气候变化领域集成服务门户(CCPortal): Reconstruction of a daily gridded snow water equivalent product for the land region above 45 degrees N based on a ridge regression machine learning approach

CCPortal

DOI	10.5194/essd-14-795-2022
	Reconstruction of a daily gridded snow water equivalent product for the land region above 45 degrees N based on a ridge regression machine learning approach
	Shao, Donghang; Li, Hongyi; Wang, Jian; Hao, Xiaohua; Che, Tao; Ji, Wenzheng
通讯作者	Li, HY (通讯作者)，Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China. ; Li, HY (通讯作者)，Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China.
发表日期	2022
ISSN	1866-3508
EISSN	1866-3516
起始页码	795
结束页码	809
卷号	14 期号:2
英文摘要	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 degrees 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 < 100m elevation to 0.29 within the 800-900m elevation range. The MAE is best in the Russian region and worst in the Canadian region. The RMSE ranges from 4.71mm for areas within < 100m elevation to 31.14mm within the > 1000m 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).
关键词	PASSIVE MICROWAVE BRITISH-COLUMBIA CLIMATE DEPTH
语种	英语
WOS研究方向	Geology ; Meteorology & Atmospheric Sciences
WOS类目	Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000760833600001
来源期刊	EARTH SYSTEM SCIENCE DATA
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/253802
作者单位	[Shao, Donghang; Li, Hongyi; Wang, Jian; Hao, Xiaohua; Che, Tao; Ji, Wenzheng] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China; [Shao, Donghang; Li, Hongyi; Wang, Jian; Hao, Xiaohua; Che, Tao; Ji, Wenzheng] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China; [Ji, Wenzheng] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Wang, Jian] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
推荐引用方式 GB/T 7714	Shao, Donghang,Li, Hongyi,Wang, Jian,et al. Reconstruction of a daily gridded snow water equivalent product for the land region above 45 degrees N based on a ridge regression machine learning approach[J]. 中国科学院西北生态环境资源研究院,2022,14(2).
APA	Shao, Donghang,Li, Hongyi,Wang, Jian,Hao, Xiaohua,Che, Tao,&Ji, Wenzheng.(2022).Reconstruction of a daily gridded snow water equivalent product for the land region above 45 degrees N based on a ridge regression machine learning approach.EARTH SYSTEM SCIENCE DATA,14(2).
MLA	Shao, Donghang,et al."Reconstruction of a daily gridded snow water equivalent product for the land region above 45 degrees N based on a ridge regression machine learning approach".EARTH SYSTEM SCIENCE DATA 14.2(2022).