In VeruGHub/easyclimate: Easy Access to High-Resolution Daily Climate Data for Europe

library(knitr)
knitr::opts_chunk$set(collapse = TRUE, warning = FALSE, message = FALSE, 
                      fig.width = 6, fig.height = 4, cache = TRUE)

First, let's download daily climatic data for a specific location and store it in a dataframe.

library(easyclimate)
library(tidyr)
library(dplyr)

coords <- matrix(c(-5.36, 37.40), ncol = 2)

daily <- get_daily_climate(coords,
                           period = 2001:2005,
                           climatic_var = c("Prcp","Tmin","Tmax"))  

Here we calculate the daily mean temperature:

daily <- daily |> 
  mutate(Tmean = (Tmin + Tmax) / 2, #Daily mean temperature
         date = as.Date(date),
         month = format(date, format = "%m"),
         year = format(date, format = "%Y"))

Average climatic values per site or time period

To calculate average temperatures by site or time period we can use group_by and summarise from dplyr, or by and aggregate from base R:

daily |> 
  group_by(ID_coords) |> 
  summarise(Tmin.site = mean(Tmin),
            Tmean.site = mean(Tmean),
            Tmax.site = mean(Tmax))

daily |> 
  group_by(year) |>  
  summarise(Tmin.year = mean(Tmin),
            Tmean.year = mean(Tmean),
            Tmax.year = mean(Tmax)) |> 
  kable(digits = 1)

daily |>  
  group_by(month) |> 
  summarise(Tmin.month = mean(Tmin),
            Tmean.month = mean(Tmean),
            Tmax.month = mean(Tmax)) |> 
  kable(digits = 1)

Similarly, you can calculate accumulated precipitation over different time periods:

daily |> 
  group_by(year) |>  
  summarise(sumPrec = sum(Prcp)) |> 
  kable(digits = 0)

daily |> 
  group_by(year, month) |> 
  summarise(sumPrec = sum(Prcp)) |>  
  pivot_wider(names_from = month, values_from = sumPrec) |> 
  kable(digits = 0)

Climatic anomalies

Sometimes it can be useful to calculate the deviation of climate values from a reference value. For example, if we consider as the reference the mean value for the period 2001-2005, we can calculate the deviations per site and year as follows:

daily |>    
  group_by(ID_coords, year) |>  
  summarise(avgTm = mean(Tmean)) |>  
  group_by(ID_coords) |>  
  mutate(refTm = mean(avgTm)) |>  #Reference mean temperature
  mutate(devTm = avgTm - refTm) |>  
  kable(digits = 1)

daily |>    
  group_by(ID_coords, year) |>  
  summarise(sumPrec = sum(Prcp)) |> 
  group_by(ID_coords) |> 
  mutate(refPrec = mean(sumPrec)) |>  #Reference precipitation
  mutate(devPrec =sumPrec - refPrec) |>  
  kable(digits = 0)

Climatic events

Having daily values allows us to calculate climatic indices such as the number of consecutive days above a given temperature (e.g. heat waves or growing degree days), the number of spring frosts or the number of consecutive days without rain.

Let's start with an example on heat waves, or number of days with maximum temperatures above a given threshold:

threshold <- 32  

daily |>  
  mutate(ID_coords = as.factor(ID_coords),
         year = factor(year)) |>  
  group_by(ID_coords, year) |>  
  mutate(event = ifelse(Tmax >= threshold, 1, 0), # NAs will be 0 
         start = c(1, diff(event) != 0),
         run = cumsum(start)) |>  
  filter(event == 1) |> 
  group_by(run, .add = TRUE) |>  
  mutate(length = n()) |>  
  group_by(ID_coords, year) |> 
  summarise(max_heatwave = max(length), # longest heatwave per year (in days)
            mean_heatwave = mean(length), # average length of heatwaves per year
            n_heatwave = n()) |>  # No. of events per year
  kable(digits = 1)

To calculate the number of days with spring frosts (minimum temperatures below 0) we can use the next code:

spring.months <- c("03","04","05") # March to May

daily |>  
  mutate(ID_coords = as.factor(ID_coords),
         year = factor(year)) |>  
  filter(month %in% spring.months) |>  
  mutate(event = ifelse(Tmin < 0, 1, 0)) |>  # NAs will be 0 
  group_by(ID_coords, year) |>  
  mutate(n_frost = sum(event)) |>  # No. of days with minimum temperature < 0 per year
  filter(event == 1) |>  
  group_by(ID_coords, year, n_frost) |>  
  summarise(Tmin_frost_avg = mean(Tmin)/100) |>  # Mean minimum temperature of frost days
  ungroup() |>  
  complete(ID_coords, year, fill = list(n_frost = 0, Tmin_frost_avg = NA)) |>  
  kable()

A dry periods can be defined as a specific number of consecutive days with no precipitation.

threshold <- 30 #threshold in days to count extreme long events of no-rain

daily %>%  
  mutate(event = ifelse(Prcp < 0.01, 1, 0), # NAs will be 0 
         start = c(1, diff(event) != 0)) |>  
    group_by(ID_coords, year) |>  
    mutate(run = cumsum(start)) |> 
    filter(event == 1) |>  
    group_by(ID_coords, year, run) |>  
    summarise(length = n()) |>  
    ungroup(run) |> 
    summarise(max_days_norain = max(length), # Maximum length of periods of consecutive days without rain per year
              mean_days_norain = mean(length), 
              nextreme_events = sum(length > threshold)) %>% # No. of events per year
  ungroup() |>  
  complete(ID_coords, year, 
           fill = list(max_days_norain = NA, mean_days_norain = NA, nevents = 0))  |>  
  kable(digits = 1)

Using ClimInd in combination with easyclimate

The package climInd can be used to calculate multiple climatic indices from data obtained through easyclimate. For example:

Number of days with maximum temperature > 32ºC in summer (June-August):

library(ClimInd)

Tmax <- as.vector(daily$Tmax)
names(Tmax) <- as.character(format(daily$date, format = "%m/%d/%Y"))

d32(Tmax)

Growing degree days:

Tmean <- as.vector(daily$Tmean)
names(Tmean) <- as.character(format(daily$date, format = "%m/%d/%Y"))

gd4(data = Tmean, time.scale = "year") 

Growing season length:

gsl(data = Tmean, time.scale = "year") 

Heavy precipitation days:

Prec <- as.vector(daily$Prcp)
names(Prec) <- as.character(format(daily$date, format = "%m/%d/%Y"))

d50mm(data = Prec, time.scale = "month")

Learn more

To learn more about downloading daily climatic data, please consult this vignette for point coordinates, or this other if you need to extract data for several polygons or a region.

VeruGHub/easyclimate documentation built on Nov. 24, 2024, 10:37 a.m.