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Rainfall interception loss (Ei) is a difficult to study and poorly understood flux compared to transpiration and soil evaporation. The influence of climate and vegetation on Ei is not well known at continental‐to‐global and annual‐to‐decadal scales. Here, we use a long‐term multi‐product approach to examine the global trends in Ei, and further utilize a recently developed and validated dataset to isolate the relative contributions of precipitation, vegetation and evaporative demand. At decadal timescales, increasing Ei is largely driven by global vegetation greening through an increase in the intercepting surface and storage capacity, while its inter‐annual variations are mainly controlled by changes in precipitation, largely related to El Niño/Southern Oscillation. Increasing evaporative demand, driven by atmospheric warming, also positively contributes to the global rise in Ei. This study provides new perspectives for further understanding the impacts of climate change on the terrestrial hydrological cycle. Plain Language Summary Rainfall interception loss is the volume of rain that gets caught by plants before reaching the ground and evaporated back into the atmosphere. It is among the least understood components of the global water cycle. In our research, we used satellite data over a long time (from 1981 to 2020) and a recently developed global model to study how rainfall interception has changed in time and space. We discovered that globally, more rain is being caught by vegetation over the years. This increase happens because our planet is greening, increasing the surface over which rain can be intercepted. On the other hand, changes in how much it rains dominate the year‐to‐year differences in interception loss. At the same time, as the atmosphere gets warmer, water can evaporate faster from vegetation, which adds to the growing trend in interception loss. These results match with the expectation of an intensified water cycle over the continents. Key Points Rainfall interception loss exhibits increasing trends globally Its multi‐decadal trends are driven by vegetation greening and warming, whereas interannual variations are controlled by precipitation ENSO regulates rainfall interception loss largely through its influence on precipitation dynamics

Understanding the interaction between canopy structure and the parameters of interception loss is essential in predicting the variations in partitioning rainfall and water resources as affected by changes in canopy structure and in implementing water-based management in semiarid forest plantations. In this study, seasonal variations in rainfall interception loss and canopy storage capacity as driven by canopy structure were predicted and the linkages were tested using seasonal filed measurements. The study was conducted in nine 50 m × 50 m Robinia pseudoacacia plots in the semiarid region of China’s Loess Plateau. Gross rainfall, throughfall and stemflow were measured in seasons with and without leaves in 2015 and 2016. Results show that measured average interception loss for the nine plots were 17.9% and 9.4% of gross rainfall during periods with leaves (the growing season) and without leaves, respectively. Average canopy storage capacity estimated using an indirect method was 1.3 mm in the growing season and 0.2 mm in the leafless season. Correlations of relative interception loss and canopy storage capacity to canopy variables were highest for leaf/wood area index (LAI/WAI) and canopy cover, followed by bark area, basal area, tree height and stand density. Combined canopy cover, leaf/wood area index and bark area multiple regression models of interception loss and canopy storage capacity were established for the growing season and in the leafless season in 2015. It explained 97% and 96% of the variations in relative interception loss during seasons with and without leaves, respectively. It also explained 98% and 99% of the variations in canopy storage capacity during seasons with and without leaves, respectively. The empirical regression models were validated using field data collected in 2016. The models satisfactorily predicted relative interception loss and canopy storage capacity during seasons with and without leaves. This study provides greater understanding about the effects of changes in tree canopy structure (e.g., dieback or mortality) on hydrological processes.

This study aimed to build urban green space with environmental functions (e.g., canopy interception of rainfall) and adjust hydrographic balance to some extent for forecasting the potential canopy rainfall interception capacity of landscape trees and the effects on rainfall distribution. The effects of urban green space on interception and runoff reduction have been conceptualized, but not quantified. Therefore, the leaf area index and the water storage abilities of 17 kinds of landscape trees in common use were measured, at Shanghai, and canopy rainfall interception capacity was calculated using the interception formula. The predicted rainfall interception capacity models were established choosing tree morphological characteristics (diameter at the breast height, height, and crown width) as variables. The model test showed that the errors of 12 models were less than 5% between the predicted and the measured data and the errors of four models were within 5 and 10%, with the error for only one model being between 10 and 11%. Also, the study indicated that conifer trees were able to hold more rainfall compared with broad-leaved trees per unit area ( k ). The results showed that these models could effectively predict the potential capacity of canopy rainfall interception for landscape trees in Shanghai area and were beneficial for species selection in constructing plant communities, aiming to improve the rainfall interception capacity of urban green space.

Evaporative loss of interception ( E i ) is the first process occurring during rainfall, yet its role in large-scale surface water balance has been largely underexplored. Here we show that E i can be inferred from flux tower evapotranspiration measurements using physics-informed hybrid machine learning models built under wet versus dry conditions. Forced by satellite and reanalysis data, this framework provides an observationally constrained estimate of E i , which is on average 84.1 ± 1.8 mm per year and accounts for 8.6 ± 0.2% of total rainfall globally during 2000–2020. Rainfall frequency regulates long-term average E i changes, and rainfall intensity, rather than vegetation attributes, determines the fraction of E i in gross precipitation ( E i / P ). Rain events have become less frequent and more intense since 2000, driving a global decline in E i (and E i / P ) by 4.9% (6.7%). This suggests that ongoing rainfall changes favor a partitioning towards more soil moisture and runoff, benefiting ecosystem functions but simultaneously increasing flood risks. Canopy rainfall interception ( E i ) is a key component of global water cycle. Here, the authors quantify E i using flux tower data and machine learning, and find that rainfall gets less partitioned into E i as it gets more intense and less frequent.

This study aims to assess the hydrological effects of four herbs and four shrubs planted in a selfestablished test area in Xining Basin of northeastern Qinghai-Tibet Plateau, China. The Rainfall-Intercepting Capability (RIC) of the herbs and shrubs was evaluated in rainfall interception experiment at the end of the third, fourth and fifth month of the growth period in 2007. The leaf transpiration rate and the effects of roots on promoting soil moisture evaporation in these plants were also assessed in transpiration experiment and root-soil composite system evaporation experiment in the five month’s growth period. It is found that the RIC of the four studied herbs follows the order of E. repens , E. dahuricus , A. trachycaulum and L. secalinus ; the RIC of the four shrubs follows the order of A. canescens , Z. xanthoxylon , C. korshinskii and N. tangutorum . The RIC of all the herbs is related linearly to their mean height and canopy area ( R 2 ≥ 0.9160). The RIC of all the shrubs bears a logarithmic relationship with their mean height ( R 2 ≥ 0.9164), but a linear one with their canopy area ( R 2 ≥ 0.9356). Moreover, different species show different transpiration rates. Of the four herbs, E. repens has the highest transpiration rate of 1.07 mg/(m 2 ·s), and of the four shrubs, A. canescens has the highest transpiration rate (0.74 mg/(m 2 ·s)). The roots of all the herbs and shrubs can promote soil moisture evaporation. Of the four herbs, the evaporation rate of E. repens root-soil composite system is the highest (2.14%), and of the four shrubs, the root-soil composite system of A. canescens has the highest evaporation rate (1.41%). The evaporation rate of the root-soil composite system of E. dahuricus and Z. xanthoxylon bears a second-power linear relationship with evaporation time ( R 2 ≥ 0.9924). The moisture content of all the eight root-soil composite systems decreases exponentially with evaporation time ( R 2 ≥ 0.8434). The evaporation rate and moisture content of all the plants’ root-soil composite systems increases logarithmically ( R 2 ≥ 0.9606) and linearly ( R 2 ≥ 0.9777) with root volume density. The findings of this study indicate that among the four herbs and four shrubs, E. repens and A. canescens possess the most effective hydrological effects in reducing the soil erosion and shallow landslide in this region.

Rainfall interception loss remains one of the most uncertain fluxes in the global water balance, hindering water management in forested regions and precluding an accurate formulation in climate models. Here, a synthesis of interception loss data from past field experiments conducted worldwide is performed, resulting in a meta-analysis comprising 166 forest sites and 17 agricultural plots. This meta-analysis is used to constrain a global process-based model driven by satellite-observed vegetation dynamics, potential evaporation and precipitation. The model considers sub-grid heterogeneity and vegetation dynamics and formulates rainfall interception for tall and short vegetation separately. A global, 40-year (1980–2019), 0.1∘ spatial resolution, daily temporal resolution dataset is created, analysed and validated against in situ data. The validation shows a good consistency between the modelled interception and field observations over tall vegetation, both in terms of correlations and bias. While an underestimation is found in short vegetation, the degree to which it responds to in situ representativeness errors and difficulties inherent to the measurement of interception in short vegetated ecosystems is unclear. Global estimates are compared to existing datasets, showing overall comparable patterns. According to our findings, global interception averages to 73.81 mm yr−1 or 10.96 × 103 km3 yr−1, accounting for 10.53 % of continental rainfall and approximately 14.06 % of terrestrial evaporation. The seasonal variability of interception follows the annual cycle of canopy cover, precipitation, and atmospheric demand for water. Tropical rainforests show low intra-annual vegetation variability, and seasonal patterns are dictated by rainfall. Interception shows a strong variance among vegetation types and biomes, supported by both the modelling and the meta-analysis of field data. The global synthesis of field observations and the new global interception dataset will serve as a benchmark for future investigations and facilitate large-scale hydrological and climate research.

Canopy interception affects the effective rainfall for plant growth. Extensive studies of canopy interception have focused on trees, but few on crops, due to the longer canopy duration of trees. However, overlooking the canopy rainfall interception results in an overestimate of effective water for crop growth and development. It is still unclear how crop canopy interception is influenced. In this study we examined the effect of leaf area index (LAI) and rainfall characteristics on soybean canopy interception. The results showed that the LAI, rainfall intensity and rainfall duration were the most relevant factors affecting canopy interception. The relationship between canopy interception and LAI was expressed by a linear function, as well as the relationship between canopy interception and rainfall amount. We proposed a canopy interception model versus LAI and rainfall characteristics to simulated the water loss by canopy interception. The results indicated that canopy interception loss increased with bigger LAI and decreased with rainfall amount increasing, indicating that canopy interception can’t be ignored in the crop production, especially with small LAI and high precipitation.

Non-vascular vegetation has been shown to capture considerable quantities of rainfall, which may affect the hydrological cycle and climate at continental scales. However, direct measurements of rainfall interception by non-vascular vegetation are confined to the local scale, which makes extrapolation to the global effects difficult. Here we use a process-based numerical simulation model to show that non-vascular vegetation contributes substantially to global rainfall interception. Inferred average global water storage capacity including non-vascular vegetation was 2.7 mm, which is consistent with field observations and markedly exceeds the values used in land surface models, which average around 0.4 mm. Consequently, we find that the total evaporation of free water from the forest canopy and soil surface increases by 61% when non-vascular vegetation is included, resulting in a global rainfall interception flux that is 22% of the terrestrial evaporative flux (compared with only 12% for simulations where interception excludes non-vascular vegetation). We thus conclude that non-vascular vegetation is likely to significantly influence global rainfall interception and evaporation with consequences for regional- to continental-scale hydrologic cycling and climate.

• Variation in canopy water content (CWC) that can be detected from microwave remote sensing of vegetation optical depth (VOD) has been proposed as an important measure of vegetation water stress. However, the contribution of leaf surface water (LWs), arising from dew formation and rainfall interception, to CWC is largely unknown, particularly in tropical forests and other high-humidity ecosystems. • We compared VOD data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and CWC predicted by a plant hydrodynamics model at four tropical sites in Brazil spanning a rainfall gradient. We assessed how LWs influenced the relationship between VOD and CWC. • The analysis indicates that while CWC is strongly correlated with VOD (R² = 0.62 across all sites), LWs accounts for 61–76% of the diurnal variation in CWC despite being < 10% of CWC. Ignoring LWs weakens the near-linear relationship between CWC and VOD and reduces the consistency in diurnal variation. The contribution of LWs to CWC variation, however, decreases at longer, seasonal to inter-annual, time scales. • Our results demonstrate that diurnal patterns of dew formation and rainfall interception can be an important driver of diurnal variation in CWC and VOD over tropical ecosystems and therefore should be accounted for when inferring plant diurnal water stress from VOD measurements.

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.