Estimate degree days from daily min and max temperature
dd_calc(
daily_min,
daily_max,
nextday_min = daily_min,
thresh_low = NULL,
thresh_up = NULL,
method = c("sng_tri", "dbl_tri", "sng_sine", "dbl_sine", "simp_avg")[0],
cutoff = c("horizontal", "vertical", "intermediate")[1],
digits = 2,
cumulative = FALSE,
no_neg = TRUE,
simp_avg_zero_method = 1,
interpolate_na = FALSE,
quiet = FALSE,
debug = FALSE
)
dd_simp_avg(
daily_min,
daily_max,
thresh_low,
thresh_up = NULL,
simp_avg_zero_method = 1,
digits = 2,
cumulative = FALSE,
quiet = FALSE
)
dd_sng_tri(
daily_min,
daily_max,
thresh_low = NULL,
thresh_up = NULL,
cutoff = c("horizontal", "vertical", "intermediate")[1],
digits = 2,
cumulative = FALSE,
quiet = FALSE
)
dd_sng_sine(
daily_min,
daily_max,
thresh_low = NULL,
thresh_up = NULL,
cutoff = c("horizontal", "vertical", "intermediate")[1],
digits = 2,
cumulative = FALSE,
quiet = FALSE
)
dd_dbl_tri(
daily_min,
daily_max,
nextday_min = daily_min,
thresh_low = NULL,
thresh_up = NULL,
cutoff = c("horizontal", "vertical", "intermediate")[1],
digits = 2,
cumulative = FALSE,
quiet = FALSE
)
dd_dbl_sine(
daily_min,
daily_max,
nextday_min = daily_min,
thresh_low = NULL,
thresh_up = NULL,
cutoff = c("horizontal", "vertical", "intermediate")[1],
digits = 2,
cumulative = FALSE,
quiet = FALSE
)Daily minimum temperature
Daily maximum temperature
Minimum temp the day after
Lower development threshold temperature
Upper development threshold temperature
Estimation method
Estimation cutoff method
Number of decimal places to round results to
Return cumulative values
Set negative values to zero
How to handle temperatures in the simple average method that fall outside the upper and lower thresholds (see details)
Interpolate missing values, logical
Suppress messages, logical
Show additional messages
A vector of estimated degree day values (either daily or cumulative, depending on the value of cumulative)
Units for daily_min, daily_max, thresh_low, and thresh_up should all be the same
(i.e., all Fahrenheit or all Celsius). The function does not check for unit consistency.
nextday_min is required for the double-triangle and the double-sine methods. These methods use the minimum temperature
of the following day to model temperatures in the 2nd half of the day. If omitted or NA, the daily minimum temperature will be
substituted.
no_neg = TRUE sets negative values to zero. This is generally preferred when using degree days to predict the timing of
development milestones, if one assumes that growth can not go backwards.
The simple average method is taken from McMaster and Wilhelm (1997). This method requires passing a lower threshold (also called the base temp). There are two ways of handling temperatures that fall below the base temperature. Most studies and applications use the default method (simp_avg_zero_method = 1) which simply 'zeroes out' average daily temperatures that fall below the base temp. Some studies (notably corn) use method 2, which truncates the daily minimum and maximum temperature before computing the simple average. Method 2 also allows you to pass an upper threshold. For details, see McMaster and Wilhelm.
Missing values (NAs) in the temperatures will result in NA degree days. If interpolate_na = TRUE, missing degree days will
be interpolated. NAs in the middle of the series will be linearly interpolated, and NAs at the ends will be filled with the
adjacent values.
dd_simp_avg(): Estimate degree days using the simple avg method
dd_sng_tri(): Estimate degree days using the single-triangle method
dd_sng_sine(): Estimate degree days using the single-sine method
dd_dbl_tri(): Estimate degree days using the double-triangle method
dd_dbl_sine(): Estimate degree days using the double-sine method
daily_temps <- system.file("extdata/espartoa-weather-2020.csv", package = "degday") %>%
read.csv() %>%
dplyr::mutate(date = as.Date(date)) %>%
dplyr::slice(1:10)
daily_temps
#> station date tmin tmax
#> 1 Esparto.A 2020-01-01 38 55
#> 2 Esparto.A 2020-01-02 36 67
#> 3 Esparto.A 2020-01-03 33 59
#> 4 Esparto.A 2020-01-04 37 59
#> 5 Esparto.A 2020-01-05 38 63
#> 6 Esparto.A 2020-01-06 36 58
#> 7 Esparto.A 2020-01-07 30 53
#> 8 Esparto.A 2020-01-08 41 50
#> 9 Esparto.A 2020-01-09 37 53
#> 10 Esparto.A 2020-01-10 32 55
## Simple average method
dd_simp_avg(daily_min = daily_temps$tmin,
daily_max = daily_temps$tmax,
thresh_low = 55)
#> - using simple average method
#> [1] 0 0 0 0 0 0 0 0 0 0
## Single sine method
dd_sng_sine(daily_min = daily_temps$tmin, daily_max = daily_temps$tmax,
thresh_low = 55, thresh_up = 93.9)
#> - using single sine method
#> [1] 0.00 3.31 0.68 0.74 1.99 0.48 0.00 0.00 0.00 0.00
## Single triangle method
dd_sng_tri(daily_min = daily_temps$tmin, daily_max = daily_temps$tmax,
thresh_low = 55, thresh_up = 93.9)
#> - using single triangle method
#> [1] 0.00 2.32 0.31 0.36 1.28 0.20 0.00 0.00 0.00 0.00
## Add next day min temp as an additional column
daily_temps_plus_tmin_next <- daily_temps %>% dplyr::mutate(tmin_next = dplyr::lead(tmin, n = 1))
daily_temps_plus_tmin_next
#> station date tmin tmax tmin_next
#> 1 Esparto.A 2020-01-01 38 55 36
#> 2 Esparto.A 2020-01-02 36 67 33
#> 3 Esparto.A 2020-01-03 33 59 37
#> 4 Esparto.A 2020-01-04 37 59 38
#> 5 Esparto.A 2020-01-05 38 63 36
#> 6 Esparto.A 2020-01-06 36 58 30
#> 7 Esparto.A 2020-01-07 30 53 41
#> 8 Esparto.A 2020-01-08 41 50 37
#> 9 Esparto.A 2020-01-09 37 53 32
#> 10 Esparto.A 2020-01-10 32 55 NA
## Double-triangle method
dd_dbl_tri(daily_min = daily_temps_plus_tmin_next$tmin,
daily_max = daily_temps_plus_tmin_next$tmax,
nextday_min = daily_temps_plus_tmin_next$tmin_next,
thresh_low = 55, thresh_up = 93.9)
#> - using double triangle method
#> [1] 0.00 2.22 0.34 0.37 1.23 0.18 0.00 0.00 0.00 0.00