Articles | Volume 18, issue 13
https://doi.org/10.5194/bg-18-4091-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-4091-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests
Boris Sakschewski
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
Werner von Bloh
Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
Markus Drüke
Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
Humboldt Universität zu Berlin, Unter den Linden 6, 10099 Berlin,
Germany
Anna Amelia Sörensson
Centro de Investigaciones del Mar y la
Atmósfera (CIMA), Universidad de Buenos Aires – Consejo Nacional de Investigaciones
Científicas y Técnicas (UBA-CONICET), Buenos Aires, Argentina
Institut Franco-Argentin d'Études sur le Climat et ses Impacts,
Unité Mixte Internationale (UMI-IFAECI CNRS-CONICET-UBA), Buenos Aires, Argentina
Romina Ruscica
Centro de Investigaciones del Mar y la
Atmósfera (CIMA), Universidad de Buenos Aires – Consejo Nacional de Investigaciones
Científicas y Técnicas (UBA-CONICET), Buenos Aires, Argentina
Institut Franco-Argentin d'Études sur le Climat et ses Impacts,
Unité Mixte Internationale (UMI-IFAECI CNRS-CONICET-UBA), Buenos Aires, Argentina
Fanny Langerwisch
Department of Ecology and
Environmental Sciences, Palacký University Olomouc, 78371 Olomouc, Czech Republic
Maik Billing
Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
Sarah Bereswill
University of Potsdam, 14469 Potsdam, Germany
Marina Hirota
Federal University of Santa Catarina (UFSC), Campus Universitário Reitor João David Ferreira Lima, Trindade, CEP: 88040-900, Florianópolis, Santa Catarina, Brazil
University of Campinas (UNICAMP), Cidade Universitária “Zeferino
Vaz”, CEP 13083-970, Campinas, Sao Paulo, Brazil
Rafael Silva Oliveira
University of Campinas (UNICAMP), Cidade Universitária “Zeferino
Vaz”, CEP 13083-970, Campinas, Sao Paulo, Brazil
Jens Heinke
Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
Kirsten Thonicke
Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
Related authors
Jéssica Schüler, Sarah Bereswill, Werner von Bloh, Maik Billing, Boris Sakschewski, Luke Oberhagemann, Kirsten Thonicke, and Mercedes M. C. Bustamante
EGUsphere, https://doi.org/10.5194/egusphere-2025-2225, https://doi.org/10.5194/egusphere-2025-2225, 2025
Short summary
Short summary
We introduced a new plant type into a global vegetation model to better represent the ecology of the Cerrado, South America's second largest biome. This improved the model’s ability to simulate vegetation structure, root systems, and fire dynamics, aligning more closely with observations. Our results enhance understanding of tropical savannas and provide a stronger basis for studying their responses to fire and climate change at regional and global scales.
Rodrigo San Martin, Catherine Ottlé, Anna Sorenssön, Pradeebane Vattinada Ayar, Florent Mouillot, and Marielle Malfante
EGUsphere, https://doi.org/10.5194/egusphere-2025-3484, https://doi.org/10.5194/egusphere-2025-3484, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We studied wildfires in the Gran Chaco, one of the world's largest dry forests, to understand why some fires grow larger than others. By analyzing fire size and weather conditions during burning, we found that strong winds and low humidity were key drivers of fire expansion. This work helps improve our understanding of extreme fire events and supports better fire risk management in dry ecosystems.
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
Jéssica Schüler, Sarah Bereswill, Werner von Bloh, Maik Billing, Boris Sakschewski, Luke Oberhagemann, Kirsten Thonicke, and Mercedes M. C. Bustamante
EGUsphere, https://doi.org/10.5194/egusphere-2025-2225, https://doi.org/10.5194/egusphere-2025-2225, 2025
Short summary
Short summary
We introduced a new plant type into a global vegetation model to better represent the ecology of the Cerrado, South America's second largest biome. This improved the model’s ability to simulate vegetation structure, root systems, and fire dynamics, aligning more closely with observations. Our results enhance understanding of tropical savannas and provide a stronger basis for studying their responses to fire and climate change at regional and global scales.
Marie Brunel, Stephen Wirth, Markus Drüke, Kirsten Thonicke, Henrique Barbosa, Jens Heinke, and Susanne Rolinski
EGUsphere, https://doi.org/10.5194/egusphere-2025-922, https://doi.org/10.5194/egusphere-2025-922, 2025
Short summary
Short summary
Farmers often use fire to clear dead pasture biomass, impacting vegetation and soil nutrients. This study integrates fire management into a DGVM to assess its effects, focusing on Brazil. The results show that combining grazing and fire management reduces vegetation carbon and soil nitrogen over time. The research highlights the need to include these practices in models to improve pasture management assessments and calls for better data on fire usage and its long-term effects.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
Short summary
Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Detlef van Vuuren, Brian O'Neill, Claudia Tebaldi, Louise Chini, Pierre Friedlingstein, Tomoko Hasegawa, Keywan Riahi, Benjamin Sanderson, Bala Govindasamy, Nico Bauer, Veronika Eyring, Cheikh Fall, Katja Frieler, Matthew Gidden, Laila Gohar, Andrew Jones, Andrew King, Reto Knutti, Elmar Kriegler, Peter Lawrence, Chris Lennard, Jason Lowe, Camila Mathison, Shahbaz Mehmood, Luciana Prado, Qiang Zhang, Steven Rose, Alexander Ruane, Carl-Friederich Schleussner, Roland Seferian, Jana Sillmann, Chris Smith, Anna Sörensson, Swapna Panickal, Kaoru Tachiiri, Naomi Vaughan, Saritha Vishwanathan, Tokuta Yokohata, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3765, https://doi.org/10.5194/egusphere-2024-3765, 2025
Short summary
Short summary
We propose a set of six plausible 21st century emission scenarios, and their multi-century extensions, that will be used by the international community of climate modeling centers to produce the next generation of climate projections. These projections will support climate, impact and mitigation researchers, provide information to practitioners to address future risks from climate change, and contribute to policymakers’ considerations of the trade-offs among various levels of mitigation.
Matthew Forrest, Jessica Hetzer, Maik Billing, Simon P. K. Bowring, Eric Kosczor, Luke Oberhagemann, Oliver Perkins, Dan Warren, Fátima Arrogante-Funes, Kirsten Thonicke, and Thomas Hickler
Biogeosciences, 21, 5539–5560, https://doi.org/10.5194/bg-21-5539-2024, https://doi.org/10.5194/bg-21-5539-2024, 2024
Short summary
Short summary
Climate change is causing an increase in extreme wildfires in Europe, but drivers of fire are not well understood, especially across different land cover types. We used statistical models with satellite data, climate data, and socioeconomic data to determine what affects burning in cropland and non-cropland areas of Europe. We found different drivers of burning in cropland burning vs. non-cropland to the point that some variables, e.g. population density, had the complete opposite effects.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Jamir Priesner, Boris Sakschewski, Maik Billing, Werner von Bloh, Sebastian Fiedler, Sarah Bereswill, Kirsten Thonicke, and Britta Tietjen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3066, https://doi.org/10.5194/egusphere-2024-3066, 2024
Short summary
Short summary
Our simulations suggest that increased drought frequencies lead to a drastic reduction in biomass in pine monoculture and mixed forest. Mixed forest eventually recovered, as long as drought frequencies was not too high. The higher resilience of mixed forests was due to higher adaptive capacity. After adaptation mixed forests were mainly composed of smaller, broad-leaved trees with higher wood density and slower growth.This would have strong implications for forestry and other ecosystem services.
Markus Drüke, Wolfgang Lucht, Werner von Bloh, Stefan Petri, Boris Sakschewski, Arne Tobian, Sina Loriani, Sibyll Schaphoff, Georg Feulner, and Kirsten Thonicke
Earth Syst. Dynam., 15, 467–483, https://doi.org/10.5194/esd-15-467-2024, https://doi.org/10.5194/esd-15-467-2024, 2024
Short summary
Short summary
The planetary boundary framework characterizes major risks of destabilization of the Earth system. We use the comprehensive Earth system model POEM to study the impact of the interacting boundaries for climate change and land system change. Our study shows the importance of long-term effects on carbon dynamics and climate, as well as the need to investigate both boundaries simultaneously and to generally keep both boundaries within acceptable ranges to avoid a catastrophic scenario for humanity.
Stephen Björn Wirth, Arne Poyda, Friedhelm Taube, Britta Tietjen, Christoph Müller, Kirsten Thonicke, Anja Linstädter, Kai Behn, Sibyll Schaphoff, Werner von Bloh, and Susanne Rolinski
Biogeosciences, 21, 381–410, https://doi.org/10.5194/bg-21-381-2024, https://doi.org/10.5194/bg-21-381-2024, 2024
Short summary
Short summary
In dynamic global vegetation models (DGVMs), the role of functional diversity in forage supply and soil organic carbon storage of grasslands is not explicitly taken into account. We introduced functional diversity into the Lund Potsdam Jena managed Land (LPJmL) DGVM using CSR theory. The new model reproduced well-known trade-offs between plant traits and can be used to quantify the role of functional diversity in climate change mitigation using different functional diversity scenarios.
Anthony Schrapffer, Jan Polcher, Anna Sörensson, and Lluís Fita
Geosci. Model Dev., 16, 5755–5782, https://doi.org/10.5194/gmd-16-5755-2023, https://doi.org/10.5194/gmd-16-5755-2023, 2023
Short summary
Short summary
The present paper introduces a floodplain scheme for a high-resolution land surface model river routing. It was developed and evaluated over one of the world’s largest floodplains: the Pantanal in South America. This shows the impact of tropical floodplains on land surface conditions (soil moisture, temperature) and on land–atmosphere fluxes and highlights the potential impact of floodplains on land–atmosphere interactions and the importance of integrating this module in coupled simulations.
Jenny Niebsch, Werner von Bloh, Kirsten Thonicke, and Ronny Ramlau
Geosci. Model Dev., 16, 17–33, https://doi.org/10.5194/gmd-16-17-2023, https://doi.org/10.5194/gmd-16-17-2023, 2023
Short summary
Short summary
The impacts of climate change require strategies for climate adaptation. Dynamic global vegetation models (DGVMs) are used to study the effects of multiple processes in the biosphere under climate change. There is a demand for a better computational performance of the models. In this paper, the photosynthesis model in the Lund–Potsdam–Jena managed Land DGVM (4.0.002) was examined. We found a better numerical solution of a nonlinear equation. A significant run time reduction was possible.
Phillip Papastefanou, Christian S. Zang, Zlatan Angelov, Aline Anderson de Castro, Juan Carlos Jimenez, Luiz Felipe Campos De Rezende, Romina C. Ruscica, Boris Sakschewski, Anna A. Sörensson, Kirsten Thonicke, Carolina Vera, Nicolas Viovy, Celso Von Randow, and Anja Rammig
Biogeosciences, 19, 3843–3861, https://doi.org/10.5194/bg-19-3843-2022, https://doi.org/10.5194/bg-19-3843-2022, 2022
Short summary
Short summary
The Amazon rainforest has been hit by multiple severe drought events. In this study, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon. Using nine different precipitation datasets and three drought indicators we find large differences in drought stress across the Amazon region. We conclude that future studies should use multiple rainfall datasets and drought indicators when estimating the impact of drought stress in the Amazon region.
Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, https://doi.org/10.5194/gmd-14-4117-2021, 2021
Short summary
Short summary
In this study, we couple the well-established and comprehensively validated state-of-the-art dynamic LPJmL5 global vegetation model to the CM2Mc coupled climate model (CM2Mc-LPJmL v.1.0). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. The new climate model is able to capture important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the the impacts of managed land (crop growth and irrigation).
Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos P. M. Aidar, Scott Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson-Teixeira, Luiza Maria Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, Bethany Blakely, Johnny Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, Cesar Cisneros, Kenneth Clark, Edoardo Cremonese, Hongzhong Dang, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frederic C. Do, Michal Dohnal, Jean-Christophe Domec, Sebinasi Dzikiti, Colin Edgar, Rebekka Eichstaedt, Tarek S. El-Madany, Jan Elbers, Cleiton B. Eller, Eugénie S. Euskirchen, Brent Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar Garcia-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, John Grace, André Granier, Anne Griebel, Yan Guangyu, Mark B. Gush, Paul J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernandez-Santana, Valentine Herrmann, Teemu Hölttä, Friso Holwerda, James Irvine, Supat Isarangkool Na Ayutthaya, Paul G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean-Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, Anders Lindroth, Pilar Llorens, Álvaro López-Bernal, Michael M. Loranty, Dietmar Lüttschwager, Cate Macinnis-Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley Matheny, Nate McDowell, Sean McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, Richard Norby, Kimberly Novick, Walter Oberhuber, Nikolaus Obojes, A. Christopher Oishi, Rafael S. Oliveira, Ram Oren, Jean-Marc Ourcival, Teemu Paljakka, Oscar Perez-Priego, Pablo L. Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine Rascher, George Robinson, Humberto Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, Alexandr Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor-ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan Wullschleger, Koong Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, and Jordi Martínez-Vilalta
Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, https://doi.org/10.5194/essd-13-2607-2021, 2021
Short summary
Short summary
Transpiration is a key component of global water balance, but it is poorly constrained from available observations. We present SAPFLUXNET, the first global database of tree-level transpiration from sap flow measurements, containing 202 datasets and covering a wide range of ecological conditions. SAPFLUXNET and its accompanying R software package
sapfluxnetrwill facilitate new data syntheses on the ecological factors driving water use and drought responses of trees and forests.
Yvonne Jans, Werner von Bloh, Sibyll Schaphoff, and Christoph Müller
Hydrol. Earth Syst. Sci., 25, 2027–2044, https://doi.org/10.5194/hess-25-2027-2021, https://doi.org/10.5194/hess-25-2027-2021, 2021
Short summary
Short summary
Growth of and irrigation water demand on cotton may be challenged by future climate change. To analyze the global cotton production and irrigation water consumption under spatially varying present and future climatic conditions, we use the global terrestrial biosphere model LPJmL. Our simulation results suggest that the beneficial effects of elevated [CO2] on cotton yields overcompensate yield losses from direct climate change impacts, i.e., without the beneficial effect of [CO2] fertilization.
L. Cappelletti, A. Sörensson, R. Ruscica, M. M. Salvia, E. Jobbágy, S. Kuppel, and L. Fita
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 279–283, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-279-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-279-2020, 2020
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferrar, R., Xi, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present), J. Hydrometeor, 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003.
Allen, C. D., Breshears, D. D., and McDowell, N. G.: On underestimation of
global vulnerability to tree mortality and forest die-off from hotter
drought in the Anthropocene, Ecosphere, 6, 1–55,
https://doi.org/10.1890/ES15-00203.1, 2015.
Aragão, L. E. O. C., Malhi, Y., Roman-Cuesta, R. M., Saatchi, S.,
Anderson, L. O., and Shimabukuro, Y. E.: Spatial patterns and fire response
of recent Amazonian droughts, Geophys. Res. Lett., 34, L07701,
https://doi.org/10.1029/2006GL028946, 2007.
Arnold, J. G., Williams, J. R., Nicks, A. D., and Sammons, N. B.: SWRRB: A Basin Scale Simulation Model for Soil and Water Resources Management,
J. Environ. Qual., 20, 309, https://doi.org/10.2134/jeq1991.00472425002000010050x, 1990.
Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O.
L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry,
N. J., Boeckx, P., de Jong, B. H. J., Devries, B., Girardin, C. A. J.,
Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y.,
Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C.
M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini,
R., Verbeeck, H., Wijaya, A., and Willcock, S.: An integrated pan-tropical
biomass map using multiple reference datasets, Global Change Biol., 22,
1406–1420, https://doi.org/10.1111/gcb.13139, 2016.
Baker, I. T., Prihodko, L., Denning, A. S., Goulden, M., Miller, S., and Da Rocha, H. R.: Seasonal drought stress in the amazon: Reconciling models and observations, J. Geophys. Res.-Biogeo., 114, 1–10,
https://doi.org/10.1029/2007JG000644, 2008.
Balsamo, G., Viterbo, P., Beijaars, A., van den Hurk, B., Hirschi, M.,
Betts, A. K., and Scipal, K.: A revised hydrology for the ECMWF model:
Verification from field site to terrestrial water storage and impact in the
integrated forecast system, J. Hydrometeorol., 10, 623–643,
https://doi.org/10.1175/2008JHM1068.1, 2009.
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., and Vitart, F.: ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, 2015.
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G., Martens, B., and de Roo, A.: MSWEP: 3-hourly 0.25∘ global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci., 21, 589–615, https://doi.org/10.5194/hess-21-589-2017, 2017.
Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K., Schneider, U., and Ziese, M.: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present, Earth Syst. Sci. Data, 5, 71–99, https://doi.org/10.5194/essd-5-71-2013, 2013.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Bonal, D., Bosc, A., Ponton, S., Goret, J., Burban, B., Gross, P.,
Bonnefonds, J. M., Elbers, J. A., Longdoz, B., Epron, D., Guehl, J., and
Granier, A.: Impact of severe dry season on net ecosystem exchange in the
Neotropical rainforest of French Guiana, Global Change Biol., 14,
1917–1933, https://doi.org/10.1111/j.1365-2486.2008.01610.x, 2008.
Brum, M., Vadeboncoeur, M. A., Ivanov, V., Asbjornsen, H., Saleska, S.,
Alves, L. F., Penha, D., Dias, J. D., Aragão, L. E. O. C., Barros, F.,
Bittencourt, P., Pereira, L., and Oliveira, R. S.: Hydrological niche
segregation defines forest structure and drought tolerance strategies in a
seasonal Amazon forest, J. Ecol., 107, 318–333,
https://doi.org/10.1111/1365-2745.13022, 2019.
Brunner, I., Herzog, C., Dawes, M. A., Arend, M., and Sperisen, C.: How tree
roots respond to drought, Front. Plant Sci., 6, 547,
https://doi.org/10.3389/fpls.2015.00547, 2015.
Canadell, J., Jackson, R. B., Ehleringer, J. R., Mooney, H. A., Sala, O. E.,
and Schulze, E.-D.: Max rooting depth of vegetation types at the global
scale, Oecologica, 108, 583–595, https://doi.org/10.1007/s10705-016-9812-z, 1996.
Carvalhais, N., Forkel, M., Khomik, M., Bellarby, J., Jung, M., Migliavacca,
M., Saatchi, S., Santoro, M., Thurner, M., and Weber, U.: Global covariation
of carbon turnover times with climate in terrestrial ecosystems, Nature,
514, 213–217, 2014.
Ciemer, C., Boers, N., Hirota, M., Kurths, J., Müller-Hansen, F.,
Oliveira, R. S., and Winkelmann, R.: Higher resilience to climatic
disturbances in tropical vegetation exposed to more variable rainfall, Nat.
Geosci., 12, 174–179, https://doi.org/10.1038/s41561-019-0312-z, 2019.
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T.: A statistical
exploration of the relationships of soil moisture characteristics to the
physical properties of soils, Water Resour. Res., 20, 682–690, 1984.
Costa, M. H., Biajoli, M. C., Sanches, L., Malhado, A. C. M., Hutyra, L. R.,
Da Rocha, H. R., Aguiar, R. G., and De Araújo, A. C.: Atmospheric versus
vegetation controls of Amazonian tropical rain forest evapotranspiration:
Are the wet and seasonally dry rain forests any different?, J. Geophys. Res.-Biogeo., 115, G04021, https://doi.org/10.1029/2009JG001179, 2010.
da Rocha, H. R., Goulden, M. L., Miller, S. D., Menton, M., Pinto, L. D. V. O., Freitas, H. C. De, and Figueira Silva, E. M. A.: Seasonality of water and heat fluxes over a tropical forest in eastern Amazonia, Ecol. Appl., 14, 22–32, https://doi.org/10.1890/02-6001, 2004.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., Mcnally, A. P., Monge-Sanz, B. M.,
Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C.,
Thépaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: Configuration
and performance of the data assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T., and Hanasaki, N.: GSWP-2: Multimodel analysis and implications for our perception of the land surface, B. Am. Meteorol. Soc., 87, 1381–1398, https://doi.org/10.1175/BAMS-87-10-1381, 2006.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V.,
Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model
advances in the National Centers for Environmental Prediction operational
mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851,
https://doi.org/10.1029/2002jd003296, 2003.
Eshel, A. and Grünzweig, J. M.: Root-shoot allometry of tropical forest
trees determined in a large-scale aeroponic system, Ann. Bot.-London, 112,
291–296, https://doi.org/10.1093/aob/mcs275, 2013.
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B., and
Otero-Casal, C.: Hydrologic regulation of plant rooting depth,
P. Natl. Acad. Sci. USA, 114, 10572–10577, https://doi.org/10.1073/pnas.1712381114,
2017.
Fearnside, P. M.: Brazil's Amazonian forest carbon: the key to Southern
Amazonia's significance for global climate, Reg. Environ. Change, 18,
47–61, https://doi.org/10.1007/s10113-016-1007-2, 2016.
Forkel, M., Carvalhais, N., Schaphoff, S., v. Bloh, W., Migliavacca, M., Thurner, M., and Thonicke, K.: Identifying environmental controls on vegetation greenness phenology through model–data integration, Biogeosciences, 11, 7025–7050, https://doi.org/10.5194/bg-11-7025-2014, 2014.
Guimberteau, M., Zhu, D., Maignan, F., Huang, Y., Yue, C., Dantec-Nédélec, S., Ottlé, C., Jornet-Puig, A., Bastos, A., Laurent, P., Goll, D., Bowring, S., Chang, J., Guenet, B., Tifafi, M., Peng, S., Krinner, G., Ducharne, A., Wang, F., Wang, T., Wang, X., Wang, Y., Yin, Z., Lauerwald, R., Joetzjer, E., Qiu, C., Kim, H., and Ciais, P.: ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation, Geosci. Model Dev., 11, 121–163, https://doi.org/10.5194/gmd-11-121-2018, 2018.
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated
high-resolution grids of monthly climatic observations – the CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014.
Hijmans, R. J. and van Etten, J.: raster: Geographic data analysis and modeling, R Packag. version 2 (8), 2016.
Hirota, M., Holmgren, M., Van New, E. H., and Scheffer, M.: Global Resilience
of Tropical Forest, Science, 334, 232–235,
https://doi.org/10.1126/science.1210657, 2011.
Huang, S., Titus, S. J., and Wiens, D. P.: Comparison of nonlinear
height-diameter functions for major Alberta tree species, Can. J. Forest Res., 22, 1297–1304, 1992.
Ichii, K., Hashimoto, H., White, M. A., Potter, C., Hutyra, L. R., Huete, A.
R., Myneni, R. B., and Nemani, R. R.: Constraining rooting depths in tropical
rainforests using satellite data and ecosystem modeling for accurate
simulation of gross primary production seasonality, Global Change Biol.,
13, 67–77, https://doi.org/10.1111/j.1365-2486.2006.01277.x, 2007.
Jackson, R. B., Canadell, J., Ehleringer, J., Mooney, H., Sala, O., and
Schulze, E.: A global analysis of root distributions for terrestrial biomes,
Oecologica, 108, 389–411, 1996.
Jenik, J.: Roots and root systems in tropical trees, Trop. trees as living
Syst., p. 323, 1978.
Johnson, D. M., Domec, J.-C., Berry, Z. C., Schwantes, A. M., McCulloh, K. A., Woodruff, D. R., Polley, H. W., Wortemann, R., Swenson, J. J., Mackay, D. S., McDowell, N. G., and Jackson, R. B.: Co-occurring woody species have diverse hydraulic strategies and mortality rates during an extreme drought, Plant Cell Environ., 41, 576–588,
https://doi.org/10.1111/pce.13121, 2018.
Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C.,
Leadley, P., Tautenhahn, S., Werner, G. D. A., Aakala, T., and Abedi, M.: TRY
plant trait database – enhanced coverage and open access, Global Change Biol., 26, 119–188, https://doi.org/10.1111/gcb.14904, 2020.
Kelley, D. I., Prentice, I. C., Harrison, S. P., Wang, H., Simard, M., Fisher, J. B., and Willis, K. O.: A comprehensive benchmarking system for evaluating global vegetation models, Biogeosciences, 10, 3313–3340, https://doi.org/10.5194/bg-10-3313-2013, 2013.
Kim, Y., Knox, R. G., Longo, M., Medvigy, D., Hutyra, L. R., Pyle, E. H.,
Wofsy, S. C., Bras, R. L., and Moorcroft, P. R.: Seasonal carbon dynamics and
water fluxes in an Amazon rainforest, Global Change Biol., 18, 1322–1334,
https://doi.org/10.1111/j.1365-2486.2011.02629.x, 2012.
Kleidon, A. and Heimann, M.: A method of determining rooting depth from a
terrestrial biosphere model and its impacts on the global water and carbon
cycle, Global Change Biol., 4, 275–286,
https://doi.org/10.1046/j.1365-2486.1998.00152.x, 1998.
Kleidon, A. and Heimann, M.: Deep-rooted vegetation, Amazonian
deforestation, and climate: Results from a modelling study,
Global Ecol. Biogeogr., 8, 397–405, https://doi.org/10.1046/j.1365-2699.1999.00150.x, 1999.
Kleidon, A. and Heimann, M.: Assessing the role of deep rooted vegetation in
the climate system with model simulations: Mechanism, comparison to
observations and implications for Amazonian deforestation, Clim. Dynam.,
16, 183–199, https://doi.org/10.1007/s003820050012, 2000.
Krysanova, V., Müller-Wohlfeil, D.-I., and Becker, A.: Development and
test of a spatially distributed hydrological/water quality model for
mesoscale watersheds, Ecol. Model., 106, 261–289, 1998.
Langan, L., Higgins, S. I., and Scheiter, S.: Climate-biomes, pedo-biomes or
pyro-biomes: which world view explains the tropical forest-savanna boundary
in South America?, J. Biogeogr., 44, 2319–2330, https://doi.org/10.1111/jbi.13018,
2017.
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson, S.
C., Lawrence, P. J., Zeng, X., Yang, Z.-L., Levis, S., Sakaguchi, K., Bonan,
G. B., and Slater, A. G.: Parameterization improvements and functional and
structural advances in Version 4 of the Community Land Model, J. Adv. Model. Earth Sy., 3, M03001, https://doi.org/10.1029/2011MS00045, 2011.
Lee, J. E., Oliveira, R. S., Dawson, T. E., and Fung, I.: Root functioning
modifies seasonal climate, P. Natl. Acad. Sci. USA, 102,
17576–17581, https://doi.org/10.1073/pnas.0508785102, 2005.
Leuschner, C., Moser, G., Bertsch, C., Röderstein, M., and Hertel, D.:
Large altitudinal increase in tree root/shoot ratio in tropical mountain
forests of Ecuador, Basic Appl. Ecol., 8, 219–230, 2007.
Lewis, S. L., Brando, P. M., Phillips, O. L., Van Der Heijden, G. M. F., and
Nepstad, D.: The 2010 Amazon drought, Science, 331, p. 554, https://doi.org/10.1126/science.1200807, 2011.
Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A., and Peng, S.: Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015), Earth Syst. Sci. Data, 10, 219–234, https://doi.org/10.5194/essd-10-219-2018, 2018.
Liu, Y. Y., Dorigo, W. A., Parinussa, R. M., De Jeu, R. A. M., Wagner, W.,
McCabe, M. F., Evans, J. P., and Van Dijk, A. I. J. M.: Trend-preserving
blending of passive and active microwave soil moisture retrievals, Remote
Sens. Environ., 123, 280–297, https://doi.org/10.1016/j.rse.2012.03.014, 2012.
Liu, Y. Y., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P., and de Jeu,
R. A. M.: Global vegetation biomass change (1988–2008) and attribution to
environmental and human drivers, Global Ecol. Biogeogr., 22, 692–705,
https://doi.org/10.1111/geb.12024, 2013.
Malhi, Y., Aragao, L. E. O. C., Galbraith, D., Huntingford, C., Fisher, R.,
Zelazowski, P., Sitch, S., McSweeney, C., and Meir, P.: Exploring the
likelihood and mechanism of a climate-change-induced dieback of the Amazon
rainforest, P. Natl. Acad. Sci. USA, 106, 20610–20615,
https://doi.org/10.1073/pnas.0804619106, 2009.
Markewitz, D., Devine, S., Davidson, E. A., Brando, P., and Nepstad, D. C.:
Soil moisture depletion under simulated drought in the Amazon: Impacts on
deep root uptake, New Phytol., 187, 592–607,
https://doi.org/10.1111/j.1469-8137.2010.03391.x, 2010.
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.
Masson, V., Champeaux, J. L., Chauvin, F., Meriguet, C., and Lacaze, R.: A
global database of land surface parameters at 1 km resolution in
meteorological and climate models, J. Climate, 16, 1261–1282,
https://doi.org/10.1175/1520-0442(2003)16<1261:AGDOLS>2.0.CO;2, 2003.
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, 2011.
Mokany, K., Raison, R. J., and Prokushkin, A. S.: Critical analysis of root:
Shoot ratios in terrestrial biomes, Global Change Biol., 12, 84–96,
https://doi.org/10.1111/j.1365-2486.2005.001043.x, 2006.
Nachtergaele, F., van Velthuizen, H. T., Verelst, L., Batjes, N., Dijkshoorn,
K., van Engelen, V., Fischer, G., Jones, A., Montanarella, L., and Petri, M.:
Harmonized world soil database, Food and Agriculture Organization of the
United Nations, FAO, Rome, Italy and IIASA, Laxenburg, Austria, available at: http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (last access: 7 July 2021), 2009.
Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C.,
Tucker, C. J., Myneni, R. B., and Running, S. W.: Climate-driven increases in
global terrestrial net primary production from 1982 to 1999, Science, 300, 1560–1563, https://doi.org/10.1126/science.1082750, 2003.
New, M., Hulme, M., and Jones, P.: Representing twentieth century space-time
climate variability, Part II: development of a 1901–1996 monthly grids of
terrestrial surface climate, J. Climate, 13, 2217–2238, 2000.
Nikolova, P. S., Zang, C., and Pretzsch, H.: Combining tree-ring analyses on
stems and coarse roots to study the growth dynamics of forest trees: A case
study on Norway spruce (Picea abies [L.] H. Karst), Trees-Struct. Funct.,
25, 859–872, https://doi.org/10.1007/s00468-011-0561-y, 2011.
Ostle, N. J., Smith, P., Fisher, R., Woodward, F. I., Fisher, J. B., Smith, J. U., Galbraith, D., Levy, P., Meir, P., McNamara, N. P., and Bardgett, R. D.: Integrating plant-soil interactions into global carbon cycle models, J. Ecol., 97, 851–863, https://doi.org/10.1111/j.1365-2745.2009.01547.x, 2009.
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A.,
Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D.: A gridded global
data set of soil, intact regolith, and sedimentary deposit thicknesses for
regional and global land surface modeling, J. Adv. Model. Earth Sy., 8,
41–65, https://doi.org/10.1002/2017MS001065, 2016.
Poorter, H., Niklas, K. J., Reich, P. B., Oleksyn, J., Poot, P., and Mommer,
L.: Biomass allocation to leaves, stems and roots: meta-analyses of
interspecific variation and environmental control, New Phytol., 193,
30–50, 2012.
Prentice, I. C., Sykes, M. T., and Cramer, W.: A simulation model for the
transient effects of climate change on forest landscapes, Ecol. Model.,
65, 51–70, 1993.
R Core Team: A language and environment for statistical computing, R
Foundatoin for Statistical Computing, available at:
https://www.r-project.org/ (last access: 23 January 2020), 2019.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteorol. Soc., 85, 381–394, 2004.
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A.,
Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S.,
White, L., Silman, M., and Morel, A.: Benchmark map of forest carbon stocks
in tropical regions across three continents, P. Natl. Acad. Sci. USA,
108, 9899–9904, https://doi.org/10.1073/pnas.1019576108, 2011.
Sakschewski, B., von Bloh, W., Drüke, M., Sörensson, A. A., Ruscica,
R., Langerwisch, F., Billing, M., Bereswill, S., Hirota, M., Oliveira, R.
S., Heinke, J., and Thonicke, K.: LPJmL4.0-VR Model Code, Zenodo [code],
https://doi.org/10.5281/zenodo.4709250, 2021a.
Sakschewski, B., von Bloh, W., Drüke, M., Sörensson, A. A., Ruscica,
R., Langerwisch, F., Billing, M., Bereswill, S., Hirota, M., Oliveira, R.
S., Heinke, J., and Thonicke, K.: LPJmL4.0-VR Model Output, Zenodo [code], https://doi.org/10.5281/zenodo.4709166, 2021b.
Saleska, S. R., Da Rocha, H. R., Huete, A. R., Nobre, A. D., Artaxo, P. E.,
and Shimabukuro, Y. E.: LBA-ECO CD-32 Flux Tower Network Data Compilation,
Brazilian Amazon: 1999–2006, ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1174, 2013.
Schaphoff, S., von Bloh, W., Rammig, A., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Knauer, J., Langerwisch, F., Lucht, W., Müller, C., Rolinski, S., and Waha, K.: LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description, Geosci. Model Dev., 11, 1343–1375, https://doi.org/10.5194/gmd-11-1343-2018, 2018a.
Schaphoff, S., von Bloh, W., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Müller, C., Rolinski, S., Waha, K., Stehfest, E., de Waal, L., Heyder, U., Gumpenberger, M., and Beringer, T.: LPJmL4 Model Code, V. 4.0, GFZ Data Services [code], https://doi.org/10.5880/pik.2018.002, 2018b.
Schymanski, S. J., Sivapalan, M., Roderick, M. L., Beringer, J., and Hutley, L. B.: An optimality-based model of the coupled soil moisture and root dynamics, Hydrol. Earth Syst. Sci., 12, 913–932, https://doi.org/10.5194/hess-12-913-2008, 2008.
Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-year
high-resolution global dataset of meteorological forcings for land surface
modeling, J. Climate, 19, 3088–3111, https://doi.org/10.1175/JCLI3790.1, 2006.
Shinozaki, K., Yoda, K., and Kira, T.: A quantitative analysis of plant form
– The pipe model theory, Jpn. J. Ecol., 14, 133–139, https://doi.org/10.18960/seitai.14.4_133, 1964.
Smith, B., Wårlind, D., Arneth, A., Hickler, T., Leadley, P., Siltberg, J., and Zaehle, S.: Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model, Biogeosciences, 11, 2027–2054, https://doi.org/10.5194/bg-11-2027-2014, 2014.
Sörensson, A. A. and Ruscica, R. C.: Intercomparison and Uncertainty
Assessment of Nine Evapotranspiration Estimates Over South America, Water
Resour. Res., 54, 2891–2908, https://doi.org/10.1002/2017WR021682, 2018.
Staal, A., Tuinenburg, O. A., Bosmans, J. H. C., Holmgren, M., Van Nes, E.
H., Scheffer, M., Zemp, D. C., and Dekker, S. C.: Forest-rainfall cascades
buffer against drought across the Amazon, Nat. Clim. Change, 8, 539–543,
https://doi.org/10.1038/s41558-018-0177-y, 2018.
Staver, A. C., Archibald, S., and Levin, S. A.: The global extent and
determinants of savanna and forest as alternative biome states, Science, 334, 230–232, https://doi.org/10.1126/science.1210465, 2011.
Tans, P. and Keeling, R.: Trends in Atmospheric Carbon Dioxide, NOAA Earth System Research Laboratories (ESRL), Boulder, Colorado, USA, available at: http://www.esrl.noaa.gov/gmd/ccgg/trends (last access: 7 July 2021), 2015.
Thonicke, K., Venevsky, S., Sitch, S., and Cramer, W.: The role of fire
disturbance for global vegetation dynamics: Coupling fire into a dynamic
global vegetation model, Global Ecol. Biogeogr., 10, 661–677,
https://doi.org/10.1046/j.1466-822X.2001.00175.x, 2001.
Waring, R. H., Schroeder, P. E., and Oren, R.: Application of the pipe model
theory to predict canopy leaf area, Can. J. Forest Res., 12, 556–560,
https://doi.org/10.1139/x82-086, 1982.
Warren, J. M., Hanson, P. J., Iversen, C. M., Kumar, J., Walker, A. P., and
Wullschleger, S. D.: Root structural and functional dynamics in terrestrial
biosphere models – evaluation and recommendations, New Phytol., 205,
59–78, https://doi.org/10.1111/nph.13034, 2015.
Weedon, G. P., Gomes, S., Viterbo, P., Shuttleworth, W. J., Blyth, E.,
Österle, H., Adam, J. C., Bellouin, N., Boucher, O., and Best, M.:
Creation of the WATCH Forcing Data and Its Use to Assess Global and Regional
Reference Crop Evaporation over Land during the Twentieth Century, J.
Hydrometeorol., 12, 823–848, https://doi.org/10.1175/2011jhm1369.1, 2011.
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and
Viterbo, P.: Data methodology applied to ERA-Interim reanalysis data, Water
Resour. Res., 50, 7505–7514, https://doi.org/10.1002/2014WR015638, 2014.
Wu, J., Albert, L. P., Lopes, A. P., Restrepo-Coupe, N., Hayek, M.,
Wiedemann, K. T., Guan, K., Stark, S. C., Christoffersen, B., Prohaska, N.,
Tavares, J. V., Marostica, S., Kobayashi, H., Ferreira, M. L., Campos, K.
S., Da Silva, R., Brando, P. M., Dye, D. G., Huxman, T. E., Huete, A. R.,
Nelson, B. W., and Saleska, S. R.: Leaf development and demography explain
photosynthetic seasonality in Amazon evergreen forests, Science,
351, 972–976, https://doi.org/10.1126/science.aad5068, 2016.
Wuyts, B., Champneys, A. R., and House, J. I.: Amazonian forest-savanna
bistability and human impact, Nat. Commun., 8, 15519,
https://doi.org/10.1038/ncomms15519, 2017.
Xiao, C. W., Yuste, J. C., Janssens, I. A., Roskams, P., Nachtergale, L.,
Carrara, A., Sanchez, B. Y., and Ceulemans, R.: Above- and belowground
biomass and net primary production in a 73-year-old Scots pine forest,
Tree Physiol., 23, 505–516, https://doi.org/10.1093/treephys/23.8.505, 2003.
Xiao, X., Hagen, S., Zhang, Q., Keller, M., and Moore, B.: Detecting leaf
phenology of seasonally moist tropical forests in South America with
multi-temporal MODIS images, Remote Sens. Environ., 103, 465–473,
https://doi.org/10.1016/j.rse.2006.04.013, 2006.
Zemp, D. C., Schleussner, C. F., Barbosa, H. M. J., Hirota, M., Montade, V.,
Sampaio, G., Staal, A., Wang-Erlandsson, L., and Rammig, A.: Self-amplified
Amazon forest loss due to vegetation-atmosphere feedbacks, Nat. Commun., 8,
14681, https://doi.org/10.1038/ncomms14681, 2017.
Short summary
This study shows how local adaptations of tree roots across tropical and sub-tropical South America explain patterns of biome distribution, productivity and evapotranspiration on this continent. By allowing for high diversity of tree rooting strategies in a dynamic global vegetation model (DGVM), we are able to mechanistically explain patterns of mean rooting depth and the effects on ecosystem functions. The approach can advance DGVMs and Earth system models.
This study shows how local adaptations of tree roots across tropical and sub-tropical South...
Altmetrics
Final-revised paper
Preprint