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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.

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

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.

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.

• 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.

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time‐evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5. Plain Language Summary The Community Land Model (CLM) is the land component of the widely used Community Earth System Model (CESM). Here, we introduce model developments included in CLM version 5 (CLM5), the default land component for CESM2 which will be used for the Coupled Model Intercomparison Project (CMIP6). CLM5 includes many new and updated processes including (1) hydrology and snow features such as spatially explicit soil depth, canopy snow processes, a simple firn model, and a more mechanistic river model, (2) plant hydraulics and hydraulic redistribution, (3) revised nitrogen cycling with flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake, (4) expansion to six crop types (global) and time‐evolving irrigated areas and fertilization rates, (5) improved urban building energy model, and (6) carbon isotopes. New optional features include a demographically structured dynamic vegetation model, ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Model performance is generally improved for most assessed variables and metrics, though clear establishment of improvement or degradation is challenging due to model complexity as well as observational data limitations. Nonetheless, CLM5 is increasingly suited for research into a broad range of societally relevant scientific questions related to the terrestrial system. Key Points Updated Community Land Model has more hydrological and ecological process fidelity and more comprehensive representation of land management. The model is systematically evaluated using International Land Model Benchmarking system and shows marked improvement over prior versions.

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.

The ratio of plant transpiration to total terrestrial evapotranspiration (T/ET) captures the role of vegetation in surface–atmosphere interactions. However, its magnitude remains highly uncertain at the global scale. Here we apply an emergent constraint approach that integrates CMIP5 Earth system models (ESMs) with 33 field T/ET measurements to re-estimate the global T/ET value. Our observational constraint strongly increases the original ESM estimates (0.41 ± 0.11) and greatly alleviates intermodel discrepancy, which leads to a new global T/ET estimate of 0.62 ± 0.06. For all the ESMs, the leaf area index is identified as the primary driver of spatial variations of T/ET, but to correct its bias generates a larger T/ET underestimation than the original ESM output. We present evidence that the ESM underestimation of T/ET is, instead, attributable to inaccurate representation of canopy light use, interception loss and root water uptake processes in the ESMs. These processes should be prioritized to reduce model uncertainties in the global hydrological cycle.

Increasing planting density is a key strategy for enhancing maize yields 1 – 3 . An ideotype for dense planting requires a ‘smart canopy’ with leaf angles at different canopy layers differentially optimized to maximize light interception and photosynthesis 4 – 6 , among other features. Here we identified leaf angle architecture of smart canopy 1 ( lac1 ), a natural mutant with upright upper leaves, less erect middle leaves and relatively flat lower leaves. lac1 has improved photosynthetic capacity and attenuated responses to shade under dense planting. lac1 encodes a brassinosteroid C-22 hydroxylase that predominantly regulates upper leaf angle. Phytochrome A photoreceptors accumulate in shade and interact with the transcription factor RAVL1 to promote its degradation via the 26S proteasome, thereby inhibiting activation of lac1 by RAVL1 and decreasing brassinosteroid levels. This ultimately decreases upper leaf angle in dense fields. Large-scale field trials demonstrate that lac1 boosts maize yields under high planting densities. To quickly introduce lac1 into breeding germplasm, we transformed a haploid inducer and recovered homozygous lac1 edits from 20 diverse inbred lines. The tested doubled haploids uniformly acquired smart-canopy-like plant architecture. We provide an important target and an accelerated strategy for developing high-density-tolerant cultivars, with lac1 serving as a genetic chassis for further engineering of a smart canopy in maize. A natural mutant of maize exhibits leaf characteristics in line with the ‘smart canopy’ ideotype for high-density planting and boosts yields in large-scale field trials.