identify_winter_events: Identify harsh weather winter events

In bniebuhr/winteranalyzer: WInTer conditions and CHaracterization for reindeer husbandry

Description

This functions identifies the winter events related to temperature and precipitation described in Lundqvist et al. (2007). The functions uses daily temperature (min, max, or average, in degrees Celcius) and precipitation (in mm) to identify events that may be harsh weather conditions for reindeer husbandry.

Usage

identify_winter_event3(
  date,
  temp_min,
  temp_max,
  precipitation,
  temp_min_thr = -2,
  temp_max_thr = 2,
  prec_thr = 3
)

identify_winter_event4(
  date,
  temp_avg,
  precipitation,
  temp_avg_day = +1,
  temp_avg_next_day = -1,
  prec_thr = 3
)

identify_winter_eventX(
  date,
  temp_min = c(),
  temp_max = c(),
  temp_avg = c(),
  precipitation = c(),
  temp_avg_day = +1,
  temp_avg_next_day = -1,
  temp_max_thr_day = +1,
  temp_min_thr_next_day = -2,
  number_days_after = 3,
  prec_thr = 3
)

Arguments

date	date object. vector. Vector of dates in a time series of a given winter.
temp_min	numeric. vector. Minimum daily temperature, in Celsius.
temp_max	numeric. vector. Minimum daily temperature, in Celsius.
precipitation	numeric. vector. Precipitation accumulated each day, in mm.
temp_min_thr	numeric. Threshold of minimum temperature, in Celsius, used for event 3.
temp_max_thr	numeric. Threshold of maximum temperature, in Celsius, used for event 3.
prec_thr	numeric. Threshold of precipitation, in mm, used for events 3 and 4.
temp_avg	numeric. vector. Average daily temperature, in Celsius.
temp_avg_day	numeric. Threshold of average temperature in the day, in Celsius, used for event 4.
temp_avg_next_day	numeric. Threshold of average temperature in the following day, in Celsius, used for event 4.

Details

The events are:

Events 1 and 2

Still to be implemented

Event 3

Day when max temp > 2, min temp < -2, and precipitation > 3 mm.

Event 4

Day when average temp > 1, precipitation > 3 mm, followed by day with average temp < -1.

Event X

Day when max temp > 1, precipitation > 3 mm, followed minimum temp < -2 in any of the following 3 days.

Value

A list with the elements:

events

time series with 0 for days with no event and 1 with days when the event occurred.

n_days

number of days when the event occurred.

n_events

number of events, by aggregating subsequent days when it repeatedly happened.

duration_events

duration of each event, in days.

event_dates

starting date of each event.

n_event_date

time series with the counter of the number of events. Similar to events.

These elements are calculated for both the whole winter and only the first half of the winter.

References

Lundqvist, H., Norell, L., Danel, O. 2007. Multivariate characterisation of environmental conditions for reindeer husbandry in Sweden. Rangifer, 27(1): 5-23.

Examples

library(ggplot2)
library(dplyr)
data(weather_mittadalen)

weather <- weather_mittadalen %>%
  dplyr::filter(year == 2015)

# events 3
with(weather, identify_winter_event3(date, temp_min, temp_max, prec))

# One can also change the parameters
with(weather, identify_winter_event3(date, temp_min, temp_max, prec,
                                     temp_max_thr = 0))

# events 4
with(weather, identify_winter_event4(date, temp_avg, prec))

# events X
with(weather, identify_winter_eventX(date, temp_max = temp_max, temp_min = temp_min,
                                     precipitation = prec,
                                     temp_max_thr_day = 1, temp_min_thr_next_day = -2,
                                     number_days_after = 3))

# visualize events 4 for this year
weather_condition <-
  with(weather, analyze_weather(date, snow_depth, prec, temp_min, temp_max,
                                temp_avg, start = "first_date",
                                plot_first_snow = T))

to_plot <- weather_condition

to_plot$weather_indices <- weather_condition$weather_indices %>%
  dplyr::mutate(temp_avg_next = c(temp_avg[-1], NA)) %>%
  dplyr::filter(lubridate::month(date) == 12)

to_plot %>%
  plot_weather(term = c("temp_avg", "prec", "temp_avg_next"), factor_mult = c(1,1)) +
  geom_hline(yintercept = c(-1,1), linetype = 2) +
  ylim(-10, 10)

bniebuhr/winteranalyzer documentation built on Dec. 31, 2020, 8:56 p.m.