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Substantial Contribution of Woody Components to Rainfall Interception in Chinese Forests: Insights From a Refined Analytical Model
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Substantial Contribution of Woody Components to Rainfall Interception in Chinese Forests: Insights From a Refined Analytical Model
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Journal Article
Substantial Contribution of Woody Components to Rainfall Interception in Chinese Forests: Insights From a Refined Analytical Model
2025
Overview
Assessing rainfall interception (IR) is a critical yet uncertain aspect in hydrological cycle, particularly the quantification of relative contributions from leaves and woody components (e.g., branches, stems, and trunks) to IR. Nevertheless, the role of woody components in IR estimation remains largely unexplored and thereby has been constantly overlooked. This study addressed this challenge and refined the widely‐used Gash model to distinguish woody interception (IW) from leaf interception (IL). We incorporated the spatial variability of vegetation traits alongside satellite data in 2019 into the refined model, and spanned China's major forest types. The refined model showed a strong agreement with field observations in estimating IR (r = 0.83, p < 0.01) and the fraction of rainfall interception to precipitation (IR/P) (r = 0.77, p < 0.01). The average IR was 112.4 ± 32.1 mm (with IR/P of 14.7 ± 8.2%) in 2019, of which IL accounted for 77.9% and IW contributed the rest 22.1%. Among different forest types, IW/IR exhibited the highest values in deciduous needle‐leaf forests (DNF, mean: 51.9%) but lowest values in evergreen broad‐leaf (EBF, mean: 14.3%). In addition, IW/IR was larger in the non‐growing season than that of growing season in some forest types, such as exceeding 60% in winter for DNF, indicating that more rainwater was intercepted by woody components than by leaves. Our study underscores the substantial role of woody components in IR, particularly in needle‐leaf forests, that are prevalent globally, a finding that can provide novel methods and valuable parameters for global hydrological models to improve the accuracy of model predictions.
