Climate Data Processing

Import and summarise hourly climate records; compute daily temperatures and growing degree-days (GDD) per sowing season.

Overview

This section imports hourly weather station data, aggregates it to daily summaries, and derives the accumulated growing degree-days (GDD) used as the thermal-time variable for all subsequent growth models.

Note

GDD is calculated as the mean daily temperature minus a base temperature of 5 °C, which is commonly adopted for flax (Linum usitatissimum L.).

Required packages

Show code 
library(rio)        # Flexible data import (import())
library(tidyverse)  # Data manipulation and visualisation (dplyr, ggplot2, …)
library(lubridate)  # Date parsing helpers (dmy(), ymd())
library(ggridges)   # Ridge/density plots (geom_density_ridges_gradient())

Import and structure climate data

Show code 
# Import the raw CSV file.
# 'dec = ","' handles the Brazilian decimal comma format.
dft <-
  import("../clima.csv", dec = ",") |>
  # The 'hora' column stores date as "DD/MM/YYYY HH:MM" → split into parts
  separate(hora, into = c("dia", "mes", "ano")) |>
  # Rebuild a single date string and parse it with lubridate
  unite("data", dia, mes, ano, sep = "/") |>
  mutate(data = dmy(data))

Daily temperature summary

Show code 
# Aggregate hourly records to daily statistics.
# Variables:
#   tmin / tmed / tmax : minimum, mean, maximum temperature (°C)
#   prec               : daily rainfall (mm)
#   ur                 : mean relative humidity (%)
#   gd                 : growing degree-days via mean temperature  (Tmean – 5)
#   gd2                : growing degree-days via Tmax+Tmin average ((Tmax+Tmin)/2 – 5)
dftemp <-
  dft |>
  group_by(data) |>
  summarise(
    tmin = min(tmin),
    tmed = mean(tmed),
    tmax = max(tmax),
    prec = sum(prec),
    ur   = mean(ur)
  ) |>
  mutate(
    gd  = tmed - 5,                  # GDD method 1: mean temperature
    gd2 = ((tmax + tmin) / 2) - 5   # GDD method 2: (Tmax + Tmin) / 2
  ) |>
  mutate(data = ymd(data))

Split data by sowing season

Show code 
# Season E1: rows 1–79  (first sowing date)
dftempe1 <-
  dftemp |>
  slice(1:79) |>
  mutate(season = "E1")

# Season E2: rows 79–148 (second sowing date, one row overlap ensures continuity)
dftempe2 <-
  dftemp |>
  slice(79:148) |>
  mutate(season = "E2")

# Combine seasons and compute cumulative GDD within each season
dftemp2 <-
  bind_rows(dftempe1, dftempe2) |>
  relocate(season, .before = data) |>
  group_by(season) |>
  mutate(
    gda  = cumsum(gd),   # Cumulative GDD – method 1
    gda2 = cumsum(gd2)   # Cumulative GDD – method 2
  ) |>
  # Reformat the date for display (DD/MM)
  separate(data, into = c("ano", "mes", "dia")) |>
  unite("data", dia, mes, sep = "/")

dftemp2 |> group_by(season) |> summarise(sum(prec))

Ridge plot – temperature distribution by month

Show code 
dft |>
  # Extract month from the date column for grouping on the y-axis
  separate(data, into = c("ano", "mes", "dia")) |>
  ggplot(aes(x = tmax, y = mes, fill = after_stat(x))) +
  geom_density_ridges_gradient() +
  scale_fill_viridis_c() +
  labs(
    x    = "Maximum temperature (°C)",
    y    = "Month of the year",
    fill = "Max. temperature\n(°C)"
  ) +
  theme_bw(base_size = 14)

Distribution of maximum daily temperatures by month. Higher temperatures in summer months (Dec–Feb) contrast with cooler autumn/winter records.

Temperature and rainfall overview

Show code 
ggplot() +
  # Rainfall bars (scaled to secondary axis: raw mm × 30/100)
  geom_bar(
    dftemp,
    mapping  = aes(x = data, y = prec * 30 / 100),
    stat     = "identity",
    fill     = "skyblue"
  ) +
  # Raw daily Tmax and Tmin lines (transparent)
  geom_line(dftemp, mapping = aes(x = data, y = tmax, colour = "red"),  linewidth = 1, alpha = 0.1) +
  geom_line(dftemp, mapping = aes(x = data, y = tmin, colour = "blue"), linewidth = 1, alpha = 0.1) +
  # LOESS smoothed trends for Tmax and Tmin
  geom_smooth(dftemp, mapping = aes(x = data, y = tmax, colour = "red"),  linewidth = 1, se = FALSE) +
  geom_smooth(dftemp, mapping = aes(x = data, y = tmin, colour = "blue"), linewidth = 1, se = FALSE) +
  # X-axis: dates every 15 days
  scale_x_date(
    date_breaks = "15 days",
    date_labels = "%d/%m",
    expand      = expansion(c(0, 0))
  ) +
  # Primary Y-axis: temperature; secondary Y-axis: rainfall (rescaled back to mm)
  scale_y_continuous(
    name      = expression("Temperature (" ~ degree ~ "C)"),
    sec.axis  = sec_axis(~ . * 100 / 30, name = "Rainfall (mm)")
  ) +
  # Colour legend with descriptive labels
  scale_color_identity(
    breaks = c("red", "blue"),
    labels = c("Maximum temperature (°C)", "Minimum temperature (°C)"),
    guide  = "legend"
  ) +
  labs(
    x     = "Day of the year",
    color = ""
  ) +
  theme_bw(base_size = 16) +
  theme(
    panel.grid.major  = element_blank(),
    legend.background = element_rect(fill = "transparent"),
    legend.position   = "bottom",
    axis.text.x       = element_text(angle = 45, vjust = 1, hjust = 1)
  )

Daily maximum (red) and minimum (blue) temperatures with LOESS smoothing, and daily rainfall (sky-blue bars) across the study period.
Show code 

ggsave("../figs/temperature.jpg", width = 12, height = 6)