References

• MGAP. Anuario estadístico agropecuario 2021a. Estadísticas Agropecuarias. Ministerio de Ganadería, Agricultura y Pesca (Uruguay). 2021a [ accedido 2022 July 25]. Disponible en: https://www.gub.uy/ministerio-ganaderia-agricultura-pesca/comunicacion/publicaciones/anuario-estadistico-agropecuario-2021
   Google Scholar
• Vasallo M. Dinámica y competencia intrasectorial en la agricultura uruguaya, los cambios en la última década. Agrociencia Uruguay. 2013 julio/diciembre; 17(2):170–179.
   Google Scholar
• MGAP. Cartografía Nacional Forestal 2021. Dirección General Forestal, División Evaluación e Información. Ministerio de Ganadería, Agricultura y Pesca (Uruguay). 2021b [ accedido 2022 Aug 3]. Disponible en: https://www.gub.uy/ministerio-ganaderia-agricultura-pesca/comunicacion/publicaciones/cartografia-nacional-forestal-2021
   Google Scholar
• Silveira L, y Alonso J. Runoff modifications due to the conversion of natural grasslands to forests in a large basin in Uruguay. Hydrol Process. 2009;23(2):320–329.
   Web of Science ®Google Scholar
• Birkinshaw SJ, Bathurst JC, Robinson M. 45 years of non-stationary hydrology over a forest plantation growth cycle, Coalburn catchment, Northern England. JouR of Hydro. 2014;519 559–573. doi: 10.1016/j.jhydrol.2014.07.050
   Web of Science ®Google Scholar
• Carlyle-Moses DE, y Gash JHC. Rainfall Interception Loss by Forest Canopies. In: Levia D, Carlyle-Moses DE, Tanaka T, editors. Forest hydrology and biogeochemistry: synthesis of past research and future directions. Vol. 216. Dordrecht: Springer; 2011. p. 407–423.
   Google Scholar
• Calder IR. Maidment, DR Hydrologic effects of land-use change. New York: McGraw-Hill Inc; 1992. p. 13–50.
   Google Scholar
• Zhang L, Dawes WR, Walker GR. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Wat Reso Resea. 2001;37(3):701–708. doi: 10.1029/2000WR900325
   Web of Science ®Google Scholar
• de Paula Lima W. Hidrologia florestal aplicada ao manejo de bacias hidrográficas. Piracicaba: USP-ESALQ. 2008;245.
   Google Scholar
• Rutter A, Kershaw K, Robins P, et al. A predictive model of rainfall interception in forests, 1. Derivation of the model from observations in a plantation of Corsican pine. Agri Mete. 1971;9:367–384. doi: 10.1016/0002-1571(71)90034-3
   Google Scholar
• Huber A, Y Iroumé A. Variability of annual rainfall partitioning for different sites and forest covers in Chile. J Hydrol. 2001;248(1–4):78–92.
   Web of Science ®Google Scholar
• Yáñez-Díaz MI, Cantú-Silva I, González-Rodríguez H, et al. Redistribución de la precipitación en tres especies arbustivas nativas y una plantación de eucalipto del noreste de México. Tecnología y ciencias del agua. 2014;5(2):71–84.
   Google Scholar
• Crockford RH, y Richardson DP. Partitioning of rainfall into throughfall, stemflow and interception: effect of forest type, ground cover and climate. Hydrol Process. 2000;14(16‐17):2903–2920.
   Web of Science ®Google Scholar
• Huber A. Cambios en el balance hídrico provocado por la forestación con Pinus radiata D. Don en el secado interior del centro de Chile. Gestión Ambiental. 2003;9:57–66.
   Google Scholar
• Keim RF, Skaugset AE, y Weiler M. Storage of water on vegetation under simulated rainfall of varying intensity. Adv Water Res. 2006;29(7):974–986.
   Web of Science ®Google Scholar
• Momolli DR, Schumacher MV, Viera M, et al. Incident precipitation partitioning: throughfall, stemflow and canopy interception in Eucalyptus dunnii stand. J Agric Sci. 2019;11(5):372.
   Google Scholar
• Magliano PN, Whitworth-Hulse JI, y Baldi G. Interception, throughfall and stemflow partition in drylands: global synthesis and meta-analysis. J Hydrol. 2019;568:638–645.
   Web of Science ®Google Scholar
• Cayuela C, Llorens P, Sánchez-Costa E, et al. Effect of biotic and abiotic factors on inter-and intra-event variability in stemflow rates in oak and pine stands in a Mediterranean mountain area. J Hydrol. 2018;560:396–406.
   Web of Science ®Google Scholar
• Bulcock H, y Jewitt G. Field data collection and analysis of canopy and litter interception in commercial forest plantations in the KwaZulu-Natal Midlands, South Africa. Hydrol Earth Syst Sci. 2012;16(10):3717–3728.
   Web of Science ®Google Scholar
• Tamosiunas M. Complejo Forestal. In: Vassallo M, editor. Dinámica y competencia intrasectorial en el agro: uruguay 2000-2010. Montevideo: Facultad de Agronomía; 2012. p. 105–122.
   Google Scholar
• Resquin F, Duque-Lazo J, Acosta-Muñoz C, et al. Modelling Current and Future Potential Habitats for Plantations of Eucalyptus grandis Hill ex Maiden and E. dunnii Maiden in Uruguay. Forests. 2020:11(9):948. doi: 10.3390/f11090948
   Web of Science ®Google Scholar
• INUMET. Estadísticas climáticas del período 1991-2020. Instituto Uruguayo de Meteorología. 2022. [ accedido 2022 Aug 4]. Disponible en: https://www.inumet.gub.uy/clima/estadisticas-climatologicas/tablas-estadisticas
   Google Scholar
• Dunkerley D. Measuring interception loss and canopy storage in dryland vegetation: a brief review and evaluation of available research strategies. Hydrol Process. 2000;14(4):669–678.
   Web of Science ®Google Scholar
• Silveira L, Alonso J, y Martínez L. Efecto de las plantaciones forestales sobre el recurso agua en el Uruguay. Agrociencia Uruguay. 2006;10(2):75–93.
   Google Scholar
• Vernimmen RRE, Bruijnzeel LA, Romdoni A, et al. Rainfall interception in three contrasting lowland rain forest types in Central Kalimantan, Indonesia. J Hydrol. 2007;340(3–4):217–232.
   Web of Science ®Google Scholar
• Besteiro S. Evaluación de la influencia hidrológica de forestaciones en la llanura pampeana [Tesis Doctoral] Universidad Nacional de La Plata; 2014 . http://sedici.unlp.edu.ar/bitstream/handle/10915/33806/Documento_completo__.pdf?sequence=1
   Google Scholar
• Mateos Rodríguez AB, y Leco F. Distribución espacial de la lluvia sobre el suelo en la dehesa: influencia de la poda del arbolado. Cuaternario y geomorfología: Revista de la Sociedad Española de Geomorfología y Asociación Española para el Estudio del Cuaternario. 2010;24(3–4):41–51.
   Google Scholar
• WMO. Guide to meteorological instruments and methods of observation (WMO-No. 8) Volume I- Measurement of Meteorological Variables. Geneva Switzerland: World Meteorological Organization; 2021.
   Google Scholar
• Ford E, y Deans J. The effects of canopy structure on stemflow, thoughfall and interception loss in a young Sitka Spruce plantation. J Appl Ecol. 1978;15(3):905–917.
   Web of Science ®Google Scholar
• Perazza G, Alonso J, Pais P. Development of new equipment for measurement of rainfall interception in eucalyptus planted forests. HydroSenSoft Symposium; Madrid. 2017. https://www.iahr.org/library/infor?pid=19564
   Google Scholar
• JCGM. Evaluation of measurement data - Guide to the expression of uncertainty in measurement JCGM 100:2008. Joint Committee for Guides in Metrology (JCGM/WG 1). 2008.
   Google Scholar
• Herbst M, Rosier PT, McNeil DD, et al. Seasonal variability of interception evaporation from the canopy of a mixed deciduous forest. Agric Forest Meteorol. 2008;148(11):1655–1667.
   Web of Science ®Google Scholar
• Peláez JJZ. Hidrologia comparativa em bacias hidrográficas com eucalipto e campo. [Tesis doctoral]. Universidad Federal de Santa Maria, Brasil; 2014 [ accedido 2023 Jun 6]. Disponible en: https://repositorio.ufsm.br/handle/1/21387
   Google Scholar
• Benyon RG, Doody TM. Comparison of interception, forest floor evaporation and transpiration in Pinus radiata and Eucalyptus globulus plantations. Hydro Proc. 2015;29(6):1173–1187. doi: 10.1002/hyp.10237
   Web of Science ®Google Scholar
• Kottegoda NT, y Rosso R. Random variables and their properties. Applied statistics for civil and environmental engineers. United Kingdom: Blackwell Publishing. Chichester; 2008. p. 83–164.
   Google Scholar
• Caffo B. Residuals, variation, diagnostics. A companion book for the Coursera Regression Models class. Victoria BC Canada: Lean Publishing; 2015. p. 137.
   Google Scholar
• Wickham, Wickham H, Averick M, et al. Welcome to the tidyverse. J Open Source Software. 2019;4(43):1686.
   Google Scholar
• Kassambara A. ggpubr: ‘ggplot2’ based publication ready plots. Rpackage version 0.4.0. 2020. [cited 2022 July 2]. Available from: https://CRAN.R-project.org/package=ggpubr.
   Google Scholar
• Aphalo PJ. ggpmisc: miscellaneous Extensions to ‘ggplot2.’ R package version 0.5.0. 2022. [cited 2022 June 9]. Available from: https://CRAN.R-project.org/package=ggpmisc.
   Google Scholar
• R Core Team. R: a language and environment for statistical computing. Vienna Austria: R Foundation for Statistical Computing; 2022. [cited 2022 June 20]. Available from: https://www.R-project.org/
   Google Scholar
• Ferreto DOC, Reichert JM, Cavalcante RBL, et al. Rainfall partitioning in young clonal plantations Eucalyptus species in a subtropical environment, and implications for water and forest management. Int Soil Water Conserv Res. 2021;9(3):474–484.
   Web of Science ®Google Scholar
• Návar J. Modeling rainfall interception loss components of forests. J Hydrol. 2019. DOI:10.1016/j.jhydrol.2019.124449
   Google Scholar
• Coops NC, Tompalski P, Goodbody TR, et al. Modelling lidar-derived estimates of forest attributes over space and time: a review of approaches and future trends. Remote Sens Environ. 2021;260:112477.
   Web of Science ®Google Scholar