map_urbanET: map_urbanET

In AlbyDR/rSCOPE: Run SCOPE model through R

map_urbanET

Description

This is function map Urban ET based on the SCOPE outputs according to a selected period.

Usage

map_urbanET(
  dataset,
  function_var,
  function_time,
  output_vars,
  period_var = "annual",
  input_raster,
  NA_cells,
  Input_vector,
  veg_fraction,
  extract_fun = "max"
)

Arguments

dataset	a data.frame with SCOPE outputs, a datetime variable and pixel numbers (timestamp, id_pixel) from the get_prediction function
function_var	aggregation function, default = sum .
function_time	to define the period use data.table::year, data.table::quarters, data.table::month, data.table::week, data.table::yday" for day of the year
output_vars	variables to map, default c("ET", "ET_soil", "ET_canopy"),
period_var	name for the ET, default "annual"
input_raster	the grip template (raster object, the same as the interpolation)
NA_cells	a vector the raster grid id_pixel masked and excluded from the SCOPE run
Input_vector	name of the sf polygon map object
veg_fraction	name of the vegetation fraction variable
extract_fun	get the max ET form the 1km grid, default = 'max', faster and suitable in case of coarse grid

Value

It will save the sf map with the Urban ET aggregated by the selected period .

Examples

Map Urban ET based on the SCOPE predictions

Urban_ET_map <- map_urbanET(dataset = Berlin2020_pred,
                            input_raster = krg_grid,
                            NA_cells = cellNA,
                            Input_vector = Green_vol,
                            veg_fraction = "vegproz",
                            function_var = sum,
                            function_time = data.table::year,
                            output_vars = c("ET", "ET_soil", "ET_canopy"),
                            period_var = "annual",
                            extract_fun = 'max')

plot(Urban_ET_map, border = "transparent", nbreaks=11,
     pal=RColorBrewer::brewer.pal('RdYlBu', n = 11), reset=FALSE)

Urban_ET_map_hotday <- map_urbanET(dataset = Berlin2020_pred[as.IDate(timestamp) == "2020-08-08",], # hottest day subset
                                   input_raster = krg_grid,
                                   NA_cells = cellNA,
                                   Input_vector = Green_vol,
                                   veg_fraction = "vegproz",
                                   function_var = sum,
                                   function_time = data.table::yday,
                                   output_vars = c("ET"),
                                   period_var = "hottest_day",
                                   extract_fun = 'max')

plot(Urban_ET_map_hotday, border = "transparent", nbreaks=11,
     pal=RColorBrewer::brewer.pal('RdYlBu', n = 11), reset=FALSE)

Urban_ET_map_month <- map_urbanET(input_raster = krg_grid,
                                  NA_cells = cellNA,
                                  Input_vector = Green_vol,
                                 veg_fraction = "vegproz",
                                  dataset = Berlin2020_pred,
                                  function_var = sum,
                                  function_time = data.table::month,
                                  output_vars = c("ET"),
                                  period_var = "monthly",
                                  extract_fun = 'max')

plot(Urban_ET_map_month[c(3,9,17,21)], border = "transparent", nbreaks=11,
     pal=RColorBrewer::brewer.pal('RdYlBu', n = 11), reset=FALSE)

Urban_ET_map_quarter <- map_urbanET(dataset = Berlin2020_pred, # data.table with datetime, id_pixel and SCOPE outputs
                                    input_raster = krg_grid, # raster used to interpolate and modelling
                                    NA_cells = cellNA, # vector with the NA cells mask by the city (Berlin) border
                                    Input_vector = Green_vol, # vector map (sf) with the vegetation fraction
                                    veg_fraction = "vegproz", # name of the vegetation fraction var in the map (sf)
                                    function_var = sum, # function to summarize (sum, mean, max, min)
                                    function_time = quarter, # time to summarize (year, quarter, month, week, yday, hour). If hour or day maybe is need to reduce the range of the timestamp before run
                                    output_vars = c("ET", "ET_canopy"), # possible outputs (ET, ET_soil, Tsave - see names(Berlin2020_pred)). If many, better year, quarter or up tp month
                                    period_var = "quarter", # name to enumerate the period
                                    extract_fun = 'max') # function to extract the raster values into the vector. If the raster is high-resolution use "mean",  otherwise "max" (e.g. 1km grid)

plot(Urban_ET_map_quarter[c(3,7,11,15)], border = "transparent", nbreaks=11,
     pal=RColorBrewer::brewer.pal('RdYlBu', n = 11), reset=FALSE)

AlbyDR/rSCOPE documentation built on Dec. 19, 2024, 7:29 p.m.