气候变化领域集成服务门户(CCPortal): Developing machine learning-based snow depletion curves and analysing their sensitivity over complex mountainous areas

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DOI	10.1002/hyp.14303
	Developing machine learning-based snow depletion curves and analysing their sensitivity over complex mountainous areas
	Hou, Jinliang; Huang, Chunlin; Chen, Weijing; Zhang, Ying
通讯作者	Huang, CL (通讯作者)，Donggang West Rd 318, Lanzhou, Gansu, Peoples R China.
发表日期	2021
ISSN	0885-6087
EISSN	1099-1085
卷号	35 期号:8
英文摘要	A snow depletion curve (SDC), the relationship between snow mass (e.g., snow depth [SD]) and fractional snow cover area (SCF), is essential to parameterize the effect of snowpack within a physically based snow model. Existing SDCs are constructed using traditional statistic methods may not be applicable in complex mountainous areas. In this study, we developed an information fusion framework to define the relationship between SCF and SD as well as 12 auxiliary factors by using a traditional statistical method and four prevailing machine learning (ML) algorithms, which have comprehensively considered the variable conditions that cause spatiotemporal heterogeneity of snow cover. We also performed a single-dimensional sensitivity analysis to investigate the physical rationality of the newly developed SDCs. The Northern Xinjiang, Northwest China, is selected as the study area, and the data from 46 meteorological stations covering five snow seasons from 2010 to 2015 are used. The results illustrated that ML techniques can be used to establish high-accuracy and robust SDCs for complex mountainous areas. Compared with SDCs constructed by traditional statistical, the performance of the four ML-based SDCs is significantly improved, the RMSE values can be reduced by 50%, R-2 above 0.75, and an average relative variance close to 0. ML-based SDCs predicted SCF values showed a range of sensitivities to different input variables (e.g., Land surface temperature, aspect, longwave radiation and land cover type), in addition to SD, that were physically representative of effects that snow cover is sensitive to. Moreover, the complexity of SDCs can be reduced by removing insensitive input variables.
关键词	LAND-SURFACE MODEL COVERED AREA MODIS DATA ASSIMILATION PARAMETERIZATION INFORMATION VALIDATION FRACTION SCHEME
英文关键词	fractional snow cover; machine learning; sensitivity analysis; snow depletion curve; snow depth
语种	英语
WOS研究方向	Water Resources
WOS类目	Water Resources
WOS记录号	WOS:000691011400017
来源期刊	HYDROLOGICAL PROCESSES
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/254078
作者单位	[Hou, Jinliang; Huang, Chunlin; Zhang, Ying] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Heihe Remote Sensing Expt Res Stn, Key Lab Remote Sensing Gansu Prov, Lanzhou, Peoples R China; [Chen, Weijing] Univ Texas Austin, Dept Geol Sci, Jackson Sch Geosci, Austin, TX USA
推荐引用方式 GB/T 7714	Hou, Jinliang,Huang, Chunlin,Chen, Weijing,et al. Developing machine learning-based snow depletion curves and analysing their sensitivity over complex mountainous areas[J]. 中国科学院西北生态环境资源研究院,2021,35(8).
APA	Hou, Jinliang,Huang, Chunlin,Chen, Weijing,&Zhang, Ying.(2021).Developing machine learning-based snow depletion curves and analysing their sensitivity over complex mountainous areas.HYDROLOGICAL PROCESSES,35(8).
MLA	Hou, Jinliang,et al."Developing machine learning-based snow depletion curves and analysing their sensitivity over complex mountainous areas".HYDROLOGICAL PROCESSES 35.8(2021).