Evaluating the performance of snow depth reanalysis products in the arid region of Central Asia
Central Asia (CA) faces water scarcity issues and heavily relies on snowmelt; however, the limited number of monitoring stations cannot meet snow monitoring needs. Reanalysis data could fill this gap, but their accuracy in CA remains uncertain. This study evaluates snow depth (SD) products from ERA5...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2447368 |
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| author | Liancheng Zhang Guli∙Jiapaer Tao Yu Hongwu Liang Kaixiong Lin Tongwei Ju Philippe De Maeyer Tim Van de Voorde |
| author_facet | Liancheng Zhang Guli∙Jiapaer Tao Yu Hongwu Liang Kaixiong Lin Tongwei Ju Philippe De Maeyer Tim Van de Voorde |
| author_sort | Liancheng Zhang |
| collection | DOAJ |
| description | Central Asia (CA) faces water scarcity issues and heavily relies on snowmelt; however, the limited number of monitoring stations cannot meet snow monitoring needs. Reanalysis data could fill this gap, but their accuracy in CA remains uncertain. This study evaluates snow depth (SD) products from ERA5, ERA5-Land, JRA-55, MERRA-2, and GLDAS in CA using in situ data and the three-cornered hat (TCH) method. In situ data evaluations indicate that JRA-55 outperforms the other SD products overall. However, each of the other four SD products demonstrates unique strengths under different conditions. The TCH method indicates that the ERA5, JRA-55, MERRA-2 and GLDAS SD products have low uncertainty, with regions of uncertainty less than 1.0 cm covering more than 70% of CA, whereas ERA5-Land displays relatively high uncertainty. Both in situ data and TCH indicate poor performance for all five SD products in Tajikistan and in high-altitude areas. Errors in the five SD reanalysis datasets in certain regions may stem from inaccuracies in precipitation and air temperature forcing data. The use of a multidataset ensemble average SD product significantly enhances the ability to capture SDs in CA. Our study provides reliable data support for SD monitoring in the CA. |
| format | Article |
| id | doaj-art-298aa2b5d5a141a2a3aec9df515be314 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-298aa2b5d5a141a2a3aec9df515be3142025-02-04T08:10:22ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-12-0118110.1080/17538947.2024.2447368Evaluating the performance of snow depth reanalysis products in the arid region of Central AsiaLiancheng Zhang0Guli∙Jiapaer1Tao Yu2Hongwu Liang3Kaixiong Lin4Tongwei Ju5Philippe De Maeyer6Tim Van de Voorde7State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, People’s Republic of ChinaDepartment of Geography, Ghent University, Ghent, BelgiumCentral Asia (CA) faces water scarcity issues and heavily relies on snowmelt; however, the limited number of monitoring stations cannot meet snow monitoring needs. Reanalysis data could fill this gap, but their accuracy in CA remains uncertain. This study evaluates snow depth (SD) products from ERA5, ERA5-Land, JRA-55, MERRA-2, and GLDAS in CA using in situ data and the three-cornered hat (TCH) method. In situ data evaluations indicate that JRA-55 outperforms the other SD products overall. However, each of the other four SD products demonstrates unique strengths under different conditions. The TCH method indicates that the ERA5, JRA-55, MERRA-2 and GLDAS SD products have low uncertainty, with regions of uncertainty less than 1.0 cm covering more than 70% of CA, whereas ERA5-Land displays relatively high uncertainty. Both in situ data and TCH indicate poor performance for all five SD products in Tajikistan and in high-altitude areas. Errors in the five SD reanalysis datasets in certain regions may stem from inaccuracies in precipitation and air temperature forcing data. The use of a multidataset ensemble average SD product significantly enhances the ability to capture SDs in CA. Our study provides reliable data support for SD monitoring in the CA.https://www.tandfonline.com/doi/10.1080/17538947.2024.2447368Snow depthreanalysis datathree-cornered hatCentral Asia |
| spellingShingle | Liancheng Zhang Guli∙Jiapaer Tao Yu Hongwu Liang Kaixiong Lin Tongwei Ju Philippe De Maeyer Tim Van de Voorde Evaluating the performance of snow depth reanalysis products in the arid region of Central Asia International Journal of Digital Earth Snow depth reanalysis data three-cornered hat Central Asia |
| title | Evaluating the performance of snow depth reanalysis products in the arid region of Central Asia |
| title_full | Evaluating the performance of snow depth reanalysis products in the arid region of Central Asia |
| title_fullStr | Evaluating the performance of snow depth reanalysis products in the arid region of Central Asia |
| title_full_unstemmed | Evaluating the performance of snow depth reanalysis products in the arid region of Central Asia |
| title_short | Evaluating the performance of snow depth reanalysis products in the arid region of Central Asia |
| title_sort | evaluating the performance of snow depth reanalysis products in the arid region of central asia |
| topic | Snow depth reanalysis data three-cornered hat Central Asia |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2447368 |
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