Calculating basic climatic indices with data from easyclimate

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))
## # A tibble: 1 × 4
##   ID_coords Tmin.site Tmean.site Tmax.site
##       <dbl>     <dbl>      <dbl>     <dbl>
## 1         1      11.9       18.4      24.9

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

year	Tmin.year	Tmean.year	Tmax.year
2001	12.2	18.5	24.8
2002	12.0	18.3	24.6
2003	12.5	18.7	24.9
2004	11.7	18.3	24.8
2005	11.3	18.3	25.3

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

month	Tmin.month	Tmean.month	Tmax.month
01	4.7	10.3	15.9
02	4.8	11.0	17.2
03	8.8	14.5	20.3
04	9.8	16.4	23.0
05	12.9	19.9	27.0
06	17.6	25.6	33.5
07	18.9	27.1	35.3
08	19.8	27.5	35.3
09	17.2	24.0	30.8
10	14.1	19.5	25.0
11	8.2	13.6	18.9
12	6.2	11.1	16.1

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

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

year	sumPrec
2001	568
2002	564
2003	643
2004	373
2005	261

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

year	01	02	03	04	05	06	07	08	09	10	11	12
2001	136	17	123	3	44	0	0	0	36	76	65	68
2002	31	9	101	53	14	3	0	0	80	32	141	100
2003	69	68	43	62	3	0	0	0	30	195	77	96
2004	10	80	63	40	61	0	0	2	2	70	9	37
2005	0	58	22	20	18	0	0	0	0	103	14	27

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)

ID_coords	year	avgTm	refTm	devTm
1	2001	18.5	18.4	0.1
1	2002	18.3	18.4	-0.1
1	2003	18.7	18.4	0.3
1	2004	18.3	18.4	-0.2
1	2005	18.3	18.4	-0.1

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)

ID_coords	year	sumPrec	refPrec	devPrec
1	2001	568	482	86
1	2002	564	482	82
1	2003	643	482	161
1	2004	373	482	-109
1	2005	261	482	-221

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)

ID_coords	year	max_heatwave	mean_heatwave	n_heatwave
1	2001	28	14.6	94
1	2002	13	8.1	74
1	2003	31	14.3	97
1	2004	30	15.5	99
1	2005	27	15.0	93

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()

ID_coords	year	n_frost	Tmin_frost_avg
1	2001	0	NA
1	2002	0	NA
1	2003	0	NA
1	2004	1	-0.0050
1	2005	1	-0.0021

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)

ID_coords	year	max_days_norain	mean_days_norain	nextreme_events
1	2001	124	10.5	2
1	2002	102	9.9	1
1	2003	146	8.9	1
1	2004	87	12.0	2
1	2005	132	17.3	3

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)
## 2001 2002 2003 2004 2005 
##   77   64   72   75   76

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") 
##     2001     2002     2003     2004     2005 
## 5304.165 5211.840 5371.645 5218.365 5234.140

Growing season length:

gsl(data = Tmean, time.scale = "year") 
## 2001 2002 2003 2004 2005 
##  365  365  365  366  365

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")
##      Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 2001   0   0   0   0   0   0   0   0   0   0   0   0
## 2002   0   0   0   0   0   0   0   0   0   0   0   0
## 2003   0   0   0   0   0   0   0   0   0   0   0   0
## 2004   0   0   0   0   0   0   0   0   0   0   0   0
## 2005   0   0   0   0   0   0   0   0   0   0   0   0

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.