Towards large-scale daily snow density mapping with spatiotemporally aware model and multi-source data
Snow density plays a critical role in estimating water resources and predicting natural disasters such as floods, avalanches, and snowstorms. However, gridded products for snow density are lacking for understanding its spatiotemporal patterns. In this study, considering the strong spatiotemporal het...
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Copernicus Publications
2023
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00064278 2023-05-15T18:32:33+02:00 Towards large-scale daily snow density mapping with spatiotemporally aware model and multi-source data Wang, Huadong Zhang, Xueliang Xiao, Pengfeng Che, Tao Zheng, Zhaojun Dai, Liyun Luan, Wenbo 2023-01 electronic https://doi.org/10.5194/tc-17-33-2023 https://noa.gwlb.de/receive/cop_mods_00064278 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00063097/tc-17-33-2023.pdf https://tc.copernicus.org/articles/17/33/2023/tc-17-33-2023.pdf eng eng Copernicus Publications The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-17-33-2023 https://noa.gwlb.de/receive/cop_mods_00064278 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00063097/tc-17-33-2023.pdf https://tc.copernicus.org/articles/17/33/2023/tc-17-33-2023.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess CC-BY article Verlagsveröffentlichung article Text doc-type:article 2023 ftnonlinearchiv https://doi.org/10.5194/tc-17-33-2023 2023-01-16T00:13:44Z Snow density plays a critical role in estimating water resources and predicting natural disasters such as floods, avalanches, and snowstorms. However, gridded products for snow density are lacking for understanding its spatiotemporal patterns. In this study, considering the strong spatiotemporal heterogeneity of snow density, as well as the weak and nonlinear relationship between snow density and the meteorological, topographic, vegetation, and snow variables, the geographically and temporally weighted neural network (GTWNN) model is constructed for estimating daily snow density in China from 2013 to 2020, with the support of satellite, ground, and reanalysis data. The leaf area index of high vegetation, total precipitation, snow depth, and topographic variables are found to be closely related to snow density among the 20 potentially influencing variables. The 10-fold cross-validation results show that the GTWNN model achieves an R2 of 0.531 and RMSE of 0.043 g cm−3, outperforming the geographically and temporally weighted regression model (R2=0.271), geographically weighted neural network model (R2=0.124), and reanalysis snow density product (R2=0.095), which demonstrates the superiority of the GTWNN model in capturing the spatiotemporal heterogeneity of snow density and the nonlinear relationship to the influencing variables. The performance of the GTWNN model is closely related to the state and amount of snow, in which more stable and plentiful snow would result in higher snow density estimation accuracy. With the benefit of the daily snow density map, we are able to obtain knowledge of the spatiotemporal pattern and heterogeneity of snow density in China. The proposed GTWNN model holds the potential for large-scale daily snow density mapping, which will be beneficial for snow parameter estimation and water resource management. Article in Journal/Newspaper The Cryosphere Niedersächsisches Online-Archiv NOA The Cryosphere 17 1 33 50 |
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article Verlagsveröffentlichung Wang, Huadong Zhang, Xueliang Xiao, Pengfeng Che, Tao Zheng, Zhaojun Dai, Liyun Luan, Wenbo Towards large-scale daily snow density mapping with spatiotemporally aware model and multi-source data |
topic_facet |
article Verlagsveröffentlichung |
description |
Snow density plays a critical role in estimating water resources and predicting natural disasters such as floods, avalanches, and snowstorms. However, gridded products for snow density are lacking for understanding its spatiotemporal patterns. In this study, considering the strong spatiotemporal heterogeneity of snow density, as well as the weak and nonlinear relationship between snow density and the meteorological, topographic, vegetation, and snow variables, the geographically and temporally weighted neural network (GTWNN) model is constructed for estimating daily snow density in China from 2013 to 2020, with the support of satellite, ground, and reanalysis data. The leaf area index of high vegetation, total precipitation, snow depth, and topographic variables are found to be closely related to snow density among the 20 potentially influencing variables. The 10-fold cross-validation results show that the GTWNN model achieves an R2 of 0.531 and RMSE of 0.043 g cm−3, outperforming the geographically and temporally weighted regression model (R2=0.271), geographically weighted neural network model (R2=0.124), and reanalysis snow density product (R2=0.095), which demonstrates the superiority of the GTWNN model in capturing the spatiotemporal heterogeneity of snow density and the nonlinear relationship to the influencing variables. The performance of the GTWNN model is closely related to the state and amount of snow, in which more stable and plentiful snow would result in higher snow density estimation accuracy. With the benefit of the daily snow density map, we are able to obtain knowledge of the spatiotemporal pattern and heterogeneity of snow density in China. The proposed GTWNN model holds the potential for large-scale daily snow density mapping, which will be beneficial for snow parameter estimation and water resource management. |
format |
Article in Journal/Newspaper |
author |
Wang, Huadong Zhang, Xueliang Xiao, Pengfeng Che, Tao Zheng, Zhaojun Dai, Liyun Luan, Wenbo |
author_facet |
Wang, Huadong Zhang, Xueliang Xiao, Pengfeng Che, Tao Zheng, Zhaojun Dai, Liyun Luan, Wenbo |
author_sort |
Wang, Huadong |
title |
Towards large-scale daily snow density mapping with spatiotemporally aware model and multi-source data |
title_short |
Towards large-scale daily snow density mapping with spatiotemporally aware model and multi-source data |
title_full |
Towards large-scale daily snow density mapping with spatiotemporally aware model and multi-source data |
title_fullStr |
Towards large-scale daily snow density mapping with spatiotemporally aware model and multi-source data |
title_full_unstemmed |
Towards large-scale daily snow density mapping with spatiotemporally aware model and multi-source data |
title_sort |
towards large-scale daily snow density mapping with spatiotemporally aware model and multi-source data |
publisher |
Copernicus Publications |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-17-33-2023 https://noa.gwlb.de/receive/cop_mods_00064278 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00063097/tc-17-33-2023.pdf https://tc.copernicus.org/articles/17/33/2023/tc-17-33-2023.pdf |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_relation |
The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-17-33-2023 https://noa.gwlb.de/receive/cop_mods_00064278 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00063097/tc-17-33-2023.pdf https://tc.copernicus.org/articles/17/33/2023/tc-17-33-2023.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5194/tc-17-33-2023 |
container_title |
The Cryosphere |
container_volume |
17 |
container_issue |
1 |
container_start_page |
33 |
op_container_end_page |
50 |
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1766216737647230976 |