Degree days transformation and aggregation of climate data

The function staggregate_degree_days() aggregates climate data to the daily level, performs a degree days transformation on these daily values, and aggregates the transformed values to the polygon level and desired temporal scale

Usage

staggregate_degree_days(
  data,
  overlay_weights,
  time_agg = "month",
  start_date = NA,
  time_interval = "1 hour",
  thresholds
)

Arguments

data: The raster brick with the data to be transformed and aggregated
overlay_weights: A table of weights which can be generated using the function overlay_weights()
time_agg: the temporal scale to aggregate data to. Options are 'day', 'month', or 'year'
start_date: the date (and time, if applicable) of the first layer in the raster. To be input in a format compatible with lubridate::as_datetime(), e.g. "1991-10-29" or "1991-10-29 00:00:00". The default is NA since the rasters usually already contain temporal information in the layer names and they do not need to be manually supplied.
time_interval: the time interval between layers in the raster to be aggregated. To be input in a format compatible with seq(), e.g. '1 day' or '3 months'. The default is '1 hour' and this argument is required if the start_date argument is not NA.
thresholds: A vector of temperature thresholds critical to a crop

Examples

degree_days_output <- staggregate_degree_days(
  data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and
                                         # aggregate
  overlay_weights = overlay_weights_nj, # Output from overlay_weights()
  time_agg = "month", # Sum the transformed daily values across months
  start_date = "2024-06-01 00:00:00", # The start date of the supplied data,
                                      # only required if the layer name
                                      # format is not compatible with stagg
  time_interval = "1 hour", # The temporal interval of the supplied data,
                            # only required if the start_date is not NA
  thresholds = c(0, 10, 20) # Calculate degree days above 0, 10, and 20
                            # degrees Celsius
  )
#> Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of 1 hour
#> Skipping pre-transformation aggregation to daily level
#> Executing degree days transformation
#> Aggregating by polygon and month

head(degree_days_output)
#>     year month poly_id threshold_ninf_to_0 threshold_0_to_10 threshold_10_to_20
#>    <num> <num>  <char>               <num>             <num>              <num>
#> 1:  2024     6     011                   0              7200           7057.782
#> 2:  2024     6     033                   0              7200           6994.112
#> 3:  2024     6     015                   0              7200           6929.559
#> 4:  2024     6     009                   0              7200           7135.649
#> 5:  2024     6     007                   0              7200           6935.083
#> 6:  2024     6     041                   0              7200           6480.811
#>    threshold_20_to_inf
#>                  <num>
#> 1:            3179.867
#> 2:            3414.202
#> 3:            3365.634
#> 4:            2146.668
#> 5:            3407.180
#> 6:            2724.483