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 Návar 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 Návar 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.

中文翻译：

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半干旱地区高海拔青海云杉林冠层截留模型的不确定性


基于物理的降雨拦截研究对于理解森林生态系统内的水平衡和植被对水文循环的贡献至关重要，特别是在干旱/半干旱生态系统中。尽管它很重要，但缺乏全面的敏感性分析和参数优化，导致预测准确性不确定或次优。为了弥补这些缺点，本研究涉及在两个不同海拔的半干旱森林环境中建立和评估三种典型的森林冠层拦截模型，即功率纳瓦尔模型、重新制定的加什模型和刘模型。对三个物理模型进行的全局敏感性分析表明，冠层饱和点和冠层饱和所需的平均降雨强度是重新制定的 Gash 和 Liu 模型应用于高海拔环境时最敏感的参数。相反，对于 Návar 模型，最敏感的参数是线性方程的截距系数以及幂方程 k 和 c 的参数。模型灵敏度的量化指标对参数灵敏度的排序有一定的影响。然而，对于参数数量有限的模型，这些结果的影响受到限制。相反，识别和利用参数调整过程特有的特征可以显着提高模型校准的效率。研究所采用的三个模型在模拟亚高山松树冠层拦截过程方面均表现出了值得称赞的性能。 干旱、高海拔地区的 crassifolia 获得“良好”评级，纳什-萨特克利夫效率值超过 0.7。在实际应用中，我们建议优先使用Liu模型。本研究结果为海拔高度变化较大的干旱地区广泛的冠层拦截建模中的模型选择、敏感性分析、参数标定和模型评估提供参考。这为干旱区高海拔森林水文过程的精细化建模提供了重要的理论支撑。