干旱区生态环境知识资源中心(Arid): Uncertainty of canopy interception modeling in high-altitude Picea crassifolia forests of Semi-arid regions

Arid

DOI	10.1016/j.agrformet.2024.110190
	Uncertainty of canopy interception modeling in high-altitude Picea crassifolia forests of Semi-arid regions
	Yang, Junjun; He, Zhibin; Lin, Pengfei; Du, Jun; Shi, Dong; Bai, Meng
通讯作者	Yang, JJ
来源期刊	AGRICULTURAL AND FOREST METEOROLOGY
ISSN	0168-1923
EISSN	1873-2240
出版年	2024
卷号	356
英文摘要	The study of physically-based rainfall interception is crucial for comprehending the water balance within forest ecosystems and the contribution of vegetation to the hydrological cycle, particularly in arid/semi-arid ecosystems. Despite its importance, there is a lack of comprehensive sensitivity analysis and parameter optimization, resulting in uncertain or suboptimal predictive accuracy. To mitigate these shortcomings, this research involved the establishment and assessment of three quintessential forest canopy interception models namely, the power Navar model, the reformulated Gash model, and the Liu model, within semi-arid forest environments at two different elevations. A global sensitivity analysis conducted on the three physical models indicated that the canopy saturation point and the mean rainfall intensity required for canopy saturation were the parameters to which the reformulated Gash and Liu models were most sensitive when applied to high-altitude settings. Conversely, for the Navar model, the most sensitive parameters were the interception coefficient of the linear equation, and the parameters of the power equation k and c . The quantification indices of model sensitivity exert a certain influence on the ranking of parameter sensitivities. However, for models with a limited number of parameters, the impact of these results is constrained. Conversely, the identification and utilization of characteristics specific to the parameter tuning process can significantly enhance the efficiency of model calibration. The three models employed by the research institute have all demonstrated commendable performance in modeling the canopy interception process of subalpine P. crassifolia in arid, high-altitude regions, achieving a good rating with Nash-Sutcliffe Efficiency values exceeding 0.7. In practical applications, we recommend giving priority to the use of the Liu model. The findings of this study provide a reference for model selection, sensitivity analysis, parameter calibration, and model evaluation in the context of extensive canopy interception modeling in arid areas with significant altitudinal variation. This constitutes an important theoretical support for the refined modeling of hydrological processes in high-altitude forests within arid zones.
英文关键词	Qilian mountains Forest hydrology Interception modeling Uncertainty analysis Physical-based
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001295832200001
WOS关键词	RAINFALL INTERCEPTION ; GLOBAL SENSITIVITY ; SPATIAL VARIABILITY ; OAK STAND ; THROUGHFALL ; TREE ; PLANTATION ; DYNAMICS ; STEMFLOW
WOS类目	Agronomy ; Forestry ; Meteorology & Atmospheric Sciences
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/402638
推荐引用方式 GB/T 7714	Yang, Junjun,He, Zhibin,Lin, Pengfei,et al. Uncertainty of canopy interception modeling in high-altitude Picea crassifolia forests of Semi-arid regions[J],2024,356.
APA	Yang, Junjun,He, Zhibin,Lin, Pengfei,Du, Jun,Shi, Dong,&Bai, Meng.(2024).Uncertainty of canopy interception modeling in high-altitude Picea crassifolia forests of Semi-arid regions.AGRICULTURAL AND FOREST METEOROLOGY,356.
MLA	Yang, Junjun,et al."Uncertainty of canopy interception modeling in high-altitude Picea crassifolia forests of Semi-arid regions".AGRICULTURAL AND FOREST METEOROLOGY 356(2024).