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Investigating climatic drivers of snow phenology by considering key-substage heterogeneity

文献类型：期刊论文

作者Ma, Xinqi1,2,3; Lin, Kai6; Sun, Xueyan5; Luo, Lun7; Ma, Ning4; Zha, Hang1,2; Zhang, Longhui1,2; Tang, Shizhen1,2; Tang, Zhiguang8; Zhang, Hongbo1,2,4,5
刊名JOURNAL OF HYDROLOGY
出版日期2024-12-01
卷号645页码:19
关键词Snow cover changes Snow cover phenology Climatic cause aid region of China
ISSN号0022-1694
DOI10.1016/j.jhydrol.2024.132215
产权排序4
英文摘要Investigating the main climatic drivers responsible for changes in snow cover phenology (SCP) is crucial for making scientific countermeasures to ensure water resources security in global mountainous regions. However, most studies have explored drivers of SCP changes using a fixed substage division scheme and correlation analysis (referred to as the traditional method), potentially limiting reliability and accuracy in mountainous areas with complex terrain and climate. Here, a novel method is developed to efficiently identify main climatic drivers of SCP changes. This method employs a flexible scheme to account for the spatial heterogeneity of the dominant sub-period (the sub-period with the major climatic effect) in combination with regression analysis. Using the arid region of China as a case study, the new method was applied to three SCP parameters including snow cover days, snow start date, and snow end date, based on a seamless snow cover dataset from 2002 to 2019. The method's effectiveness was evaluated by comparing it with the traditional method. The results indicate significant spatial heterogeneity in the dominant sub-period(s), closely associated with local temperature and elevation. The traditional method failed to accurately identify the main drivers of SCP changes, as evidenced by sub-region and elevation zone analyses showing adjusted coefficient of determination (R2) of < 0.5 in most cases. This inadequacy is attributed to its fixed and inappropriate scheme of substage division. In contrast, the new method, with its flexible scheme, achieved much higher adjusted R2 values (mostly > 0.5) and exhibited better performances in predicting SCP changes. Thanks to the new method, climatic causes of SCP changes were successfully identified in 12 hotspots (regions with significant SCP changes), all with adjusted R2 > 0.5. The climatic causes were found to vary significantly across different hotspot regions and SCP parameters. The proposed method holds significant potential to enhance the reliability of analyses concerning main climatic drivers of SCP changes in mountainous regions globally.
WOS关键词COVER VARIABILITY ; MOUNTAIN REGIONS ; CENTRAL-ASIA ; TIME-SERIES ; MODIS ; CHINA ; PRODUCTS ; PRECIPITATION ; TEMPERATURE ; VALIDATION
资助项目National Natural Science Foundation of China[U2243217] ; National Natural Science Foundation of China[42271029] ; Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences[WL2023005] ; Shandong Provincial Natural Science Foundation[ZR2021MD029]
WOS研究方向Engineering ; Geology ; Water Resources
语种英语
WOS记录号WOS:001349508000001
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences ; Shandong Provincial Natural Science Foundation
源URL[http://ir.igsnrr.ac.cn/handle/311030/210977]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者Zhang, Hongbo
作者单位1.China Agr Univ, State Key Lab Efficient Utilizat Agr Water Resourc, Beijing, Peoples R China
2.China Agr Univ, Coll Water Resources & Civil Engn, Beijing, Peoples R China
3.Beijing Res Inst Telemetry, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China
5.China Agr Univ, Yantai Res Inst, Yantai, Peoples R China
6.Water Conservancy Bur Songshan Dist, Chifeng, Peoples R China
7.China Geol Survey, Middle Yarlung Zangbo River Nat Resources Observat, Res Ctr Appl Geol, Chengdu, Peoples R China
8.Hunan Univ Sci & Technol, Hunan Prov Key Lab Geoinformat Engn Surveying Mapp, Xiangtan, Peoples R China
推荐引用方式
GB/T 7714		 Ma, Xinqi,Lin, Kai,Sun, Xueyan,et al. Investigating climatic drivers of snow phenology by considering key-substage heterogeneity[J]. JOURNAL OF HYDROLOGY,2024,645:19.
APA Ma, Xinqi.,Lin, Kai.,Sun, Xueyan.,Luo, Lun.,Ma, Ning.,...&Zhang, Hongbo.(2024).Investigating climatic drivers of snow phenology by considering key-substage heterogeneity.JOURNAL OF HYDROLOGY,645,19.
MLA Ma, Xinqi,et al."Investigating climatic drivers of snow phenology by considering key-substage heterogeneity".JOURNAL OF HYDROLOGY 645(2024):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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