Contribución de las componentes de la evapotranspiración a la interacción suelo-atmósfera en Sudamérica
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https://doi.org/10.24215/1850468Xe033Palabras clave:
hotspots de interacción suelo-atmósfera, transpiración, inLand, LPJmL4, evapotranspiraciónResumen
La evapotranspiración es una variable clave del ciclo hidrológico ya que modifica aspectos físicos del sistema climático, cómo la humedad del suelo y de la atmósfera, la cantidad de agua en ríos o acuíferos, y la temperatura del suelo y del aire en superficie. Una correcta representación de la evapotranspiración es de suma importancia para el estudio del sistema climático, por ejemplo para la identificación de eventos extremos como inundaciones o sequías, u olas de calor o de frío. En particular, es de relevancia distinguir regiones de interacción suelo-atmósfera, es decir dónde variaciones en el suelo modifican la atmósfera. En este trabajo investigamos la representación de la evapotranspiración en Sudamérica según cinco estimaciones diferentes: cuatro simulaciones provenientes de dos modelos globales de vegetación dinámica, y un producto satelital, durante el período 1981-2010. Principalmente, estudiamos la partición de la evapotranspiración en sus componentes: transpiración, evaporación desde la vegetación, y desde el suelo; y cómo éstas contribuyen a la interacción suelo-atmósfera en diciembre-enero-febrero. Hallamos que, aunque las estimaciones de la evapotranspiración anual media presentan un patrón espacial similar, no ocurre lo mismo con la partición en componentes. Encontramos regiones de interacción suelo-atmósfera que son reconocidas habitualmente en la literatura: el centro de Argentina y el noreste de Brasil, que son, además, regiones de transición entre climas secos y húmedos. Nuestro resultado principal es que la transpiración es la componente de la evapotranspiración que más contribuye a la interacción suelo-atmósfera.
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Baker, J. C., and Coauthors, 2021: An assessment of land–atmosphere interactions over South America using satellites, reanalysis, and two global climate models. Journal of Hydrometeorology, 22, 905–922, https://doi.org/10.1175/JHM-D-20-0132.1
Baldocchi, D., and Coauthors, 2001: FLUXNET: A new tool to study the temporal and spatial variability of ecosystem–scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society, 82, 2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2
Beck, H. E., A. I. van Dijk, P. R. Larraondo, T. R. McVicar, M. Pan, E. Dutra, and D. G. Miralles, 2022: MSWX: Global 3-hourly 0.1° bias-corrected meteorological data including near-real-time updates and forecast ensembles. Bulletin of the American Meteorological Society, 103, https://doi.org/10.1175/BAMS-D-21-0145.1
Berg, A., and J. Sheffield, 2019: Evapotranspiration partitioning in CMIP5 models: Uncertainties and future projections. Journal of Climate, 32, 2653–2671, https://doi.org/10.1175/JCLI-D-18-0583.1
Bonan, G. B., S. Levis, L. Kergoat, and K. W. Oleson, 2002: Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models. Global Biogeochemical Cycles, 16, https://doi.org/10.1029/2000GB001360
Carlyle-Moses, D. E., 2004: Throughfall, stemflow, and canopy interception loss fluxes in a semi-arid Sierra Madre Oriental Matorral Community. Journal of Arid Environments, 58, 181–202, https://doi.org/10.1016/S0140-1963(03)00125-3
Chen, S., W. Wei, B. Tong, and L. Chen, 2023: Effects of soil moisture and vapor pressure deficit on canopy transpiration for two coniferous forests in the Loess Plateau of China. Agricultural and Forest Meteorology, 339, 109581, https://doi.org/10.1016/j.agrformet.2023.109581
Christoffersen, B. O., and Coauthors, 2014: Mechanisms of water supply and vegetation demand govern the seasonality and magnitude of evapotranspiration in Amazonia and Cerrado. Agricultural and Forest Meteorology, 191, 33–50, https://doi.org/10.1016/j.agrformet.2014.02.008
Coronato, T., and Coauthors, 2020: The impact of soil moisture–atmosphere coupling on daily maximum surface temperatures in southeastern South America. Climate Dynamics, 55, 2543–2556, https://doi.org/10.1007/s00382-020-05399-9
David, J. S., F. Valente, and J. H. Gash, 2005: Evaporation of intercepted rainfall. Encyclopedia of Hydrological Sciences, https://doi.org/10.1002/0470848944.hsa046
Dunkerley, D., 2000: Measuring interception loss and canopy storage in dryland vegetation: A brief review and evaluation of available research strategies. Hydrological Processes, 14, 669–678, https://doi.org/10.1002/(SICI)1099-1085(200003)14:4<669::AID-HYP965>3.0.CO;2-I
Federer, C. A., 1982: Transpirational supply and demand: Plant, soil, and atmospheric effects evaluated by simulation. Water Resources Research, 18, 355–362, https://doi.org/10.1029/WR018i002p00355
Gash, J. H., 1979: An analytical model of rainfall interception by forests. Quarterly Journal of the Royal Meteorological Society, 105, 43–55, https://doi.org/10.1002/qj.49710544304
Goergen, G., R. H. Valdés, G. A. Degrazia, R. A. Gotuzzo, D. L. Herdies, L. G. de Gonçalves, and D. R. Roberti, 2020: Energy and CO2 fluxes over native fields of southern Brazil through multi-objective calibration of Inland Model. Geosciences, 10, 479, https://doi.org/10.3390/geosciences10120479
Goymer, P., 2017: Spotlight on South America. Nature Ecology & Evolution, 1, https://doi.org/10.1038/s41559-017-0129
Hartley, A. J., N. MacBean, G. Georgievski, and S. Bontemps, 2017: Uncertainty in plant functional type distributions and its impact on land surface models. Remote Sensing of Environment, 203, 71–89, https://doi.org/10.1016/j.rse.2017.07.037
Jahromi, M. N., D. Miralles, A. Koppa, D. Rains, S. Zand-Parsa, H. Mosaffa, and S. Jamshidi, 2022: Ten Years of gleam: A review of scientific advances and applications. Computational Intelligence for Water and Environmental Sciences, 525–540, https://doi.org/10.1007/978-981-19-2519-1_25
Jung, M., and Coauthors, 2020: Scaling carbon fluxes from eddy covariance sites to Globe: Synthesis and evaluation of the FLUXCOM approach. Biogeosciences, 17, 1343–1365, https://doi.org/10.5194/bg-17-1343-2020
Koster, R. D., and Coauthors, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138–1140, https://www.science.org/doi/10.1126/science.1100217
Kumar, S., T. Holmes, D. Mocko, S. Wang, and C. Peters-Lidard, 2018: Attribution of flux partitioning variations between land surface models over the continental U.S. Remote Sensing, 10, 751, https://doi.org/10.3390/rs10050751
Lawrence, D. M., P. E. Thornton, K. W. Oleson, and G. B. Bonan, 2007: The partitioning of evapotranspiration into transpiration, soil evaporation, and canopy evaporation in a GCM: Impacts on land–atmosphere interaction. Journal of Hydrometeorology, 8, 862–880, https://doi.org/10.1175/JHM596.1
Li, W., H.-J. Hendricks Franssen, P. Brunner, Z. Li, Z. Wang, Y. Wang, and W. Wang, 2022: The role of soil texture on diurnal and seasonal cycles of potential evaporation over saturated bare soils – lysimeter studies. Journal of Hydrology, 613, 128194, https://doi.org/10.1016/j.jhydrol.2022.128194
Liu, Y. Y., A. I. van Dijk, M. F. McCabe, J. P. Evans, and R. A. de Jeu, 2013: Global vegetation biomass change (1988-2008) and attribution to environmental and human drivers. Global Ecology and Biogeography, 22, 692–705, https://doi.org/10.1111/geb.12024
Liu, Y., Q. Yue, Q. Wang, J. Yu, Y. Zheng, X. Yao, and S. Xu, 2021: A framework for actual evapotranspiration assessment and projection based on meteorological, vegetation and hydrological remote sensing products. Remote Sensing, 13, 3643, https://doi.org/10.3390/rs13183643
Magliano, P. N., D. D. Breshears, R. J. Fernández, and E. G. Jobbágy, 2015: Rainfall intensity switches ecohydrological runoff/runon redistribution patterns in dryland vegetation patches. Ecological Applications, 25, 2094–2100, https://doi.org/10.1890/15-0550.1
Martens, B., and Coauthors, 2017: Gleam v3: Satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017
Martens, B., W. Waegeman, W. A. Dorigo, N. E. Verhoest, and D. G. Miralles, 2018: Terrestrial evaporation response to modes of climate variability. npj Climate and Atmospheric Science, 1, https://doi.org/10.1038/s41612-018-0053-5
Melo, D. C., and Coauthors, 2021: Are remote sensing evapotranspiration models reliable across South American ecoregions? Water Resources Research, 57, https://doi.org/10.1029/2020WR028752
Menéndez, C. G., and Coauthors, 2019: Temperature variability and soil–atmosphere interaction in South America simulated by two regional climate models. Climate Dynamics, 53, 2919–2930, https://doi.org/10.1007/s00382-019-04668-6
Miralles, D. G., T. R. Holmes, R. A. De Jeu, J. H. Gash, A. G. Meesters, and A. J. Dolman, 2011: Global land-surface evaporation estimated from satellite-based observations. Hydrology and Earth System Sciences, 15, 453–469, https://doi.org/10.5194/hess-15-453-2011
Miralles, D., and Coauthors, 2016: The WACMOS-ET project – part 2: Evaluation of global terrestrial evaporation data sets. Hydrology and Earth System Sciences, 20, 823–842, https://doi.org/10.5194/hess-20-823-2016
Miralles, D. G., P. Gentine, S. I. Seneviratne, and A. J. Teuling, 2019: Land–atmospheric feedbacks during droughts and Heatwaves: State of the science and current challenges. Annals of the New York Academy of Sciences, 1436, 19–35, https://doi.org/10.1111/nyas.13912
Moreira, A. A., A. L. Ruhoff, D. R. Roberti, V. de Souza, H. R. da Rocha, and R. C. Paiva, 2019: Assessment of terrestrial water balance using remote sensing data in South America. Journal of Hydrology, 575, 131–147, https://doi.org/10.1016/j.jhydrol.2019.05.021
Mueller, B., and Coauthors, 2011: Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations. Geophysical Research Letters, 38, https://doi.org/10.1029/2010gl046230
Newman, B. D., and Coauthors, 2006: Ecohydrology of water‐Limited Environments: A scientific vision. Water Resources Research, 42, https://doi.org/10.1029/2005wr004141
Notaro, M., 2008: Statistical identification of global hot spots in soil moisture feedbacks among IPCC AR4 models. Journal of Geophysical Research: Atmospheres, 113, https://doi.org/10.1029/2007jd009199
Oki, T., and S. Kanae, 2006: Global hydrological cycles and world water resources. Science, 313, 1068–1072, https://doi.org/10.1126/science.1128845
Pitman, A. J., 2003: The evolution of, and revolution in, land surface schemes designed for climate models. International Journal of Climatology, 23, 479–510, https://doi.org/10.1002/joc.893
Priestley, C. H., and R. J. Taylor, 1972: On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100, 81–92, https://doi.org/10.1175/1520-0493(1972)100<0081:otaosh>2.3.co;2
Qi, Y., H. Chen, and S. Zhu, 2023: Influence of land–atmosphere coupling on low temperature extremes over Southern Eurasia. Journal of Geophysical Research: Atmospheres, 128, https://doi.org/10.1029/2022jd037252
Rezende, L. F., and Coauthors, 2022: Impacts of land use change and atmospheric CO2 on gross primary productivity (GPP), evaporation, and climate in southern Amazon. Journal of Geophysical Research: Atmospheres, 127, https://doi.org/10.1029/2021jd034608
Rosales D. A., 2023: Tesis de licenciatura “Evapotranspiración modelada en Sudamérica: influencia del cambio climático y del uso del suelo”. Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires. https://hdl.handle.net/20.500.12110/seminario_nATM000005_Rosales
Ruscica, R. C., A. A. Sörensson, and C. G. Menéndez, 2015: Pathways between soil moisture and precipitation in southeastern South America. Atmospheric Science Letters, 16, 267–272, https://doi.org/10.1002/asl2.552
Ruscica, R. C., C. G. Menéndez, and A. A. Sörensson, 2016: Land surface-atmosphere interaction in future South American climate using a multi-model ensemble. Atmospheric Science Letters, 17, 141–147, https://doi.org/10.1002/asl.635
Ruscica, R. C., and Coauthors, 2022: Evapotranspiration trends and variability in southeastern South America: The roles of land‐cover change and precipitation variability. International Journal of Climatology, 42, 2019–2038, https://doi.org/10.1002/joc.7350
Sakschewski, B., and Coauthors, 2021: Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests. Biogeosciences, 18, 4091–4116, https://doi.org/10.5194/bg-18-4091-2021
Schaphoff, S., and Coauthors, 2018: LPJML4 – a dynamic global vegetation model with managed land – part 1: Model description. Geoscientific Model Development, 11, 1343–1375, https://doi.org/10.5194/gmd-11-1343-2018
Schlesinger, W. H., and S. Jasechko, 2014: Transpiration in the Global Water Cycle. Agricultural and Forest Meteorology, 189–190, 115–117, https://doi.org/10.1016/j.agrformet.2014.01.011
Seneviratne, S. I., T. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowsky, and A. J. Teuling, 2010: Investigating soil moisture–climate interactions in a changing climate: A Review. Earth-Science Reviews, 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004
Sörensson, A. A., and C. G. Menéndez, 2011: Summer soil–precipitation coupling in South America. Tellus A: Dynamic Meteorology and Oceanography, 63, 56, https://doi.org/10.1111/j.1600-0870.2010.00468.x
Sörensson, A. A., and R. C. Ruscica, 2018: Intercomparison and uncertainty assessment of nine evapotranspiration estimates over South America. Water Resources Research, 54, 2891–2908, https://doi.org/10.1002/2017wr021682
Spennemann, P. C., M. Salvia, R. C. Ruscica, A. A. Sörensson, F. Grings, and H. Karszenbaum, 2018: Land-atmosphere interaction patterns in southeastern South America using satellite products and Climate Models. International Journal of Applied Earth Observation and Geoinformation, 64, 96–103, https://doi.org/10.1016/j.jag.2017.08.016
Trenberth, K. E., J. T. Fasullo, and J. Kiehl, 2009: Earth’s Global Energy Budget. Bulletin of the American Meteorological Society, 90, 311–324, https://doi.org/10.1175/2008bams2634.1
Vilà‐Guerau de Arellano, J., and Coauthors, 2023: Advancing understanding of land–atmosphere interactions by breaking discipline and scale barriers. Annals of the New York Academy of Sciences, 1522, 74–97, https://doi.org/10.1111/nyas.14956
Wang, L., S. P. Good, and K. K. Caylor, 2014: Global synthesis of vegetation control on evapotranspiration partitioning. Geophysical Research Letters, 41, 6753–6757, https://doi.org/10.1002/2014gl061439
Wang, Z., C. Zhan, L. Ning, and H. Guo, 2021: Evaluation of global terrestrial evapotranspiration in CMIP6 models. Theoretical and Applied Climatology, 143, 521–531, https://doi.org/10.1007/s00704-020-03437-4
Wei, Z., K. Yoshimura, L. Wang, D. G. Miralles, S. Jasechko, and X. Lee, 2017: Revisiting the contribution of transpiration to global terrestrial evapotranspiration. Geophysical Research Letters, 44, 2792–2801, https://doi.org/10.1002/2016gl072235
Wild, M., D. Folini, C. Schär, N. Loeb, E. G. Dutton, and G. König-Langlo, 2012: The Global Energy Balance from a surface perspective. Climate Dynamics, 40, 3107–3134, https://doi.org/10.1007/s00382-012-1569-8
Wu, L., and J. Zhang, 2013: Role of land-atmosphere coupling in summer droughts and floods over eastern China for the 1998 and 1999 cases. Chinese Science Bulletin, 58, 3978–3985, https://doi.org/10.1007/s11434-013-5855-6
Zeng, X., M. Barlage, C. Castro, and K. Fling, 2010: Comparison of land–precipitation coupling strength using observations and models. Journal of Hydrometeorology, 11, 979–994, https://doi.org/10.1175/2010jhm1226.1
Zhang, Y., and Coauthors, 2016: Multi-decadal trends in global terrestrial evapotranspiration and its components. Scientific Reports, 6, https://doi.org/10.1038/srep19124
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Derechos de autor 2024 Daira A. Rosales, Romina C. Ruscica, Anna A. Sörensson
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