气候变化领域集成服务门户(CCPortal): A dual state-parameter updating scheme using the particle filter and high-spatial-resolution remotely sensed snow depths to improve snow simulation

CCPortal

DOI	10.1016/j.jhydrol.2021.125979
	A dual state-parameter updating scheme using the particle filter and high-spatial-resolution remotely sensed snow depths to improve snow simulation
	Han, Pengfei; Long, Di; Li, Xingdong; Huang, Qi; Dai, Liyun; Sun, Zhangli
通讯作者	Long, D (通讯作者)，Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China.
发表日期	2021
ISSN	0022-1694
EISSN	1879-2707
卷号	594
英文摘要	Snow varies widely in space and time over high mountain regions. Accurately representing the spatial distribution of snow water equivalent (SWE) is critically important for improving our understanding of snow accumulation and melt processes. Despite its importance, in situ observations are lacking in poorly gauged regions such as the headwater region of the Yangtze River (HRYR). Traditional remotely sensed retrievals are highly uncertain due to the effect of cloudiness (e.g., optical remote sensing-derived snow cover area) and the coarse spatial resolution (e.g., passive microwave remote sensing-derived snow depth). Hydrological modeling is a powerful way to understand the snow processes, but uncertainty still exists due to model deficiency and errors of forcing data. Assimilating high-spatial-resolution remotely sensed snow data into a snowmelt model may be potentially valuable for more accurate snow predictions. In this study, a high-spatial-resolution remotely sensed snow depth data set (500 m, derived by integrating snow cover area, land surface temperature, and passive microwave brightness temperature products) was assimilated into a snowmelt model within a particle filter (PF) assimilation framework, which updates both model state variables and parameters simultaneously. After assimilation, the time series of basin-averaged SWE estimation showed an appreciable improvement with respect to the simulation with the traditional calibration (TC) method, in terms of the Nash-Sutcliffe efficiency (NSE) coefficient increased by similar to 10-20% and root-mean-square error (RMSE) decreased by similar to 7-18%. The PF approach also greatly improved the spatial distribution of SWE estimation with RMSE decreased by similar to 15-30%. The SWE estimation from the PF was also comparable or even better than that from another assimilation scheme, namely, the direct insertion. Comparison with in situ snowfall data indicated that the simulated snowfall from the PF outperformed the TC, with RMSE decreased by similar to 15-32% and correlation coefficient increased by similar to 58-83%. Furthermore, the evolution of parameters suggested the applicability of the PF method with spatially variable parameters. With spatiotemporally variable parameters in the PF, the snow model could perfectly simulate the actual snow distribution particularly over high elevation regions where the average temperature was lower than 0 degrees C, while fixed parameters in the TC cannot simulate the variable snow distribution. The proposed data assimilation framework has large potential of improving the accuracy of snow prediction across poorly gauged high mountain areas.
关键词	WATER EQUIVALENT TIBETAN PLATEAU ENERGY EXCHANGE SIERRA-NEVADA ALPINE REGION SURFACE MODEL RUNOFF PRODUCTS CLIMATE
英文关键词	Dual state-parameter updating scheme; Particle filter; High-spatial-resolution remotely sensed snow depth; Snow water equivalent; Headwaters of the Yangtze River
语种	英语
WOS研究方向	Engineering ; Geology ; Water Resources
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS记录号	WOS:000641589600080
来源期刊	JOURNAL OF HYDROLOGY
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/254371
作者单位	[Han, Pengfei; Long, Di; Li, Xingdong; Huang, Qi; Sun, Zhangli] Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China; [Dai, Liyun] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Han, Pengfei,Long, Di,Li, Xingdong,et al. A dual state-parameter updating scheme using the particle filter and high-spatial-resolution remotely sensed snow depths to improve snow simulation[J]. 中国科学院西北生态环境资源研究院,2021,594.
APA	Han, Pengfei,Long, Di,Li, Xingdong,Huang, Qi,Dai, Liyun,&Sun, Zhangli.(2021).A dual state-parameter updating scheme using the particle filter and high-spatial-resolution remotely sensed snow depths to improve snow simulation.JOURNAL OF HYDROLOGY,594.
MLA	Han, Pengfei,et al."A dual state-parameter updating scheme using the particle filter and high-spatial-resolution remotely sensed snow depths to improve snow simulation".JOURNAL OF HYDROLOGY 594(2021).