Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach
The snow water equivalent (SWE) is an important parameter of surface hydrological and climate systems, and it has a profound impact on Arctic amplification and climate change. However, there are great differences among existing SWE products. In the land region above 45 ∘ N, the existing SWE products...
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ftcopernicus:oai:publications.copernicus.org:essd98326 2023-05-15T15:19:36+02:00 Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach Shao, Donghang Li, Hongyi Wang, Jian Hao, Xiaohua Che, Tao Ji, Wenzheng 2022-02-21 application/pdf https://doi.org/10.5194/essd-14-795-2022 https://essd.copernicus.org/articles/14/795/2022/ eng eng doi:10.5194/essd-14-795-2022 https://essd.copernicus.org/articles/14/795/2022/ eISSN: 1866-3516 Text 2022 ftcopernicus https://doi.org/10.5194/essd-14-795-2022 2022-02-28T17:22:17Z The snow water equivalent (SWE) is an important parameter of surface hydrological and climate systems, and it has a profound impact on Arctic amplification and climate change. However, there are great differences among existing SWE products. In the land region above 45 ∘ N, the existing SWE products are associated with a limited time span and limited spatial coverage, and the spatial resolution is coarse, which greatly limits the application of SWE data in cryosphere change and climate change studies. In this study, utilizing the ridge regression model (RRM) of a machine learning algorithm, we integrated various existing SWE products to generate a spatiotemporally seamless and high-precision RRM SWE product. The results show that it is feasible to utilize a ridge regression model based on a machine learning algorithm to prepare SWE products on a global scale. We evaluated the accuracy of the RRM SWE product using hemispheric-scale snow course (HSSC) observational data and Russian snow survey data. The mean absolute error (MAE), RMSE, R , and R 2 between the RRM SWE products and observed SWEs are 0.21, 25.37 mm, 0.89, and 0.79, respectively. The accuracy of the RRM SWE dataset is improved by 28 %, 22 %, 37 %, 11 %, and 11 % compared with the original AMSR-E/AMSR2 (SWE), ERA-Interim SWE, Global Land Data Assimilation System (GLDAS) SWE, GlobSnow SWE, and ERA5-Land SWE datasets, respectively, and it has a higher spatial resolution. The RRM SWE product production method does not rely heavily on an independent SWE product; it takes full advantage of each SWE dataset, and it takes into consideration the altitude factor. The MAE ranges from 0.16 for areas within <100 m elevation to 0.29 within the 800–900 m elevation range. The MAE is best in the Russian region and worst in the Canadian region. The RMSE ranges from 4.71 mm for areas within <100 m elevation to 31.14 mm within the >1000 m elevation range. The RMSE is best in the Finland region and worst in the Canadian region. This method has good stability, is extremely suitable for the production of snow datasets with large spatial scales, and can be easily extended to the preparation of other snow datasets. The RRM SWE product is expected to provide more accurate SWE data for the hydrological model and climate model and provide data support for cryosphere change and climate change studies. The RRM SWE product is available from “A Big Earth Data Platform for Three Poles” ( https://doi.org/10.11888/Snow.tpdc.271556 ) (Li et al., 2021). Text Arctic Climate change Copernicus Publications: E-Journals Arctic Earth System Science Data 14 2 795 809 |
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Copernicus Publications: E-Journals |
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English |
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The snow water equivalent (SWE) is an important parameter of surface hydrological and climate systems, and it has a profound impact on Arctic amplification and climate change. However, there are great differences among existing SWE products. In the land region above 45 ∘ N, the existing SWE products are associated with a limited time span and limited spatial coverage, and the spatial resolution is coarse, which greatly limits the application of SWE data in cryosphere change and climate change studies. In this study, utilizing the ridge regression model (RRM) of a machine learning algorithm, we integrated various existing SWE products to generate a spatiotemporally seamless and high-precision RRM SWE product. The results show that it is feasible to utilize a ridge regression model based on a machine learning algorithm to prepare SWE products on a global scale. We evaluated the accuracy of the RRM SWE product using hemispheric-scale snow course (HSSC) observational data and Russian snow survey data. The mean absolute error (MAE), RMSE, R , and R 2 between the RRM SWE products and observed SWEs are 0.21, 25.37 mm, 0.89, and 0.79, respectively. The accuracy of the RRM SWE dataset is improved by 28 %, 22 %, 37 %, 11 %, and 11 % compared with the original AMSR-E/AMSR2 (SWE), ERA-Interim SWE, Global Land Data Assimilation System (GLDAS) SWE, GlobSnow SWE, and ERA5-Land SWE datasets, respectively, and it has a higher spatial resolution. The RRM SWE product production method does not rely heavily on an independent SWE product; it takes full advantage of each SWE dataset, and it takes into consideration the altitude factor. The MAE ranges from 0.16 for areas within <100 m elevation to 0.29 within the 800–900 m elevation range. The MAE is best in the Russian region and worst in the Canadian region. The RMSE ranges from 4.71 mm for areas within <100 m elevation to 31.14 mm within the >1000 m elevation range. The RMSE is best in the Finland region and worst in the Canadian region. This method has good stability, is extremely suitable for the production of snow datasets with large spatial scales, and can be easily extended to the preparation of other snow datasets. The RRM SWE product is expected to provide more accurate SWE data for the hydrological model and climate model and provide data support for cryosphere change and climate change studies. The RRM SWE product is available from “A Big Earth Data Platform for Three Poles” ( https://doi.org/10.11888/Snow.tpdc.271556 ) (Li et al., 2021). |
format |
Text |
author |
Shao, Donghang Li, Hongyi Wang, Jian Hao, Xiaohua Che, Tao Ji, Wenzheng |
spellingShingle |
Shao, Donghang Li, Hongyi Wang, Jian Hao, Xiaohua Che, Tao Ji, Wenzheng Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach |
author_facet |
Shao, Donghang Li, Hongyi Wang, Jian Hao, Xiaohua Che, Tao Ji, Wenzheng |
author_sort |
Shao, Donghang |
title |
Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach |
title_short |
Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach |
title_full |
Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach |
title_fullStr |
Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach |
title_full_unstemmed |
Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach |
title_sort |
reconstruction of a daily gridded snow water equivalent product for the land region above 45° n based on a ridge regression machine learning approach |
publishDate |
2022 |
url |
https://doi.org/10.5194/essd-14-795-2022 https://essd.copernicus.org/articles/14/795/2022/ |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change |
genre_facet |
Arctic Climate change |
op_source |
eISSN: 1866-3516 |
op_relation |
doi:10.5194/essd-14-795-2022 https://essd.copernicus.org/articles/14/795/2022/ |
op_doi |
https://doi.org/10.5194/essd-14-795-2022 |
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Earth System Science Data |
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