fruclimadapt (version 0.4.5)

spring_frost: Calculates the risk of spring frosts for a climate series

Description

The function evaluates the number of early and spring frosts and the expected frost damage on each season within a climate data series. Frost damage is assumed to be multiplicative, directly related to the minimum temperature and unrelated to the duration of the frost. The function is an enhanced version of the Damage Estimator Excel application program (DEST.xls) created by de Melo-Abreu and Snyder and bundled with FAO Environment and Natural Resources Series 10 manual (Snyder and de Melo-Abreu, 2005). The function compares daily minimum temperature (Tmin) with the critical temperatures (Tcrit) for that day. Daily Tcrit are linearly interpolated from a user-provided dataframe with the day of occurrence of the stages on each season and a vector of critical temperatures (the lethal temperatures for 10% (LT_10) and 90% (LT_90) of the organs) for each phenological stage. The main difference of spring_frost with DEST.xls is that the latter uses the same dates of phenological occurrence for all the years evaluated (up to 50 years of data), while spring_frost is able to use the expected dates of occurrence for each year from historical records or estimations produced by the functions phenology_thermal_time or phenology_sequential, included in this package. There is no limit for the number of years evaluated.

Usage

spring_frost(tempdata, fendata, tcrit, lastday = 181)

Value

a list with two data frames. The df Days_frost has the columns Year, DOY, Tmin, Tcrit and Day_Frost (indicates a day of frost with a 1 if Tmin<=Tcrit). The df Damage_frosts indicates the total number of frost days and the expected damage (as % of organs) for every year in the series. It has the columns Year, Frost_d, Damage.

Arguments

tempdata

a dataframe with daily minimum temperatures for each year in a series. Must contain the columns Year, julian day of year (DOY) and the minimum daily temperature (Tmin).

fendata

a dataframe with julian day of occurrence of the phenological stages that can be produced by phenology_thermal_time or phenology_sequential functions. Must contain the columns Year, and Pheno_date.

tcrit

a dataframe with columns LT_10 and LT_90, critical temperatures for all the phenological stages indicated in fendata.

lastday

the last day (day of the year) to evaluate. By default, lastday = 181 (June 30th).

Author

Carlos Miranda,

Details

The last day in the year for the evaluation can be defined by the user, and it is set by default set at DOY 181, to avoid computing autumn and winter frosts.

The function currently works only with phenological dates occurring within the same year.

References

Snyder RL, de Melo-Abreu JP. 2005. Frost Protection: fundamentals, practice and economics (2 volumes). FAO Environment and Natural Resources Service Series, No. 10 - FAO, Rome.

Examples

# Generate hourly temperatures from the first season from
# the example dataset Tudela_DW
library(magrittr)
library(dplyr)
library(lubridate)
Tudela_Sel <- Tudela_DW %>% filter(Tudela_DW$Year<=2002)
Tudela_HT <- hourly_temps(Tudela_Sel,42.13132)
# Calculate chill as chill portions, starting on DOY 305
Chill <- chill_portions(Tudela_HT,305)
# Calculate forcing heat as growing degree hours (GDH) with the linear model,
# using base temperature 4.7 C and no upper thresholds
GDH <- GDH_linear(Tudela_HT,4.7,999,999)
# Combine Chill and GDH values in a dataframe with a format compatible with
# the function phenology_sequential
Tudela_CH <- merge(Chill,GDH) %>%
   select(Date, Year, Month, Day, DOY, Chill,GDH) %>%
   arrange(Date) %>%
   rename(GD=GDH)
# Obtain the predicted dates using the example dataset with the requirements 
# indicated in the Bigtop_reqs example dataset and create a dataframe with a
# format compatible with the function spring_frost
Phenology_BT <- phenology_sequential(Tudela_CH, Bigtop_reqs, 305) %>% 
   select(Freq_Year,Freq_DOY) %>%
   rename(Year=Freq_Year,Pheno_date=Freq_DOY) %>%
   filter (Year==2002)
# Create a dataframe with daily minimum temperatures with the 
# format required by spring_frost
Tmin_Tudela <- Tudela_Sel %>% filter(Year==2002) %>%
  mutate(Date=make_date(Year,Month,Day), DOY=yday(Date)) %>%
  select(Year, DOY, Tmin) 
# Predict the number and accumulated damage of the spring frosts using the
# critical values contained in the example dataset Tcrits_peach and extract
# the dataframe with the total results for each year
Frost_BT <- spring_frost(Tmin_Tudela, Phenology_BT, Tcrits_peach, 181)
Frost_results <- as.data.frame(Frost_BT[['Damage_frosts']])