documentation/02-ed2-r-interface.md
In FoRTExperiment/ed4forte: FoRTE-specific utilities for working with ED2
ED 2.2 R interface
Alexey Shikloamnov
Prepare inputs
For this tutorial, in your working directory, create a folder called
ed-input-data.
Download meteorology inputs from the GitHub releases
page.
For this tutorial, use the NARR-ED2.tar.gz file (North American
Regional Reanalysis). Download the file into ed-input-data and extract
it.
cd ed-input-data
tar -xf NARR-ED2.tar.gz
cd ..
You should now have a directory ed-input-data/NARR-ED2 containing a
bunch of files like 1979JAN.h5 as well as a file called
ED_MET_DRIVER_HEADER.
Open the ed-input-data/NARR-ED2/ED_MET_DRIVER_HEADER file in a text
editor, and change the third line (/Users/shik544/...) so it matches
the path of the directory
(e.g. /path/to/current/directory/ed-input-data/NARR-ED2/). You can
also do this with the following shell command:
# Use `gsed` instead of `sed` if on MacOS
sed -i "3s:.*:$PWD/ed-input-data/NARR-ED2/:" ed-input-data/NARR-ED2/ED_MET_DRIVER_DATA
The remaining inputs required for a basic ED2 simulation ship with the
ed4forte package. Install the package from your local clone
(devtools::install("/path/to/ed4forte")) or from GitHub
(devtools::install_github("FoRTExperiment/ed4forte")).
Basic ED2 run
First, locate the ED2 executable and set the R option ed4forte.ed2_exe
to the absolute path to this executable.
options(ed4forte.ed2_exe = "/path/to/ED/build/ed_2.2-dbg")
Use the following R code to perform a simple ED2 simulation at UMBS for
the year 2000 starting from bare ground:
library(ed4forte)
outdir <- "test-ed-outputs"
ed_input_dir <- "ed-input-data"
assertthat::assert_that(dir.exists("ed-input-data"))
p <- run_ed2(
outdir,
"2000-01-01",
"2001-01-01",
ED_MET_DRIVER_DB = file.path(ed_input_dir, "NARR-ED2", "ED_MET_DRIVER_HEADER")
)
The first argument to run_ed2 is the output directory (which will be
created if it doesn’t exist). The next two arguments are the start and
end date (-times), respectively. The last argument indicates that the
default ED2IN value of the ED_MET_DRIVER_DB tag should be modified to
that value. Any default values for ED2IN tags can be modified in this
way.
Note that the code above will return immediately. This is because it
triggers ED2 to run in the background. The return object (p) is a
processx process, which can
be examined.
p$get_status()
## [1] "running"
p$is_alive()
## [1] TRUE
For more details, see the processx::process
documentation. To
wait for the run to complete, use the wait() method.
p$wait()
You can tell that the run finished by examining the contents of the
output directory.
list.files(outdir)
## [1] "analysis-E-2000-01-00-000000-g01.h5" "analysis-E-2000-02-00-000000-g01.h5"
## [3] "analysis-E-2000-03-00-000000-g01.h5" "analysis-E-2000-04-00-000000-g01.h5"
## [5] "analysis-E-2000-05-00-000000-g01.h5" "analysis-E-2000-06-00-000000-g01.h5"
## [7] "analysis-E-2000-07-00-000000-g01.h5" "analysis-E-2000-08-00-000000-g01.h5"
## [9] "analysis-E-2000-09-00-000000-g01.h5" "analysis-E-2000-10-00-000000-g01.h5"
## [11] "analysis-E-2000-11-00-000000-g01.h5" "analysis-E-2000-12-00-000000-g01.h5"
## [13] "ED2IN" "stderr.log"
## [15] "stdout.log"
You should see the 12 monthly output files, along with a copy of the
ED2IN file used for the run and two log files corresponding to the input
(stdout) and error (stderr) streams.
Reading output
ED2 outputs are in HDF5 format, of which NetCDF is a special case.
Therefore, they can be read by NetCDF utilities, like the ncdf4
package.
outfiles <- list.files(outdir, "analysis-E-", full.names = TRUE)
nc <- ncdf4::nc_open(outfiles[1])
ncdf4::ncvar_get(nc, "AGB_CO")
## [1] 0.0002973947 0.0002414269 0.0001701059 0.0001030530 0.0001030530
ncdf4::ncvar_get(nc, "MMEAN_GPP_PY")
## [1] 0
Unfortunately, ED2’s produces only one file per timestep, which makes
reading in results from long simulations pain. ed4forte provides some
utilities for reading a subset of ED2 file types and variables in bulk.
results <- read_monthly_dir(outdir)
## Reading all HDF5 files
results
## # A tibble: 1 x 6
## basename df_scalar df_cohort df_soil df_pft outdir
## <chr> <list> <list> <list> <list> <chr>
## 1 test-ed-out… <tibble [12 ×… <tibble [65 … <tibble [84… <tibble [20… test-ed-o…
scalar_results <- tidyr::unnest(results, df_scalar)
scalar_results
## # A tibble: 12 x 353
## basename datetime PYSI_ID PYSI_N XATM YATM LONGITUDE LATITUDE
## <chr> <dttm> <int> <int> <int> <int> <dbl> <dbl>
## 1 test-ed… 2000-01-01 00:00:00 1 1 1 1 -84.7 45.6
## 2 test-ed… 2000-02-01 00:00:00 1 1 1 1 -84.7 45.6
## 3 test-ed… 2000-03-01 00:00:00 1 1 1 1 -84.7 45.6
## 4 test-ed… 2000-04-01 00:00:00 1 1 1 1 -84.7 45.6
## 5 test-ed… 2000-05-01 00:00:00 1 1 1 1 -84.7 45.6
## 6 test-ed… 2000-06-01 00:00:00 1 1 1 1 -84.7 45.6
## 7 test-ed… 2000-07-01 00:00:00 1 1 1 1 -84.7 45.6
## 8 test-ed… 2000-08-01 00:00:00 1 1 1 1 -84.7 45.6
## 9 test-ed… 2000-09-01 00:00:00 1 1 1 1 -84.7 45.6
## 10 test-ed… 2000-10-01 00:00:00 1 1 1 1 -84.7 45.6
## 11 test-ed… 2000-11-01 00:00:00 1 1 1 1 -84.7 45.6
## 12 test-ed… 2000-12-01 00:00:00 1 1 1 1 -84.7 45.6
## # … with 345 more variables: CROP_HARVEST_PY <dbl>, LOGGING_HARVEST_PY <dbl>,
## # COMBUSTED_FUEL_PY <dbl>, MMEAN_GPP_PY <dbl>, MMEAN_NPP_PY <dbl>,
## # MMEAN_LEAF_RESP_PY <dbl>, MMEAN_ROOT_RESP_PY <dbl>,
## # MMEAN_LEAF_GROWTH_RESP_PY <dbl>, MMEAN_ROOT_GROWTH_RESP_PY <dbl>,
## # MMEAN_SAPA_GROWTH_RESP_PY <dbl>, MMEAN_SAPB_GROWTH_RESP_PY <dbl>,
## # MMEAN_BARKA_GROWTH_RESP_PY <dbl>, MMEAN_BARKB_GROWTH_RESP_PY <dbl>,
## # MMEAN_LEAF_STORAGE_RESP_PY <dbl>, MMEAN_ROOT_STORAGE_RESP_PY <dbl>,
## # MMEAN_SAPA_STORAGE_RESP_PY <dbl>, MMEAN_SAPB_STORAGE_RESP_PY <dbl>,
## # MMEAN_BARKA_STORAGE_RESP_PY <dbl>, MMEAN_BARKB_STORAGE_RESP_PY <dbl>,
## # MMEAN_PLRESP_PY <dbl>, MMEAN_LEAF_ENERGY_PY <dbl>,
## # MMEAN_LEAF_WATER_PY <dbl>, MMEAN_LEAF_WATER_IM2_PY <dbl>,
## # MMEAN_LEAF_HCAP_PY <dbl>, MMEAN_LEAF_VPDEF_PY <dbl>,
## # MMEAN_LEAF_TEMP_PY <dbl>, MMEAN_LEAF_FLIQ_PY <dbl>,
## # MMEAN_LEAF_GSW_PY <dbl>, MMEAN_LEAF_GBW_PY <dbl>,
## # MMEAN_WOOD_ENERGY_PY <dbl>, MMEAN_WOOD_WATER_PY <dbl>,
## # MMEAN_WOOD_WATER_IM2_PY <dbl>, MMEAN_WOOD_HCAP_PY <dbl>,
## # MMEAN_WOOD_TEMP_PY <dbl>, MMEAN_WOOD_FLIQ_PY <dbl>,
## # MMEAN_WOOD_GBW_PY <dbl>, MMEAN_FS_OPEN_PY <dbl>, MMEAN_FSW_PY <dbl>,
## # MMEAN_FSN_PY <dbl>, MMEAN_A_OPEN_PY <dbl>, MMEAN_A_CLOSED_PY <dbl>,
## # MMEAN_A_NET_PY <dbl>, MMEAN_A_LIGHT_PY <dbl>, MMEAN_A_RUBP_PY <dbl>,
## # MMEAN_A_CO2_PY <dbl>, MMEAN_PSI_OPEN_PY <dbl>, MMEAN_PSI_CLOSED_PY <dbl>,
## # MMEAN_WATER_SUPPLY_PY <dbl>, MMEAN_PAR_L_PY <dbl>,
## # MMEAN_PAR_L_BEAM_PY <dbl>, MMEAN_PAR_L_DIFF_PY <dbl>,
## # MMEAN_RSHORT_L_PY <dbl>, MMEAN_RLONG_L_PY <dbl>,
## # MMEAN_SENSIBLE_LC_PY <dbl>, MMEAN_VAPOR_LC_PY <dbl>, MMEAN_TRANSP_PY <dbl>,
## # MMEAN_WFLUX_WL_PY <dbl>, MMEAN_WFLUX_GW_PY <dbl>,
## # MMEAN_INTERCEPTED_AL_PY <dbl>, MMEAN_WSHED_LG_PY <dbl>,
## # MMEAN_RSHORT_W_PY <dbl>, MMEAN_RLONG_W_PY <dbl>,
## # MMEAN_SENSIBLE_WC_PY <dbl>, MMEAN_VAPOR_WC_PY <dbl>,
## # MMEAN_INTERCEPTED_AW_PY <dbl>, MMEAN_WSHED_WG_PY <dbl>, MMEAN_RH_PY <dbl>,
## # MMEAN_FGC_RH_PY <dbl>, MMEAN_FSC_RH_PY <dbl>, MMEAN_STGC_RH_PY <dbl>,
## # MMEAN_STSC_RH_PY <dbl>, MMEAN_MSC_RH_PY <dbl>, MMEAN_SSC_RH_PY <dbl>,
## # MMEAN_PSC_RH_PY <dbl>, MMEAN_NEP_PY <dbl>, MMEAN_RK4STEP_PY <dbl>,
## # MMEAN_AVAILABLE_WATER_PY <dbl>, MMEAN_VEG_DISPLACE_PY <dbl>,
## # MMEAN_ROUGH_PY <dbl>, MMEAN_CAN_THEIV_PY <dbl>, MMEAN_CAN_THETA_PY <dbl>,
## # MMEAN_CAN_VPDEF_PY <dbl>, MMEAN_CAN_TEMP_PY <dbl>, MMEAN_CAN_SHV_PY <dbl>,
## # MMEAN_CAN_CO2_PY <dbl>, MMEAN_CAN_RHOS_PY <dbl>, MMEAN_CAN_DMOL_PY <dbl>,
## # MMEAN_CAN_PRSS_PY <dbl>, MMEAN_GND_TEMP_PY <dbl>, MMEAN_GND_SHV_PY <dbl>,
## # MMEAN_CAN_GGND_PY <dbl>, MMEAN_SFCW_DEPTH_PY <dbl>,
## # MMEAN_SFCW_ENERGY_PY <dbl>, MMEAN_SFCW_MASS_PY <dbl>,
## # MMEAN_SFCW_TEMP_PY <dbl>, MMEAN_SFCW_FLIQ_PY <dbl>,
## # MMEAN_RSHORT_GND_PY <dbl>, MMEAN_PAR_GND_PY <dbl>,
## # MMEAN_RLONG_GND_PY <dbl>, MMEAN_RLONGUP_PY <dbl>, …
plot(MMEAN_GPP_PY ~ datetime, data = scalar_results, type = "o",
main = "Total plot GPP")
pft_results <- tidyr::unnest(results, df_pft)
pft_results
## # A tibble: 204 x 43
## basename df_scalar df_cohort df_soil datetime pft NPLANT_PY
## <chr> <list> <list> <list> <dttm> <int> <dbl>
## 1 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 1 0.0771
## 2 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 2 0.0771
## 3 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 3 0.0773
## 4 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 4 0.0775
## 5 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 5 0
## 6 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 6 0
## 7 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 7 0
## 8 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 8 0
## 9 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 9 0
## 10 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 10 0
## # … with 194 more rows, and 36 more variables: AGB_PY <dbl>, WAI_PY <dbl>,
## # BASAL_AREA_PY <dbl>, BDEADA_PY <dbl>, BDEADB_PY <dbl>, BTIMBER_PY <dbl>,
## # BSAPWOODA_PY <dbl>, BSAPWOODB_PY <dbl>, BSEEDS_PY <dbl>, BYIELD_PY <dbl>,
## # BDEADA_N_PY <dbl>, BDEADB_N_PY <dbl>, BSAPWOODA_N_PY <dbl>,
## # BSAPWOODB_N_PY <dbl>, BSEEDS_N_PY <dbl>, MMEAN_THBARK_PY <dbl>,
## # MMEAN_LAI_PY <dbl>, MMEAN_BLEAF_PY <dbl>, MMEAN_BROOT_PY <dbl>,
## # MMEAN_BBARKA_PY <dbl>, MMEAN_BBARKB_PY <dbl>, MMEAN_BALIVE_PY <dbl>,
## # MMEAN_BSTORAGE_PY <dbl>, MMEAN_BLEAF_N_PY <dbl>, MMEAN_BROOT_N_PY <dbl>,
## # MMEAN_BBARKA_N_PY <dbl>, MMEAN_BBARKB_N_PY <dbl>, MMEAN_BALIVE_N_PY <dbl>,
## # MMEAN_BSTORAGE_N_PY <dbl>, MMEAN_LEAF_MAINTENANCE_PY <dbl>,
## # MMEAN_ROOT_MAINTENANCE_PY <dbl>, MMEAN_BARKA_MAINTENANCE_PY <dbl>,
## # MMEAN_BARKB_MAINTENANCE_PY <dbl>, MMEAN_LEAF_DROP_PY <dbl>,
## # MMEAN_ROOT_DROP_PY <dbl>, outdir <chr>
plot(MMEAN_LAI_PY ~ datetime, data = pft_results, col = pft, pch = 19,
main = "LAI by PFT")
Configuring ED2 runs
ED2 runs are configured using two files: ED2IN is a Fortran namelist
file generally used for settings that affect the entire run (e.g. start
and end dates; integration timestep; submodel configurations). A
reference ED2IN with documentation and default values (lightly tweaked
for FoRTE) is in the inst/ED2IN file. config.xml is
an XML file that contains PFT-specific parameters, plus a few others. A
complete example featuring all of the ED2 default values can be found in
the
inst/ed2-default-parameters.xml
file. Confusingly, some parameters can be set from both the config.xml
and the ED2IN – where both are used, parameters in the config.xml
override the ED2IN values.
Both sets of parameters can be modified through the run_ed2 function.
To modify ED2IN values, pass the name of the tag (without the leading
NL%) directly as an argument to run_ed2. For instance, in the below
example, we modify the ED_MET_DRIVER_HEADER and CROWN_MOD tags.
run_ed2(
outdir, start_dt, end_dt,
ED_MET_DRIVER_HEADER = "/path/to/ED_MET_DRIVER_HEADER",
CROWN_MOD = 1
)
To see the default values of the ED2IN file, and for some
documentation about what they mean,
The parameter configuration XML file (typically called config.xml) can
be configured via the configxml argument, which can be either a path
to a complete config.xml file or (probably easier) a list of values.
For example:
run_ed2(
outdir, start_dt, end_dt,
configxml = list(
pft = list(num = 9, SLA = 35),
pft = list(num = 10, Vm0 = 27.4),
phenology = list(theta_crit = -1.25)
),
ED_MET_DRIVER_HEADER = "/path/to/ED_MET_DRIVER_HEADER"
)
Note the format: A nested list of lists, with the name of each
first-level list indicating the type of parameter and each sub-list
consisting of name-value pairs. In the above example, we are setting the
SLA of PFT number 9 (Temperate Early-successional Hardwood) to 35 and
the Vcmax of PFT 10 (Temperate Mid-successional Hardwood) to 27.4, as
well as setting the PFT-independent phenology submodel parameter
theta_crit to -1.25.
Because the most common use of the config.xml is setting PFT
parameters, which may be well-suited to a tabular data structure, you
can also pass parameters as a data.frame (but only if you’re only
setting PFT parameters — if you have a mix of PFT parameters and other
values, you need to give the explicit nested list. Also, for this to
work, the same set of PFT parameters has to be set for all PFTs.):
run_ed2(
outdir, start_dt, end_dt,
configxml = data.frame(
num = c(9, 10),
SLA = c(35.3, 38.2),
Vm0 = c(27.4, 24.3)
),
ED_MET_DRIVER_HEADER = "/path/to/ED_MET_DRIVER_HEADER"
)
If ED2 finds and reads a config.xml file, it will also write out a
file called history.xml in the same directory that contains a complete
list of all parameters and values used. This is useful for figuring out
what ED2’s default parameters are, and confirming that parameters from
config.xml have been read in correctly. (The history.xml file from a
test run is actually the basis for the ed2-default-parameters.xml file
that ships with this package. Also, for a tabular version of the default
PFT parameters, see the
inst/ed2-default-pft-parameters.csv
file).
NOTE: A key difference between the ED2IN and config.xml files is
how strictly they are parsed and how values unknown to ED2 are treated.
The ED2IN file must be complete and have no values that ED2 does not
know about – both missing entries and entries that ED2 doesn’t know
about will result in errors during run initialization. On the other
hand, unknown values in config.xml are silently ignored – i.e. ED2
will only read in parameters that it knows about. This is why it’s a
good idea to check the history.xml file to make sure that your
parameters from the config.xml are being correctly set.
FoRTExperiment/ed4forte documentation built on March 21, 2020, 6:54 p.m.
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ED 2.2 R interface
Alexey Shikloamnov
Prepare inputs
For this tutorial, in your working directory, create a folder called
ed-input-data.
Download meteorology inputs from the GitHub releases
page.
For this tutorial, use the NARR-ED2.tar.gz file (North American
Regional Reanalysis). Download the file into ed-input-data and extract
it.
cd ed-input-data
tar -xf NARR-ED2.tar.gz
cd ..
You should now have a directory ed-input-data/NARR-ED2 containing a
bunch of files like 1979JAN.h5 as well as a file called
ED_MET_DRIVER_HEADER.
Open the ed-input-data/NARR-ED2/ED_MET_DRIVER_HEADER file in a text
editor, and change the third line (/Users/shik544/...) so it matches
the path of the directory
(e.g. /path/to/current/directory/ed-input-data/NARR-ED2/). You can
also do this with the following shell command:
# Use `gsed` instead of `sed` if on MacOS
sed -i "3s:.*:$PWD/ed-input-data/NARR-ED2/:" ed-input-data/NARR-ED2/ED_MET_DRIVER_DATA
The remaining inputs required for a basic ED2 simulation ship with the
ed4forte package. Install the package from your local clone
(devtools::install("/path/to/ed4forte")) or from GitHub
(devtools::install_github("FoRTExperiment/ed4forte")).
Basic ED2 run
First, locate the ED2 executable and set the R option ed4forte.ed2_exe
to the absolute path to this executable.
options(ed4forte.ed2_exe = "/path/to/ED/build/ed_2.2-dbg")
Use the following R code to perform a simple ED2 simulation at UMBS for the year 2000 starting from bare ground:
library(ed4forte)
outdir <- "test-ed-outputs"
ed_input_dir <- "ed-input-data"
assertthat::assert_that(dir.exists("ed-input-data"))
p <- run_ed2(
outdir,
"2000-01-01",
"2001-01-01",
ED_MET_DRIVER_DB = file.path(ed_input_dir, "NARR-ED2", "ED_MET_DRIVER_HEADER")
)
The first argument to run_ed2 is the output directory (which will be
created if it doesn’t exist). The next two arguments are the start and
end date (-times), respectively. The last argument indicates that the
default ED2IN value of the ED_MET_DRIVER_DB tag should be modified to
that value. Any default values for ED2IN tags can be modified in this
way.
Note that the code above will return immediately. This is because it
triggers ED2 to run in the background. The return object (p) is a
processx process, which can
be examined.
p$get_status()
## [1] "running"
p$is_alive()
## [1] TRUE
For more details, see the processx::process
documentation. To
wait for the run to complete, use the wait() method.
p$wait()
You can tell that the run finished by examining the contents of the output directory.
list.files(outdir)
## [1] "analysis-E-2000-01-00-000000-g01.h5" "analysis-E-2000-02-00-000000-g01.h5"
## [3] "analysis-E-2000-03-00-000000-g01.h5" "analysis-E-2000-04-00-000000-g01.h5"
## [5] "analysis-E-2000-05-00-000000-g01.h5" "analysis-E-2000-06-00-000000-g01.h5"
## [7] "analysis-E-2000-07-00-000000-g01.h5" "analysis-E-2000-08-00-000000-g01.h5"
## [9] "analysis-E-2000-09-00-000000-g01.h5" "analysis-E-2000-10-00-000000-g01.h5"
## [11] "analysis-E-2000-11-00-000000-g01.h5" "analysis-E-2000-12-00-000000-g01.h5"
## [13] "ED2IN" "stderr.log"
## [15] "stdout.log"
You should see the 12 monthly output files, along with a copy of the
ED2IN file used for the run and two log files corresponding to the input
(stdout) and error (stderr) streams.
Reading output
ED2 outputs are in HDF5 format, of which NetCDF is a special case.
Therefore, they can be read by NetCDF utilities, like the ncdf4
package.
outfiles <- list.files(outdir, "analysis-E-", full.names = TRUE)
nc <- ncdf4::nc_open(outfiles[1])
ncdf4::ncvar_get(nc, "AGB_CO")
## [1] 0.0002973947 0.0002414269 0.0001701059 0.0001030530 0.0001030530
ncdf4::ncvar_get(nc, "MMEAN_GPP_PY")
## [1] 0
Unfortunately, ED2’s produces only one file per timestep, which makes
reading in results from long simulations pain. ed4forte provides some
utilities for reading a subset of ED2 file types and variables in bulk.
results <- read_monthly_dir(outdir)
## Reading all HDF5 files
results
## # A tibble: 1 x 6
## basename df_scalar df_cohort df_soil df_pft outdir
## <chr> <list> <list> <list> <list> <chr>
## 1 test-ed-out… <tibble [12 ×… <tibble [65 … <tibble [84… <tibble [20… test-ed-o…
scalar_results <- tidyr::unnest(results, df_scalar)
scalar_results
## # A tibble: 12 x 353
## basename datetime PYSI_ID PYSI_N XATM YATM LONGITUDE LATITUDE
## <chr> <dttm> <int> <int> <int> <int> <dbl> <dbl>
## 1 test-ed… 2000-01-01 00:00:00 1 1 1 1 -84.7 45.6
## 2 test-ed… 2000-02-01 00:00:00 1 1 1 1 -84.7 45.6
## 3 test-ed… 2000-03-01 00:00:00 1 1 1 1 -84.7 45.6
## 4 test-ed… 2000-04-01 00:00:00 1 1 1 1 -84.7 45.6
## 5 test-ed… 2000-05-01 00:00:00 1 1 1 1 -84.7 45.6
## 6 test-ed… 2000-06-01 00:00:00 1 1 1 1 -84.7 45.6
## 7 test-ed… 2000-07-01 00:00:00 1 1 1 1 -84.7 45.6
## 8 test-ed… 2000-08-01 00:00:00 1 1 1 1 -84.7 45.6
## 9 test-ed… 2000-09-01 00:00:00 1 1 1 1 -84.7 45.6
## 10 test-ed… 2000-10-01 00:00:00 1 1 1 1 -84.7 45.6
## 11 test-ed… 2000-11-01 00:00:00 1 1 1 1 -84.7 45.6
## 12 test-ed… 2000-12-01 00:00:00 1 1 1 1 -84.7 45.6
## # … with 345 more variables: CROP_HARVEST_PY <dbl>, LOGGING_HARVEST_PY <dbl>,
## # COMBUSTED_FUEL_PY <dbl>, MMEAN_GPP_PY <dbl>, MMEAN_NPP_PY <dbl>,
## # MMEAN_LEAF_RESP_PY <dbl>, MMEAN_ROOT_RESP_PY <dbl>,
## # MMEAN_LEAF_GROWTH_RESP_PY <dbl>, MMEAN_ROOT_GROWTH_RESP_PY <dbl>,
## # MMEAN_SAPA_GROWTH_RESP_PY <dbl>, MMEAN_SAPB_GROWTH_RESP_PY <dbl>,
## # MMEAN_BARKA_GROWTH_RESP_PY <dbl>, MMEAN_BARKB_GROWTH_RESP_PY <dbl>,
## # MMEAN_LEAF_STORAGE_RESP_PY <dbl>, MMEAN_ROOT_STORAGE_RESP_PY <dbl>,
## # MMEAN_SAPA_STORAGE_RESP_PY <dbl>, MMEAN_SAPB_STORAGE_RESP_PY <dbl>,
## # MMEAN_BARKA_STORAGE_RESP_PY <dbl>, MMEAN_BARKB_STORAGE_RESP_PY <dbl>,
## # MMEAN_PLRESP_PY <dbl>, MMEAN_LEAF_ENERGY_PY <dbl>,
## # MMEAN_LEAF_WATER_PY <dbl>, MMEAN_LEAF_WATER_IM2_PY <dbl>,
## # MMEAN_LEAF_HCAP_PY <dbl>, MMEAN_LEAF_VPDEF_PY <dbl>,
## # MMEAN_LEAF_TEMP_PY <dbl>, MMEAN_LEAF_FLIQ_PY <dbl>,
## # MMEAN_LEAF_GSW_PY <dbl>, MMEAN_LEAF_GBW_PY <dbl>,
## # MMEAN_WOOD_ENERGY_PY <dbl>, MMEAN_WOOD_WATER_PY <dbl>,
## # MMEAN_WOOD_WATER_IM2_PY <dbl>, MMEAN_WOOD_HCAP_PY <dbl>,
## # MMEAN_WOOD_TEMP_PY <dbl>, MMEAN_WOOD_FLIQ_PY <dbl>,
## # MMEAN_WOOD_GBW_PY <dbl>, MMEAN_FS_OPEN_PY <dbl>, MMEAN_FSW_PY <dbl>,
## # MMEAN_FSN_PY <dbl>, MMEAN_A_OPEN_PY <dbl>, MMEAN_A_CLOSED_PY <dbl>,
## # MMEAN_A_NET_PY <dbl>, MMEAN_A_LIGHT_PY <dbl>, MMEAN_A_RUBP_PY <dbl>,
## # MMEAN_A_CO2_PY <dbl>, MMEAN_PSI_OPEN_PY <dbl>, MMEAN_PSI_CLOSED_PY <dbl>,
## # MMEAN_WATER_SUPPLY_PY <dbl>, MMEAN_PAR_L_PY <dbl>,
## # MMEAN_PAR_L_BEAM_PY <dbl>, MMEAN_PAR_L_DIFF_PY <dbl>,
## # MMEAN_RSHORT_L_PY <dbl>, MMEAN_RLONG_L_PY <dbl>,
## # MMEAN_SENSIBLE_LC_PY <dbl>, MMEAN_VAPOR_LC_PY <dbl>, MMEAN_TRANSP_PY <dbl>,
## # MMEAN_WFLUX_WL_PY <dbl>, MMEAN_WFLUX_GW_PY <dbl>,
## # MMEAN_INTERCEPTED_AL_PY <dbl>, MMEAN_WSHED_LG_PY <dbl>,
## # MMEAN_RSHORT_W_PY <dbl>, MMEAN_RLONG_W_PY <dbl>,
## # MMEAN_SENSIBLE_WC_PY <dbl>, MMEAN_VAPOR_WC_PY <dbl>,
## # MMEAN_INTERCEPTED_AW_PY <dbl>, MMEAN_WSHED_WG_PY <dbl>, MMEAN_RH_PY <dbl>,
## # MMEAN_FGC_RH_PY <dbl>, MMEAN_FSC_RH_PY <dbl>, MMEAN_STGC_RH_PY <dbl>,
## # MMEAN_STSC_RH_PY <dbl>, MMEAN_MSC_RH_PY <dbl>, MMEAN_SSC_RH_PY <dbl>,
## # MMEAN_PSC_RH_PY <dbl>, MMEAN_NEP_PY <dbl>, MMEAN_RK4STEP_PY <dbl>,
## # MMEAN_AVAILABLE_WATER_PY <dbl>, MMEAN_VEG_DISPLACE_PY <dbl>,
## # MMEAN_ROUGH_PY <dbl>, MMEAN_CAN_THEIV_PY <dbl>, MMEAN_CAN_THETA_PY <dbl>,
## # MMEAN_CAN_VPDEF_PY <dbl>, MMEAN_CAN_TEMP_PY <dbl>, MMEAN_CAN_SHV_PY <dbl>,
## # MMEAN_CAN_CO2_PY <dbl>, MMEAN_CAN_RHOS_PY <dbl>, MMEAN_CAN_DMOL_PY <dbl>,
## # MMEAN_CAN_PRSS_PY <dbl>, MMEAN_GND_TEMP_PY <dbl>, MMEAN_GND_SHV_PY <dbl>,
## # MMEAN_CAN_GGND_PY <dbl>, MMEAN_SFCW_DEPTH_PY <dbl>,
## # MMEAN_SFCW_ENERGY_PY <dbl>, MMEAN_SFCW_MASS_PY <dbl>,
## # MMEAN_SFCW_TEMP_PY <dbl>, MMEAN_SFCW_FLIQ_PY <dbl>,
## # MMEAN_RSHORT_GND_PY <dbl>, MMEAN_PAR_GND_PY <dbl>,
## # MMEAN_RLONG_GND_PY <dbl>, MMEAN_RLONGUP_PY <dbl>, …
plot(MMEAN_GPP_PY ~ datetime, data = scalar_results, type = "o",
main = "Total plot GPP")
pft_results <- tidyr::unnest(results, df_pft)
pft_results
## # A tibble: 204 x 43
## basename df_scalar df_cohort df_soil datetime pft NPLANT_PY
## <chr> <list> <list> <list> <dttm> <int> <dbl>
## 1 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 1 0.0771
## 2 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 2 0.0771
## 3 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 3 0.0773
## 4 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 4 0.0775
## 5 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 5 0
## 6 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 6 0
## 7 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 7 0
## 8 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 8 0
## 9 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 9 0
## 10 test-ed… <tibble … <tibble … <tibbl… 2000-01-01 00:00:00 10 0
## # … with 194 more rows, and 36 more variables: AGB_PY <dbl>, WAI_PY <dbl>,
## # BASAL_AREA_PY <dbl>, BDEADA_PY <dbl>, BDEADB_PY <dbl>, BTIMBER_PY <dbl>,
## # BSAPWOODA_PY <dbl>, BSAPWOODB_PY <dbl>, BSEEDS_PY <dbl>, BYIELD_PY <dbl>,
## # BDEADA_N_PY <dbl>, BDEADB_N_PY <dbl>, BSAPWOODA_N_PY <dbl>,
## # BSAPWOODB_N_PY <dbl>, BSEEDS_N_PY <dbl>, MMEAN_THBARK_PY <dbl>,
## # MMEAN_LAI_PY <dbl>, MMEAN_BLEAF_PY <dbl>, MMEAN_BROOT_PY <dbl>,
## # MMEAN_BBARKA_PY <dbl>, MMEAN_BBARKB_PY <dbl>, MMEAN_BALIVE_PY <dbl>,
## # MMEAN_BSTORAGE_PY <dbl>, MMEAN_BLEAF_N_PY <dbl>, MMEAN_BROOT_N_PY <dbl>,
## # MMEAN_BBARKA_N_PY <dbl>, MMEAN_BBARKB_N_PY <dbl>, MMEAN_BALIVE_N_PY <dbl>,
## # MMEAN_BSTORAGE_N_PY <dbl>, MMEAN_LEAF_MAINTENANCE_PY <dbl>,
## # MMEAN_ROOT_MAINTENANCE_PY <dbl>, MMEAN_BARKA_MAINTENANCE_PY <dbl>,
## # MMEAN_BARKB_MAINTENANCE_PY <dbl>, MMEAN_LEAF_DROP_PY <dbl>,
## # MMEAN_ROOT_DROP_PY <dbl>, outdir <chr>
plot(MMEAN_LAI_PY ~ datetime, data = pft_results, col = pft, pch = 19,
main = "LAI by PFT")
Configuring ED2 runs
ED2 runs are configured using two files: ED2IN is a Fortran namelist
file generally used for settings that affect the entire run (e.g. start
and end dates; integration timestep; submodel configurations). A
reference ED2IN with documentation and default values (lightly tweaked
for FoRTE) is in the inst/ED2IN file. config.xml is
an XML file that contains PFT-specific parameters, plus a few others. A
complete example featuring all of the ED2 default values can be found in
the
inst/ed2-default-parameters.xml
file. Confusingly, some parameters can be set from both the config.xml
and the ED2IN – where both are used, parameters in the config.xml
override the ED2IN values.
Both sets of parameters can be modified through the run_ed2 function.
To modify ED2IN values, pass the name of the tag (without the leading
NL%) directly as an argument to run_ed2. For instance, in the below
example, we modify the ED_MET_DRIVER_HEADER and CROWN_MOD tags.
run_ed2(
outdir, start_dt, end_dt,
ED_MET_DRIVER_HEADER = "/path/to/ED_MET_DRIVER_HEADER",
CROWN_MOD = 1
)
To see the default values of the ED2IN file, and for some
documentation about what they mean,
The parameter configuration XML file (typically called config.xml) can
be configured via the configxml argument, which can be either a path
to a complete config.xml file or (probably easier) a list of values.
For example:
run_ed2(
outdir, start_dt, end_dt,
configxml = list(
pft = list(num = 9, SLA = 35),
pft = list(num = 10, Vm0 = 27.4),
phenology = list(theta_crit = -1.25)
),
ED_MET_DRIVER_HEADER = "/path/to/ED_MET_DRIVER_HEADER"
)
Note the format: A nested list of lists, with the name of each
first-level list indicating the type of parameter and each sub-list
consisting of name-value pairs. In the above example, we are setting the
SLA of PFT number 9 (Temperate Early-successional Hardwood) to 35 and
the Vcmax of PFT 10 (Temperate Mid-successional Hardwood) to 27.4, as
well as setting the PFT-independent phenology submodel parameter
theta_crit to -1.25.
Because the most common use of the config.xml is setting PFT
parameters, which may be well-suited to a tabular data structure, you
can also pass parameters as a data.frame (but only if you’re only
setting PFT parameters — if you have a mix of PFT parameters and other
values, you need to give the explicit nested list. Also, for this to
work, the same set of PFT parameters has to be set for all PFTs.):
run_ed2(
outdir, start_dt, end_dt,
configxml = data.frame(
num = c(9, 10),
SLA = c(35.3, 38.2),
Vm0 = c(27.4, 24.3)
),
ED_MET_DRIVER_HEADER = "/path/to/ED_MET_DRIVER_HEADER"
)
If ED2 finds and reads a config.xml file, it will also write out a
file called history.xml in the same directory that contains a complete
list of all parameters and values used. This is useful for figuring out
what ED2’s default parameters are, and confirming that parameters from
config.xml have been read in correctly. (The history.xml file from a
test run is actually the basis for the ed2-default-parameters.xml file
that ships with this package. Also, for a tabular version of the default
PFT parameters, see the
inst/ed2-default-pft-parameters.csv
file).
NOTE: A key difference between the ED2IN and config.xml files is
how strictly they are parsed and how values unknown to ED2 are treated.
The ED2IN file must be complete and have no values that ED2 does not
know about – both missing entries and entries that ED2 doesn’t know
about will result in errors during run initialization. On the other
hand, unknown values in config.xml are silently ignored – i.e. ED2
will only read in parameters that it knows about. This is why it’s a
good idea to check the history.xml file to make sure that your
parameters from the config.xml are being correctly set.
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