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Add grid region HDHCDH outputs
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1. output annual and monthly HDH/CDH at grid region scale
2. Update output structure, and remove intermediate outputs. Now only generate the final csv that combines all the intermediate outputs.
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@mengqi-z
mengqi-z committed Feb 15, 2024
1 parent 3694773 commit 15ebf8b
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14 changes: 14 additions & 0 deletions R/data.R
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Original file line number Diff line number Diff line change
Expand Up @@ -224,6 +224,20 @@
#' }
"spatial_options"

#--------------------------------
# State to Grid Region Mapping
#--------------------------------
#' mapping_states_gridregion
#'
#' @source gcamdata/inst/extdata/gcam-usa/states_subregions.csv
#' @format tibble::tribble
#' @examples
#' \dontrun{
#' library(helios);
#' mapping_states_gridregion <- helios::mapping_states_gridregion
#' }
"mapping_states_gridregion"


#.................................
# Pre-processed Population Files
Expand Down
255 changes: 215 additions & 40 deletions R/hdcd.R
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@
#' @param xml Default = FALSE. Set to TRUE to generate XML outputs for GCAM.
#' @param save Default = TRUE. Set to TRUE to save outputs.
#' @param name_append Default = ''. String for the name to append to output file name.
#' @param im3_analysis Default = T. Output annual HDCD at grid region scale for trend-representative year analysis
#' @importFrom magrittr %>%
#' @importFrom data.table :=
#' @export
Expand All @@ -33,7 +34,8 @@ hdcd <- function(ncdf = NULL,
diagnostics = F,
xml = F,
name_append = '',
save = T) {
save = T,
im3_analysis = T) {

print('Starting function process_hdcd...')

Expand Down Expand Up @@ -65,7 +67,15 @@ hdcd <- function(ncdf = NULL,
hdcd_comb_annual <- tibble::tibble()
hdcd_region_bld <- tibble::tibble()
hdcd_region_monthly <- tibble::tibble()
hdcd_region_annual<- tibble::tibble()
hdcd_region_annual <- tibble::tibble()

# for IM3 grid region analysis
if(im3_analysis){
hdcd_comb_gridregion_monthly <- tibble::tibble()
hdcd_comb_gridregion_annual <- tibble::tibble()
}



# check population input format
if (any(grepl('tbl_df|tbl|data.frame', class(population)))) {
Expand Down Expand Up @@ -208,6 +218,19 @@ hdcd <- function(ncdf = NULL,
dplyr::ungroup() %>%
dplyr::mutate(pop_weight = value / subRegion_total_value)

# for IM3 grid region analysis
if(im3_analysis){
population_j_weighted_gridregion <- population_j_grid %>%
dplyr::left_join(helios::mapping_states_gridregion, by = 'subRegion') %>%
dplyr::mutate(subRegion = grid_region,
ID = grid_region) %>%
dplyr::select(-grid_region) %>%
dplyr::group_by(region, ID, subRegion, year) %>%
dplyr::mutate(subRegion_total_value = sum(value, na.rm = T)) %>%
dplyr::ungroup() %>%
dplyr::mutate(pop_weight = value / subRegion_total_value)
}

print('Completed population weighting.')

#......................
Expand All @@ -223,6 +246,23 @@ hdcd <- function(ncdf = NULL,
dplyr::mutate(value = (((value - 273.15) * 9/5) + 32) - reference_temp_F,
value = dplyr::if_else(is.na(pop_weight), value, value * pop_weight))

# for IM3 grid region analysis
if(im3_analysis){

ncdf_pivot_gridregion <- ncdf_pivot %>%
dplyr::left_join(helios::mapping_states_gridregion, by = 'subRegion') %>%
dplyr::mutate(subRegion = grid_region,
ID = grid_region) %>%
dplyr::select(-grid_region)

ncdf_hdcd_pop_weighted_gridregion <- ncdf_pivot_gridregion %>%
dplyr::left_join(population_j_weighted_gridregion %>%
dplyr::select(-value, -subRegion_total_value),
by = c('ID', 'region', 'subRegion', 'lat', 'lon', 'year')) %>%
dplyr::mutate(value = (((value - 273.15) * 9/5) + 32) - reference_temp_F,
value = dplyr::if_else(is.na(pop_weight), value, value * pop_weight))
}

} else {
print(paste0('Population data years: ', paste(names(population_j_grid)[!grepl('RID|lat|lon', names(population_j_grid))], collapse = ',')))
print(paste0('ncdf data years: ', as.character(years)))
Expand Down Expand Up @@ -270,15 +310,15 @@ hdcd <- function(ncdf = NULL,
day = dplyr::if_else(day < 10, paste0('0', day), paste0(day)),
hour = dplyr::if_else(hour < 10, paste0('0', hour), paste0(hour)) )

# hdcd segments
# hdcd segments degree hours
hdcd_region_segments <- hdcd_region %>%
dplyr::left_join(temporal_subset, by = c('datetime', 'year')) %>%
dplyr::left_join(helios::segment_map_utc,
by = c('subRegion', 'month', 'day', 'hour')) %>%
dplyr::group_by(region, subRegion, year, segment, HDCD) %>%
dplyr::summarise(value = sum(value, na.rm = T))

# hdcd monthly
# hdcd monthly degree days
hdcd_region_monthly <- hdcd_region %>%
dplyr::left_join(temporal_subset, by = c('datetime', 'year')) %>%
dplyr::group_by(region, subRegion, year, month, day) %>%
Expand All @@ -290,7 +330,7 @@ hdcd <- function(ncdf = NULL,
dplyr::ungroup() %>%
tidyr::gather(key = 'HDCD', value = 'value', HD, CD)

# hdcd annual
# hdcd annual degree days
hdcd_region_annual <- hdcd_region %>%
dplyr::left_join(temporal_subset, by = c('datetime', 'year')) %>%
dplyr::group_by(region, subRegion, year, month, day) %>%
Expand All @@ -303,6 +343,73 @@ hdcd <- function(ncdf = NULL,
tidyr::gather(key = 'HDCD', value = 'value', HD, CD)


# for IM3 grid region analysis
if(im3_analysis){

# Equivalent to step 6: Aggregate to regions
hdcd_gridregion <- ncdf_hdcd_pop_weighted_gridregion %>%
dplyr::filter(!is.na(subRegion)) %>%
dplyr::select(-lat, -lon) %>%
dplyr::group_by(region, subRegion, ID, year, datetime) %>%
dplyr::summarise(value = dplyr::if_else(any(is.na(pop_weight)), mean(value), sum(value))) %>%
dplyr::ungroup()

# Assign HDCD categories
hdcd_gridregion <- hdcd_gridregion %>%
dplyr::mutate(HDCD = dplyr::if_else(value < 0, 'HD', 'CD')) %>%
dplyr::filter(value != 0)

#......................
# Step 6a: Aggregate over Segments, monthly and annual
#......................
#
# temporal_subset <- data.frame(ncdf_times = ncdf_times[index_subset],
# x = paste0('X', index_subset)) %>%
# dplyr::mutate(datetime = as.POSIXct(ncdf_times, format = '%Y-%m-%d_%H:%M:%S', tz = 'UTC')) %>%
# dplyr::mutate(year = lubridate::year(datetime),
# month = lubridate::month(datetime),
# day = lubridate::day(datetime),
# hour = lubridate::hour(datetime),
# timezone = lubridate::tz(datetime)) %>%
# dplyr::mutate(month = dplyr::if_else(month < 10, paste0('0', month), paste0(month)),
# day = dplyr::if_else(day < 10, paste0('0', day), paste0(day)),
# hour = dplyr::if_else(hour < 10, paste0('0', hour), paste0(hour)) )
#
# # hdcd segments
# hdcd_region_segments <- hdcd_region %>%
# dplyr::left_join(temporal_subset, by = c('datetime', 'year')) %>%
# dplyr::left_join(helios::segment_map_utc,
# by = c('subRegion', 'month', 'day', 'hour')) %>%
# dplyr::group_by(region, subRegion, year, segment, HDCD) %>%
# dplyr::summarise(value = sum(value, na.rm = T))

# hdcd monthly **degree hours**
hdcd_gridregion_monthly <- hdcd_gridregion %>%
dplyr::left_join(temporal_subset, by = c('datetime', 'year')) %>%
# dplyr::group_by(region, subRegion, year, month, day) %>%
# dplyr::summarise(value = (max(value) + min(value)) / 2) %>%
# dplyr::ungroup() %>%
dplyr::group_by(region, subRegion, year, month) %>%
dplyr::summarise(HD = sum(value[value < 0]),
CD = sum(value[value > 0])) %>%
dplyr::ungroup() %>%
tidyr::gather(key = 'HDCD', value = 'value', HD, CD)

# hdcd annual **degree hours**
hdcd_gridregion_annual <- hdcd_gridregion %>%
dplyr::left_join(temporal_subset, by = c('datetime', 'year')) %>%
# dplyr::group_by(region, subRegion, year, month, day) %>%
# dplyr::summarise(value = (max(value) + min(value)) / 2) %>%
# dplyr::ungroup() %>%
dplyr::group_by(region, subRegion, year) %>%
dplyr::summarise(HD = sum(value[value < 0]),
CD = sum(value[value > 0])) %>%
dplyr::ungroup() %>%
tidyr::gather(key = 'HDCD', value = 'value', HD, CD)

}


#......................
# Step 7a: Add in building components for GCAMUSA
#......................
Expand Down Expand Up @@ -468,6 +575,9 @@ hdcd <- function(ncdf = NULL,
dplyr::filter(!is.na(subRegion),
!is.na(year),
!is.na(segment))

hdcd_name <- 'hdhcdh'

} else if ((model_timestep == 'daily' & dispatch_segment == FALSE) |
(model_timestep == 'hourly' & dispatch_segment == FALSE)){
hdcd_comb_gcam <- hdcd_comb_gcam %>%
Expand All @@ -481,30 +591,31 @@ hdcd <- function(ncdf = NULL,
dplyr::ungroup() %>%
dplyr::filter(!is.na(subRegion),
!is.na(year))

hdcd_name <- 'hddcdd'

} else {
stop('Output by dispatch segment requires hourly climate data.')
}

if(i == length(ncdf)){

year_min_i <- min(hdcd_comb_gcam$year, na.rm = T)
year_max_i <- max(hdcd_comb_gcam$year, na.rm = T)
year_min_i <- min(hdcd_comb_gcam$year, na.rm = T)
year_max_i <- max(hdcd_comb_gcam$year, na.rm = T)

if(i < length(ncdf)){
filename_i <- file.path(
folder,
helios::create_name(c('hdcd', model, 'intermediate_gcam', name_append), 'csv'))
} else {
filename_i <- file.path(
folder,
helios::create_name(c('hdcd', model, year_min_i, year_max_i, 'gcam', name_append), 'csv'))
}
helios::create_name(c(hdcd_name, model, year_min_i, year_max_i, 'gcam',
gsub('_', '-', gsub('gcam_', '', spatial)), name_append), 'csv'))

if(save){
data.table::fwrite(hdcd_comb_gcam, file = filename_i)
print(paste0('File saved as : ', filename_i))
if(save){
data.table::fwrite(hdcd_comb_gcam, file = filename_i)
print(paste0('File saved as : ', filename_i))
}
}

}

#......................
# Step 9b: Save monthly as combined csv files
#......................
Expand All @@ -522,22 +633,53 @@ hdcd <- function(ncdf = NULL,
!is.na(year),
!is.na(month))

year_min_i <- min(hdcd_comb_monthly$year, na.rm = T)
year_max_i <- max(hdcd_comb_monthly$year, na.rm = T)
if(i == length(ncdf)){

year_min_i <- min(hdcd_comb_monthly$year, na.rm = T)
year_max_i <- max(hdcd_comb_monthly$year, na.rm = T)

if(i < length(ncdf)){
filename_i_monthly <- file.path(
folder,
helios::create_name(c('hdcd', model, 'intermediate_monthly', name_append), 'csv'))
} else {
filename_i_monthly <- file.path(
folder,
helios::create_name(c('hdcd', model, year_min_i, year_max_i, 'monthly', name_append), 'csv'))
helios::create_name(c('hddcdd', model, year_min_i, year_max_i, 'monthly',
gsub('_', '-', gsub('gcam_', '', spatial)), name_append), 'csv'))

if(save){
data.table::fwrite(hdcd_comb_monthly, file = filename_i_monthly)
print(paste0('File saved as : ', filename_i_monthly))
}
}


# for IM3 grid region analysis
if(im3_analysis){

hdcd_comb_gridregion_monthly <- hdcd_comb_gridregion_monthly %>%
dplyr::bind_rows(hdcd_gridregion_monthly %>%
dplyr::mutate(unit = 'Fahrenheit degree-hours')) %>%
dplyr::ungroup() %>%
dplyr::group_by(region, subRegion, year, month, HDCD, unit) %>%
dplyr::summarize(value = sum(value, na.rm = T)) %>%
dplyr::ungroup() %>%
dplyr::filter(!is.na(subRegion),
!is.na(year),
!is.na(month))

if(i == length(ncdf)){

year_min_i <- min(hdcd_comb_gridregion_monthly$year, na.rm = T)
year_max_i <- max(hdcd_comb_gridregion_monthly$year, na.rm = T)

filename_i_gridregion_monthly <- file.path(
folder,
helios::create_name(c('hdhcdh', model, year_min_i, year_max_i, 'monthly', 'gridregion', name_append), 'csv'))

if(save){
data.table::fwrite(hdcd_comb_gridregion_monthly, file = filename_i_gridregion_monthly)
print(paste0('File saved as : ', filename_i_gridregion_monthly))
}
}


if(save){
data.table::fwrite(hdcd_comb_monthly, file = filename_i_monthly)
print(paste0('File saved as : ', filename_i_monthly))
}

}
Expand All @@ -558,22 +700,53 @@ hdcd <- function(ncdf = NULL,
dplyr::filter(!is.na(subRegion),
!is.na(year))

year_min_i <- min(hdcd_comb_annual$year, na.rm = T)
year_max_i <- max(hdcd_comb_annual$year, na.rm = T)
if(i == length(ncdf)){

year_min_i <- min(hdcd_comb_annual$year, na.rm = T)
year_max_i <- max(hdcd_comb_annual$year, na.rm = T)

if(i < length(ncdf)){
filename_i_annual <- file.path(
folder,
helios::create_name(c('hdcd', model, 'intermediate_annual', name_append), 'csv'))
} else {
filename_i_annual <- file.path(
folder,
helios::create_name(c('hdcd', model, year_min_i, year_max_i, 'annual', name_append), 'csv'))
helios::create_name(c('hddcdd', model, year_min_i, year_max_i, 'annual',
gsub('_', '-', gsub('gcam_', '', spatial)), name_append), 'csv'))

if(save){
data.table::fwrite(hdcd_comb_annual, file = filename_i_annual)
print(paste0('File saved as : ', filename_i_annual))
}
}


# for IM3 grid region analysis
if(im3_analysis){

hdcd_comb_gridregion_annual <- hdcd_comb_gridregion_annual %>%
dplyr::bind_rows(hdcd_gridregion_annual %>%
dplyr::mutate(unit = 'Fahrenheit degree-hours')) %>%
dplyr::ungroup() %>%
dplyr::group_by(region, subRegion, year, HDCD, unit) %>%
dplyr::summarize(value = sum(value, na.rm = T)) %>%
dplyr::ungroup() %>%
dplyr::filter(!is.na(subRegion),
!is.na(year))

if(i == length(ncdf)){

year_min_i <- min(hdcd_comb_gridregion_annual$year, na.rm = T)
year_max_i <- max(hdcd_comb_gridregion_annual$year, na.rm = T)

filename_i_gridregion_annual <- file.path(
folder,
helios::create_name(c('hdhcdh', model, year_min_i, year_max_i, 'annual', 'gridregion', name_append), 'csv'))

if(save){
data.table::fwrite(hdcd_comb_gridregion_annual, file = filename_i_gridregion_annual)
print(paste0('File saved as : ', filename_i_gridregion_annual))
}
}

if(save){
data.table::fwrite(hdcd_comb_annual, file = filename_i_annual)
print(paste0('File saved as : ', filename_i_annual))


}

}
Expand Down Expand Up @@ -617,7 +790,9 @@ hdcd <- function(ncdf = NULL,
# return data
invisible(list(hdcd_comb_gcam = hdcd_comb_gcam,
hdcd_comb_monthly = hdcd_comb_monthly,
hdcd_comb_annual = hdcd_comb_annual))
hdcd_comb_annual = hdcd_comb_annual,
hdcd_comb_gridregion_monthly = hdcd_comb_gridregion_monthly,
hdcd_comb_gridregion_annual = hdcd_comb_gridregion_annual))

} # Close process_hdcd

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