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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.

Land-use change from forest to cocoa agroforestry and other tree-based farming systems alters the structure of forest stands and influences the magnitude of canopy water fluxes and subsequent bio-element inputs to the forest floor. The partitioning of incident rainfall (IR) into throughfall (TF), stemflow (SF) and canopy interception loss (ILC) and their associated nutrient element concentrations and fluxes was examined along a replicated chrono-sequence: forest, 3, 15 and 30-year-old smallholder shaded-cocoa systems in Ashanti Region, Ghana. Mean annual precipitation during the 2-year observational period (2007 and 2008) was 1376.2 ± 93.8 mm. TF contributed between 76.5–90.4%, and SF between 1.4–1.7% of the annual IR to the forest floor. There were significant differences in IR, TF and SF chemistry. While TF and SF were enriched in phosphorus (1.33–5.67-fold), potassium (1.1–5.69 fold), calcium (1.35–2.65 fold) and magnesium (1.4–2.68 fold) relative to IR, total N (NH4++NO3−) declined (0.5–0.91) of IR values in TF and SF in forest and shaded cocoa systems. Incident rainfall was significantly more acidic than TF and SF in both forest and shaded-cocoa systems. Mean annual total N, P, K, Ca and Mg inputs to the forest floor through IR were 5.7, 0.14, 13.6, 9.43 and 5.6 kg ha−1year−1 respectively. Though an important source of available nutrients for plant growth, incident rainfall provides only a small percentage of the annual nutrient requirements. With declining soil fertility and pervasive low cocoa yields, possible effects of the reported nutrient fluxes on nutrient budgets in cocoa systems merit further investigation. Against the background of increased TF and decreased ILC following forest conversion to shaded-cocoa, it is also recommended that more studies be carried out on rainfall partitioning and its impact on ground water recharge as a way of establishing its influence on the availability of moisture for agriculture in these systems.

The objective of this paper is to develop a spatial temporal runoff modelling of local rainfall patterns effect on the plant cover hilly lands in Kelantan River Basin. Rainfall interception loss based on leaf area index, loss/infiltration on the ground surface, and runoff calculation were considered as the main plant cover effects on the runoff volume. In this regard, a hydrological and geotechnical grid-based regional model (integrated model) was performed using Microsoft Excel® and GIS framework system for deterministic modelling of rainfall-induced runoff by incorporating plant cover effects. The infiltration process of the current model was integrated with the precipitation distribution method and rainfall interception approach while the runoff analysis of integrated model was employed based on loss/infiltration water on the ground surface with consideration of water interception loss by canopy and the remaining surface water. In the following, the spatial temporal analysis of rainfall-induced runoff was performed using 10 days of hourly rainfall events at the end of December 2014 in Kelantan River Basin. The corresponding changes in pressure head and consequent rate of infiltration were calculated during rainfall events. Subsequently, flood volume is computed using local rainfall patterns, along with water interception loss and the remaining surface water in the study area. The results showed the land cover changes caused significant differences in hydrological response to surface water. The increase in runoff volume of the Kelantan River Basin is as a function of deforestation and urbanization, especially converting the forest area to agricultural land (i.e. rubber and mixed agriculture).

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.

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.

This paper outlines a new strategy to derive evaporation from satellite observations. The approach uses a variety of satellite-sensor products to estimate daily evaporation at a global scale and 0.25 degree spatial resolution. Central to this methodology is the use of the Priestley and Taylor (PT) evaporation model. The minimalistic PT equation combines a small number of inputs, the majority of which can be detected from space. This reduces the number of variables that need to be modelled. Key distinguishing features of the approach are the use of microwave-derived soil moisture, land surface temperature and vegetation density, as well as the detailed estimation of rainfall interception loss. The modelled evaporation is validated against one year of eddy covariance measurements from 43 stations. The estimated annual totals correlate well with the stations' annual cumulative evaporation (R=0.80, N=43) and present a low average bias (−5%). The validation of the daily time series at each individual station shows good model performance in all vegetation types and climate conditions with an average correlation coefficient of R=0.83, still lower than the R=0.90 found in the validation of the monthly time series. The first global map of annual evaporation developed through this methodology is also presented.

Rainfall, throughfall and stemflow were measured and canopy interception loss was derived for 14 rainfall events from June 22 to August 30, 2008 within a mature declining hybrid white spruce (Picea glauca (Moench) Voss × P. engelmannii Parry ex Engelm.)-subalpine fir (Abies lasiocarpa (Hook.) Nutt.)-lodgepole pine (Pinus contorta var. latifolia Dougl. ex Loud.) stand in south-central British Columbia, Canada. Stemflow was negligible during the study period, while, respectively, throughfall and canopy interception loss accounted for approximately 59.4% and 40.6% of the 50.1 mm of cumulative rainfall. Throughfall variability was assessed with three approaches involving roving and stationary wedge-type gauges, and stationary trough gauges. Throughfall exhibited large spatial variability with the coefficient of variability of study period throughfall sampled using 16 stationary trough gauges being 30.3%, while it was 38.0% and 28.7% for 32 stationary and 32 roving wedge gauges, respectively. Our analysis suggests that a roving gauge method is better than a stationary approach since the errors associated with event mean throughfalls are summed quadratically and a greater portion of the canopy area is sampled. Trough gauges were more efficient than wedge gauges; however, this efficiency was less than expected given their much larger sampling areas, suggesting that spatial autocorrelation lengths of throughfall may be longer than the trough systems. The spatial distribution of throughfall showed a high degree of temporal persistence throughout the study suggesting the existence of stable “wet” and “dry” inputs to the floors of these coniferous forests.

The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project aims to advance the development of land evaporation estimates on global and regional scales. Its main objective is the derivation, validation, and intercomparison of a group of existing evaporation retrieval algorithms driven by a common forcing data set. Three commonly used process-based evaporation methodologies are evaluated: the Penman–Monteith algorithm behind the official Moderate Resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Global Land Evaporation Amsterdam Model (GLEAM), and the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL). The resulting global spatiotemporal variability of evaporation, the closure of regional water budgets, and the discrete estimation of land evaporation components or sources (i.e. transpiration, interception loss, and direct soil evaporation) are investigated using river discharge data, independent global evaporation data sets and results from previous studies. In a companion article (Part 1), Michel et al. (2016) inspect the performance of these three models at local scales using measurements from eddy-covariance towers and include in the assessment the Surface Energy Balance System (SEBS) model. In agreement with Part 1, our results indicate that the Priestley and Taylor products (PT-JPL and GLEAM) perform best overall for most ecosystems and climate regimes. While all three evaporation products adequately represent the expected average geographical patterns and seasonality, there is a tendency in PM-MOD to underestimate the flux in the tropics and subtropics. Overall, results from GLEAM and PT-JPL appear more realistic when compared to surface water balances from 837 globally distributed catchments and to separate evaporation estimates from ERA-Interim and the model tree ensemble (MTE). Nonetheless, all products show large dissimilarities during conditions of water stress and drought and deficiencies in the way evaporation is partitioned into its different components. This observed inter-product variability, even when common forcing is used, suggests that caution is necessary in applying a single data set for large-scale studies in isolation. A general finding that different models perform better under different conditions highlights the potential for considering biome- or climate-specific composites of models. Nevertheless, the generation of a multi-product ensemble, with weighting based on validation analyses and uncertainty assessments, is proposed as the best way forward in our long-term goal to develop a robust observational benchmark data set of continental evaporation.

A process-based methodology is applied to estimate land-surface evaporation from multi-satellite information. GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) combines a wide range of remotely-sensed observations to derive daily actual evaporation and its different components. Soil water stress conditions are defined from a root-zone profile of soil moisture and used to estimate transpiration based on a Priestley and Taylor equation. The methodology also derives evaporationfrom bare soil and snow sublimation. Tall vegetation rainfall interception is independently estimated by means of the Gash analytical model. Here, GLEAM is applied daily, at global scale and a quarter degree resolution. Triple collocation is used to calculate the error structure of the evaporation estimates and test the relative merits of two different precipitation inputs. The spatial distribution of evaporation – and its different components – is analysed to understand the relative importance of each component over different ecosystems. Annual land evaporation is estimated as 67.9 × 103 km3, 80% corresponding to transpiration, 11% to interception loss, 7% to bare soil evaporation and 2% snow sublimation. Results show that rainfall interception plays an important role in the partition of precipitation into evaporation and water available for runoff at a continental scale. This study gives insights into the relative importance of precipitation and net radiation in driving evaporation, and how the seasonal influence of these controls varies over different regions. Precipitation is recognised as an important factor driving evaporation, not only in areas that have limited soil water availability, but also in areas of high rainfall interception and low available energy.