Three-Dimensional Canopy Morphology and Wind Dynamics Govern Global Rainfall Interception

This dataset provides global monthly estimates of canopy interception loss (I) and interception ratio (I/P) from 1982 to 2020, associated with the study "Three-Dimensional Canopy Morphology and Wind Dynamics Govern Global Rainfall Interception". Generated using the Feature-Constrained Deep Symbolic Regression (FC-DSR) framework, this product explicitly incorporates 3D canopy architecture (Canopy Depth and Width) and wind dynamics into the parameterization, offering improved hydrological realism compared to traditional LAI-based models.The data is formatted as a NetCDF file with a spatial resolution of 0.1° × 0.1° (1800 *3600 grid). It contains two primary variables: I (Monthly canopy interception in mm/month) and I_P (Monthly interception rate in %).本数据集提供了1982年至2020年全球逐月冠层截留量（I）和截留率（I/P）的估算结果，其关联研究论文为《三维冠层形态与风力动力学调控全球降雨截留》。本数据集基于特征约束深度符号回归（Feature-Constrained Deep Symbolic Regression, FC-DSR）框架生成，明确将三维冠层结构（冠层深度与冠层宽度）及风力动力学过程纳入参数化方案，相较于传统基于叶面积指数（Leaf Area Index, LAI）的模型，其水文模拟真实性得到显著提升。数据以NetCDF格式存储，空间分辨率为0.1°×0.1°，共包含1800×3600个网格。数据集包含两个核心变量：I（逐月冠层截留量，单位：毫米/月）与I_P（逐月截留率，单位：%）。