Savenije, H. H. G. The importance of interception and why we should delete the term evapotranspiration from our vocabulary. Hydrol. Process. 18, 1507–1511 (2004).
Google Scholar
Gerrits, A. M. J., Pfister, L. & Savenije, H. H. G. Spatial and temporal variability of canopy and forest floor interception in a beech forest. Hydrol. Process. 24, 3011–3025 (2010).
Google Scholar
Porada, P., Van Stan, J. T. & Kleidon, A. Significant contribution of non-vascular vegetation to global rainfall interception. Nat. Geosci. 11, 563–567 (2018).
Google Scholar
van der Ent, R. J., Wang-Erlandsson, L., Keys, P. W. & Savenije, H. H. G. Contrasting roles of interception and transpiration in the hydrological cycle – Part 2: moisture recycling. Earth Syst. Dyn. 5, 471–489 (2014).
Google Scholar
Lian, X. et al. Partitioning global land evapotranspiration using CMIP5 models constrained by observations. Nat. Clim. Change 8, 640–646 (2018).
Google Scholar
Coenders-Gerrits, A. M. et al. Uncertainties in transpiration estimates. Nature 506, E1–E2 (2014).
Google Scholar
Chang, L.-L. et al. Why do large-scale land surface models produce a low ratio of transpiration to evapotranspiration? J. Geophys. Res. Atmos. 123, 9109–9130 (2018).
Google Scholar
Zwieback, S., Chang, Q., Marsh, P. & Berg, A. Shrub tundra ecohydrology: rainfall interception is a major component of the water balance. Environ. Res. Lett. 14, 055005 (2019).
Google Scholar
Cuartas, L. A. et al. Interception water-partitioning dynamics for a pristine rainforest in Central Amazonia: Marked differences between normal and dry years. Agric. For. Meteorol. 145, 69–83 (2007).
Google Scholar
Yue, K. et al. Global patterns and drivers of rainfall partitioning by trees and shrubs. Glob. Change Biol. 27, 3350–3357 (2021).
Google Scholar
Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).
Google Scholar
Tramontana, G. et al. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences 13, 4291–4313 (2016).
Google Scholar
Jung, M., Reichstein, M. & Bondeau, A. Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model. Biogeosciences 6, 2001–2013 (2009).
Google Scholar
Li, X. et al. Spatiotemporal pattern of terrestrial evapotranspiration in China during the past thirty years. Agric. For. Meteorol. 259, 131–140 (2018).
Google Scholar
Koppa, A., Rains, D., Hulsman, P., Poyatos, R. & Miralles, D. G. A deep learning-based hybrid model of global terrestrial evaporation. Nat. Commun. 13, 1912 (2022).
Google Scholar
Zheng, C. & Jia, L. Global canopy rainfall interception loss derived from satellite Earth observations. Ecohydrology 13, e2186 (2019).
Muzylo, A. et al. A review of rainfall interception modelling. J. Hydrol. 370, 191–206 (2009).
Google Scholar
Miralles, D. G., Gash, J. H., Holmes, T. R. H., de Jeu, R. A. M., & Dolman, A. J. Global canopy interception from satellite observations. J. Geophys. Res. 115, D16122 (2010).
Google Scholar
Martens, B. et al. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).
Google Scholar
Oleson, K. et al. Technical Description of Version 4.5 of the Community Land Model (CLM) Report NCAR/TN-503+STR, https://doi.org/10.5065/D6RR1W7M (2013).
Gash, J. An analytical model of rainfall interception by forests. Q. J. Roy. Meteor. Soc. 105, 43–55 (1979).
Google Scholar
Fan, Y. et al. Reconciling canopy interception parameterization and rainfall forcing frequency in the Community Land Model for simulating evapotranspiration of rainforests and oil palm plantations in Indonesia. J. Adv. Model. Earth Syst. 11, 732–751 (2019).
Google Scholar
Návar, J. Modeling rainfall interception loss components of forests. J. Hydrol. 584, 124449 (2019).
Google Scholar
Kang, M., Kwon, H., Cheon, J. H. & Kim, J. On estimating wet canopy evaporation from deciduous and coniferous forests in the Asian monsoon climate. J. Hydrometeorol. 13, 950–965 (2012).
Google Scholar
Llorens, P., Domingo, F., Garcia-Estringana, P., Muzylo, A. & Gallart, F. Canopy wetness patterns in a Mediterranean deciduous stand. J. Hydrol. 512, 254–262 (2014).
Google Scholar
Czikowsky, M. J. & Fitzjarrald, D. R. Detecting rainfall interception in an Amazonian rain forest with eddy flux measurements. J. Hydrol. 377, 92–105 (2009).
Google Scholar
Renninger, H. J., Phillips, N. & Salvucci, G. D. Wet- vs. dry-season transpiration in an Amazonian rain forest palm iriartea deltoidea. Biotropica 42, 470–478 (2010).
Google Scholar
Zhao, W. et al. Physics-constrained machine learning of evapotranspiration. Geophys. Res. Lett. 46, 14496–14507 (2019).
Google Scholar
Zabret, K. & Šraj, M. How characteristics of a rainfall event and the meteorological conditions determine the development of stemflow: A case study of a birch tree. Front. Glob. Change 4, 663100 (2022).
Google Scholar
Calder, I. R. Dependence of rainfall interception on drop size: 1. Development of the two-layer stochastic model. J. Hydrol. 185, 363–378 (1996).
Google Scholar
Niinemets, Ü. A review of light interception in plant stands from leaf to canopy in different plant functional types and in species with varying shade tolerance. Ecol. Res. 25, 693–714 (2010).
Google Scholar
Gordon, D. A. R., Coenders-Gerrits, M., Sellers, B. A., Sadeghi, S., & Van Stan II, J. T. Rainfall interception and redistribution by a common North American understory and pasture forb, Eupatorium capillifolium (Lam. dogfennel). Hydrol. Earth Syst. Sci. 24, 4587–4599 (2020).
Google Scholar
Ciruzzi, D. M. & Loheide, S. P. II Monitoring tree sway as an indicator of interception dynamics before, during, and following a storm. Geophys. Res. Lett. 48, e2021GL094980 (2021).
Google Scholar
Karimi, P., Bastiaanssen, W. G. & Molden, D. Water Accounting Plus (WA+)–a water accounting procedure for complex river basins based on satellite measurements. Hydrol. Earth Syst. Sci. 17, 2459–2472 (2013).
Google Scholar
del Campo, A. D., González-Sanchis, M., Lidón, A., Ceacero, C. J. & García-Prats, A. Rainfall partitioning after thinning in two low-biomass semiarid forests: Impact of meteorological variables and forest structure on the effectiveness of water-oriented treatments. J. Hydrol. 565, 74–86 (2018).
Google Scholar
Lian, X. et al. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 2, 232–250 (2021).
Google Scholar
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).
Google Scholar
Feng, X. et al. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Change 6, 1019–1022 (2016).
Google Scholar
Dawson, T. E. & Goldsmith, G. R. The value of wet leaves. N. Phytol. 219, 1156–1169 (2018).
Google Scholar
Aparecido, L. M. T., Miller, G. R., Cahill, A. T. & Moore, G. W. Comparison of tree transpiration under wet and dry canopy conditions in a Costa Rican premontane tropical forest. Hydrol. Process. 30, 5000–5011 (2016).
Google Scholar
Huang, L. & Zhang, Z. Effect of rainfall pulses on plant growth and transpiration of two xerophytic shrubs in a revegetated desert area: Tengger Desert, China. CATENA 137, 269–276 (2016).
Google Scholar
Fathizadeh, O., Hosseini, S., Zimmermann, A., Keim, R. & Boloorani, A. D. Estimating linkages between forest structural variables and rainfall interception parameters in semi-arid deciduous oak forest stands. Sci. Total Environ. 601, 1824–1837 (2017).
Google Scholar
Zhang, Z.-S., Zhao, Y., Li, X.-R., Huang, L. & Tan, H.-J. Gross rainfall amount and maximum rainfall intensity in 60-minute influence on interception loss of shrubs: a 10-year observation in the Tengger Desert. Sci. Rep. 6, 26030 (2016).
Google Scholar
de Groen, M. M. & Savenije, H. H. G. A monthly interception equation based on the statistical characteristics of daily rainfall. Water Resour. Res. 42, W12417 (2006).
Google Scholar
Chinita, M. J., Richardson, M., Teixeira, J. & Miranda, P. M. A. Global mean frequency increases of daily and sub-daily heavy precipitation in ERA5. Environ. Res. Lett. 16, 074035 (2021).
Google Scholar
Donat, M. G., Lowry, A. L., Alexander, L. V., O’Gorman, P. A. & Maher, N. More extreme precipitation in the world’s dry and wet regions. Nat. Clim. Change 6, 508–513 (2016).
Google Scholar
IPCC. The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al) (Cambridge Univ. Press, 2021).
Ficklin, D. L., Null, S. E., Abatzoglou, J. T., Novick, K. A. & Myers, D. T. Hydrological intensification will increase the complexity of water resource management. Earth’s Futur. 10, e2021EF002487 (2022).
Google Scholar
Haslwanter, A., Hammerle, A. & Wohlfahrt, G. Open-path vs. closed-path eddy covariance measurements of the net ecosystem carbon dioxide and water vapour exchange: a long-term perspective. Agric. For. Meteorol. 149, 291–302 (2009).
Google Scholar
Migliavacca, M. et al. The three major axes of terrestrial ecosystem function. Nature 598, 468–472 (2021).
Google Scholar
Zhang, W. et al. The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation. Preprint at https://doi.org/10.2139/ssrn.4106267 (2022).
van Dijk, A. I. J. M. et al. Rainfall interception and the coupled surface water and energy balance. Agric. For. Meteorol. 214–215, 402–415 (2015).
Google Scholar
Barr, A. G., Morgenstern, K., Black, T. A., McCaughey, J. H. & Nesic, Z. Surface energy balance closure by the eddy-covariance method above three boreal forest stands and implications for the measurement of the CO2 flux. Agric. Meteorol. 140, 322–337 (2006).
Google Scholar
Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019).
Google Scholar
Zhi, W. et al. From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale? Environ. Sci. Technol. 55, 2357–2368 (2021).
Google Scholar
Kraft, B., Jung, M., Körner, M. & Reichstein, M. Hybrid modeling: fusion of a deep approach and physics-based model for global hydrological modeling. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 43, 1537–1544 (2020).
Google Scholar
Hoffmann, L. et al. From ERA-Interim to ERA5: the considerable impact of ECMWF’s next-generation reanalysis on Lagrangian transport simulations. Atmos. Chem. Phys. 19, 3097–3124 (2019).
Google Scholar
Wang, D., Wang, G. & Anagnostou, E. N. Evaluation of canopy interception schemes in land surface models. J. Hydrol. 347, 308–318 (2007).
Google Scholar
Wang, G. & Eltahir, E. A. Modeling the biosphere–atmosphere system: The impact of the subgrid variability in rainfall interception. J. Clim. 13, 2887–2899 (2000).
Google Scholar
Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).
Google Scholar
Le Quéré, C. et al. Global carbon budget 2018. Earth Syst. Sci. Data 10, 2141–2194 (2018).
Google Scholar
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