Description

This is to run the same evaluation of GPP simulated by the P-model as done for Stocker et al. (2020), using data from the FLUXNET2015 Tier 1 ensemble. Model forcing and observational GPP data are prepared as detailed in the vignette prepare_inputs_FLUXNET2015_ensemble.Rmd. Respective files are available on Euler XXXpathXXX.

This assumes that the model is already calibrated (calibratable parameters are prescribed).

Note: For simulations used in Stocker et al. (2020), forcing data was written to files and read by Fortran. With the updated rsofun model, this is passed through R, using an object formatted like rsofun::df_drivers.

Load data

Load drivers data frame (created by prepare_inputs_FLUXNET2015_ensemble.Rmd).

load("~/data/rsofun_benchmarking/df_drivers_fluxnet2015.Rdata")

There seem to be some leap year dates which create problems for rsofun. Drop Feb. 29 dates.

df_drivers_fluxnet2015 <- df_drivers_fluxnet2015 %>% 
  dplyr::select(sitename, forcing) %>% 
  unnest(forcing) %>% 
  dplyr::filter(!(month(date)==2 & mday(date)==29)) %>% 
  
  ## model requires flux per seconds now
  mutate(prec = prec / (60*60*24), ppfd = ppfd / (60*60*24)) %>% 
  
  group_by(sitename) %>% 
  nest() %>%
  rename(forcing = data) %>% 
  right_join(
    df_drivers_fluxnet2015 %>% 
      dplyr::select(-forcing),
    by = "sitename"
  ) %>% 
  ungroup()

# save(df_drivers_fluxnet2015, file = "~/data/rsofun_benchmarking/df_drivers_fluxnet2015.Rdata")

Calibrate model

Define calibration sites.

flue_sites <- readr::read_csv( "~/data/flue/flue_stocker18nphyt.csv" ) %>%
              dplyr::filter( !is.na(cluster) ) %>% 
              distinct(site) %>% 
              pull(site)
## Parsed with column specification:
## cols(
##   site = col_character(),
##   date = col_date(format = ""),
##   year = col_double(),
##   doy = col_double(),
##   flue = col_double(),
##   is_flue_drought = col_logical(),
##   cluster = col_character()
## )
calibsites <- siteinfo_fluxnet2015 %>% 
  dplyr::filter(!(sitename %in% c("DE-Akm", "IT-Ro1"))) %>%  # excluded because fapar data could not be downloaded (WEIRD)
  # dplyr::filter(!(sitename %in% c("AU-Wom"))) %>%  # excluded because no GPP data was found in FLUXNET file
  dplyr::filter(sitename != "FI-Sod") %>%  # excluded because some temperature data is missing
  dplyr::filter( c4 %in% c(FALSE, NA) & classid != "CRO" & classid != "WET" ) %>%
  dplyr::filter( sitename %in% flue_sites ) %>%
  pull(sitename)

Define calibration settings.

settings_calib <- list(
  method              = "gensa",
  targetvars          = c("gpp"),
  timescale           = list( gpp = "d" ),
  maxit               = 5,
  sitenames           = calibsites,
  metric              = "rmse",
  dir_results         = "./",
  name                = "FULL",
  par                 = list( kphio       = list( lower=0.03, upper=0.1, init= 0.05 ),
                              soilm_par_a = list( lower=0.0,  upper=1.0, init=0.0 ),
                              soilm_par_b = list( lower=0.0,  upper=1.5, init=0.6 ) )
 )

Use the ingestr package once again, now for collecting calibration target data. I.e., GPP based on the nighttime flux decomposition method.

settings_ingestr_fluxnet <- list(
  dir_hh = "~/data/FLUXNET-2015_Tier1/20191024/HH/", 
  getswc = FALSE,
  filter_ntdt = TRUE,
  threshold_GPP = 0.8,
  remove_neg = FALSE
  )

filn <- "~/data/rsofun_benchmarking/ddf_fluxnet_gpp.Rdata"
if (!file.exists(filn)){
  ddf_fluxnet_gpp <- ingestr::ingest(
    siteinfo = siteinfo_fluxnet2015 %>% 
      dplyr::filter(sitename %in% calibsites),
    source    = "fluxnet",
    getvars = list(gpp = "GPP_NT_VUT_REF",
                   gpp_unc = "GPP_NT_VUT_SE"),
    dir = "~/data/FLUXNET-2015_Tier1/20191024/DD/",
    settings = settings_ingestr_fluxnet,
    timescale = "d"
    )
  save(ddf_fluxnet_gpp, file = filn)
} else {
  load(filn)
}

Calibrate the model.

set.seed(1982)
settings_calib <- calib_sofun(
  df_drivers = dplyr::filter(df_drivers_fluxnet2015, sitename %in% calibsites),  # use only one site
  ddf_obs = ddf_fluxnet_gpp,
  settings = settings_calib
  )
##       kphio soilm_par_a soilm_par_b 
##  0.09423773  0.33349283  1.45602286 
## [1] "writing output from GenSA function to .//out_gensa_FULL.Rdata"
## [1] "writing calibrated parameters to .//params_opt_FULL.csv"

The calibrated parameters are returned by calib_sofun() as part of the list:

print(settings_calib$par_opt)
##       kphio soilm_par_a soilm_par_b 
##  0.09423773  0.33349283  1.45602286
save(settings_calib, file = "./settings_calib.Rdata")

Update model parameters.

params_modl <- list(
    kphio           = 0.05,
    soilm_par_a     = 1.0,
    soilm_par_b     = 0.0,
    vpdstress_par_a = 9999,
    vpdstress_par_b = 9999,
    vpdstress_par_m = 9999
    )
params_modl <- update_params(params_modl, settings_calib)

Run model

df_output <- runread_pmodel_f(
     df_drivers_fluxnet2015,
     params_modl = params_modl, 
     makecheck = TRUE,
     parallel = FALSE
     )

Run evaluation

Do evaluation only for sites where simulation was run.

evalsites <- df_output %>% 
  mutate(ntsteps = purrr::map_dbl(data, ~nrow(.))) %>% 
  dplyr::filter(ntsteps > 0) %>% 
  pull(sitename)

Load standard benchmarking file with observational data for evaluation.

load("~/data/rsofun_benchmarking/obs_eval_fluxnet2015.Rdata")

Define evaluation settings.

settings_eval <- list(
  benchmark = list( gpp = c("fluxnet") ),
  sitenames = evalsites,
  agg       = 8  # An integer specifying the number of days used to define the width of bins for daily data aggregated to several days
  )

And finally run the evaluation.

out_eval <- eval_sofun( 
  df_output, 
  settings_eval, 
  settings_sims, 
  obs_eval = obs_eval, 
  overwrite = TRUE, 
  light = FALSE 
  )
## Error in approx(seq(length(vec)), vec, xout = seq(length(vec))) : 
##   need at least two non-NA values to interpolate
## Error in approx(seq(length(vec)), vec, xout = seq(length(vec))) : 
##   need at least two non-NA values to interpolate
## Error in approx(seq(length(vec)), vec, xout = seq(length(vec))) : 
##   need at least two non-NA values to interpolate
## Error in approx(seq(length(vec)), vec, xout = seq(length(vec))) : 
##   need at least two non-NA values to interpolate
## Error in approx(seq(length(vec)), vec, xout = seq(length(vec))) : 
##   need at least two non-NA values to interpolate
## Error in approx(seq(length(vec)), vec, xout = seq(length(vec))) : 
##   need at least two non-NA values to interpolate
## Error in approx(seq(length(vec)), vec, xout = seq(length(vec))) : 
##   need at least two non-NA values to interpolate
## Error in approx(seq(length(vec)), vec, xout = seq(length(vec))) : 
##   need at least two non-NA values to interpolate
## Error in approx(seq(length(vec)), vec, xout = seq(length(vec))) : 
##   need at least two non-NA values to interpolate
## Error in approx(seq(length(vec)), vec, xout = seq(length(vec))) : 
##   need at least two non-NA values to interpolate
## Error in approx(seq(length(vec)), vec, xout = seq(length(vec))) : 
##   need at least two non-NA values to interpolate
## Error in approx(seq(length(vec)), vec, xout = seq(length(vec))) : 
##   need at least two non-NA values to interpolate

Evaluation results

Metrics table

out_eval$gpp$fluxnet$metrics %>% 
  bind_rows(.id = "Level") %>% 
  kable
Level rsq rmse slope bias nvals
daily_pooled 0.6647013 2.317325 0.9005069 0.0505925 237777
xdaily_pooled 0.7278124 2.013727 0.9948575 0.0583899 33152
annual_pooled 0.6992443 386.054715 1.1002031 -0.7718708 598
monthly_pooled 0.7547680 1.822366 1.0311406 -0.0564397 7428
spatial 0.7067811 408.008044 1.0448012 -19.5171391 109
anomalies_annual 0.0741801 176.567490 0.4227650 -3.6159965 598
meandoy 0.7222098 1.862512 0.9807113 0.1339092 42844
anomalies_daily 0.2615536 1.647578 0.4124326 -0.0356895 237603
meanxoy 0.7494810 1.738624 1.0077950 0.1161248 5528
anomalies_xdaily 0.1414565 1.247923 0.3838807 -0.0043352 33152

Visualisations

out_eval$gpp$fluxnet$plot$gg_modobs_xdaily

out_eval$gpp$fluxnet$plot$gg_modobs_spatial_annual

Appendix

Site list

siteinfo_fluxnet2015 %>% 
  dplyr::filter(sitename %in% evalsites) %>% 
  kable()
sitename lon lat elv year_start year_end classid c4 whc koeppen_code igbp_land_use plant_functional_type
AR-SLu -66.4598 -33.4648 499.0 2009 2011 MF FALSE 268.42401 Bwk NA NA
AR-Vir -56.1886 -28.2395 97.0 2009 2012 ENF FALSE 302.42755 Csb NA NA
AT-Neu 11.3175 47.1167 970.0 2002 2012 GRA FALSE 270.08563 Dfc Mixed Forests Evergreen Broadleaf Trees
AU-Ade 131.1178 -13.0769 81.0 2007 2009 WSA FALSE 269.21866 Aw Savannas Grass
AU-ASM 133.2490 -22.2830 606.0 2010 2013 ENF FALSE 214.58427 BSh Open Shrublands Shrub
AU-Cpr 140.5891 -34.0021 57.0 2010 2014 SAV FALSE 217.31125 BSk Closed Shrublands Shrub
AU-Cum 150.7225 -33.6133 28.0 2012 2014 EBF FALSE 310.86740 Cfa Woody Savannas Evergreen Broadleaf Trees
AU-DaP 131.3181 -14.0633 102.0 2007 2013 GRA FALSE 294.63034 Aw Savannas Grass
AU-DaS 131.3881 -14.1593 98.0 2008 2014 SAV FALSE 249.25092 Aw Savannas Grass
AU-Dry 132.3706 -15.2588 172.0 2008 2014 SAV FALSE 239.15613 Aw Savannas Grass
AU-Emr 148.4746 -23.8587 183.0 2011 2013 GRA FALSE 212.93277 Bwk NA NA
AU-Gin 115.7138 -31.3764 51.0 2011 2014 WSA FALSE 194.44785 Csa Woody Savannas Shrub
AU-GWW 120.6541 -30.1913 452.0 2013 2014 SAV FALSE 183.37975 Bwk NA NA
AU-Lox 140.6551 -34.4704 48.0 2008 2009 DBF FALSE 248.41380 Bsh NA NA
AU-RDF 132.4776 -14.5636 181.0 2011 2013 WSA FALSE 290.04077 Bwh NA NA
AU-Rig 145.5759 -36.6499 153.0 2011 2014 GRA FALSE 251.43498 Cfb Croplands Cereal crop
AU-Rob 145.6301 -17.1175 772.0 2014 2014 EBF FALSE 265.16736 Csb NA NA
AU-Stp 133.3502 -17.1507 227.0 2008 2014 GRA FALSE 283.41589 BSh Grasslands Grass
AU-TTE 133.6400 -22.2870 552.0 2012 2013 OSH FALSE 217.10576 BWh Open Shrublands Shrub
AU-Tum 148.1517 -35.6566 932.0 2001 2014 EBF FALSE 248.48276 Cfb Evergreen Broadleaf Forest Evergreen Broadleaf Trees
AU-Wac 145.1878 -37.4259 562.0 2005 2008 EBF FALSE 163.75224 Cfb Evergreen Broadleaf Forest Evergreen Broadleaf Trees
AU-Whr 145.0294 -36.6732 152.0 2011 2014 EBF FALSE 271.49738 Cfb Woody Savannas Shrub
AU-Wom 144.0944 -37.4222 705.0 2010 2012 EBF FALSE 189.57100 Cfb Evergreen Broadleaf Forest Evergreen Broadleaf Trees
AU-Ync 146.2907 -34.9893 125.0 2012 2014 GRA FALSE 199.15875 BSk Croplands Cereal crop
BE-Bra 4.5206 51.3092 16.0 1996 2014 MF FALSE 85.68380 Cfb Mixed Forests Deciduous Broadleaf Trees
BE-Vie 5.9981 50.3051 493.0 1996 2014 MF FALSE 312.76520 Cfb Mixed Forests Deciduous Broadleaf Trees
BR-Sa3 -54.9714 -3.0180 100.0 2000 2004 EBF FALSE 207.06451 Am Evergreen Broadleaf Forest Evergreen Broadleaf Trees
CA-Man -98.4808 55.8796 259.0 1994 2008 ENF FALSE 52.67784 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-NS1 -98.4839 55.8792 260.0 2001 2005 ENF FALSE 50.25988 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-NS2 -98.5247 55.9058 260.0 2001 2005 ENF FALSE 59.02733 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-NS3 -98.3822 55.9117 260.0 2001 2005 ENF FALSE 115.96288 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-NS4 -98.3822 55.9117 260.0 2002 2005 ENF FALSE 115.96288 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-NS5 -98.4850 55.8631 260.0 2001 2005 ENF FALSE 32.74040 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-NS6 -98.9644 55.9167 244.0 2001 2005 OSH FALSE 28.60807 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-NS7 -99.9483 56.6358 297.0 2002 2005 OSH FALSE 90.78813 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-Qfo -74.3421 49.6925 382.0 2003 2010 ENF FALSE 176.82556 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-SF1 -105.8176 54.4850 536.0 2003 2006 ENF FALSE 265.99557 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-SF2 -105.8775 54.2539 520.0 2001 2005 ENF FALSE 286.65930 Dfc Mixed Forests Evergreen Needleleaf Trees
CA-SF3 -106.0053 54.0916 540.0 2001 2006 OSH FALSE 272.68091 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CH-Cha 8.4104 47.2102 393.0 2005 2014 GRA FALSE 343.09296 Cfb Cropland/Natural Vegetation Mosaic Grass
CH-Dav 9.8559 46.8153 1639.0 1997 2014 ENF FALSE 171.52492 ET Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CH-Fru 8.5378 47.1158 982.0 2005 2014 GRA FALSE 285.33804 Cfb Cropland/Natural Vegetation Mosaic Grass
CH-Lae 8.3650 47.4781 689.0 2004 2014 MF FALSE 292.45551 Cfb Mixed Forests Deciduous Broadleaf Trees
CH-Oe1 7.7319 47.2858 450.0 2002 2008 GRA FALSE 316.34888 Cfb Croplands Cereal crop
CN-Cha 128.0958 42.4025 754.0 2003 2005 MF FALSE 320.64484 Dwb Mixed Forests Deciduous Broadleaf Trees
CN-Cng 123.5092 44.5934 140.0 2007 2010 GRA FALSE 244.44846 Bsh NA NA
CN-Dan 91.0664 30.4978 4751.0 2004 2005 GRA FALSE 229.43036 ET Grasslands Grass
CN-Din 112.5361 23.1733 261.0 2003 2005 EBF FALSE 274.25833 Cfa Evergreen Broadleaf Forest Evergreen Broadleaf Trees
CN-Du2 116.2836 42.0467 1331.0 2006 2008 GRA FALSE 327.09930 Dwb Grasslands Grass
CN-HaM 101.1800 37.3700 3932.0 2002 2004 GRA FALSE 222.66310 NA NA NA
CN-Qia 115.0581 26.7414 64.0 2003 2005 ENF FALSE 303.67596 Cfa Woody Savannas Shrub
CN-Sw2 111.8971 41.7902 1439.0 2010 2012 GRA FALSE 313.57028 Bsh NA NA
CZ-BK1 18.5369 49.5021 875.0 2004 2008 ENF FALSE 260.95676 Dfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CZ-BK2 18.5429 49.4944 855.0 2004 2006 GRA FALSE 260.13770 Dfb Mixed Forests Evergreen Needleleaf Trees
DE-Gri 13.5125 50.9495 385.0 2004 2014 GRA FALSE 338.52594 Cfb Mixed Forests Deciduous Broadleaf Trees
DE-Hai 10.4530 51.0792 430.0 2000 2012 DBF FALSE 282.66736 Cfb Mixed Forests Deciduous Broadleaf Trees
DE-Lkb 13.3047 49.0996 1308.0 2009 2013 ENF FALSE 189.99904 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
DE-Obe 13.7196 50.7836 735.0 2008 2014 ENF FALSE 246.86536 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
DE-RuR 6.3041 50.6219 514.7 2011 2014 GRA FALSE 327.45950 Cfb Grasslands Grass
DE-Tha 13.5669 50.9636 380.0 1996 2014 ENF FALSE 295.66315 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
DK-Sor 11.6446 55.4859 40.0 1996 2014 DBF FALSE 226.43781 Cfb Deciduous Broadleaf Forest Deciduous Broadleaf Trees
DK-ZaH -20.5503 74.4732 38.0 2000 2014 GRA FALSE 241.88185 ET Open Shrublands Shrub
ES-LgS -2.9658 37.0979 2267.0 2007 2009 OSH FALSE 272.30676 Csa Woody Savannas Evergreen Needleleaf Trees
ES-Ln2 -3.4758 36.9695 2249.0 2009 2009 OSH FALSE 246.17299 Csa Closed Shrublands Shrub
FI-Hyy 24.2950 61.8475 181.0 1996 2014 ENF FALSE 255.05896 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
FR-Fon 2.7801 48.4764 103.0 2005 2014 DBF FALSE 335.19290 Cfb Deciduous Broadleaf Forest Deciduous Broadleaf Trees
FR-LBr -0.7693 44.7171 61.0 1996 2008 ENF FALSE 269.57657 Cfb Cropland/Natural Vegetation Mosaic Shrub
FR-Pue 3.5958 43.7414 270.0 2000 2014 EBF FALSE 239.64929 Csa Mixed Forests Evergreen Needleleaf Trees
GF-Guy -52.9249 5.2788 48.0 2004 2014 EBF FALSE 230.85413 Af Evergreen Broadleaf Forest Evergreen Broadleaf Trees
IT-CA1 12.0266 42.3804 200.0 2011 2014 DBF FALSE 269.10382 Csa Croplands Cereal crop
IT-CA3 12.0222 42.3800 197.0 2011 2014 DBF FALSE 269.12970 Csa Croplands Cereal crop
IT-Col 13.5881 41.8494 1560.0 1996 2014 DBF FALSE 267.97675 Cfa Deciduous Broadleaf Forest Deciduous Broadleaf Trees
IT-Cp2 12.3573 41.7043 19.0 2012 2014 EBF FALSE 306.13284 Csa Evergreen Needleleaf Forest Evergreen Needleleaf Trees
IT-Cpz 12.3761 41.7052 68.0 1997 2009 EBF FALSE 305.06644 Csa Evergreen Needleleaf Forest Evergreen Needleleaf Trees
IT-Isp 8.6336 45.8126 210.0 2013 2014 DBF FALSE 320.68103 Cfb Woody Savannas Deciduous Broadleaf Trees
IT-La2 11.2853 45.9542 1350.0 2000 2002 ENF FALSE 237.59509 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
IT-Lav 11.2813 45.9562 1353.0 2003 2014 ENF FALSE 249.79709 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
IT-MBo 11.0458 46.0147 1550.0 2003 2013 GRA FALSE 264.44385 Dfb Grasslands Grass
IT-Noe 8.1515 40.6061 28.0 2004 2014 CSH FALSE 237.01605 - Woody Savannas Shrub
IT-PT1 9.0610 45.2009 60.0 2002 2004 DBF FALSE 317.98535 Cfa Croplands Cereal crop
IT-Ren 11.4337 46.5869 1730.0 1998 2013 ENF FALSE 167.45172 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
IT-Ro2 11.9209 42.3903 160.0 2002 2012 DBF FALSE 273.45822 Csa Cropland/Natural Vegetation Mosaic Cereal crop
IT-SR2 10.2910 43.7320 12.0 2013 2014 ENF FALSE 286.22598 Csa Mixed Forests Evergreen Needleleaf Trees
IT-SRo 10.2844 43.7279 6.0 1999 2012 ENF FALSE 286.22598 Csa Water Water
IT-Tor 7.5781 45.8444 2160.0 2008 2014 GRA FALSE 156.72546 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
JP-MBF 142.3186 44.3869 545.0 2003 2005 DBF FALSE 214.18483 Dfb Mixed Forests Deciduous Broadleaf Trees
JP-SMF 137.0788 35.2617 175.0 2002 2006 MF FALSE 294.94739 Cfa Croplands Cereal crop
NL-Hor 5.0713 52.2404 2.2 2004 2011 GRA FALSE 335.84946 Cfb Mixed Forests Deciduous Broadleaf Trees
NL-Loo 5.7436 52.1666 25.0 1996 2013 ENF FALSE 71.05942 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
NO-Blv 11.8311 78.9216 25.0 2008 2009 SNO FALSE 203.84015 ET Snow and Ice Snow and Ice
RU-Cok 147.4943 70.8291 48.0 2003 2014 OSH FALSE 376.49628 Dfc Open Shrublands Shrub
RU-Fyo 32.9221 56.4615 265.0 1998 2014 ENF FALSE 301.45709 Dfb Mixed Forests Evergreen Needleleaf Trees
RU-Ha1 90.0022 54.7252 446.0 2002 2004 GRA FALSE 357.77884 Dfc Grasslands Grass
SD-Dem 30.4783 13.2829 500.0 2005 2009 SAV FALSE 200.05038 BWh Grasslands Grass
SN-Dhr -15.4322 15.4028 40.0 2010 2013 SAV FALSE 196.49721 BWh Grasslands Grass
US-AR1 -99.4200 36.4267 611.0 2009 2012 GRA FALSE 356.77850 Cfa Grasslands Grass
US-AR2 -99.5975 36.6358 646.0 2009 2012 GRA FALSE 222.08368 Cfa Grasslands Grass
US-ARb -98.0402 35.5497 424.0 2005 2006 GRA FALSE 334.74182 Cfa Croplands Cereal crop
US-ARc -98.0400 35.5465 424.0 2005 2006 GRA FALSE 327.44418 Cfa Grasslands Grass
US-Blo -120.6328 38.8953 1315.0 1997 2007 ENF FALSE 323.68643 Csb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
US-Cop -109.3900 38.0900 1520.0 2001 2007 GRA FALSE 334.22589 BSk Grasslands Grass
US-GBT -106.2397 41.3658 3191.0 1999 2006 ENF FALSE 219.37785 Dfc Evergreen Needleleaf Forests (null)
US-GLE -106.2399 41.3665 3197.0 2004 2014 ENF FALSE 207.54053 Dfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
US-Ha1 -72.1715 42.5378 340.0 1991 2012 DBF FALSE 193.85033 Dfb Mixed Forests Deciduous Broadleaf Trees
US-KS2 -80.6715 28.6086 3.0 2003 2006 CSH FALSE 205.27802 Cfa Woody Savannas Shrub
US-Me1 -121.5000 44.5794 896.0 2004 2005 ENF FALSE 316.75296 Csb Croplands Cereal crop
US-Me2 -121.5574 44.4523 1253.0 2002 2014 ENF FALSE 244.58331 Csb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
US-Me6 -121.6078 44.3233 998.0 2010 2014 ENF FALSE 226.51639 Csb Woody Savannas Shrub
US-MMS -86.4131 39.3232 275.0 1999 2014 DBF FALSE 343.01581 Cfa Deciduous Broadleaf Forest Deciduous Broadleaf Trees
US-NR1 -105.5464 40.0329 3050.0 1998 2014 ENF FALSE 170.75986 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
US-PFa -90.2723 45.9459 470.0 1995 2014 MF FALSE 203.73933 Dfb Mixed Forests Deciduous Broadleaf Trees
US-Prr -147.4876 65.1237 210.0 2010 2013 ENF FALSE 382.20374 Dfc Evergreen Needleleaf Forests Evergreen Needleleaf Trees
US-SRG -110.8277 31.7894 1291.0 2008 2014 GRA FALSE 154.87320 BSk Grasslands Shrub
US-SRM -110.8661 31.8214 1120.0 2004 2014 WSA FALSE 201.24539 BSk Open Shrublands Shrub
US-Syv -89.3477 46.2420 540.0 2001 2014 MF FALSE 222.69208 Dfb Mixed Forests Deciduous Broadleaf Trees
US-Ton -120.9660 38.4316 177.0 2001 2014 WSA FALSE 304.46140 Csa Woody Savannas Shrub
US-UMB -84.7138 45.5598 234.0 2000 2014 DBF FALSE 174.07025 Dfb Deciduous Broadleaf Forest Deciduous Broadleaf Trees
US-UMd -84.6975 45.5625 239.0 2007 2014 DBF FALSE 235.31183 Dfb Mixed Forests Deciduous Broadleaf Trees
US-Var -120.9507 38.4133 129.0 2000 2014 GRA FALSE 307.51373 Csa Woody Savannas Shrub
US-WCr -90.0799 45.8059 520.0 1999 2014 DBF FALSE 264.96152 Dfb Deciduous Broadleaf Forest Deciduous Broadleaf Trees
US-Whs -110.0522 31.7438 1370.0 2007 2014 OSH FALSE 233.38818 BSk Open Shrublands Shrub
US-Wi0 -91.0814 46.6188 349.0 2002 2002 ENF FALSE 325.71179 Dfb Mixed Forests Evergreen Needleleaf Trees
US-Wi3 -91.0987 46.6347 411.0 2002 2004 DBF FALSE 343.67532 Dfb Deciduous Broadleaf Forest Deciduous Broadleaf Trees
US-Wi4 -91.1663 46.7393 352.0 2002 2005 ENF FALSE 299.29538 Dfb Mixed Forests Evergreen Needleleaf Trees
US-Wi6 -91.2982 46.6249 371.0 2002 2003 OSH FALSE 334.28870 Dfb Cropland/Natural Vegetation Mosaic Cereal crop
US-Wi9 -91.0814 46.6188 350.0 2004 2005 ENF FALSE 325.71179 Dfb Mixed Forests Evergreen Needleleaf Trees
US-Wkg -109.9419 31.7365 1531.0 2004 2014 GRA FALSE 209.49074 BSk Grasslands Grass
ZA-Kru 31.4969 -25.0197 359.0 2000 2010 SAV FALSE 276.68857 BSh Savannas Grass
ZM-Mon 23.2528 -15.4378 1053.0 2000 2009 DBF FALSE 132.62697 Aw Savannas Grass