DOI: 10.5281/zenodo.3634131
title: Supporting Dataset for "Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests"

description: This data set is a supplement for: Longo, M., S. S. Saatchi, M. Keller, K. W. Bowman, A. Ferraz, P. R. Moorcroft, D. Morton, D. Bonal, P. Brando, B. Burban, G. Derroire, M. N. dos-Santos, V. Meyer, S. R. Saleska, S. Trumbore, and G. Vin- cent, 2020: Impacts of degradation on water, energy, and carbon cycling of the Amazon tropical forests.<em> J. Geophys. Res.-Biogeosci</em>., <strong>125</strong> (<strong>8</strong>), e2020JG005 677, doi:10.1029/2020JG005677. This data set contains the following files (which should be all downloaded and uncompressed in the same root directory): 00_SiteLidar.zip – R scripts to process forest inventory plots and Airborne LiDAR point clouds. Sub-directories contains a directory Template, which should be copied for each site for which data are to be processed. 01_LidarSynthesis.zip – R scripts to fit the statistical models of aggregated properties, and to evaluate both the statistical model and the prediction of Airborne LiDAR profiles to be used to initialize ED-2.2. 02_model_eval.zip – R scripts to compare the ED-2.2 model output and evaluate the model against tower observations. 03_degrad_mtr – R scripts to visualize the ED-2.2 simulation results. InputData – Miscellaneous data to be used by the scripts. Util – Additional R scripts Rsc – Mostly R functions, which may be called by other R scripts OutsideLAS – List of plots that were not fully overlapped by the Airborne LiDAR surveys GenMERRA2_ED2 – Utility scripts to process MERRA-2 to generate the met drivers needed by ED-2.2 GenMSWEP2_ED2 – Utility scripts to process MSWEP-2.2 to generate the met drivers needed by ED-2.2 ED2IN_Config – list of ED2IN files used in the runs. To see the input data used for this analysis, load any of the objects available in 01_LidarSynthesis/01_eval_multivar, and look for the following structures: List of variables and units of data structure <strong>census[[1]]</strong>,<strong> rlidar[[1]]</strong>, and <strong>tchdat[[1]]</strong>. Variable Structure Description Units identifier census[[1]], rlidar[[1]], tchdat[[1]] Plot identifier. This always has the site identifier (see below), the area within each site, the nominal year of the campaign, the unique sub-plot ID (Pxx_Byy for rectangular plots, and Txx_Pyy for long transects) iata census[[1]], rlidar[[1]], tchdat[[1]] Site identifier: <strong>115:</strong> Km 115 of BR-163 highway, PA, BRA <strong>ana:</strong> Anambé, PA, BRA <strong>and:</strong> Fazenda Andiroba, PA, BRA <strong>bon:</strong> Fazenda Bonal, AC, BRA <strong>cau:</strong> Fazenda Cauaxi, PA, BRA <strong>duc:</strong> Reserva Ducke, AM, BRA <strong>fc2:</strong> Feliz Natal (zone C, area 2), MT, BRA <strong>fd1:</strong> Feliz Natal (zone D, area 1), MT, BRA <strong>fd2:</strong> Feliz Natal (zone D, area 2), MT, BRA <strong>fd3:</strong> Feliz Natal (zone D, area 3), MT, BRA <strong>fn2:</strong> Feliz Natal (long transect 2), MT, BRA <strong>fna:</strong> Feliz Natal (zone A), MT, BRA <strong>fst:</strong> Saracá-Taquera National Forest, PA, BRA <strong>gf1:</strong> Paracou (Guyaflux plots), GUF <strong>gf2:</strong> Paracou (Logging experiment plots), GUF <strong>hum:</strong> Fazenda Humaitá, AC, BRA <strong>jm2:</strong> Jamari National Forest (area 2), RO, BRA <strong>jm3:</strong> Jamari National Forest (area 3), RO, BRA <strong>par:</strong> Fazenda Nova Neonita, PA, BRA <strong>sbe:</strong> local census[[1]], rlidar[[1]], tchdat[[1]] Region identifier (used for regional cross-validation): <strong>bte:</strong> Belterra, PA, BRA <strong>duc:</strong> Manaus (Reserva Ducke), AM, BRA <strong>fst:</strong> Saracá-Taquera National Forest, PA, BRA <strong>fzn:</strong> Feliz Natal, MT, BRA <strong>gyf:</strong> Paracou, GUF <strong>jam:</strong> Jamari National Forest, RO, BRA <strong>prg:</strong> Paragominas, PA, BRA <strong>rib:</strong> Rio Branco, AC, BRA <strong>sfx:</strong> São Félix do Xingu, PA, BRA <strong>tan:</strong> Tanguro, MT, BRA <strong>sbe:</strong> Southeastern Belterra, PA, BRA <strong>sx1:</strong> São Félix do Xingu (area 1), PA, BRA <strong>sx2:</strong> São Félix do Xingu (area 2), PA, BRA <strong>tac:</strong> Tomé-Açu, PA, BRA <strong>tal:</strong> Fazenda Talismã, AC, BRA <strong>tn1:</strong> Fazenda Tanguro (Sustainable Landscapes transects), MT, BRA <strong>tn2:</strong> Fazenda Tanguro (fire experiment transects), MT, BRA <strong>tp1:</strong> Tapajós National Forest, PA, BRA <strong>tp2:</strong> São Jorge (area 2), PA, BRA <strong>tp3:</strong> São Jorge (area 3), PA, BRA poi census[[1]], rlidar[[1]], tchdat[[1]] Nominal size of each plot when census[[1]], rlidar[[1]], tchdat[[1]] Date of measurement col census[[1]], rlidar[[1]], tchdat[[1]] Colour associated with plot (for plotting only) pch census[[1]], rlidar[[1]], tchdat[[1]] Symbol associated with plot (for plotting only) dist.key census[[1]], rlidar[[1]], tchdat[[1]] Disturbance flag: <strong>bnm:</strong> Burnt multiple times <strong>bno:</strong> Burnt once <strong>cvl:</strong> Conventional logging <strong>int:</strong> Intact (minimally disturbed) forest <strong>lbn:</strong> Logged and burnt once <strong>lth:</strong> Logged and thinned <strong>ril:</strong> Reduced-impact logging <strong>sbn:</strong> Secondary growth then burnt <strong>sec:</strong> Secondary growth <strong>ukn:</strong> Unknown/Unclassified dist.age census[[1]], rlidar[[1]], tchdat[[1]] Age since last disturbance yr dist.col census[[1]], rlidar[[1]], tchdat[[1]] Colour associated with disturbance (for plotting only) dist.pch census[[1]], rlidar[[1]], tchdat[[1]] Symbol associated with disturbance (for plotting only) agb.std census[[1]] Above-ground biomass of individuals with DBH ≥ 10 cm kgC m<sup>−2</sup> ba.std census[[1]] Basal area of individuals with DBH ≥ 10 cm cm<sup>2</sup> m<sup>−2</sup> lai.std census[[1]] Potential (allometry-based) leaf area index of individuals with DBH ≥ 10 cm m<sup>2</sup> m<sup>−2</sup> nplant.std census[[1]] Stem number density of individuals with DBH ≥ 10 cm m<sup>−2</sup> elev.mean rlidar[[1]] Mean elevation of point cloud return distribution (all returns) m elev.sdev rlidar[[1]] Standard deviation of point cloud return distribution (all returns) m elev.skew rlidar[[1]] Skewness of point cloud return distribution (all returns) m elev.kurt rlidar[[1]] Kurtosis of point cloud return distribution (all returns) m elev.p01 rlidar[[1]] 1<sup>st</sup> percentile of the point cloud return distribution (all returns) m elev.p05 rlidar[[1]] 5<sup>th</sup> percentile of the point cloud return distribution (all returns) m elev.p10 rlidar[[1]] 10<sup>th</sup> percentile of the point cloud return distribution (all returns) m elev.p25 rlidar[[1]] 25<sup>th</sup> percentile of the point cloud return distribution (all returns) m elev.p50 rlidar[[1]] 50<sup>th</sup> percentile (median) of the point cloud return distribution (all returns) m elev.p75 rlidar[[1]] 75<sup>th</sup> percentile of the point cloud return distribution (all returns) m elev.p90 rlidar[[1]] 90<sup>th</sup> percentile of the point cloud return distribution (all returns) m elev.p95 rlidar[[1]] 95<sup>th</sup> percentile of the point cloud return distribution (all returns) m elev.p99 rlidar[[1]] 99<sup>th</sup> percentile of the point cloud return distribution (all returns) m elev.iqr rlidar[[1]] Interquartile range of the point cloud return distribution (all returns) m elev.max rlidar[[1]] Maximum of the point cloud return distribution (all returns) m fcan.elev.1.0.to.2.5.m rlidar[[1]] Fraction of returns between 1.0 and 2.5 m fraction [0-1] fcan.elev.2.5.to.5.0.m rlidar[[1]] Fraction of returns between 2.5 and 5.0 m fraction [0-1] fcan.elev.5.0.to.7.5.m rlidar[[1]] Fraction of returns between 5.0 and 7.5 m fraction [0-1] fcan.elev.7.5.to.10.0.m rlidar[[1]] Fraction of returns between 7.5 and 10.0 m fraction [0-1] fcan.elev.10.0.to.15.0.m rlidar[[1]] Fraction of returns between 10.0 and 15.0 m fraction [0-1] fcan.elev.15.0.to.20.0.m rlidar[[1]] Fraction of returns between 15.0 and 20.0 m fraction [0-1] fcan.elev.20.0.to.25.0.m rlidar[[1]] Fraction of returns between 20.0 and 25.0 m fraction [0-1] fcan.elev.25.0.to.30.0.m rlidar[[1]] Fraction of returns between 25.0 and 30.0 m fraction [0-1] fcan.elev.above.1.0.m rlidar[[1]] Fraction of returns above 1.0 m fraction [0-1] fcan.elev.above.2.5.m rlidar[[1]] Fraction of returns above 2.5 m fraction [0-1] fcan.elev.above.5.0.m rlidar[[1]] Fraction of returns above 5.0 m fraction [0-1] fcan.elev.above.7.5.m rlidar[[1]] Fraction of returns above 7.5 m fraction [0-1] fcan.elev.above.10.0.m rlidar[[1]] Fraction of returns above 10.0 m fraction [0-1] fcan.elev.above.15.0.m rlidar[[1]] Fraction of returns above 15.0 m fraction [0-1] fcan.elev.above.20.0.m rlidar[[1]] Fraction of returns above 20.0 m fraction [0-1] fcan.elev.above.25.0.m rlidar[[1]] Fraction of returns above 25.0 m fraction [0-1] fcan.elev.above.30.0.m rlidar[[1]] Fraction of returns above 30.0 m fraction [0-1] ztch tchdat[[1]] Mean top canopy height (0.25ha average from 1-m pixels) m