Supporting Dataset for "Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests"

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. J. Geophys. Res.-Biogeosci., 125 (8), 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 census[[1]], rlidar[[1]], and tchdat[[1]].

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:

115: Km 115 of BR-163 highway, PA, BRA

ana: Anambé, PA, BRA

and: Fazenda Andiroba, PA, BRA

bon: Fazenda Bonal, AC, BRA

cau: Fazenda Cauaxi, PA, BRA

duc: Reserva Ducke, AM, BRA

fc2: Feliz Natal (zone C, area 2), MT, BRA

fd1: Feliz Natal (zone D, area 1), MT, BRA

fd2: Feliz Natal (zone D, area 2), MT, BRA

fd3: Feliz Natal (zone D, area 3), MT, BRA

fn2: Feliz Natal (long transect 2), MT, BRA

fna: Feliz Natal (zone A), MT, BRA

fst: Saracá-Taquera National Forest, PA, BRA

gf1: Paracou (Guyaflux plots), GUF

gf2: Paracou (Logging experiment plots), GUF

hum: Fazenda Humaitá, AC, BRA

jm2: Jamari National Forest (area 2), RO, BRA

jm3: Jamari National Forest (area 3), RO, BRA

par: Fazenda Nova Neonita, PA, BRA

sbe: 

local
census[[1]], rlidar[[1]], tchdat[[1]]

Region identifier (used for regional cross-validation):

bte: Belterra, PA, BRA

duc: Manaus (Reserva Ducke), AM, BRA

fst: Saracá-Taquera National Forest, PA, BRA

fzn: Feliz Natal, MT, BRA

gyf: Paracou, GUF

jam: Jamari National Forest, RO, BRA

prg: Paragominas, PA, BRA

rib: Rio Branco, AC, BRA

sfx: São Félix do Xingu, PA, BRA

tan: Tanguro, MT, BRA

sbe: Southeastern Belterra, PA, BRA

sx1: São Félix do Xingu (area 1), PA, BRA

sx2: São Félix do Xingu (area 2), PA, BRA

tac: Tomé-Açu, PA, BRA

tal: Fazenda Talismã, AC, BRA

tn1: Fazenda Tanguro (Sustainable Landscapes transects), MT, BRA

tn2: Fazenda Tanguro (fire experiment transects), MT, BRA

tp1: Tapajós National Forest, PA, BRA

tp2: São Jorge (area 2), PA, BRA

tp3: 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:

bnm: Burnt multiple times

bno: Burnt once

cvl: Conventional logging

int: Intact (minimally disturbed) forest

lbn: Logged and burnt once

lth: Logged and thinned

ril: Reduced-impact logging

sbn: Secondary growth then burnt

sec: Secondary growth

ukn: 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−2

ba.std
census[[1]]
Basal area of individuals with DBH ≥ 10 cm
cm2 m−2

lai.std
census[[1]]
Potential (allometry-based) leaf area index of individuals with DBH ≥ 10 cm
m2 m−2

nplant.std
census[[1]]
Stem number density of individuals with DBH ≥ 10 cm
m−2

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]]
1st percentile of the point cloud return distribution (all returns)
m

elev.p05
rlidar[[1]]
5th percentile of the point cloud return distribution (all returns)
m

elev.p10
rlidar[[1]]
10th percentile of the point cloud return distribution (all returns)
m

elev.p25
rlidar[[1]]
25th percentile of the point cloud return distribution (all returns)
m

elev.p50
rlidar[[1]]
50th percentile (median) of the point cloud return distribution (all returns)
m

elev.p75
rlidar[[1]]
75th percentile of the point cloud return distribution (all returns)
m

elev.p90
rlidar[[1]]
90th percentile of the point cloud return distribution (all returns)
m

elev.p95
rlidar[[1]]
95th percentile of the point cloud return distribution (all returns)
m

elev.p99
rlidar[[1]]
99th 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