Newer
Older
# written by Fabian Stenzel, based on work by Sebastian Ostberg
# 2022-2023 - stenzel@pik-potsdam.de
################# EcoRisk calc functions ###################
#' Wrapper for calculating the ecosystem change metric EcoRisk
#'
#' Function to read in data for ecorisk, and call the calculation function once,
#' if overtime is FALSE, or for each timeslice of length window years, if
#' overtime is TRUE
#'
#' @param path_ref folder of reference run
#' @param path_scen folder of scenario run
#' @param read_saved_data whether to read in previously saved data
#' (default: FALSE)
#' @param save_data file to save read in data to (default NULL)
#' @param save_ecorisk file to save EcoRisk data to (default NULL)
#' @param nitrogen include nitrogen outputs for pools and fluxes into EcoRisk
#' calculation (default FALSE)
#' @param weighting apply "old" (Ostberg-like), "new", or "equal" weighting of
#' vegetation_structure_change weights (default "equal")
#' @param time_span_reference vector of years to use as scenario period
#' @param time_span_scenario vector of years to use as scenario period
#' @param dimensions_only_local flag whether to use only local change component
#' for water/carbon/nitrogen fluxes and pools, or use an average of
#' local change, global change and ecosystem balance (default FALSE)
#' @param overtime logical: calculate ecorisk as time-series? (default: FALSE)
#' @param window integer, number of years for window length (default: 30)
#' @param debug_mode write out all nitrogen state variables (default FALSE)

Fabian Stenzel
committed
#' @param replace_input_file_names list with alternative names for output

Fabian Stenzel
committed
#' identifiers to replace the ones in inst/ext_files/metric_files.yml.

Fabian Stenzel
committed
#' e.g. list(npp="mnpp") would replace the expected output for npp with

Fabian Stenzel
committed
#' mnpp followed by the automatically detected file extension (.bin.json)
#' @param suppress_warnings suppress warnings - default: TRUE

Fabian Stenzel
committed
#' @param external_variability use externally supplied variability for the
#' reference period? experimental! (default: FALSE)
#' @param c2vr external variability array
#'
#' @return list data object containing arrays of ecorisk_total,
#' vegetation_structure_change, local_change, global_importance,
#' ecosystem_balance, carbon_stocks, carbon_fluxes, water_fluxes
#' @examples
#' \dontrun{
#' ecorisk_wrapper(
#' path_ref = pnv_folder,
#' path_scen = run_folder,
#' read_saved_data = FALSE,
#' nitrogen = TRUE,
#' save_data = NULL,
#' save_ecorisk = NULL,
#' time_span_reference = c(1550:1579),
#' time_span_scenario = c(1987:2016)
#' }
#'
#' @md
#' @export
ecorisk_wrapper <- function(path_ref,
path_scen,
read_saved_data = FALSE,
save_data = NULL,
save_ecorisk = NULL,
nitrogen = TRUE,
weighting = "equal",
time_span_reference,
time_span_scenario,
dimensions_only_local = FALSE,
overtime = FALSE,
window = 30,
debug_mode = FALSE,

Fabian Stenzel
committed
replace_input_file_names = NULL,
c2vr = NULL,
suppress_warnings = TRUE) {

Fabian Stenzel
committed
# check timespan consistency
nyears <- length(time_span_reference)
nyears_scen <- length(time_span_scenario)
# prepare slices for overtime calculation
slices <- (nyears_scen - window + 1)
window_half <- round(window / 2.0)
slice_years <- time_span_scenario[
(window_half + 1):(nyears_scen - window_half + 1)
]
if ((!nyears == window) || nyears_scen < window) {
stop(
"Timespan in reference is not equal to window size (",
window,
"), or scenario timespan is smaller than window size."
)
if (!read_saved_data) {
# translate output names (from metric_files.yml) and
# folders to files_scenarios/reference lists
metric_files <- system.file(
"extdata",
"metric_files.yml",
package = "biospheremetrics"
) %>%
yaml::read_yaml()
file_extension <- get_major_file_ext(paste0(path_scen))
files_scenario <- list()
files_reference <- list()

Fabian Stenzel
committed
for (output in names(metric_files$metric$ecorisk_nitrogen$output)) {
# Iterate over all outputs
if (is.null(replace_input_file_names[[output]])) {
for (file in metric_files$file_name[[output]]) {
full_file_path_lu <- paste0(path_scen, file, ".", file_extension)
if (file.exists(full_file_path_lu)) {
files_scenario[[output]] <- full_file_path_lu
}
full_file_path_pnv <- paste0(path_ref, file, ".", file_extension)
if (file.exists(full_file_path_pnv)) {
files_reference[[output]] <- full_file_path_pnv
}

Fabian Stenzel
committed
}
stop(
"None of the default file names for ", output,
" were found in ", path_scen, " - please check or define manually",
" using argument 'replace_input_file_names'. Stopping."
)
}
if (is.null(files_reference[[output]])) {
stop(
"None of the default file names for ", output,
" were found in ", path_ref, " - please check or define manually",
" using argument 'replace_input_file_names'. Stopping."
)

Fabian Stenzel
committed
}
files_scenario[[output]] <- paste0(
path_scen,
replace_input_file_names[[output]], ".", file_extension
)
files_reference[[output]] <- paste0(
path_ref,
replace_input_file_names[[output]], ".", file_extension
)

Fabian Stenzel
committed
}
} # end if !read_saved_data
if (overtime && (window != nyears)) stop("Overtime is enabled, but window \
length (", window, ") does not match the reference nyears.")
if (read_saved_data) {
if (!is.null(save_data)) {
message("Loading saved data from:", save_data)
stop(
"save_data is not specified as parameter, ",
"nothing to load ... exiting"
)
# first read in all lpjml output files required for computing EcoRisks
returned_vars <- read_ecorisk_data(
files_reference = files_reference,
files_scenario = files_scenario,
save_file = save_data,
nitrogen = nitrogen,
time_span_reference = time_span_reference,
time_span_scenario = time_span_scenario,
debug_mode = debug_mode,
suppress_warnings = suppress_warnings
if (overtime == FALSE && slices > 1) {
print("Overtime == FALSE, but too many time slices.
I assume this is just a read all data operation. Exiting.")
return(NULL)
}
# extract variables from return list object and give them proper names
state_ref <- returned_vars$state_ref
state_scen <- returned_vars$state_scen
fpc_ref <- returned_vars$fpc_ref
fpc_scen <- returned_vars$fpc_scen
bft_ref <- returned_vars$bft_ref
bft_scen <- returned_vars$bft_scen
cft_ref <- returned_vars$cft_ref
cft_scen <- returned_vars$cft_scen
lat <- returned_vars$lat
lon <- returned_vars$lon
cell_area <- returned_vars$cell_area
rm(returned_vars)
}
ncells <- length(cell_area)
ecorisk_total = array(0,
dim = c(ncells, slices),
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
dimnames = list(cell = cell_ids, year = slice_years)
),
vegetation_structure_change = array(0,
dim = c(ncells, slices),
dimnames = list(cell = cell_ids, year = slice_years)
),
local_change = array(0,
dim = c(ncells, slices),
dimnames = list(cell = cell_ids, year = slice_years)
),
global_importance = array(0,
dim = c(ncells, slices),
dimnames = list(cell = cell_ids, year = slice_years)
),
ecosystem_balance = array(0,
dim = c(ncells, slices),
dimnames = list(cell = cell_ids, year = slice_years)
),
c2vr = array(0,
dim = c(4, ncells, slices),
dimnames = list(part = 1:4, cell = cell_ids, year = slice_years)
),
carbon_stocks = array(0,
dim = c(ncells, slices),
dimnames = list(cell = cell_ids, year = slice_years)
),
carbon_fluxes = array(0,
dim = c(ncells, slices),
dimnames = list(cell = cell_ids, year = slice_years)
),
carbon_total = array(0,
dim = c(ncells, slices),
dimnames = list(cell = cell_ids, year = slice_years)
),
water_total = array(0,
dim = c(ncells, slices),
dimnames = list(cell = cell_ids, year = slice_years)
),
water_fluxes = array(0,
dim = c(ncells, slices),
dimnames = list(cell = cell_ids, year = slice_years)
),
nitrogen_stocks = array(0,
dim = c(ncells, slices),
dimnames = list(cell = cell_ids, year = slice_years)
),
nitrogen_fluxes = array(0,
dim = c(ncells, slices),
dimnames = list(cell = cell_ids, year = slice_years)
),
nitrogen_total = array(0,
dim = c(ncells, slices),
dimnames = list(cell = cell_ids, year = slice_years)
),

Fabian Stenzel
committed
lat = lat,
year_range <- as.character((as.numeric(y) - window_half):
(as.numeric(y) + window_half - 1))
message("Calculating time slice ", y, " of ", slice_years[1], "-", slice_years[length(slice_years)])
weighting = weighting,
lat = lat,
lon = lon,
cell_area = cell_area,
dimensions_only_local = dimensions_only_local,
nitrogen = nitrogen,
external_variability = external_variability,
c2vr = c2vr
ecorisk$ecorisk_total[, as.character(y)] <- returned$ecorisk_total
ecorisk$vegetation_structure_change[, as.character(y)] <- (
ecorisk$local_change[, as.character(y)] <- returned$local_change
ecorisk$global_importance[, as.character(y)] <- returned$global_importance
ecorisk$ecosystem_balance[, as.character(y)] <- returned$ecosystem_balance
ecorisk$c2vr[, , as.character(y)] <- returned$c2vr
ecorisk$carbon_stocks[, as.character(y)] <- returned$carbon_stocks
ecorisk$carbon_fluxes[, as.character(y)] <- returned$carbon_fluxes
ecorisk$carbon_total[, as.character(y)] <- returned$carbon_total
ecorisk$water_total[, as.character(y)] <- returned$water_total
ecorisk$water_fluxes[, as.character(y)] <- returned$water_fluxes
ecorisk$nitrogen_stocks[, as.character(y)] <- returned$nitrogen_stocks
ecorisk$nitrogen_fluxes[, as.character(y)] <- returned$nitrogen_fluxes
ecorisk$nitrogen_total[, as.character(y)] <- returned$nitrogen_total
}
############## export and save data if requested #############
if (!(is.null(save_ecorisk))) {
message("Saving EcoRisk data to: ", save_ecorisk)
save(ecorisk, file = save_ecorisk)
}
#
###
return(ecorisk)
}
#' Calculate the ecosystem change metric EcoRisk between 2 sets of states

Fabian Stenzel
committed
#' This function is called by the wrapper function (ecorisk_wrapper),
#' unless you know what you are doing, don't use this function directly.
#'
#' Function to calculate the ecosystem change metric EcoRisk, based on
#' gamma/vegetation_structure_change
#' work from Sykes (1999), Heyder (2011), and Ostberg (2015,2018).
#' This is a reformulated version in R, not producing 100% similar values
#' than the C/bash version from Ostberg et al. 2018, but similar the methodology
#'
#' @param fpc_ref reference run data for fpc
#' @param fpc_scen scenario run data for fpc
#' @param bft_ref reference run data for fpc_bft
#' @param bft_scen scenario run data for fpc_bft
#' @param cft_ref reference run data for cftfrac
#' @param cft_scen scenario run data for cftfrac
#' @param state_ref reference run data for state variables
#' @param state_scen scenario run data for state variables
#' @param weighting apply "old" (Ostberg-like), "new", or "equal" weighting of
#' vegetation_structure_change weights (default "equal")
#' @param lat latitude array
#' @param lon longitude array
#' @param cell_area cellarea array
#' @param dimensions_only_local flag whether to use only local change component
#' for water/carbon/nitrogen fluxes and pools, or use an average of
#' local change, global change and ecosystem balance (default FALSE)
#' @param nitrogen include nitrogen outputs (default: TRUE)
#' @param external_variability include external change_to_variability_ratio?
#' (default: FALSE)
#' @param c2vr list with external change_to_variability_ratios for each
#' component (default: NULL)
#'
#' @return list data object containing arrays of ecorisk_total,
#' vegetation_structure_change, local_change, global_importance,
#' ecosystem_balance, carbon_stocks, carbon_fluxes, water_fluxes
#' (+ nitrogen_stocks and nitrogen_fluxes)
#'
#' @export
calc_ecorisk <- function(fpc_ref,
fpc_scen,
bft_ref,
bft_scen,
cft_ref,
cft_scen,
state_ref,
state_scen,
weighting = "equal",
lat,
lon,
cell_area,
dimensions_only_local = FALSE,
nitrogen = TRUE,
external_variability = FALSE,
c2vr = NULL) {
if (external_variability && is.null(c2vr)) {
stop("external_variability enabled, but not supplied (c2vr). Aborting.")
di_ref <- dim(fpc_ref)
di_scen <- dim(fpc_scen)
ncells <- di_ref[1]
nyears <- di_ref[3]
if (di_ref[3] != di_scen[3]) {
stop("Dimension year does not match between fpc_scen and fpc_ref.")
}
# calc vegetation_structure_change and variability of
# vegetation_structure_change within
# reference period S(vegetation_structure_change,
# sigma_vegetation_structure_change)
fpc_ref_mean <- apply(fpc_ref, c(1, 2), mean)
bft_ref_mean <- apply(bft_ref, c(1, 2), mean)
cft_ref_mean <- apply(cft_ref, c(1, 2), mean)
sigma_vegetation_structure_change_ref_list <- array(
)
# calculate for every year of the reference period,
# vegetation_structure_change between that year and the average reference
# period year
# this gives the variability of vegetation_structure_change within the
# reference period
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
sigma_vegetation_structure_change_ref_list[, y] <- calc_delta_v( # nolint
fpc_ref = fpc_ref_mean,
fpc_scen = fpc_ref[, , y],
bft_ref = bft_ref_mean,
bft_scen = bft_ref[, , y],
cft_ref = cft_ref_mean,
cft_scen = cft_ref[, , y],
weighting = weighting
)
}
# calculate the std deviation over the reference period for each gridcell
vegetation_structure_changesd <- apply(
sigma_vegetation_structure_change_ref_list,
c(1),
stats::sd
)
# calculate vegetation_structure_change between average reference and average
# scenario period
vegetation_structure_change <- calc_delta_v(
fpc_ref = fpc_ref_mean,
fpc_scen = apply(fpc_scen, c(1, 2), mean),
bft_ref = bft_ref_mean,
bft_scen = apply(bft_scen, c(1, 2), mean),
cft_ref = cft_ref_mean,
cft_scen = apply(cft_scen, c(1, 2), mean),
weighting = weighting
)
#
####
############## calc EcoRisk components ################

Fabian Stenzel
committed
# dimensions in the state vector
# 1 "vegetation_carbon_pool"
# 2 "soil_carbon_pool"
# 3 "carbon_influx"
# 4 "carbon_outflux"
# 5 "soil_water_pool"
# 6 "water_influx"
# 7 "water_outflux"
# 8 "other"
# 9 "vegetation_nitrogen_pool"
# 10 "soil_mineral_nitrogen_pool"
# 11 "nitrogen_influx"
# 12 "nitrogen_outflux"
delta_var <- s_change_to_var_ratio(
vegetation_structure_change,
vegetation_structure_changesd
nitrogen_dimensions <- c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool",
"nitrogen_influx",
"nitrogen_outflux"
)
all_dimensions <- dimnames(state_scen)$class
non_nitrogen_dimensions <- setdiff(all_dimensions, nitrogen_dimensions)
ref = state_ref,
scen = state_scen,

Fabian Stenzel
committed
ref = state_ref,
scen = state_scen,
local = FALSE,
cell_area = cell_area
) # global importance
ref = state_ref,
scen = state_scen
) # ecosystem balance
ref = state_ref[, , non_nitrogen_dimensions],
scen = state_scen[, , non_nitrogen_dimensions],
ref = state_ref[, , non_nitrogen_dimensions],
scen = state_scen[, , non_nitrogen_dimensions],
ref = state_ref[, , non_nitrogen_dimensions],
scen = state_scen[, , non_nitrogen_dimensions]
) # ecosystem balance
}
if (dimensions_only_local == TRUE) {
# carbon stocks (local change)
cs <- calc_component(
ref = state_ref[, , c("vegetation_carbon_pool", "soil_carbon_pool")],
scen = state_scen[, , c("vegetation_carbon_pool", "soil_carbon_pool")],
local = TRUE,
cell_area = cell_area
)$full
# carbon fluxes (local change)
cf <- calc_component(
ref = state_ref[, , c("carbon_influx", "carbon_outflux")],
scen = state_scen[, , c("carbon_influx", "carbon_outflux")],
local = TRUE,
cell_area = cell_area
)$full
# total carbon (local change)
ct <- calc_component(
ref = state_ref[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool",
"carbon_influx",
"carbon_outflux"
)],
scen = state_scen[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool",
"carbon_influx",
"carbon_outflux"
)],
local = TRUE,
cell_area = cell_area
)$full
# water fluxes (local change)
wf <- calc_component(
ref = state_ref[, , c(
"water_influx",
"water_outflux"
)],
scen = state_scen[, , c(
"water_influx",
"water_outflux"
)],
local = TRUE,
cell_area = cell_area
)$full
# total water (local change)
wt <- calc_component(
ref = state_ref[, , c(
"water_influx",
"water_outflux",
"soil_water_pool"
)],
scen = state_scen[, , c(
"water_influx",
"water_outflux",
"soil_water_pool"
)],
local = TRUE,
cell_area = cell_area
)$full
# nitrogen stocks (local change)
if (nitrogen) {
ns <- calc_component(
ref = state_ref[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool"
)],
scen = state_scen[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool"
)],
# nitrogen fluxes (local change)
nf <- calc_component(
ref = state_ref[, , c("nitrogen_influx", "nitrogen_outflux")],
scen = state_scen[, , c("nitrogen_influx", "nitrogen_outflux")],
local = TRUE,
ref = state_ref[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool",
"nitrogen_influx",
"nitrogen_outflux"
)],
scen = state_scen[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool",
"nitrogen_influx",
"nitrogen_outflux"
)],
}
} else { # local == FALSE
cf <- (
calc_component(
ref = state_ref[, , c(
"carbon_influx",
"carbon_outflux"
)],
scen = state_scen[, , c(
"carbon_influx",
"carbon_outflux"
)],
ref = state_ref[, , c(
"carbon_influx",
"carbon_outflux"
)],
scen = state_scen[, , c(
"carbon_influx",
"carbon_outflux"
)],
local = FALSE,
cell_area = cell_area
ref = state_ref[, , c(
"carbon_influx",
"carbon_outflux"
)],
scen = state_scen[, , c(
"carbon_influx",
"carbon_outflux"
)]
ref = state_ref[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool"
)],
scen = state_scen[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool"
)],
ref = state_ref[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool"
)],
scen = state_scen[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool"
)],
ref = state_ref[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool"
)],
scen = state_scen[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool"
)]
ref = state_ref[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool",
"carbon_influx",
"carbon_outflux"
)],
scen = state_scen[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool",
"carbon_influx",
"carbon_outflux"
)],
calc_component(
ref = state_ref[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool",
"carbon_influx",
"carbon_outflux"
)],
scen = state_scen[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool",
"carbon_influx",
"carbon_outflux"
)],
local = FALSE,
cell_area = cell_area
calc_ecosystem_balance(
ref = state_ref[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool",
"carbon_influx",
"carbon_outflux"
)],
scen = state_scen[, , c(
"vegetation_carbon_pool",
"soil_carbon_pool",
"carbon_influx",
"carbon_outflux"
)]
ref = state_ref[, , c(
"water_influx",
"water_outflux"
)],
scen = state_scen[, , c(
"water_influx",
"water_outflux"
)],
ref = state_ref[, , c(
"water_influx",
"water_outflux"
)],
scen = state_scen[, , c(
"water_influx",
"water_outflux"
)],
local = FALSE,
cell_area = cell_area
ref = state_ref[, , c(
"water_influx",
"water_outflux"
)],
scen = state_scen[, , c(
"water_influx",
"water_outflux"
)]
ref = state_ref[, , c(
"water_influx",
"water_outflux",
"soil_water_pool"
)],
scen = state_scen[, , c(
"water_influx",
"water_outflux",
"soil_water_pool"
)],
ref = state_ref[, , c(
"water_influx",
"water_outflux",
"soil_water_pool"
)],
scen = state_scen[, , c(
"water_influx",
"water_outflux",
"soil_water_pool"
)],
local = FALSE,
cell_area = cell_area
ref = state_ref[, , c(
"water_influx",
"water_outflux",
"soil_water_pool"
)],
scen = state_scen[, , c(
"water_influx",
"water_outflux",
"soil_water_pool"
)]
if (nitrogen) {
# nitrogen stocks (local change)
ns <- (
calc_component(
ref = state_ref[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool"
)],
scen = state_scen[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool"
)],
ref = state_ref[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool"
)],
scen = state_scen[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool"
)],
ref = state_ref[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool"
)],
scen = state_scen[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool"
)]
# nitrogen fluxes (local change)
nf <- (
calc_component(
ref = state_ref[, , c(
"nitrogen_influx",
"nitrogen_outflux"
)],
scen = state_scen[, , c(
"nitrogen_influx",
"nitrogen_outflux"
)],
ref = state_ref[, , c(
"nitrogen_influx",
"nitrogen_outflux"
)],
scen = state_scen[, , c(
"nitrogen_influx",
"nitrogen_outflux"
)],
local = FALSE,
cell_area = cell_area
ref = state_ref[, , c(
"nitrogen_influx",
"nitrogen_outflux"
)],
scen = state_scen[, , c(
"nitrogen_influx",
"nitrogen_outflux"
)]
ref = state_ref[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool",
"nitrogen_influx",
"nitrogen_outflux"
)],
scen = state_scen[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool",
"nitrogen_influx",
"nitrogen_outflux"
)],
calc_component(
ref = state_ref[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool",
"nitrogen_influx",
"nitrogen_outflux"
)],
scen = state_scen[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool",
"nitrogen_influx",
"nitrogen_outflux"
)],
local = FALSE,
cell_area = cell_area
calc_ecosystem_balance(
ref = state_ref[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool",
"nitrogen_influx",
"nitrogen_outflux"
)],
scen = state_scen[, , c(
"vegetation_nitrogen_pool",
"soil_mineral_nitrogen_pool",
"nitrogen_influx",
"nitrogen_outflux"
)]

Fabian Stenzel
committed
delta <- vegetation_structure_change * c2vr["vs", ] # vegetation_structure
lc <- lc_raw$value * c2vr["lc", ]
gi <- gi_raw$value * c2vr["gi", ]
eb <- eb_raw$value * c2vr["eb", ]

Fabian Stenzel
committed
delta <- vegetation_structure_change * delta_var # vegetation_structure
lc <- lc_raw$value * lc_raw$var
gi <- gi_raw$value * gi_raw$var
eb <- eb_raw$value * eb_raw$var

Fabian Stenzel
committed
c2vr <- rbind(delta_var, lc_raw$var, gi_raw$var, eb_raw$var) # dim=(4,ncell)
dimnames(c2vr) <- list(
component = c("vs", "lc", "gi", "eb"),
cell = 0:(ncells - 1)
)
# calc total EcoRisk as the average of the 4 components
ecorisk_full <- (delta + lc + gi + eb) / 4 # check for NAs
if (nitrogen) {
ecorisk <- list(
ecorisk_total = ecorisk_full,
vegetation_structure_change = delta,
local_change = lc,
global_importance = gi,
ecosystem_balance = eb,
carbon_total = ct,
water_stocks = NA,
water_total = wt,
nitrogen_fluxes = nf,
nitrogen_total = nt
)
} else {
ecorisk <- list(
ecorisk_total = ecorisk_full,
vegetation_structure_change = delta,
local_change = lc,
global_importance = gi,
ecosystem_balance = eb,
carbon_total = ct,
water_stocks = NA,
water_total = wt,
nitrogen_stocks = NA,
nitrogen_fluxes = NA,
nitrogen_total = NA
)
}
###
return(ecorisk)
}
#' Read in output data from LPJmL to calculate the ecosystem change metric

Fabian Stenzel
committed
#' EcoRisk. This function is called by the wrapper function (ecorisk_wrapper),
#' unless you know what you are doing, don't use this function directly.
#'
#' Utility function to read in output data from LPJmL for calculation of EcoRisk
#'
#' @param files_reference folder of reference run
#' @param files_scenario folder of scenario run
#' @param save_file file to save read in data to (default NULL)
#' @param time_span_reference vector of years to use as scenario period
#' @param time_span_scenario vector of years to use as scenario period
#' @param nitrogen include nitrogen outputs for pools and fluxes into EcoRisk
#' calculation (default FALSE)
#' @param debug_mode write out all nitrogen state variables (default FALSE)
#' @param suppress_warnings suppress writing of Warnings, default: TRUE
#'
#' @return list data object containing arrays of state_ref, mean_state_ref,
#' state_scen, mean_state_scen, fpc_ref, fpc_scen, bft_ref, bft_scen,
#' cft_ref, cft_scen, lat, lon, cell_area
#'
#' @export
read_ecorisk_data <- function(
files_reference, # nolint
files_scenario,
save_file = NULL,
time_span_reference,
time_span_scenario,
nitrogen,
debug_mode = FALSE,
suppress_warnings = TRUE) {
file_type <- tools::file_ext(files_reference$grid)
if (file_type %in% c("json", "clm")) {
# read grid
grid <- lpjmlkit::read_io(

Fabian Stenzel
committed
files_reference$grid

Fabian Stenzel
committed
cell_area <- drop(lpjmlkit::read_io(
filename = files_reference$terr_area

Fabian Stenzel
committed
ncells <- length(lat)
nyears <- length(time_span_scenario)
nyears_ref <- length(time_span_reference)
### read in lpjml output
# for vegetation_structure_change (fpc,fpc_bft,cftfrac)
message("Reading in fpc, fpc_bft, cftfrac")
cft_scen <- aperm(lpjmlkit::read_io(
files_scenario$cftfrac,
subset = list(year = as.character(time_span_scenario))
) %>%
lpjmlkit::transform(to = c("year_month_day")) %>%
lpjmlkit::as_array(aggregate = list(month = sum)), c(1, 3, 2)) %>%
suppressWarnings()
bft_scen <- aperm(lpjmlkit::read_io(
files_scenario$fpc_bft,
subset = list(year = as.character(time_span_scenario))
) %>%
lpjmlkit::transform(to = c("year_month_day")) %>%
lpjmlkit::as_array(aggregate = list(month = sum)), c(1, 3, 2)) %>%
suppressWarnings()
fpc_scen <- aperm(lpjmlkit::read_io(
files_scenario$fpc,
subset = list(year = as.character(time_span_scenario))
) %>%
lpjmlkit::transform(to = c("year_month_day")) %>%
lpjmlkit::as_array(aggregate = list(month = sum)), c(1, 3, 2)) %>%
suppressWarnings()
if (file.exists(files_reference$cftfrac)) {
cft_ref <- aperm(lpjmlkit::read_io(
files_reference$cftfrac,
subset = list(year = as.character(time_span_reference))
) %>%
lpjmlkit::transform(to = c("year_month_day")) %>%
lpjmlkit::as_array(aggregate = list(month = sum)), c(1, 3, 2)) %>%
suppressWarnings()
} else {
cft_ref <- cft_scen * 0
}
if (file.exists(files_reference$fpc_bft)) {
bft_ref <- aperm(lpjmlkit::read_io(
files_reference$fpc_bft,
subset = list(year = as.character(time_span_reference))
) %>%
lpjmlkit::transform(to = c("year_month_day")) %>%
lpjmlkit::as_array(aggregate = list(month = sum)), c(1, 3, 2)) %>%
suppressWarnings()
} else {
bft_ref <- bft_scen * 0
}
fpc_ref <- aperm(lpjmlkit::read_io(
files_reference$fpc,
subset = list(year = as.character(time_span_reference))
) %>%
lpjmlkit::transform(to = c("year_month_day")) %>%
lpjmlkit::as_array(aggregate = list(month = sum)), c(1, 3, 2)) %>%
suppressWarnings()

Fabian Stenzel
committed
#### new input reading ###
metric_files <- system.file(
"extdata",
"metric_files.yml",
package = "biospheremetrics"
) %>%
yaml::read_yaml()

Fabian Stenzel
committed
nclasses <- length(metric_files$metric$ecorisk_nitrogen$metric_class)
nstate_dimensions <- 0
for (i in seq_len(nclasses)) {
nstate_dimensions <- nstate_dimensions +
length(metric_files$metric$ecorisk_nitrogen$metric_class[[i]])
}
state_ref <- array(0, dim = c(ncells, nyears_ref, nstate_dimensions))
state_scen <- array(0, dim = c(ncells, nyears, nstate_dimensions))
class_names <- seq_len(nstate_dimensions)

Fabian Stenzel
committed
index <- 1
# iterate over main classes (carbon pools, water fluxes ...)

Fabian Stenzel
committed
classe <- metric_files$metric$ecorisk_nitrogen$metric_class[[c]]
nsubclasses <- length(classe)
# iterate over subclasses (vegetation carbon, soil water ...)

Fabian Stenzel
committed
subclass <- classe[s]
class_names[index] <- names(subclass)
vars <- split_sign(unlist(subclass))

Fabian Stenzel
committed
path_scen_file <- files_scenario[[vars[v, "variable"]]]
if (file.exists(path_scen_file)) {
header_scen <- lpjmlkit::read_meta(filename = path_scen_file)

Fabian Stenzel
committed
"Reading in ", path_scen_file, " with unit ", header_scen$unit,
" -> as part of ", class_names[index]

Fabian Stenzel
committed
var_scen <- lpjmlkit::read_io(
path_scen_file,
subset = list(year = as.character(time_span_scenario))
) %>%
lpjmlkit::transform(to = c("year_month_day")) %>%
lpjmlkit::as_array(aggregate = list(month = sum, band = sum), ) %>%
drop() %>%
suppressWarnings()

Fabian Stenzel
committed
} else {
stop(paste("Couldn't read in:", path_scen_file, " - stopping!"))

Fabian Stenzel
committed
}
path_ref_file <- files_reference[[vars[v, "variable"]]]
if (file.exists(path_ref_file)) {
header_ref <- lpjmlkit::read_meta(path_ref_file)

Fabian Stenzel
committed
"Reading in ", path_ref_file, " with unit ", header_ref$unit,
" -> as part of ", class_names[index]

Fabian Stenzel
committed
var_ref <- lpjmlkit::read_io(
path_ref_file,
subset = list(year = as.character(time_span_reference))
) %>%
lpjmlkit::transform(to = c("year_month_day")) %>%
lpjmlkit::as_array(aggregate = list(month = sum, band = sum)) %>%
drop() %>%
suppressWarnings()

Fabian Stenzel
committed
} else {
stop(paste("Couldn't read in:", path_ref_file, " - stopping!"))

Fabian Stenzel
committed
}
# if (vars[v,"sign"] == "+"){
# state_scen[,,index,] <- state_scen[,,index,] + var_scen
# state_ref[,,index,] <- state_ref[,,index,] + var_ref
# } else { # vars[v,"sign"] == "-"
# state_scen[,,index,] <- state_scen[,,index,] - var_scen
# state_ref[,,index,] <- state_ref[,,index,] - var_ref
# }
# }else{
if (vars[v, "sign"] == "+") {
state_scen[, , index] <- state_scen[, , index] + var_scen
state_ref[, , index] <- state_ref[, , index] + var_ref
} else { # vars[v,"sign"] == "-"
state_scen[, , index] <- state_scen[, , index] - var_scen
state_ref[, , index] <- state_ref[, , index] - var_ref

Fabian Stenzel
committed
}

Fabian Stenzel
committed
}
index <- index + 1

Fabian Stenzel
committed
}
dimnames(state_scen) <- list(
cell = 0:(ncells - 1),
year = as.character(time_span_scenario), class = class_names
)
dimnames(state_ref) <- list(
cell = 0:(ncells - 1),
year = as.character(time_span_reference), class = class_names
)
} else if (file_type == "nc") { # to be added
stop(
"nc reading has not been updated to latest functionality. ",
"Please contact Fabian Stenzel"
)
} else {
stop("Unrecognized file type (", file_type, ")")
}
if (!(is.null(save_file))) {
message("Saving data to: ", save_file)
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
save(state_ref, state_scen, fpc_ref, fpc_scen,
bft_ref, bft_scen, cft_ref, cft_scen, lat, lon, cell_area,
file = save_file
)
}
return(
list(
state_ref = state_ref,
state_scen = state_scen,
fpc_ref = fpc_ref,
fpc_scen = fpc_scen,
bft_ref = bft_ref,
bft_scen = bft_scen,
cft_ref = cft_ref,
cft_scen = cft_scen,
lat = lat,
lon = lon,
cell_area = cell_area
)
)
}
#' Calculates changes in vegetation structure (vegetation_structure_change)
#'
#' Utility function to calculate changes in vegetation structure
#' (vegetation_structure_change) for calculation of EcoRisk
#'
#' @param fpc_ref reference fpc array (dim: [ncells,npfts+1])
#' @param fpc_scen scenario fpc array (dim: [ncells,npfts+1])
#' @param bft_ref reference bft array (dim: [ncells,nbfts])
#' @param bft_scen scenario bft array (dim: [ncells,nbfts])
#' @param cft_ref reference cft array (dim: [ncells,ncfts])
#' @param cft_scen scenario cft array (dim: [ncells,ncfts])
#' @param weighting apply "old" (Ostberg-like), "new", or "equal" weighting of
#' vegetation_structure_change weights (default "equal")
#'
#' @return vegetation_structure_change array of size ncells with the
#' vegetation_structure_change value [0,1] for each cell
#'
#' @examples
#' \dontrun{
#' vegetation_structure_change <- calc_delta_v(
#' fpc_ref = fpc_ref_mean,
#' fpc_scen = apply(fpc_scen, c(1, 2), mean),
#' bft_ref = bft_ref_mean,
#' bft_scen = apply(bft_scen, c(1, 2), mean),
#' cft_ref = cft_ref_mean,
#' cft_scen = apply(cft_scen, c(1, 2), mean),
#' weighting = "equal"
#' )
#' }
#' @export
calc_delta_v <- function(fpc_ref, # nolint
fpc_scen,
bft_ref,
bft_scen,
cft_ref,
cft_scen,
weighting = "equal") {
di <- dim(fpc_ref)
ncells <- di[1]
npfts <- di[2] - 1
fpc_ref[fpc_ref < 0] <- 0
fpc_scen[fpc_scen < 0] <- 0
bft_ref[bft_ref < 0] <- 0
bft_scen[bft_scen < 0] <- 0
cft_ref[cft_ref < 0] <- 0
cft_scen[cft_scen < 0] <- 0
if (npfts == 9) {
# barren = 1 - crop area - natural vegetation area +
# barren under bioenergy trees
barren_area_ref <- (
1 - rowSums(cft_ref) -
rowSums(fpc_ref[, 2:10]) * fpc_ref[, 1] +
rowSums(cft_ref[, c(16, 32)]) * (1 - rowSums(bft_ref[, c(1:4, 7:10)]))
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
)
barren_area_ref[barren_area_ref < 0] <- 0
tree_area_ref <- array(0, dim = c(ncells, 11))
# natural tree area fractions scaled by total natural frac
tree_area_ref[, 1:7] <- (
fpc_ref[, 2:8] * fpc_ref[, 1]
)
# fraction of rainfed tropical and temperate BE trees scaled by total
# rainfed bioenergy tree area and relative fpc of bioenergy trees and
# grass under bioenergy trees
tree_area_ref[, 8:9] <- (
cft_ref[, 16] * bft_ref[, 1:2] / rowSums(bft_ref[, c(1, 2, 4)])
)
# fraction of irrigated tropical and temperate BE trees scaled by total
# irrigated bioenergy tree area and relative fpc of bioenergy trees and
# grass under bioenergy trees
tree_area_ref[, 10:11] <- (
cft_ref[, 32] * bft_ref[, 7:8] / rowSums(bft_ref[, c(7, 8, 10)])
)
grass_area_ref <- array(0, dim = c(ncells, 20))
# natural grass
grass_area_ref[, 1:2] <- fpc_ref[, 9:10] * fpc_ref[, 1]
# crops
grass_area_ref[, 3:15] <- cft_ref[, 1:13] + cft_ref[, 17:29]
# managed grass rf
grass_area_ref[, 16] <- cft_ref[, 14]
# managed grass irr
grass_area_ref[, 17] <- cft_ref[, 30]
# bioenergy grass
grass_area_ref[, 18] <- cft_ref[, 15] + cft_ref[, 31]
# fraction of rainfed grass under bioenergy trees
grass_area_ref[, 19] <- (
cft_ref[, 16] * bft_ref[, 4] / rowSums(bft_ref[, c(1, 2, 4)])
)
# fraction of irrigated grass under bioenergy trees
grass_area_ref[, 20] <- (
cft_ref[, 32] * bft_ref[, 10] / rowSums(bft_ref[, c(7, 8, 10)])
)
rowSums(fpc_scen[, 2:10]) * fpc_scen[, 1] +
rowSums(cft_scen[, c(16, 32)]) * (1 - rowSums(bft_scen[, c(1:4, 7:10)]))
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
)
barren_area_scen[barren_area_scen < 0] <- 0
tree_area_scen <- array(0, dim = c(ncells, 11))
# natural tree area fractions scaled by total natural frac
tree_area_scen[, 1:7] <- (
fpc_scen[, 2:8] * fpc_scen[, 1]
)
# fraction of rainfed tropical and temperate BE trees scaled by total
# rainfed bioenergy tree area and relative fpc of bioenergy trees and
# grass under bioenergy trees
tree_area_scen[, 8:9] <- (
cft_scen[, 16] * bft_scen[, 1:2] / rowSums(bft_scen[, c(1, 2, 4)])
)
# fraction of irrigated tropical and temperate BE trees scaled by total
# irrigated bioenergy tree area and relative fpc of bioenergy trees and
# grass under bioenergy trees
tree_area_scen[, 10:11] <- (
cft_scen[, 32] * bft_scen[, 7:8] / rowSums(bft_scen[, c(7, 8, 10)])
)
grass_area_scen <- array(0, dim = c(ncells, 20))
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
grass_area_scen[, 1:2] <- fpc_scen[, 9:10] * fpc_scen[, 1]
# crops
grass_area_scen[, 3:15] <- cft_scen[, 1:13] + cft_scen[, 17:29]
# managed grass rf
grass_area_scen[, 16] <- cft_scen[, 14]
# managed grass irr
grass_area_scen[, 17] <- cft_scen[, 30]
# bioenergy grass
grass_area_scen[, 18] <- cft_scen[, 15] + cft_scen[, 31]
# fraction of rainfed grass under bioenergy trees
grass_area_scen[, 19] <- (
cft_scen[, 16] * bft_scen[, 4] / rowSums(bft_scen[, c(1, 2, 4)])
)
# fraction of irrigated grass under bioenergy trees
grass_area_scen[, 20] <- (
cft_scen[, 32] * bft_scen[, 10] / rowSums(bft_scen[, c(7, 8, 10)])
)
# evergreenness, needleleavedness, tropicalness, borealness, naturalness
tree_attributes <- matrix(
c(
c(1, 0, 1, 0, 1), # 1 TrBE
c(0, 0, 1, 0, 1), # 2 TrBR
c(1, 1, 0, 0, 1), # 3 TeNE
c(1, 0, 0, 0, 1), # 4 TeBE
c(0, 0, 0, 0, 1), # 5 TeBS
c(1, 1, 0, 1, 1), # 6 BoNE
c(0, 0.25, 0, 1, 1), # 7 BoS (including larchs)
c(1, 0, 1, 0, 0), # 8 TrBi tropical bioenergy rainfed
c(0, 0, 0, 0, 0), # 9 TeBi temperate bioenergy rainfed
c(1, 0, 1, 0, 0), # 10 TrBi tropical bioenergy irrigated
c(0, 0, 0, 0, 0) # 11 TeBi temperate bioenergy irrigated
),
nrow = 11,
byrow = TRUE
)
if (weighting == "equal") {
tree_weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
} else if (weighting == "new") {
tree_weights <- c(0.2, 0.2, 0.3, 0.3, 0.3) / 1.3
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
} else if (weighting == "old") {
tree_weights <- c(0.2, 0.2, 0.3, 0.3, 0.3)
} else {
stop("Unknown method of weighting.")
}
grass_attributes <- array(0, dim = c(ncells, 20, 2))
# 1 C3grass
# 2 C4grass
# 3 TemperateCereals
# 4 Rice
# 5 Maize
# 6 TropicalCereals
# 7 Pulses
# 8 TemperateRoots
# 9 TropicalRoots
# 10 Sunflower
# 11 Soybean
# 12 Groundnut
# 13 Rapeseed
# 14 Sugarcane
# 15 Others
# 16 Managed grass rainfed
# 17 Managed grass irrigated
# 18 Bioenergy grass
# 19 Grass under rainfed Bioenergy trees
# 20 Grass under irrigated Bioenergy trees
# tropicalness
grass_attributes[, , 1] <- rep(
c(0, 1, 0, 1, 1, 1, 0.5, 0, 1, 0.5, 1, 1, 0.5, 1, 0.5, NA, NA, 1, NA, NA),
each = ncells
)
# naturalness
grass_attributes[, , 2] <- rep(
c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
each = ncells
)
# dynamic share of tropicalness for rf/irr grasslands taken from ratio of
# bioenergy grasses
dyn_grass_attributes <- cbind(
bft_scen[, 6] / rowSums(bft_scen[, 5:6]),
bft_scen[, 12] / rowSums(bft_scen[, 11:12])
)
dyn_grass_attributes[!is.finite(dyn_grass_attributes)] <- 0
# managed grass rf/irr
grass_attributes[, 16:17, 1] <- dyn_grass_attributes
# grass under biotrees rf/irr (taken from managed grass)
grass_attributes[, 19:20, 1] <- dyn_grass_attributes
if (weighting == "equal") {
grass_weights <- c(0.2, 0.2)
} else if (weighting == "new") {
grass_weights <- c(0.5, 0.5)
# Sebastian Ostbergs's method (no downscaling to weightsum 1)
} else if (weighting == "old") {
grass_weights <- c(0.3, 0.3)
} else {
stop("Unknown method of weighting.")
}
} else if (npfts == 11) {
# barren = 1 - crop area - natural vegetation area +
# barren under bioenergy trees
barren_area_ref <- (
1 - rowSums(cft_ref) -
rowSums(fpc_ref[, 2:12]) * fpc_ref[, 1] +
rowSums(cft_ref[, c(16, 32)]) * (1 - rowSums(bft_ref[, c(4:9, 13:18)]))
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
)
barren_area_ref[barren_area_ref < 0] <- 0
tree_area_ref <- array(0, dim = c(ncells, 12))
# natural tree area fractions scaled by total natural frac
tree_area_ref[, 1:8] <- fpc_ref[, 2:9] * fpc_ref[, 1]
# fraction of rainfed tropical and temperate BE trees scaled by total
# rainfed bioenergy tree area and relative fpc of bioenergy trees and
# grass under bioenergy trees
tree_area_ref[, 9:10] <- (
cft_ref[, 16] * bft_ref[, 7:8] / rowSums(bft_ref[, 4:8])
)
# fraction of irrigated tropical and temperate BE trees scaled by total
# irrigated bioenergy tree area and relative fpc of bioenergy trees and
# grass under bioenergy trees
tree_area_ref[, 11:12] <- (
cft_ref[, 32] * bft_ref[, 16:17] / rowSums(bft_ref[, 13:17])
)
grass_area_ref <- array(0, dim = c(ncells, 21))
# natural grass
grass_area_ref[, 1:3] <- fpc_ref[, 10:12] * fpc_ref[, 1]
# crops
grass_area_ref[, 4:16] <- cft_ref[, 1:13] + cft_ref[, 17:29]
# managed grass rf
grass_area_ref[, 17] <- cft_ref[, 14]
# managed grass irr
grass_area_ref[, 18] <- cft_ref[, 30]
# bioenergy grass
grass_area_ref[, 19] <- cft_ref[, 15] + cft_ref[, 31]
# fraction of rainfed grass under bioenergy trees
grass_area_ref[, 20] <- (
cft_ref[, 16] * rowSums(bft_ref[, 4:6]) / rowSums(bft_ref[, 4:8])
)
# fraction of irrigated grass under bioenergy trees
grass_area_ref[, 21] <- (
cft_ref[, 32] * rowSums(bft_ref[, 13:15]) / rowSums(bft_ref[, 13:17])
)
# barren = 1 - crop area - natural vegetation area +
# barren under bioenergy trees
barren_area_scen <- (
1 - rowSums(cft_scen) -
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
)
barren_area_scen[barren_area_scen < 0] <- 0
tree_area_scen <- array(0, dim = c(ncells, 12))
# natural tree area fractions scaled by total natural frac
tree_area_scen[, 1:8] <- fpc_scen[, 2:9] * fpc_scen[, 1]
# fraction of rainfed tropical and temperate BE trees scaled by total
# rainfed bioenergy tree area and relative fpc of bioenergy trees and
# grass under bioenergy trees
tree_area_scen[, 9:10] <- (
cft_scen[, 16] * bft_scen[, 7:8] / rowSums(bft_scen[, 4:8])
)
# fraction of irrigated tropical and temperate BE trees scaled by total
# irrigated bioenergy tree area and relative fpc of bioenergy trees and
# grass under bioenergy trees
tree_area_scen[, 11:12] <- (
cft_scen[, 32] * bft_scen[, 16:17] / rowSums(bft_scen[, 13:17])
)
grass_area_scen <- array(0, dim = c(ncells, 21))
# natural grass
grass_area_scen[, 1:3] <- fpc_scen[, 10:12] * fpc_scen[, 1]
# crops
grass_area_scen[, 4:16] <- cft_scen[, 1:13] + cft_scen[, 17:29]
# managed grass rf
grass_area_scen[, 17] <- cft_scen[, 14]
# managed grass irr
grass_area_scen[, 18] <- cft_scen[, 30]
# bioenergy grass
grass_area_scen[, 19] <- cft_scen[, 15] + cft_scen[, 31]
# fraction of rainfed grass under bioenergy trees
grass_area_scen[, 20] <- (
cft_scen[, 16] * rowSums(bft_scen[, 4:6]) / rowSums(bft_scen[, 4:8])
)
# fraction of irrigated grass under bioenergy trees
grass_area_scen[, 21] <- (
cft_scen[, 32] * rowSums(bft_scen[, 13:15]) / rowSums(bft_scen[, 13:17])
)
# evergreenness, needleleavedness, tropicalness, borealness, naturalness
tree_attributes <- matrix(
c(
c(1, 0, 1, 0, 1), # 1 TrBE
c(0, 0, 1, 0, 1), # 2 TrBR
c(1, 1, 0, 0, 1), # 3 TeNE
c(1, 0, 0, 0, 1), # 4 TeBE
c(0, 0, 0, 0, 1), # 5 TeBS
c(1, 1, 0, 1, 1), # 6 BoNE
c(0, 0, 0, 1, 1), # 7 BoBS
c(0, 1, 0, 1, 1), # 8 BoNS
c(1, 0, 1, 0, 0), # 9 TrBi tropical bioenergy rainfed
c(0, 0, 0, 0, 0), # 10 TeBi temperate bioenergy rainfed
c(1, 0, 1, 0, 0), # 11 TrBi tropical bioenergy irrigated
c(0, 0, 0, 0, 0) # 12 TeBi temperate bioenergy irrigated
),
nrow = 12,
byrow = TRUE
)
if (weighting == "equal") {
tree_weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
} else if (weighting == "new") {
tree_weights <- c(0.2, 0.2, 0.3, 0.3, 0.3) / 1.3
# Sebastian Ostberg's method (no downscaling to weightsum 1)
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
} else if (weighting == "old") {
tree_weights <- c(0.2, 0.2, 0.3, 0.3, 0.3)
} else {
stop("Unknown method of weighting.")
}
grass_attributes <- array(0, dim = c(ncells, 21, 3))
# 1 C4grass tropic
# 2 C3grass temperate
# 3 C3grass polar
# 4 TemperateCereals
# 5 Rice
# 6 Maize
# 7 TropicalCereals
# 8 Pulses
# 9 TemperateRoots
# 10 TropicalRoots
# 11 Sunflower
# 12 Soybean
# 13 Groundnut
# 14 Rapeseed
# 15 Sugarcane
# 16 Others
# 17 Managed grass rainfed
# 18 Managed grass irrigated
# 19 Bioenergy grass
# 20 Grass under rainfed Bioenergy trees
# 21 Grass under irrigated Bioenergy trees
# tropicalness
grass_attributes[, , 1] <- rep(
c(
1, 0, 0, 0, 1, 1, 1, 0.5, 0, 1, 0.5,
1, 1, 0.5, 1, 0.5, NA, NA, 1, NA, NA
),
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
each = ncells
)
# borealness
grass_attributes[, , 2] <- rep(
c(0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, NA, NA),
each = ncells
)
# naturalness
grass_attributes[, , 3] <- rep(
c(1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
each = ncells
)
# dynamic share of tropicalness for grass under irr biotrees
dyn_trop_grass_attributes <- cbind(
# dynamic share of tropicalness for rf grasslands
bft_scen[, 1] / rowSums(bft_scen[, 1:3]),
# dynamic share of tropicalness for irr grasslands
bft_scen[, 10] / rowSums(bft_scen[, 10:12]),
# dynamic share of tropicalness for grass under rf biotrees
bft_scen[, 4] / rowSums(bft_scen[, 4:6]),
bft_scen[, 13] / rowSums(bft_scen[, 13:15])
)
dyn_trop_grass_attributes[!is.finite(dyn_trop_grass_attributes)] <- 0
# managed grass rf/irr, grass under biotrees rf/irr
grass_attributes[, c(17, 18, 20, 21), 1] <- dyn_trop_grass_attributes
# dynamic share of borealness for grass under irr biotrees
dyn_boreal_grass_attributes <- cbind(
# dynamic share of borealness for rf grasslands
bft_scen[, 3] / rowSums(bft_scen[, 1:3]),
# dynamic share of borealness for irr grasslands
bft_scen[, 12] / rowSums(bft_scen[, 10:12]),
# dynamic share of borealness for grass under rf biotrees
bft_scen[, 6] / rowSums(bft_scen[, 4:6]),
bft_scen[, 15] / rowSums(bft_scen[, 13:15])
)
dyn_boreal_grass_attributes[!is.finite(dyn_boreal_grass_attributes)] <- 0
# managed grass rf/irr, grass under biotrees rf/irr
grass_attributes[, c(17, 18, 20, 21), 2] <- dyn_boreal_grass_attributes
if (weighting == "equal") {
grass_weights <- c(0.2, 0.2, 0.2)
} else if (weighting == "old" || weighting == "new") {
grass_weights <- c(0.3333333, 0.3333333, 0.3333333)
} else {
stop("Unknown method of weighting.")
}
} else {
stop("Unknown number of pfts.")
}
# compute vegetation_structure_change
barren_v <- fBasics::rowMins(cbind(barren_area_ref, barren_area_scen))
trees_v <- fBasics::rowMins(
cbind(
rowSums(tree_area_ref, na.rm = TRUE),
rowSums(tree_area_scen, na.rm = TRUE)
)
cbind(
rowSums(grass_area_ref, na.rm = TRUE),
rowSums(grass_area_scen, na.rm = TRUE)
)
)
inner_sum_trees <- (
# evergreenness
abs(
rep(tree_attributes[, 1], each = ncells), na.rm = TRUE) -
rep(tree_attributes[, 1], each = ncells), na.rm = TRUE)
rep(tree_attributes[, 2], each = ncells), na.rm = TRUE) -
rep(tree_attributes[, 2], each = ncells), na.rm = TRUE)
rep(tree_attributes[, 3], each = ncells), na.rm = TRUE) -
rep(tree_attributes[, 3], each = ncells), na.rm = TRUE)
rep(tree_attributes[, 4], each = ncells), na.rm = TRUE) -
rep(tree_attributes[, 4], each = ncells), na.rm = TRUE)
rep(tree_attributes[, 5], each = ncells), na.rm = TRUE) -
rep(tree_attributes[, 5], each = ncells), na.rm = TRUE)
abs(
rowSums(grass_area_ref[, ] * grass_attributes[, , 1], na.rm = TRUE) -
rowSums(grass_area_scen[, ] * grass_attributes[, , 1], na.rm = TRUE)
# naturalness
abs(
rowSums(grass_area_ref[, ] * grass_attributes[, , 2], na.rm = TRUE) -
rowSums(grass_area_scen[, ] * grass_attributes[, , 2], na.rm = TRUE)
) * grass_weights[2]
)
} else if (npfts == 11) {
inner_sum_grasses <- (
# tropicalness
abs(
rowSums(grass_area_ref[, ] * grass_attributes[, , 1], na.rm = TRUE) -
rowSums(grass_area_scen[, ] * grass_attributes[, , 1], na.rm = TRUE)
# borealness
abs(
rowSums(grass_area_ref[, ] * grass_attributes[, , 2], na.rm = TRUE) -
rowSums(grass_area_scen[, ] * grass_attributes[, , 2], na.rm = TRUE)
) * grass_weights[2] +
# naturalness
abs(
rowSums(grass_area_ref[, ] * grass_attributes[, , 3], na.rm = TRUE) -
rowSums(grass_area_scen[, ] * grass_attributes[, , 3], na.rm = TRUE)
) * grass_weights[3]
)
} else {
stop("Unknown number of pfts.")
}
vegetation_structure_change <- (
1 - barren_v -
trees_v * (1 - inner_sum_trees) -
grass_v * (1 - inner_sum_grasses)
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
)
vegetation_structure_change[vegetation_structure_change < 0] <- 0
vegetation_structure_change[!is.finite(vegetation_structure_change)] <- 0
return(vegetation_structure_change)
}
################# further EcoRisk utility functions ##################
t_sigmoid_trafo <- function(x) {
return(-1 / exp(3) + (1 + 1 / exp(3)) / (1 + exp(-6 * (x - 0.5))))
}
balance <- function(v1, v2) {
return(1 - sum(v1 * v2) / (sqrt(sum(v1 * v1)) * sqrt(sum(v2 * v2))))
}
std_cellwise <- function(a) {
return(apply(a, 1, stats::sd))
}
global_yearly_weighted_mean <- function(a, cell_area) {
# a is matrix with dim=c(cells,years)
# cell_area the corresponding cell_area array with dim=c(cells)
return(
sum(a * cell_area, na.rm = TRUE) /
)
}
globally_weighted_mean_foreach_var <- function(x, cell_area) { # nolint
# x is matrix with dim=c(ncells,vars)
# if vars==1 inflate x
le <- length(x)
if (le == length(cell_area)) dim(x) <- c(le, 1)
# cell_area the corresponding cell_area array to x with dim=c(ncells)
return(colSums(x * cell_area, na.rm = TRUE) / sum(cell_area, na.rm = TRUE))
}
s_change_to_var_ratio <- function(x, s) {
return(1 / (1 + exp(-4 * (x / s - 2))))
}
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
aggregate_ecorisk_data_to_lower_resolution <- function(ecorisk_data_file,
new_resolution) {
ecorisk_data_file <- "/p/projects/open/Fabian/Metrics/data/ecorisk_202311_data.RData"
aggregation_factor <- 2 # 2 means from 0.5x0.5 to 1x1
load(ecorisk_data_file) # load ecorisk data objects:
# scenario data: state_scen,cft_scen,bft_scen,fpc_scen
# reference data: state_ref,cft_ref,bft_ref,fpc_ref,
# grid data: cell_area,lat,lon
str(state_ref)
state_scen <-
state_ref <-
fpc_scen <- fpc_ref
bft_scen <- bft_ref
cft_scen <- cft_ref
di_state <- dim(state_scen)
di_fpc <- dim(fpc_scen)
di_bft <- dim(bft_scen)
di_cft <- dim(cft_scen)
}
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
#' based on Heyder 2011 eq. 6-9; epsilon case handling from code
#' by Sebastian Ostberg (not documented in papers)
#' @param ref mean reference state vector of dimension c(ncells,variables)
#' @param scen mean scenario state vector of dimension c(ncells,variables)
#' @param epsilon threshold for variables to be treated as 0
#'
#' @returns the length of the difference vector for each cell
state_diff_local <- function(ref, scen, epsilon = 10^-4) {
# Ostberg code: case change_metric_lu_comparison_jun2013.c
di <- dim(ref)
# generally normalize the scenario state vector by the reference state
s_scen <- scen / ref
s_ref <- array(1, dim = di) # initialize
# for variables in places, where ref is small (<epsilon),
# but scen larger (Ostberg code, line 798)
# Sebastian set back scenario and reference vector, to keep the unscaled
# values (Ostberg code, line 804)
cells_ref0 <- abs(ref) < epsilon & abs(scen) > epsilon
s_scen[cells_ref0] <- scen[cells_ref0]
s_ref[cells_ref0] <- ref[cells_ref0]
# for variables in places, where ref and scen are small (<epsilon),
# return 0 (both are 1, difference is 0) (Ostberg code, line 809)
s_scen[abs(ref) < epsilon & abs(scen) < epsilon] <- 1 # no change
# normalize both state vectors by the sqrt(amount of state variables) to
# ensure length(s_ref)==1 (this is part of the weighting in the Ostberg
# code)
s_ref <- s_ref / sqrt(di[2])
s_scen <- s_scen / sqrt(di[2])
# length of the local difference vector s_scen (sl2) - s_ref (sl1)
return(sqrt(rowSums((s_scen - s_ref) * (s_scen - s_ref))))
}
#' c based on Heyder 2011 eq. 10-13
#'
#' @param ref mean reference state vector of dimension c(ncells,variables)
#' @param scen mean scenario state vector of dimension c(ncells,variables)
#' @param cell_area area of each cell as a vector of dim=c(ncells)
#' @param epsilon threshold for variables to be treated as 0
#'
#' @returns the length of the difference vector for each cell
state_diff_global <- function(ref, scen, cell_area, epsilon = 10^-4) {
di <- dim(ref)
ncells <- di[1]
global_mean_ref <- globally_weighted_mean_foreach_var(ref, cell_area)
global_mean_scen <- globally_weighted_mean_foreach_var(scen, cell_area)
# if global mean state in ref period is 0 (e.g. for landuse vars in pnv run?)
# take the mean scen state instead
cells_ref0 <- abs(global_mean_ref) < epsilon & abs(global_mean_scen) > epsilon
global_mean_ref[cells_ref0] <- global_mean_scen[cells_ref0]
# if both are 0 take 1, then the division is defined but 0 - 0 leads
# to no change, which is what EcoRisk should show
cells_both0 <- (
abs(global_mean_ref) < epsilon & abs(global_mean_scen) < epsilon
)
global_mean_ref[cells_both0] <- 1
norm <- rep(global_mean_ref, each = ncells)
dim(norm) <- dim(ref)
s_scen <- scen / norm
s_ref <- ref / norm
# normalize both state vectors by the sqrt(amount of state variables) to
# ensure length(s_ref)==1
# (this is part of the weighting in the Ostberg code)
s_ref <- s_ref / sqrt(di[2])
s_scen <- s_scen / sqrt(di[2])
# length of the difference vector s_scen (sl2) - s_ref (sl1) for each cell
return(sqrt(rowSums((s_scen - s_ref) * (s_scen - s_ref))))
}
calc_component <- function(ref, scen, local, cell_area) {
di <- dim(ref)
ncells <- di[1]
nyears <- di[2]
if (length(di) == 2) {
ref_mean <- apply(ref, c(1, 3), mean)
scen_mean <- apply(scen, c(1, 3), mean)
if (local) {
x <- t_sigmoid_trafo(state_diff_local(ref = ref_mean, scen = scen_mean))
} else {
x <- t_sigmoid_trafo(
state_diff_global(ref = ref_mean, scen = scen_mean, cell_area = cell_area)
)
}
# calculation of the change-to-variability ratio in my view is mathematically
# not correctly described in Heyder and Ostberg
# - the way I understand it: recalculate the c/g/b value for each year of the
# ref period compared to the mean of the ref period as "scenario" and then
# calc the variability (sd) of that
sigma_x_ref_list <- array(0, dim = c(ncells, nyears))
if (local) {
sigma_x_ref_list[, i] <- t_sigmoid_trafo(
state_diff_local(ref = ref_mean, scen = ref[, i, ])
)
} else {
sigma_x_ref_list[, i] <- t_sigmoid_trafo(
state_diff_global(
ref = ref_mean,
scen = ref[, i, ],
cell_area = cell_area
)
)
}
}
sigma_x_ref <- apply(sigma_x_ref_list, 1, stats::sd)
c2vr <- s_change_to_var_ratio(x, sigma_x_ref)
list(
full = x * c2vr,
value = x,
var = c2vr
}
balance_shift <- function(ref, scen, epsilon = 10^-4) {
# param ref with dimension c(ncells,nvars)
# param scen with dimension c(ncells,nvars)
if (is.null(dim(ref))) dim(ref) <- c(length(ref), 1)
# first normalize as for local change
s_scen <- scen / ref
s_ref <- array(1, dim = dim(ref))
# for variables in places, where ref is small (<epsilon), but scen larger
# (Ostberg code, line 798/vector length calc in line 837)
# set back scenario vector, to keep the unscaled values (Ostberg code,
# line 805)
s_scen[abs(ref) < epsilon & abs(scen) > epsilon] <- (
scen[abs(ref) < epsilon & abs(scen) > epsilon]
)
# for variables in places, where ref and scen are small (<epsilon),
# set scen to 1 (both are 1, difference is 0 -- no change) (Ostberg code,
# line 809)
# results in no change
s_scen[abs(ref) < epsilon & abs(scen) < epsilon] <- 1
abs_ref <- sqrt(rowSums(s_ref * s_ref))
# absb(_ts) in Sebastian Ostberg's code
abs_scen <- sqrt(rowSums(s_scen * s_scen))
b1 <- 1 - (rowSums(s_ref * s_scen) / abs_ref / abs_scen) # todo: all -> 0
# restrain to the maximum range for the acos function
b1[b1 < 0] <- 0
b1[b1 > 2] <- 2
angle <- acos(1 - b1) * 360 / 2 / pi
angle[b1 == 1] <- 0
b <- b1 * 2
b[angle > 60] <- 1
return(b)
}
calc_ecosystem_balance <- function(ref, scen) {
di <- dim(ref)
ncells <- di[1]
nyears <- di[2]
if (length(di) == 2) {
}
ref_mean <- apply(ref, c(1, 3), mean)
scen_mean <- apply(scen, c(1, 3), mean)
b <- balance_shift(ref = ref_mean, scen = scen_mean)
# calculation of the change-to-variability ratio in my view is mathematically
# not correctly described in Heyder and Ostberg
# - the way I understand it: recalculate the c/g/b value for each year of the
# ref period compared to the mean
# of the ref period as "scenario" and then calc the variability (sd) of that
sigma_b_ref_list <- array(0, dim = c(ncells, nyears))
sigma_b_ref_list[, i] <- balance_shift(ref = ref_mean, scen = ref[, i, ])
}
sigma_b_ref <- apply(sigma_b_ref_list, 1, stats::sd)
c2vr <- s_change_to_var_ratio(b, sigma_b_ref)
return(
list(
full = b * c2vr,
value = b,
var = c2vr
)
)
}
#' Create modified EcoRisk data file
#'
#' Function to create a modified EcoRisk data file where each reference cell is
#' compared to the average reference biome cell. The scenario period is
#' overwritten with the original reference period and all reference cells are
#' set to the average cell of the prescribed reference biome ref_biom
#'
#' @param data_file_in path to input data
#' @param data_file_out path to save modified data to
#' @param biome_classes_in biome classes object as returned from classify_biomes
#' @param ref_biom reference biome from biome classes that all cells should
#' be compared to
#'
#' @export
replace_ref_data_with_average_ref_biome_cell <- function(
# nolint
data_file_in,
data_file_out,
biome_classes_in,
ref_biom) {
if (data_file_in == data_file_out) {
stop(
"Same file for input and output of data, would overwrite ",
"original data. Aborting."
)
}
load(data_file_in)
ref_cells <- which(biome_classes_in$biome_id == ref_biom)
# first set all scen vars to the ref vars # [1:64240, 1:30, 1:10]
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
state_scen <- state_ref
fpc_scen <- fpc_ref
bft_scen <- bft_ref
cft_scen <- cft_ref
di_state <- dim(state_scen)
di_fpc <- dim(fpc_scen)
di_bft <- dim(bft_scen)
di_cft <- dim(cft_scen)
# now replace all ref cells with that of the mean ref biom cell
# FS 2022-08-10: keeping the year-to-year variation
if (length(ref_cells) == 1) {
av_year_state <- state_scen[ref_cells, , ]
fpc_ref <- rep(fpc_scen[ref_cells, , ], each = di_fpc[1])
bft_ref <- rep(bft_scen[ref_cells, , ], each = di_bft[1])
cft_ref <- rep(cft_scen[ref_cells, , ], each = di_cft[1])
} else {
av_year_state <- apply(state_scen[ref_cells, , ], c(2, 3), mean)
fpc_ref <- rep(
apply(fpc_scen[ref_cells, , ], c(2, 3), mean),
each = di_fpc[1]
)
bft_ref <- rep(
apply(bft_scen[ref_cells, , ], c(2, 3), mean),
each = di_bft[1]
)
cft_ref <- rep(
apply(cft_scen[ref_cells, , ], c(2, 3), mean),
each = di_cft[1]
)
}
state_ref <- rep(av_year_state, each = di_state[1])
dim(state_ref) <- di_state
dimnames(state_ref) <- dimnames(state_scen)
# is the same for each year, thus for the mean just take one year
# mean_state_ref <- rep(colMeans(av_year_state), each = di_state[1])
# FS: mean states were removed from data file, removing also here
dim(fpc_ref) <- di_fpc
dimnames(fpc_ref) <- dimnames(fpc_scen)
dimnames(bft_ref) <- dimnames(bft_scen)
dimnames(cft_ref) <- dimnames(cft_scen)
# and write out the modified data
# save(state_ref,mean_state_ref,state_scen,mean_state_scen,fpc_ref,fpc_scen,
# bft_ref,bft_scen,cft_ref,cft_scen,lat,lon,cell_area,file = data_file_out)
save(state_ref,
state_scen,
fpc_ref,
fpc_scen,
bft_ref,
bft_scen,
cft_ref,
cft_scen,
lat,
lon,
cell_area,
file = data_file_out
)
}
#' Create modified EcoRisk data for crosstable
#'
#' Function to create a modified EcoRisk data file where for each biome
#' the average scenario cell is compared to the average scenario cell of all
#' other biomes. This can then be used to compute a crosstable with the average
#' difference between each of them as in the SI of Ostberg et al. 2013
#' (Critical impacts of global warming on land ecosystems)
#'
#' @param data_file_in path to input data
#' @param data_file_out path to save modified data to
#' @param biome_classes_in biome classes object as returned from classify_biomes
#' @param pick_cells pick one specific cell as representative for the biome
#' instead of computing the average state
#' @param baseline_ref logical, use reference state as baseline?
#' default is FALSE - use scenario state
#' @return c2vr array to be used in the ecorisk call
#'
#' @export
ecorisk_cross_table <- function(data_file_in,
data_file_out,
biome_classes_in,
pick_cells = NULL,
if (data_file_in == data_file_out) {
stop(
"Same file for input and output of data, would overwrite original data. ",
"Aborting."
)
}
load(data_file_in)
# calculate ecorisk to get the variability
ecorisk_c2vr <- drop(ecorisk_wrapper(
path_ref = NULL,
path_scen = NULL,
read_saved_data = TRUE,
nitrogen = TRUE,
weighting = "equal",
save_data = data_file_in,
save_ecorisk = NULL,
time_span_reference = as.character(1550:1579),
time_span_scenario = as.character(1985:2014),
dimensions_only_local = FALSE
)$c2vr)
if (baseline_ref) {
# save reference state vectors, they contain relevant data (scen can go)
state_sav <- state_ref
fpc_sav <- fpc_ref
bft_sav <- bft_ref
cft_sav <- cft_ref
# save scenario state vectors, they contain relevant data (ref can go)
state_sav <- state_scen
fpc_sav <- fpc_scen
bft_sav <- bft_scen
cft_sav <- cft_scen
}
nbiomes <- max(biome_classes_in$biome_id) # by default 19
state_ref <- array(0, dim = c(nbiomes, nbiomes, dim(state_sav)[2:3]))
fpc_ref <- array(0, dim = c(nbiomes, nbiomes, dim(fpc_sav)[2:3]))
bft_ref <- array(0, dim = c(nbiomes, nbiomes, dim(bft_sav)[2:3]))
cft_ref <- array(0, dim = c(nbiomes, nbiomes, dim(cft_sav)[2:3]))
c2vr <- array(0, dim = c(4, nbiomes))
# now replace all ref cells with that of the mean ref biome cell
for (b in sort(unique(biome_classes_in$biome_id))) {
ref_cells <- which(biome_classes_in$biome_id == b)
if (is.null(pick_cells)) {
if (length(ref_cells) == 1) {
# average over cells, keeping the average year-to-year variation
av_state <- state_sav[ref_cells, , ]
av_fpc <- fpc_sav[ref_cells, , ]
av_bft <- bft_sav[ref_cells, , ]
av_cft <- cft_sav[ref_cells, , ]
} else {
# average over cells, keeping the average year-to-year variation
av_state <- apply(state_sav[ref_cells, , ], c(2, 3), mean)
av_fpc <- apply(fpc_sav[ref_cells, , ], c(2, 3), mean)
av_bft <- apply(bft_sav[ref_cells, , ], c(2, 3), mean)
av_cft <- apply(cft_sav[ref_cells, , ], c(2, 3), mean)
av_c2vr <- apply(ecorisk_c2vr[, ref_cells], c(1), mean)
av_state <- state_sav[pick_cells[b], , ]
av_fpc <- fpc_sav[pick_cells[b], , ]
av_bft <- bft_sav[pick_cells[b], , ]
av_cft <- cft_sav[pick_cells[b], , ]
}
state_ref[b, , , ] <- rep(av_state, each = nbiomes)
state_scen[, b, , ] <- rep(av_state, each = nbiomes)
mean_state_ref <- apply(state_ref, c(1, 3), mean)
mean_state_scen <- apply(state_scen, c(1, 3), mean)
fpc_ref[b, , , ] <- rep(av_fpc, each = nbiomes)
fpc_scen[, b, , ] <- rep(av_fpc, each = nbiomes)
bft_ref[b, , , ] <- rep(av_bft, each = nbiomes)
bft_scen[, b, , ] <- rep(av_bft, each = nbiomes)
cft_ref[b, , , ] <- rep(av_cft, each = nbiomes)
cft_scen[, b, , ] <- rep(av_cft, each = nbiomes)
dim(state_ref) <- c(nbiomes * nbiomes, dim(state_sav)[2:3])
dim(state_scen) <- c(nbiomes * nbiomes, dim(state_sav)[2:3])
dim(fpc_ref) <- c(nbiomes * nbiomes, dim(fpc_sav)[2:3])
dim(fpc_scen) <- c(nbiomes * nbiomes, dim(fpc_sav)[2:3])
dim(bft_ref) <- c(nbiomes * nbiomes, dim(bft_sav)[2:3])
dim(bft_scen) <- c(nbiomes * nbiomes, dim(bft_sav)[2:3])
dim(cft_ref) <- c(nbiomes * nbiomes, dim(cft_sav)[2:3])
dim(cft_scen) <- c(nbiomes * nbiomes, dim(cft_sav)[2:3])
dimnames(state_ref) <- list(
cell = as.character(seq_len(nbiomes * nbiomes)),
year = dimnames(state_sav)$year,
class = dimnames(state_sav)$class
)
dimnames(state_scen) <- dimnames(state_ref)
dimnames(fpc_ref) <- list(
cell = as.character(seq_len(nbiomes * nbiomes)),
band = dimnames(fpc_sav)$band,
year = dimnames(fpc_sav)$year
)
dimnames(fpc_scen) <- dimnames(fpc_ref)
dimnames(bft_ref) <- list(
cell = as.character(seq_len(nbiomes * nbiomes)),
band = dimnames(bft_sav)$band,
year = dimnames(bft_sav)$year
)
dimnames(bft_scen) <- dimnames(bft_ref)
dimnames(cft_ref) <- list(
cell = as.character(seq_len(nbiomes * nbiomes)),
band = dimnames(cft_sav)$band,
year = dimnames(cft_sav)$year
)
dimnames(cft_scen) <- dimnames(cft_ref)
dimnames(c2vr) <- list(
component = c("vs", "lc", "gi", "eb"),
biome = biome_classes_in$biome_names
)
lat <- rep(0, nbiomes * nbiomes)
lon <- rep(1, nbiomes * nbiomes)
cell_area <- rep(2, nbiomes * nbiomes)
# and write out the modified data
save(state_ref,
mean_state_ref,
state_scen,
mean_state_scen,
fpc_ref,
fpc_scen,
bft_ref,
bft_scen,
cft_ref,
cft_scen,
lat,
lon,
cell_area,
file = data_file_out
)
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
#' Create modified EcoRisk data combining two time series
#'
#' Function to combine two EcoRisk data files (e.g. historic and futures) into
#' one file.
#'
#' @param hist_file path to input file with historic data
#' @param scen_file path to input file with scenario data
#' @param combined_file path to save modified data to
#'
#'
#' @export
ecorisk_combine_hist_and_scen_data <- function(hist_file, scen_file, combined_file) {
load(hist_file)
state_scen_hist <- state_scen
fpc_scen_hist <- fpc_scen
bft_scen_hist <- bft_scen
cft_scen_hist <- cft_scen
load(scen_file)
state_scen_scen <- state_scen
fpc_scen_scen <- fpc_scen
bft_scen_scen <- bft_scen
cft_scen_scen <- cft_scen
dimnames_state_hist <- dimnames(state_scen_hist)
dimnames_state_scen <- dimnames(state_scen_scen)
dimnames_state_comb <- dimnames_state_hist
dimnames_state_comb$year <- c(dimnames_state_hist$year, dimnames_state_scen$year)
di_state_hist <- dim(state_scen_hist)
di_state_scen <- dim(state_scen_scen)
di_state_comb <- di_state_hist
di_state_comb[2] <- di_state_hist[2] + di_state_scen[2]
state_scen <- array(0, dim = di_state_comb)
dimnames(state_scen) <- dimnames_state_comb
state_scen[, dimnames_state_hist$year, ] <- state_scen_hist
state_scen[, dimnames_state_scen$year, ] <- state_scen_scen
dimnames_cft_hist <- dimnames(cft_scen_hist)
dimnames_cft_scen <- dimnames(cft_scen_scen)
dimnames_cft_comb <- dimnames_cft_hist
dimnames_cft_comb$year <- c(dimnames_cft_hist$year, dimnames_cft_scen$year)
di_cft_hist <- dim(cft_scen_hist)
di_cft_scen <- dim(cft_scen_scen)
di_cft_comb <- di_cft_hist
di_cft_comb[3] <- di_cft_hist[3] + di_cft_scen[3]
cft_scen <- array(0, dim = di_cft_comb)
dimnames(cft_scen) <- dimnames_cft_comb
cft_scen[, , dimnames_cft_hist$year] <- cft_scen_hist
cft_scen[, , dimnames_cft_scen$year] <- cft_scen_scen
dimnames_fpc_hist <- dimnames(fpc_scen_hist)
dimnames_fpc_scen <- dimnames(fpc_scen_scen)
dimnames_fpc_comb <- dimnames_fpc_hist
dimnames_fpc_comb$year <- c(dimnames_fpc_hist$year, dimnames_fpc_scen$year)
di_fpc_hist <- dim(fpc_scen_hist)
di_fpc_scen <- dim(fpc_scen_scen)
di_fpc_comb <- di_fpc_hist
di_fpc_comb[3] <- di_fpc_hist[3] + di_fpc_scen[3]
fpc_scen <- array(0, dim = di_fpc_comb)
dimnames(fpc_scen) <- dimnames_fpc_comb
fpc_scen[, , dimnames_fpc_hist$year] <- fpc_scen_hist
fpc_scen[, , dimnames_fpc_scen$year] <- fpc_scen_scen
dimnames_bft_hist <- dimnames(bft_scen_hist)
dimnames_bft_scen <- dimnames(bft_scen_scen)
dimnames_bft_comb <- dimnames_bft_hist
dimnames_bft_comb$year <- c(dimnames_bft_hist$year, dimnames_bft_scen$year)
di_bft_hist <- dim(bft_scen_hist)
di_bft_scen <- dim(bft_scen_scen)
di_bft_comb <- di_bft_hist
di_bft_comb[3] <- di_bft_hist[3] + di_bft_scen[3]
bft_scen <- array(0, dim = di_bft_comb)
dimnames(bft_scen) <- dimnames_bft_comb
bft_scen[, , dimnames_bft_hist$year] <- bft_scen_hist
bft_scen[, , dimnames_bft_scen$year] <- bft_scen_scen
if (file.exists(combined_file)) {
message("Output file ", combined_file, " already exists. Aborting.")
} else {
message("Saving data to: ", combined_file)
save(state_ref, state_scen, fpc_ref, fpc_scen, bft_ref, bft_scen,
cft_ref, cft_scen, lat, lon, cell_area,
file = combined_file
)
}
}
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
################# biome (dis-)aggregation functions ##################
#' Get biome names
#'
#' Returns biome names with variable length (abbreviated, short, or full)
#'
#' @param biome_name_length integer chose from 1,2,3 for abbreviated, short,
#' or full biome names
#'
#' @export
get_biome_names <- function(biome_name_length = 2) {
biome_mapping <- utils::read.csv(
file = system.file(
"extdata",
"biomes.csv",
package = "biospheremetrics"
),
sep = ";"
)
if (biome_name_length == 1) {
biome_class_names <- biome_mapping$abbreviation
} else if (biome_name_length == 2) {
biome_class_names <- biome_mapping$short_name
} else if (biome_name_length == 3) {
biome_class_names <- biome_mapping$name
} else {
stop(
"Value for parameter biome_name_length out of range 1,2,3 - ",
"was given as: ",
biome_name_length
)
}
return(biome_class_names)
}
#' Averages EcoRisk values across regions
#'
#' Returns the average value across either 4 regions or all (19) biomes for
#' EcoRisk and each of the subcomponents for each
#'
#' @param data List object, of which every item should be disaggregated
#' @param biome_class biome class list object as returned by classify_biomes
#' @param type string controlling whether to return minimum, mean, maximum
#' ("minmeanmax") or Q10,Q50,Q90 ("quantile") - default: "quantile"
#' @param classes string for into how many regions should be disaggregated
#' "4biomes" (tropics/temperate/boreal/arctic) or "allbiomes"
#'
#' @examples
#' \dontrun{
#' disaggregate_into_biomes(
#' ecorisk = ecorisk,
#' biome_class = biome_classes,
#' type = "quantile", classes = "4biomes"
#' )
#' }
#' @export
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
biome_class,
type = "quantile",
classes = "4biomes") {
di <- dim(data[[1]])
comp_names <- names(data)
if (type == "minmeanmax") {
type_names <- c("min", "mean", "max")
} else if (type == "quantile") {
type_names <- c("Q10", "Q50", "Q90")
}
if (length(di) > 1) {
slices <- di[2]
} else {
slices <- 1
}
if (classes == "4biomes") {
tropics <- c(1, 2, 9, 10, 11)
temperate <- c(3, 4, 5, 12, 13, 14)
boreal <- c(6, 7, 8)
arctic <- c(15, 16)
cell_list <- list(
tropical_cells = which(biome_class$biome_id %in% tropics),
temperate_cells = which(biome_class$biome_id %in% temperate),
boreal_cells = which(biome_class$biome_id %in% boreal),
arctic_cells = which(biome_class$biome_id %in% arctic)
)
nclasses <- 4
} else if (classes == "allbiomes") {
nclasses <- max(unique(biome_class$biome_id))
} else {
stop(
"Unknown parameter classes: ",
classes,
", should be either '4biomes' or 'allbiomes'"
)
}
data_dims <- length(data)
data_biomes <- array(0, dim = c(nclasses, data_dims, 3, slices))
if (classes == "4biomes") { # aggregate to trop/temp/boreal/arctic
for (s in seq_len(slices)) {
for (b in seq_len(nclasses)) {
for (cc in seq_len(data_dims)) {
data_biomes[b, cc, , s] <- c(
min(data[[cc]][cell_list[[b]], s], na.rm = TRUE),
mean(data[[cc]][cell_list[[b]], s], na.rm = TRUE),
max(data[[cc]][cell_list[[b]], s], na.rm = TRUE)
probs = c(0.1, 0.5, 0.9),
na.rm = TRUE
)
)
} else {
stop(paste(
"type", type,
"unknown. please choose either 'quantile' or 'minmeanmax'"
))
} # end if
} # end for
} # end for
} # end for
biome_names <- c("tropics", "temperate", "boreal", "arctic")
dimnames(data_biomes) <- list(
biome_names, comp_names,
type_names, seq_len(slices)
)
} else if (classes == "allbiomes") { # calculate all biomes separately
for (s in seq_len(slices)) {
for (b in seq_len(nclasses)) {
for (cc in seq_len(data_dims)) {
c(
min(data[[cc]][which(biome_class$biome_id == b), s], na.rm = TRUE),
mean(data[[cc]][which(biome_class$biome_id == b), s], na.rm = TRUE),
max(data[[cc]][which(biome_class$biome_id == b), s], na.rm = TRUE)
)
probs = c(0.1, 0.5, 0.9),
na.rm = TRUE
)
)
} else {
stop(paste(
"type", type,
"unknown. please choose either 'quantile' or 'minmeanmax'"
))
} # end if
} # end for
} # end for
} # end for
biome_names <- biome_class$biome_names
dimnames(data_biomes) <- list(
biome_names,
comp_names,
type_names,
seq_len(slices)
)
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
} else {
stop(
"Unknown parameter classes: ",
classes,
", should be either '4biomes' or 'allbiomes'"
)
}
return(drop(data_biomes))
}
#' Calculate ecorisk with each biomes average cell
#'
#' Function to calculate ecorisk with each biomes average cell
#' as a measure of internal variability
#'
#' @param biome_classes biome classes object as returned by classify biomes,
#' calculated for data_file_base
#' @param data_file_base base EcoRisk to compute differences with (only ref is
#' relevant)
#' @param intra_biome_distrib_file file to additionally write results to
#' @param create create new modified files, or read already existing ones?
#' @param res how finegrained the distribution should be (resolution)
#' @param nitrogen include nitrogen outputs (default: TRUE)
#' @param plotting whether plots for each biome should be created
#' @param plot_folder folder to plot into
#' @param time_span_reference suitable 30 year reference period (e.g.
#' c(1901,1930), c(1550,1579))

Fabian Stenzel
committed
#' @param ecorisk_components integer. how many subcomponents does the
#' @return data object with distibution - dim: c(biomes,ecorisk_variables,bins)
#'
#' @export
calculate_within_biome_diffs <- function(biome_classes, # nolint
data_file_base,
intra_biome_distrib_file,
create = FALSE,
res = 0.05,
plotting = FALSE,
plot_folder,
time_span_reference,
biomes_abbrv <- get_biome_names(1)
# nbiomes, nEcoRiskvars, nHISTclasses
intra_biome_distrib <- array(
0,
dim = c(length(biome_classes$biome_names), ecorisk_components, 1 / res)
)
# start
for (b in sort(unique(biome_classes$biome_id))) {
filebase <- strsplit(data_file_base, "_data.RData")[[1]]
message(
"Calculating differences with biome ", b, " (",
biome_classes$biome_names[b], ")"
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
)
data_file <- paste0(
filebase, "_compared_to_average_", biomes_abbrv[b], "_data.RData"
)
ecorisk_file <- paste0(
filebase, "_compared_to_average_", biomes_abbrv[b], "_gamma.RData"
)
if (create) {
replace_ref_data_with_average_ref_biome_cell(
data_file_in = data_file_base,
data_file_out = data_file,
biome_classes_in = biome_classes,
ref_biom = b
)
ecorisk <- ecorisk_wrapper(
# does not need to be specified, as data is read from file
path_ref = NULL,
# does not need to be specified, as data is read from file
path_scen = NULL,
read_saved_data = TRUE,
nitrogen = nitrogen,
weighting = "equal",
save_data = data_file,
save_ecorisk = ecorisk_file,
time_span_reference = time_span_reference,
time_span_scenario = time_span_reference,
dimensions_only_local = FALSE
)
} else {
# contains ecorisk list object
load(ecorisk_file)
}
# compute average values per focus biom
ref_cells <- which(biome_classes$biome_id == b)
ecorisk[[v]][ref_cells],
breaks = seq(0, 1, res), plot = FALSE
)$counts
intra_biome_distrib[b, v, ] <- (
intra_biome_distrib[b, v, ] / sum(intra_biome_distrib[b, v, ])
)
}
if (plotting) {
plot_ecorisk_map(
file = paste0(
plot_folder, "/compare_ecorisk_to_", biomes_abbrv[b], ".png"
),
focus_biome = b, biome_classes = biome_classes$biome_id,
ecorisk_object = ecorisk,
plot_dimension = "ecorisk_total",
title = biome_classes$biome_names[b],
legendtitle = "",
eps = FALSE,
title_size = 2,
leg_yes = TRUE
)
plot_ecorisk_map(
file = paste0(
plot_folder, "/compare_vegetation_structure_change_to_",
biomes_abbrv[b], ".png" # nolint
),
focus_biome = b, biome_classes = biome_classes$biome_id,
ecorisk_object = ecorisk,
plot_dimension = "vegetation_structure_change",
title = biome_classes$biome_names[b],
legendtitle = "",
eps = FALSE,
title_size = 2,
leg_yes = TRUE
)
plot_ecorisk_map(
file = paste0(
plot_folder, "/compare_gi_to_", biomes_abbrv[b], ".png"
),
focus_biome = b,
biome_classes = biome_classes$biome_id,
ecorisk_object = ecorisk,
plot_dimension = "global_importance",
title = biome_classes$biome_names[b],
legendtitle = "",
eps = FALSE,
title_size = 2,
leg_yes = TRUE
)
plot_ecorisk_map(
file = paste0(
plot_folder, "/compare_lc_to_", biomes_abbrv[b], ".png"
),
focus_biome = b,
biome_classes = biome_classes$biome_id,
ecorisk_object = ecorisk,
plot_dimension = "local_change",
title = biome_classes$biome_names[b],
legendtitle = "",
eps = FALSE,
title_size = 2,
leg_yes = TRUE
)
plot_ecorisk_map(
file = paste0(
plot_folder, "/compare_eb_to_", biomes_abbrv[b], ".png"
),
focus_biome = b,
biome_classes = biome_classes$biome_id,
ecorisk_object = ecorisk,
plot_dimension = "ecosystem_balance",
title = biome_classes$biome_names[b],
legendtitle = "",
eps = FALSE,
title_size = 2,
leg_yes = TRUE
)
} # end if plotting
}
ecorisk_dimensions <- names(ecorisk)
dim(intra_biome_distrib) <- c(
biome = length(biome_classes$biome_names),
variable = ecorisk_components, bin = 1 / res
)
dimnames(intra_biome_distrib) <- list(
biome = biomes_abbrv, variable = ecorisk_dimensions, bin = seq(res, 1, res)
)
save(intra_biome_distrib, file = intra_biome_distrib_file)
return(intra_biome_distrib)
}