Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-795-2022
https://doi.org/10.5194/essd-14-795-2022
Data description paper
 | 
21 Feb 2022
Data description paper |  | 21 Feb 2022

Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach

Donghang Shao, Hongyi Li, Jian Wang, Xiaohua Hao, Tao Che, and Wenzheng Ji

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Cited articles

Bair, E. H., Abreu Calfa, A., Rittger, K., and Dozier, J.: Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan, The Cryosphere, 12, 1579–1594, https://doi.org/10.5194/tc-12-1579-2018, 2018. 
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., and Vitart, F.: ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, 2015. 
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. 
Bintanja, R. and Andry, O.: Towards a rain-dominated Arctic, Nat. Clim. Change, 7, 263–267, https://doi.org/10.1038/Nclimate3240, 2017. 
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Short summary
The temporal series and spatial distribution discontinuity of the existing snow water equivalent (SWE) products in the pan-Arctic region severely restricts the use of SWE data in cryosphere change and climate change studies. Using a ridge regression machine learning algorithm, this study developed a set of spatiotemporally seamless and high-precision SWE products. This product could contribute to the study of cryosphere change and climate change at large spatial scales.
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