Articles | Volume 26, issue 21
https://doi.org/10.5194/hess-26-5647-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-5647-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Nov 2022
Research article |
| 10 Nov 2022
| 10 Nov 2022
Revisiting large-scale interception patterns constrained by a synthesis of global experimental data
Feng Zhong
Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, 9000, Belgium
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
Shanhu Jiang
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
College of Hydrology and Water Resources, Hohai University, Nanjing, China
Albert I. J. M. van Dijk
Fenner School of Environment & Society, Australian National University, Canberra, ACT, Australia
Liliang Ren
CORRESPONDING AUTHOR
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
College of Hydrology and Water Resources, Hohai University, Nanjing, China
Jaap Schellekens
Planet Labs, PBC, Haarlem, the Netherlands
Diego G. Miralles
CORRESPONDING AUTHOR
Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, 9000, Belgium
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Cited articles
Acharya, S., McLaughlin, D., Kaplan, D., and Cohen, M. J.:
A proposed method for estimating interception from near-surface soil moisture response, Hydrol. Earth Syst. Sci., 24, 1859–1870, https://doi.org/10.5194/hess-24-1859-2020, 2020.
Armstrong, R., Brodzik, M., Knowles, K., and Savoie, M.:
Global monthly EASE-grid snow water equivalent climatology, version 1, National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado USA, https://doi.org/10.5067/KJVERY3MIBPS, 2005.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., Van Dijk, A. I., McVicar, T. R., and Adler, R. F.:
MSWEP V2 global 3-hourly 0.1 precipitation: methodology and quantitative assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019.
Béland, M. and Baldocchi, D. D.:
Vertical structure heterogeneity in broadleaf forests: Effects on light interception and canopy photosynthesis, Agr. Forest. Meteorol., 307, 108525, https://doi.org/10.1016/j.agrformet.2021.108525, 2021.
Braghiere, R. K., Quaife, T., Black, E., He, L., and Chen, J.:
Underestimation of global photosynthesis in Earth system models due to representation of vegetation structure, Global Biogeochem. Cy., 33, 1358–1369, https://doi.org/10.1029/2018GB006135, 2019.
Braghiere, R. K., Quaife, T., Black, E., Ryu, Y., Chen, Q., De Kauwe, M. G., and Baldocchi, D.:
Influence of sun zenith angle on canopy clumping and the resulting impacts on photosynthesis, Agr. Forest. Meteorol., 291, 108065, https://doi.org/10.1016/j.agrformet.2020.108065, 2020.
Braghiere, R. K., Wang, Y., Doughty, R., Sousa, D., Magney, T., Widlowski, J.-L., Longo, M., Bloom, A. A., Worden, J., Gentine, P., and Frankenberg, C.:
Accounting for canopy structure improves hyperspectral radiative transfer and sun-induced chlorophyll fluorescence representations in a new generation Earth System model, Remote Sens. Environ., 261, 112497, https://doi.org/10.1016/j.rse.2021.112497, 2021.
Calder, I., Wright, I., and Murdiyarso, D.:
A study of evaporation from tropical rain forest—West Java, J. Hydrol., 89, 13–31, https://doi.org/10.1016/0022-1694(86)90139-3, 1986.
Calder, I. R.:
Dependence of rainfall interception on drop size: 1. Development of the two-layer stochastic model, J. Hydrol., 185, 363–378, https://doi.org/10.1016/0022-1694(95)02998-2, 1996.
Carlson, T. N. and Ripley, D. A.:
On the relation between NDVI, fractional vegetation cover, and leaf area index, Remote Sens. Environ., 62, 241–252, https://doi.org/10.1016/S0034-4257(97)00104-1, 1997.
Carlyle-Moses, D. E., Park, A. D., and Cameron, J. L.:
Modelling rainfall interception loss in forest restoration trials in Panama, Ecohydrology, 3, 272–283, https://doi.org/10.1002/eco.105, 2010.
Chen, S., Chen, C., Zou, C. B., Stebler, E., Zhang, S., Hou, L., and Wang, D.:
Application of Gash analytical model and parameterized Fan model to estimate canopy interception of a Chinese red pine forest, J. Forestry Res., 18, 335–344, https://doi.org/10.1007/s10310-012-0364-z, 2013.
Chen, Y.-Y. and Li, M.-H.:
Quantifying Rainfall Interception Loss of a Subtropical Broadleaved Forest in Central Taiwan, Water, 8, 14, https://doi.org/10.3390/w8010014, 2016.
Cui, Y. and Jia, L.:
A modified gash model for estimating rainfall interception loss of forest using remote sensing observations at regional scale, Water, 6, 993–1012, https://doi.org/10.3390/w6040993, 2014.
Cui, Y., Zhao, P., Yan, B., Xie, H., Yu, P., Wan, W., Fan, W., and Hong, Y.:
Developing the Remote Sensing-Gash analytical model for estimating vegetation rainfall interception at very high resolution: A case study in the Heihe river basin, Remote Sens.-Basel, 9, 661, https://doi.org/10.3390/rs9070661, 2017.
David, J. S., Valente, F., and Gash, J. H.: Evaporation of Intercepted Rainfall, in: Encyclopedia of Hydrological Sciences, edited by: Anderson, M. G., John Wiley & Sons, Ltd, West Sussex, England, 627–634, https://doi.org/10.1002/0470848944.hsa046, 2006.
de Jong, S. M. and Jetten, V.:
Estimating spatial patterns of rainfall interception from remotely sensed vegetation indices and spectral mixture analysis, Int. J. Geogr. Inf. Sci., 21, 529–545, https://doi.org/10.1080/13658810601064884, 2007.
Deguchi, A., Hattori, S., and Park, H.-T.:
The influence of seasonal changes in canopy structure on interception loss: application of the revised Gash model, J. Hydrol., 318, 80–102, https://doi.org/10.1016/j.jhydrol.2005.06.005, 2006.
DiMiceli, C., Carroll, M., Sohlberg, R., Kim, D., Kelly, M., Townshend, J.: MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD44B.006, 2015.
DiMiceli, C. M., Carroll, M. L., Sohlberg, R. A., Huang, C., Hansen, M. C., and Townshend, J. R.:
Annual global automated MODIS vegetation continuous fields (MOD44B) at 250 m spatial resolution for data years beginning day 65, 2000–2014, collection 5 percent tree cover, version 6, University of Maryland, College Park, MD, USA, 2017.
Dorigo, W., Dietrich, S., Aires, F., Brocca, L., Carter, S., Cretaux, J.-F., Dunkerley, D., Enomoto, H., Forsberg, R., and Güntner, A.:
Closing the water cycle from observations across scales: where do we stand?, B. Am. Meteorol. Soc., 102, E1897–E1935, https://doi.org/10.1175/BAMS-D-19-0316.1, 2021.
Fan, J., Oestergaard, K. T., Guyot, A., and Lockington, D. A.:
Measuring and modeling rainfall interception losses by a native Banksia woodland and an exotic pine plantation in subtropical coastal Australia, J. Hydrol., 515, 156–165, https://doi.org/10.1016/j.jhydrol.2014.04.066, 2014.
Fathizadeh, O., Hosseini, S., Keim, R., and Boloorani, A. D.:
A seasonal evaluation of the reformulated Gash interception model for semi-arid deciduous oak forest stands, Forest Ecol. Manag., 409, 601–613, https://doi.org/10.1016/j.foreco.2017.11.058, 2018.
Fernandes, R. P., da Costa Silva, R. W., Salemi, L. F., de Andrade, T. M. B., de Moraes, J. M., Van Dijk, A. I., and Martinelli, L. A.:
The influence of sugarcane crop development on rainfall interception losses, J. Hydrol., 551, 532–539, https://doi.org/10.1016/j.jhydrol.2017.06.027, 2017.
Finch, J. and Riche, A.:
Interception losses from Miscanthus at a site in south-east England—An application of the Gash model, Hydrol. Process., 24, 2594–2600, https://doi.org/10.1002/hyp.7673, 2010.
Fisher, J. B., Tu, K. P., and Baldocchi, D. D.:
Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites, Remote Sens. Environ., 112, 901–919, https://doi.org/10.1016/j.rse.2007.06.025, 2008.
Friedl, M. and Sulla-Menashe, D.: MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD12C1.006, 2015.
Garcia-Estringana, P., Alonso-Blázquez, N., and Alegre, J.:
Water storage capacity, stemflow and water funneling in Mediterranean shrubs, J. Hydrol., 389, 363–372, https://doi.org/10.1016/j.jhydrol.2010.06.017, 2010.
Gash, J.:
An analytical model of rainfall interception by forests, Q. J. Roy. Meteor. Soc., 105, 43–55, https://doi.org/10.1002/qj.49710544304, 1979.
Gash, J. and Morton, A.:
An application of the Rutter model to the estimation of the interception loss from Thetford forest, J. Hydrol., 38, 49–58, https://doi.org/10.1016/0022-1694(78)90131-2, 1978.
Gash, J. and Stewart, J.:
The evaporation from Thetford Forest during 1975, J. Hydrol., 35, 385–396, https://doi.org/10.1016/0022-1694(77)90014-2, 1977.
Gash, J., Wright, I., and Lloyd, C. R.:
Comparative estimates of interception loss from three coniferous forests in Great Britain, J. Hydrol., 48, 89–105, https://doi.org/10.1016/0022-1694(80)90068-2, 1980.
Gash, J. H., Lloyd, C., and Lachaud, G.:
Estimating sparse forest rainfall interception with an analytical model, J. Hydrol., 170, 79–86, https://doi.org/10.1016/0022-1694(95)02697-N, 1995.
Gerrits, A. M. J., Pfister, L., and Savenije, H. H. G.: Spatial and temporal variability of canopy and forest floor interception in a beech forest, Hydrol. Process., 24, 3011–3025, https://doi.org/10.1002/hyp.7712, 2010.
Ghimire, C. P., Bruijnzeel, L. A., Lubczynski, M. W., and Bonell, M.:
Rainfall interception by natural and planted forests in the Middle Mountains of Central Nepal, J. Hydrol., 475, 270–280, https://doi.org/10.1016/j.jhydrol.2012.09.051, 2012.
Ginebra-Solanellas, R. M., Holder, C. D., Lauderbaugh, L. K., and Webb, R.:
The influence of changes in leaf inclination angle and leaf traits during the rainfall interception process, Agr. Forest. Meteorol., 285, 107924, https://doi.org/10.1016/j.agrformet.2020.107924, 2020.
GLEAM: https://www.gleam.eu/, last access: 31 October 2022.
Hansen, M. and Song, X.: Vegetation Continuous Fields (VCF) Yearly Global 0.05 Deg, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MEaSUREs/VCF/VCF5KYR.001, 2018.
Hassan, S. T., Ghimire, C. P., and Lubczynski, M. W.:
Remote sensing upscaling of interception loss from isolated oaks: Sardon catchment case study, Spain, J. Hydrol., 555, 489–505, https://doi.org/10.1016/j.jhydrol.2017.08.016, 2017.
Herbst, M., Roberts, J. M., Rosier, P. T., and Gowing, D. J.:
Measuring and modelling the rainfall interception loss by hedgerows in southern England, Agr. Forest. Meteorol., 141, 244–256, https://doi.org/10.1016/j.agrformet.2006.10.012, 2006.
Holder, C. D.:
Effects of leaf hydrophobicity and water droplet retention on canopy storage capacity, Ecohydrology, 6, 483–490, https://doi.org/10.1002/eco.1278, 2013.
Holwerda, F., Bruijnzeel, L., Scatena, F., Vugts, H., and Meesters, A.:
Wet canopy evaporation from a Puerto Rican lower montane rain forest: The importance of realistically estimated aerodynamic conductance, J. Hydrol., 414, 1–15, https://doi.org/10.1016/j.jhydrol.2011.07.033, 2012.
Hörmann, G., Branding, A., Clemen, T., Herbst, M., Hinrichs, A., and Thamm, F.:
Calculation and simulation of wind controlled canopy interception of a beech forest in Northern Germany, Agr. Forest. Meteorol., 79, 131–148, https://doi.org/10.1016/0168-1923(95)02275-9, 1996.
Hu, G. and Jia, L.:
Monitoring of evapotranspiration in a semi-arid inland river basin by combining microwave and optical remote sensing observations, Remote Sens.-Basel, 7, 3056–3087, https://doi.org/10.3390/rs70303056, 2015.
Iida, S., Levia, D. F., Shimizu, A., Shimizu, T., Tamai, K., Nobuhiro, T., Kabeya, N., Noguchi, S., Sawano, S., and Araki, M.:
Intrastorm scale rainfall interception dynamics in a mature coniferous forest stand, J. Hydrol., 548, 770–783, https://doi.org/10.1016/j.jhydrol.2017.03.009, 2017.
Keim, R., Skaugset, A., and Weiler, M.:
Storage of water on vegetation under simulated rainfall of varying intensity, Adv. Water Resour., 29, 974–986, https://doi.org/10.1016/j.advwatres.2005.07.017, 2006.
Klaassen, W., Lankreijer, H. J. M., and Veen, A. W. L.:
Rainfall interception near a forest edge, J. Hydrol., 185, 349–361, https://doi.org/10.1016/0022-1694(95)03011-5, 1996.
Klaassen, W., Bosveld, F., and De Water, E.:
Water storage and evaporation as constituents of rainfall interception, J. Hydrol., 212, 36–50, https://doi.org/10.1016/S0022-1694(98)00200-5, 1998.
Lankreijer, H., Hendriks, M., and Klaassen, W.:
A comparison of models simulating rainfall interception of forests, Agr. Forest. Meteorol., 64, 187–199, https://doi.org/10.1016/0168-1923(93)90028-G, 1993.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.:
The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019.
Limousin, J.-M., Rambal, S., Ourcival, J.-M., and Joffre, R.:
Modelling rainfall interception in a mediterranean Quercus ilex ecosystem: Lesson from a throughfall exclusion experiment, J. Hydrol., 357, 57–66, https://doi.org/10.1016/j.jhydrol.2008.05.001, 2008.
Linhoss, A. C. and Siegert, C. M.:
A comparison of five forest interception models using global sensitivity and uncertainty analysis, J. Hydrol., 538, 109–116, https://doi.org/10.1016/j.jhydrol.2016.04.011, 2016.
Link, T. E., Unsworth, M., and Marks, D.:
The dynamics of rainfall interception by a seasonal temperate rainforest, Agr. Forest. Meteorol., 124, 171–191, https://doi.org/10.1016/j.agrformet.2004.01.010, 2004.
Liu, Z., Wang, Y., Tian, A., Liu, Y., Webb, A. A., Wang, Y., Zuo, H., Yu, P., Xiong, W., and Xu, L.:
Characteristics of canopy interception and its simulation with a revised Gash model for a larch plantation in the Liupan Mountains, China, J. Forestry Res., 29, 187–198, https://doi.org/10.1007/s11676-017-0407-6, 2018.
Lloyd, C. R., Gash, J. H., and Shuttleworth, W. J.:
The measurement and modelling of rainfall interception by Amazonian rain forest, Agr. Forest. Meteorol., 43, 277–294, https://doi.org/10.1016/0168-1923(88)90055-X, 1988.
Lundgren, L. and Lundgren, B.:
Rainfall, interception and evaporation in the Mazumbai forest reserve, West Usambara Mts., Tanzania and their importance in the assessment of land potential, Geogr. Ann. A, 61, 157–178, https://doi.org/10.1080/04353676.1979.11879988, 1979.
Luojus, K., Pulliainen, J., Takala, M., Kangwa, M., Smolander, T., Wiesmann, A., Derksen, C., Metsämäki, S., Salminen, M., Solberg, R., Nagler, T., Bippus, G., Wunderle, S., and Hüsler, F.: ESA GlobSnow snow water equivalent (SWE) data, Finnish Meteorological Institute [data set], http://www.globsnow.info/se/ (last access: 31 October 2022), 2013.
Ma, C., Li, X., Luo, Y., Shao, M., and Jia, X.:
The modelling of rainfall interception in growing and dormant seasons for a pine plantation and a black locust plantation in semi-arid Northwest China, J. Hydrol., 577, 123849, https://doi.org/10.1016/j.jhydrol.2019.06.021, 2019.
Majasalmi, T., Stenberg, P., and Rautiainen, M.:
Comparison of ground and satellite-based methods for estimating stand-level fPAR in a boreal forest, Agr. Forest. Meteorol., 232, 422–432, https://doi.org/10.1016/j.agrformet.2016.09.007, 2017.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.:
GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Mianabadi, A., Coenders-Gerrits, M., Shirazi, P., Ghahraman, B., and Alizadeh, A.:
A global Budyko model to partition evaporation into interception and transpiration, Hydrol. Earth Syst. Sci., 23, 4983–5000, https://doi.org/10.5194/hess-23-4983-2019, 2019.
Miralles, D. G., Gash, J. H., Holmes, T. R., de Jeu, R. A., and Dolman, A.:
Global canopy interception from satellite observations, J. Geophysi. Res.-Atmos., 115, D16122, https://doi.org/10.1029/2009JD013530, 2010.
Miralles, D. G., De Jeu, R. A. M., Gash, J. H., Holmes, T. R. H., and Dolman, A. J.:
Magnitude and variability of land evaporation and its components at the global scale, Hydrol. Earth Syst. Sci., 15, 967–981, https://doi.org/10.5194/hess-15-967-2011, 2011a.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.:
Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011b.
Miralles, D. G., Brutsaert, W., Dolman, A., and Gash, J. H.:
On the use of the term “evapotranspiration”, Water Resour. Res., 56, e2020WR028055, https://doi.org/10.1029/2020WR028055, 2020.
Molina, A. J. and del Campo, A. D.:
The effects of experimental thinning on throughfall and stemflow: A contribution towards hydrology-oriented silviculture in Aleppo pine plantations, Forest Ecol. Manag., 269, 206–213, https://doi.org/10.1016/j.foreco.2011.12.037, 2012.
Monteith, J. L.: Evaporation and environment, Sym. Soc. Exp.
Biol., 19, 205–234, 1965.
Mu, Q., Zhao, M., and Running, S. W.:
Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019, 2011.
Mu, X., Song, W., Gao, Z., McVicar, T. R., Donohue, R. J., and Yan, G.:
Fractional vegetation cover estimation by using multi-angle vegetation index, Remote Sens. Environ., 216, 44–56, https://doi.org/10.1016/j.rse.2018.06.022, 2018.
Myneni, R., Knyazikhin, Y., and Park, T.: MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD15A3H.006, 2015.
Návar, J.:
The performance of the reformulated Gash's interception loss model in Mexico's northeastern temperate forests, Hydrol. Process., 27, 1626–1633, https://doi.org/10.1002/hyp.9309, 2013.
Návar, J.:
Modeling rainfall interception components of forests: Extending drip equations, Agr. Forest. Meteorol., 279, 107704, https://doi.org/10.1016/j.agrformet.2019.107704, 2019.
Návar, J.:
Modeling rainfall interception loss components of forests, J. Hydrol., 584, 124449, https://doi.org/10.1016/j.jhydrol.2019.124449, 2020.
Návar, J. and Bryan, R. B.:
Fitting the analytical model of rainfall interception of Gash to individual shrubs of semi-arid vegetation in northeastern Mexico, Agr. Forest. Meteorol., 68, 133–143, https://doi.org/10.1016/0168-1923(94)90032-9, 1994.
Návar, J., Carlyle-Moses, D. E., and Martinez, A.:
Interception loss from the Tamaulipan matorral thornscrub of north-eastern Mexico: an application of the Gash analytical interception loss model, J. Arid Environ., 41, 1–10, https://doi.org/10.1006/jare.1998.0460, 1999.
Nazari, M., Sadeghi, S. M. M., Van Stan II, J. T., and Chaichi, M. R.:
Rainfall interception and redistribution by maize farmland in central Iran, J. Hydrol.-Reg. Stud., 27, 100656, https://doi.org/10.1016/j.ejrh.2019.100656, 2020.
Paço, T. A., David, T. S., Henriques, M. O., Pereira, J. S., Valente, F., Banza, J., Pereira, F. L., Pinto, C., and David, J. S.:
Evapotranspiration from a Mediterranean evergreen oak savannah: the role of trees and pasture, J. Hydrol., 369, 98–106, https://doi.org/10.1016/j.jhydrol.2009.02.011, 2009.
Penman, H. L.:
Natural evaporation from open water, bare soil and grass, P. Roy. Soc. Lond. A-Math., 193, 120–145, https://doi.org/10.1098/rspa.1948.0037, 1948.
Pereira, F., Gash, J., David, J., and Valente, F.:
Evaporation of intercepted rainfall from isolated evergreen oak trees: Do the crowns behave as wet bulbs?, Agr. Forest. Meteorol., 149, 667–679, https://doi.org/10.1016/j.agrformet.2008.10.013, 2009.
Pereira, F., Valente, F., David, J., Jackson, N., Minunno, F., and Gash, J.:
Rainfall interception modelling: Is the wet bulb approach adequate to estimate mean evaporation rate from wet/saturated canopies in all forest types?, J. Hydrol., 534, 606–615, https://doi.org/10.1016/j.jhydrol.2016.01.035, 2016.
Pérez-Suárez, M., Arredondo-Moreno, J., Huber-Sannwald, E., and Serna-Pérez, A.:
Forest structure, species traits and rain characteristics influences on horizontal and vertical rainfall partitioning in a semiarid pine–oak forest from Central Mexico, Ecohydrology, 7, 532–543, https://doi.org/10.1002/eco.1372, 2014.
Reichle, R. H. and Koster, R. D.:
Bias reduction in short records of satellite soil moisture, Geophys. Res. Lett., 31, L19501, https://doi.org/10.1029/2004GL020938, 2004.
Reichle, R. H., Koster, R. D., De Lannoy, G. J., Forman, B. A., Liu, Q., Mahanama, S. P., and Touré, A.:
Assessment and enhancement of MERRA land surface hydrology estimates, J. Climate, 24, 6322–6338, https://doi.org/10.1175/JCLI-D-10-05033.1, 2011.
Reid, L. M. and Lewis, J.:
Rates, timing, and mechanisms of rainfall interception loss in a coastal redwood forest, J. Hydrol., 375, 459–470, https://doi.org/10.1016/j.jhydrol.2009.06.048, 2009.
Ringgaard, R., Herbst, M., and Friborg, T.:
Partitioning forest evapotranspiration: Interception evaporation and the impact of canopy structure, local and regional advection, J. Hydrol., 517, 677–690, https://doi.org/10.1016/j.jhydrol.2014.06.007, 2014.
Rutter, A., Kershaw, K., Robins, P., and Morton, A.:
A predictive model of rainfall interception in forests, 1. Derivation of the model from observations in a plantation of Corsican pine, Agr. Meteorol., 9, 367–384, https://doi.org/10.1016/0002-1571(71)90034-3, 1971.
Rutter, A., Morton, A., and Robins, P.:
A predictive model of rainfall interception in forests. II. Generalization of the model and comparison with observations in some coniferous and hardwood stands, J. Appl. Ecol., 12, 367–380, https://www.jstor.org/stable/2401739 (last access: 31 October 2022), 1975.
Sadeghi, S. M. M., Attarod, P., Van Stan, J. T., Pypker, T. G., and Dunkerley, D.:
Efficiency of the reformulated Gash's interception model in semiarid afforestations, Agr. Forest. Meteorol., 201, 76–85, https://doi.org/10.1016/j.agrformet.2014.10.006, 2015.
Schellekens, J., Scatena, F., Bruijnzeel, L., and Wickel, A.:
Modelling rainfall interception by a lowland tropical rain forest in northeastern Puerto Rico, J. Hydrol., 225, 168–184, https://doi.org/10.1016/S0022-1694(99)00157-2, 1999.
Schellekens, J., Bruijnzeel, L., Scatena, F., Bink, N., and Holwerda, F.:
Evaporation from a tropical rain forest, Luquillo Experimental Forest, eastern Puerto Rico, Water Resour. Res., 36, 2183–2196, https://doi.org/10.1029/2000WR900074, 2000.
Schellekens, J., Dutra, E., Martínez-de la Torre, A., Balsamo, G., van Dijk, A., Sperna Weiland, F., Minvielle, M., Calvet, J.-C., Decharme, B., Eisner, S., Fink, G., Flörke, M., Peßenteiner, S., van Beek, R., Polcher, J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo, W., and Weedon, G. P.:
A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data, 9, 389–413, https://doi.org/10.5194/essd-9-389-2017, 2017.
Shi, Z., Wang, Y., Xu, L., Xiong, W., Yu, P., Gao, J., and Zhang, L.:
Fraction of incident rainfall within the canopy of a pure stand of Pinus armandii with revised Gash model in the Liupan Mountains of China, J. Hydrol., 385, 44–50, https://doi.org/10.1016/j.jhydrol.2010.02.003, 2010.
Stoy, P. C., El-Madany, T. S., Fisher, J. B., Gentine, P., Gerken, T., Good, S. P., Klosterhalfen, A., Liu, S., Miralles, D. G., Perez-Priego, O., Rigden, A. J., Skaggs, T. H., Wohlfahrt, G., Anderson, R. G., Coenders-Gerrits, A. M. J., Jung, M., Maes, W. H., Mammarella, I., Mauder, M., Migliavacca, M., Nelson, J. A., Poyatos, R., Reichstein, M., Scott, R. L., and Wolf, S.:
Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities, Biogeosciences, 16, 3747–3775, https://doi.org/10.5194/bg-16-3747-2019, 2019.
Sulla-Menashe, D., Gray, J. M., Abercrombie, S. P., and Friedl, M. A.:
Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product, Remote Sens. Environ., 222, 183–194, https://doi.org/10.1016/j.rse.2018.12.013, 2019.
Sun, X., Onda, Y., Hirata, A., Kato, H., Gomi, T., and Liu, X.:
Effect of canopy openness and meteorological factors on spatial variability of throughfall isotopic composition in a Japanese cypress plantation, Hydrol. Process., 32, 1038–1049, https://doi.org/10.1002/hyp.11475, 2018.
Tarazona, T., Santa Regina, I., and Calvo, R.:
Interception, throughfall and stemflow in two forests of the” Sierra de la Demanda” in the Province of Burgos, Pirineos, 27–40, https://doi.org/10.3989/pirineos.1996.v147-148.135, 1996.
Valente, F., David, J., and Gash, J.:
Modelling interception loss for two sparse eucalypt and pine forests in central Portugal using reformulated Rutter and Gash analytical models, J. Hydrol., 190, 141–162, https://doi.org/10.1016/S0022-1694(96)03066-1, 1997.
Van Dijk, A.: The Australian Water Resources Assessment System, Technical Report 3, Landscape Model (version 0.5) Technical Description, CSIRO, 86 pp., https://doi.org/10.4225/08/5852dd9bb578c, 2010.
Van Dijk, A. and Bruijnzeel, L.:
Modelling rainfall interception by vegetation of variable density using an adapted analytical model. Part 2. Model validation for a tropical upland mixed cropping system, J. Hydrol., 247, 239–262, https://doi.org/10.1016/S0022-1694(01)00393-6, 2001a.
Van Dijk, A. and Bruijnzeel, L.:
Modelling rainfall interception by vegetation of variable density using an adapted analytical model. Part 1. Model description, J. Hydrol., 247, 230–238, https://doi.org/10.1016/S0022-1694(01)00392-4, 2001b.
Van Dijk, A. I., Peña-Arancibia, J. L., Wood, E. F., Sheffield, J., and Beck, H. E.:
Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide, Water Resour. Res., 49, 2729–2746, https://doi.org/10.1002/wrcr.20251, 2013.
Van Dijk, A. I., Gash, J. H., Van Gorsel, E., Blanken, P. D., Cescatti, A., Emmel, C., Gielen, B., Harman, I. N., Kiely, G., and Merbold, L.:
Rainfall interception and the coupled surface water and energy balance, Agr. Forest. Meteorol., 214, 402–415, https://doi.org/10.1016/j.agrformet.2015.09.006, 2015.
van Dijk, A. I. J. M., Schellekens, J., Yebra, M., Beck, H. E., Renzullo, L. J., Weerts, A., and Donchyts, G.:
Global 5 km resolution estimates of secondary evaporation including irrigation through satellite data assimilation, Hydrol. Earth Syst. Sci., 22, 4959–4980, https://doi.org/10.5194/hess-22-4959-2018, 2018.
Verger, A., Baret, F., and Weiss, M.:
A multisensor fusion approach to improve LAI time series, Remote Sens. Environ., 115, 2460–2470, https://doi.org/10.1016/j.rse.2011.05.006, 2011.
Wallace, J. and McJannet, D.:
On interception modelling of a lowland coastal rainforest in northern Queensland, Australia, J. Hydrol., 329, 477–488, https://doi.org/10.1016/j.jhydrol.2006.03.003, 2006.
Wallace, J. and McJannet, D.:
Modelling interception in coastal and montane rainforests in northern Queensland, Australia, J. Hydrol., 348, 480–495, https://doi.org/10.1016/j.jhydrol.2007.10.019, 2008.
Wallace, J., Macfarlane, C., McJannet, D., Ellis, T., Grigg, A., and Van Dijk, A.:
Evaluation of forest interception estimation in the continental scale Australian Water Resources Assessment–Landscape (AWRA-L) model, J. Hydrol., 499, 210–223, https://doi.org/10.1016/j.jhydrol.2013.06.036, 2013.
Wang, D. and Wang, L.:
Rainfall partitioning and its effects on regional water balances: Evidence from the conversion of traditional cropland to apple orchards in a semi-humid region, Hydrol. Process., 34, 4628–4639, https://doi.org/10.1002/hyp.13891, 2020.
Wang-Erlandsson, L., van der Ent, R. J., Gordon, L. J., and Savenije, H. H. G.:
Contrasting roles of interception and transpiration in the hydrological cycle – Part 1: Temporal characteristics over land, Earth Syst. Dynam., 5, 441–469, https://doi.org/10.5194/esd-5-441-2014, 2014.
Waterloo, M., Bruijnzeel, L., Vugts, H., and Rawaqa, T.:
Evaporation from Pinus caribaea plantations on former grassland soils under maritime tropical conditions, Water Resour. Res., 35, 2133–2144, https://doi.org/10.1029/1999WR900006, 1999.
Xiao, Q. and McPherson, E. G.:
Surface water storage capacity of twenty tree species in Davis, California, J. Environ. Qual., 45, 188–198, https://doi.org/10.2134/jeq2015.02.0092, 2016.
Xiao, Q., McPherson, E. G., Ustin, S. L., and Grismer, M. E.:
A new approach to modeling tree rainfall interception, J. Geophysi. Res.-Atmos., 105, 29173–29188, https://doi.org/10.1029/2000JD900343, 2000.
Yan, K., Park, T., Yan, G., Chen, C., Yang, B., Liu, Z., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B.:
Evaluation of MODIS LAI/FPAR product collection 6. Part 1: Consistency and improvements, Remote Sens.-Basel, 8, 359, https://doi.org/10.3390/rs8050359, 2016a.
Yan, K., Park, T., Yan, G., Liu, Z., Yang, B., Chen, C., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B.:
Evaluation of MODIS LAI/FPAR product collection 6. Part 2: Validation and intercomparison, Remote Sens.-Basel, 8, 460, https://doi.org/10.3390/rs8060460, 2016b.
Yan, T., Wang, Z., Liao, C., Xu, W., and Wan, L.:
Effects of the morphological characteristics of plants on rainfall interception and kinetic energy, J. Hydrol., 592, 125807, https://doi.org/10.1016/j.jhydrol.2020.125807, 2021.
Yang, M., Zuo, R., Li, X., and Wang, L.:
Improvement Test for the Canopy Interception Parameterization Scheme in the Community Land Model, SOLA, 15, 166–171, https://doi.org/10.2151/sola.2019-030, 2019.
Zabret, K., Rakovec, J., Mikoš, M., and Šraj, M.:
Influence of raindrop size distribution on throughfall dynamics under pine and birch trees at the rainfall event level, Atmosphere, 8, 240, https://doi.org/10.3390/atmos8120240, 2017.
Zabret, K., Rakovec, J., and Šraj, M.:
Influence of meteorological variables on rainfall partitioning for deciduous and coniferous tree species in urban area, J. Hydrol., 558, 29–41, https://doi.org/10.1016/j.jhydrol.2018.01.025, 2018.
Zeng, N., Shuttleworth, J. W., and Gash, J. H.:
Influence of temporal variability of rainfall on interception loss. Part I. Point analysis, J. Hydrol., 228, 228–241, https://doi.org/10.1016/S0022-1694(00)00140-2, 2000.
Zhang, S.-Y., Li, X.-Y., Jiang, Z.-Y., Li, D.-Q., and Lin, H.:
Modelling of rainfall partitioning by a deciduous shrub using a variable parameters Gash model, Ecohydrology, 11, e2011, https://doi.org/10.1002/eco.2011, 2018.
Zhang, Y., Peña-Arancibia, J. L., McVicar, T. R., Chiew, F. H., Vaze, J., Liu, C., Lu, X., Zheng, H., Wang, Y., and Liu, Y. Y.:
Multi-decadal trends in global terrestrial evapotranspiration and its components, Sci. Rep.-UK, 6, 1–12, https://doi.org/10.1038/srep19124, 2016.
Zhang, Y., Li, X. Y., Li, W., Wu, X. C., Shi, F. Z., Fang, W. W., and Pei, T. T.:
Modeling rainfall interception loss by two xerophytic shrubs in the Loess Plateau, Hydrol. Process., 31, 1926–1937, https://doi.org/10.1002/hyp.11157, 2017.
Zhang, Y., Kong, D., Gan, R., Chiew, F. H. S., McVicar, T. R., Zhang, Q., and Yang, Y.:
Coupled estimation of 500m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017, Remote Sens. Environ. 222, 165–182, https://doi.org/10.1016/j.rse.2018.12.031, 2019.
Zhang, Z. S., Zhao, Y., Li, X. R., Huang, L., and 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.-UK, 6, 1–10, https://doi.org/10.1038/srep26030, 2016.
Zheng, C. and Jia, L.:
Global canopy rainfall interception loss derived from satellite earth observations, Ecohydrology, 13, e2186, https://doi.org/10.1002/eco.2186, 2020.
Zheng, J., Fan, J., Zhang, F., Yan, S., and Xiang, Y.:
Rainfall partitioning into throughfall, stemflow and interception loss by maize canopy on the semi-arid Loess Plateau of China, Agr. Water Manage., 195, 25–36, https://doi.org/10.1016/j.agwat.2017.09.013, 2018..
Short summary
A synthesis of rainfall interception data from past field campaigns is performed, including 166 forests and 17 agricultural plots distributed worldwide. These site data are used to constrain and validate an interception model that considers sub-grid heterogeneity and vegetation dynamics. A global, 40-year (1980–2019) interception dataset is generated at a daily temporal and 0.1° spatial resolution. This dataset will serve as a benchmark for future investigations of the global hydrological cycle.
A synthesis of rainfall interception data from past field campaigns is performed, including 166...
