., Stiles, B., and Bosch-Lluis, X.: A Satellite
Synthetic Aperture Radar Concept Using P-Band Signals of Opportunity, IEEE
J. Sel. Top. Appl., 14, 2796–2816, https://doi.org/10.1109/JSTARS.2021.3059242, 2021.
Short summary
We explore satellite retrievals of snow water equivalent (SWE) along hypothetical ground tracks that would allow estimation of SWE over an entire watershed. The retrieval of SWE from satellites has proved elusive, but there are now technological options that do so along essentially one-dimensional tracks. We use machine learning (ML) algorithms as the basis for a track-to-area (TTA) transformation and show that at least one is robust enough to estimate domain-wide SWE with high accuracy.
We explore satellite retrievals of snow water equivalent (SWE) along hypothetical ground tracks...
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