Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach
<p>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<span class="i...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-02-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/14/795/2022/essd-14-795-2022.pdf |
| _version_ | 1819277691133624320 |
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| author | D. Shao D. Shao H. Li H. Li J. Wang J. Wang J. Wang X. Hao X. Hao T. Che T. Che W. Ji W. Ji W. Ji |
| author_facet | D. Shao D. Shao H. Li H. Li J. Wang J. Wang J. Wang X. Hao X. Hao T. Che T. Che W. Ji W. Ji W. Ji |
| author_sort | D. Shao |
| collection | DOAJ |
| description | <p>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<span class="inline-formula"><sup>∘</sup></span> 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, <span class="inline-formula"><i>R</i></span>, and <span class="inline-formula"><i>R</i><sup>2</sup></span>
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 <span class="inline-formula"><100</span> 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 <span class="inline-formula"><100</span> m elevation to 31.14 mm within the <span class="inline-formula">>1000</span> 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” (<a href="https://doi.org/10.11888/Snow.tpdc.271556">https://doi.org/10.11888/Snow.tpdc.271556</a>)
(Li et al., 2021).</p> |
| first_indexed | 2024-12-24T00:00:08Z |
| format | Article |
| id | doaj.art-2169efa1746c4b219e946f22abdd2b4a |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-12-24T00:00:08Z |
| publishDate | 2022-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-2169efa1746c4b219e946f22abdd2b4a2022-12-21T17:25:09ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162022-02-011479580910.5194/essd-14-795-2022Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approachD. Shao0D. Shao1H. Li2H. Li3J. Wang4J. Wang5J. Wang6X. Hao7X. Hao8T. Che9T. Che10W. Ji11W. Ji12W. Ji13Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaHeihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaHeihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaJiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, ChinaHeihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaHeihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaHeihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, China<p>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<span class="inline-formula"><sup>∘</sup></span> 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, <span class="inline-formula"><i>R</i></span>, and <span class="inline-formula"><i>R</i><sup>2</sup></span> 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 <span class="inline-formula"><100</span> 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 <span class="inline-formula"><100</span> m elevation to 31.14 mm within the <span class="inline-formula">>1000</span> 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” (<a href="https://doi.org/10.11888/Snow.tpdc.271556">https://doi.org/10.11888/Snow.tpdc.271556</a>) (Li et al., 2021).</p>https://essd.copernicus.org/articles/14/795/2022/essd-14-795-2022.pdf |
| spellingShingle | D. Shao D. Shao H. Li H. Li J. Wang J. Wang J. Wang X. Hao X. Hao T. Che T. Che W. Ji W. Ji W. Ji Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach Earth System Science Data |
| 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_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_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_sort | reconstruction of a daily gridded snow water equivalent product for the land region above 45° thinsp n based on a ridge regression machine learning approach |
| url | https://essd.copernicus.org/articles/14/795/2022/essd-14-795-2022.pdf |
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