Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published August 2020 | Supplemental Material + Published
Journal Article Open

Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests

Abstract

Selective logging, fragmentation, and understory fires directly degrade forest structure and composition. However, studies addressing the effects of forest degradation on carbon, water, and energy cycles are scarce. Here, we integrate field observations and high‐resolution remote sensing from airborne lidar to provide realistic initial conditions to the Ecosystem Demography Model (ED‐2.2) and investigate how disturbances from forest degradation affect gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H). We used forest structural information retrieved from airborne lidar samples (13,500 ha) and calibrated with 817 inventory plots (0.25 ha) across precipitation and degradation gradients in the eastern Amazon as initial conditions to ED‐2.2 model. Our results show that the magnitude and seasonality of fluxes were modulated by changes in forest structure caused by degradation. During the dry season and under typical conditions, severely degraded forests (biomass loss ≥66%) experienced water stress with declines in ET (up to 34%) and GPP (up to 35%) and increases of H (up to 43%) and daily mean ground temperatures (up to 6.5°C) relative to intact forests. In contrast, the relative impact of forest degradation on energy, water, and carbon cycles markedly diminishes under extreme, multiyear droughts, as a consequence of severe stress experienced by intact forests. Our results highlight that the water and energy cycles in the Amazon are driven by not only climate and deforestation but also the past disturbance and changes of forest structure from degradation, suggesting a much broader influence of human land use activities on the tropical ecosystems.

Additional Information

© 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Issue Online: 20 August 2020; Version of Record online: 20 August 2020; Accepted manuscript online: 30 June 2020; Manuscript accepted: 02 June 2020; Manuscript revised: 28 May 2020; Manuscript received: 12 February 2020. The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D004), and supported by NASA Earth Sciences grants (16‐IDS16‐0049 and 16‐CARBON16‐0130). Data recorded at the Guyaflux tower were obtained thanks to the support of two Investissement d'avenir grants from the Agence Nationale de la Recherche (CEBA, ref ANR‐10‐LABX‐25‐01; ARBRE, ref. ANR‐11‐LABX‐0002‐01). Data in Brazil were acquired by the Sustainable Landscapes Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the U.S. Forest Service, the USAID, and the U.S. Department of State. The study has been supported by the TRY initiative on plant traits, which is hosted, developed, and maintained by J. Kattge and G. Bönisch (Max Planck Institute for Biogeochemistry, Jena, Germany). TRY is currently supported by DIVERSITAS/Future Earth and the German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig. We thank Xiangtao Xu for sharing the trait plasticity algorithm and discussions on model results; Divino Silvério for processing and sharing the data from Tanguro; Hylke Beck for sharing the MSWEP‐2.2 data; and Marcos Scaranello, Fabian Schneider, Alexandra Konings, and A. Anthony Bloom for discussions on the lidar initialization algorithm and interpretation of model results. The model simulations were carried out at the Odyssey cluster, supported by the FAS Division of Science, Research Computing Group, at Harvard University; and at the Brazilian National Laboratory for Scientific Computing (LNCC). M. L. was supported by the São Paulo State Research Foundation (FAPESP, 2015/07227‐6) and by the NASA Postdoctoral Program, administered by Universities Space Research Association under contract with NASA. M. K. was supported in part by the Next Generation Ecosystem Experiments‐Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. Data Availability Statement: Airborne lidar and forest inventory data were obtained from Sustainable Landscapes Brazil (2019), dos‐Santos et al. (2019) (Brazil), and Paracou Portal (2016) (French Guiana). MERRA‐2 reanalyses are available from GMAO (2015a, 2015b, 2015c, 2015d), and MSWEP‐2.2 data were downloaded from https://www.gloh2o.org. The ED‐2.2 model used in this study is available at Longo, Knox, Medvigy, Levine, Dietze, Swann, et al. (2019), and the scripts and ED‐2.2 output are permanently stored at Longo et al. (2020). Trait data are available at the TRY initiative on plant traits (https://www.try-db.org), request 2751; at Gu et al. (2016); or as supporting information from the cited references (Bahar et al., 2017; Santiago & Wright, 2007; I. J. Wright et al., 2004).

Attached Files

Published - 2020JG005677.pdf

Supplemental Material - jgrg21688-sup-0001-2020jg005677-si.tex

Supplemental Material - jgrg21688-sup-0001-2020jg005677-text_si-s01.pdf

Files

jgrg21688-sup-0001-2020jg005677-text_si-s01.pdf
Files (34.8 MB)
Name Size Download all
md5:a0ecbb2f3f2b931f3b6a63799163e2a7
136.8 kB Download
md5:da4ec44e9b80bb537e4e107e6f4f3ce4
14.1 MB Preview Download
md5:8982596087982dd39658947b0aacaf09
20.6 MB Preview Download

Additional details

Created:
August 22, 2023
Modified:
October 23, 2023