exposure_indicators: Heat exposure indicators

In varunayan: Download and Process 'ERA5', IMD, 'HadEX3' and 'CRU TS' Data

Heat exposure indicators

Description

Summarizes daily temperature (and optionally humidity) into heat exposure indicators per location and year or month, as used in heat-health vulnerability indices: heatwave and warm-night frequency and duration, diurnal temperature range, WBGT, heat index and wet-bulb temperature, plus the ETCCDI indices TX90p, TN90p, WSDI and tropical nights.

Usage

exposure_indicators(
  data,
  by = c("latitude", "longitude"),
  period = c("year", "month"),
  months = NULL,
  baseline = c(1991, 2020),
  heatwave_method = c("imd", "percentile", "ehf"),
  warm_night_method = c("imd", "percentile", "absolute"),
  terrain = c("auto", "plains", "coastal", "hilly"),
  hill_elevation = 1200,
  cell_size = NULL
)

Arguments

data	A data.frame of daily values; see Details.
by	Columns identifying a location, or NULL for a single series.
period	Summarize by "year" or "month".
months	Months (1-12) to include, e.g. 3:6 for the Indian hot season; NULL for all.
baseline	Climatology years, c(first, last), for normals and percentiles.
heatwave_method	Passed to heatwave_days() as method.
warm_night_method	Passed to warm_nights() as method.
terrain	Passed to heatwave_days() for the IMD definition. "auto" classifies each location with imd_terrain() from its latitude and longitude columns and adds a terrain column.
hill_elevation, cell_size	Passed to imd_terrain() when terrain = "auto".

Details

data needs columns date, tmax and tmin (degrees Celsius), one row per day per location, covering the baseline years. With a dewpoint column (degrees Celsius), afternoon humidity metrics are computed from tmax and dewpoint: WBGT by wbgt_simple(), heat index and wet-bulb temperature. A wbgt column, such as daily maxima of wbgt_liljegren(), replaces the wbgt_simple() estimate.

Spells are found on the full series, so a heatwave spanning two periods counts as an event in the period where it starts and its days count where they fall. months filters the summary.

Value

A data.frame with one row per location and period and columns:

n_days

days with data

tmax_mean, tmin_mean

mean daily maximum and minimum temperature

dtr_mean

mean diurnal temperature range (ETCCDI DTR)

hot_days

days with Tmax of at least 40 C

tropical_nights

nights with Tmin above 20 C (ETCCDI TR)

tx90p, tn90p

percent of days above the calendar-day 90th percentile of Tmax and Tmin

wsdi

days in runs of at least 6 days with Tmax above its 90th percentile (ETCCDI WSDI)

heatwave_days, heatwave_events, heatwave_longest, severe_heatwave_days

heatwave duration (total days), frequency (events starting in the period), longest event and severe days, from heatwave_days()

warm_nights, warm_night_spells, warm_night_longest, very_warm_nights

the same for warm_nights()

With humidity: wbgt_mean, wbgt_max, wbgt_days_high (WBGT of at least 29.4 C, 85 F) and wbgt_days_extreme (at least 31.1 C, 88 F), heat_index_max, heat_index_days_danger (at least 39.4 C, 103 F), wet_bulb_max and wet_bulb_days_31 (wet bulb of at least 31 C, near the 30.55 C mean critical limit of Vecellio et al. 2022).

References

Vecellio, D.J., Wolf, S.T., Cottle, R.M. & Kenney, W.L. (2022). Evaluating the 35 C wet-bulb temperature adaptability threshold for young, healthy subjects (PSU HEAT Project). Journal of Applied Physiology, 132(2), 340-345. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1152/japplphysiol.00738.2021")}

See Also

imd_exposure_bbox() to download IMD data and compute these in one call; ndvi() and ndbi() for the land-surface indicators.

Examples

d <- seq(as.Date("2001-01-01"), as.Date("2004-12-31"), by = "day")
doy <- as.POSIXlt(d)$yday
tmax <- 33 + 8 * sin(2 * pi * (doy - 80) / 365)
tmax[d >= as.Date("2004-05-10") & d <= as.Date("2004-05-14")] <- 46.5
daily <- data.frame(date = d, tmax = tmax, tmin = tmax - 13, dewpoint = 18)
exposure_indicators(daily, by = NULL, baseline = c(2001, 2003), terrain = "plains")

# Jaipur: terrain looked up from the coordinates
daily$latitude <- 26.9
daily$longitude <- 75.8
exposure_indicators(daily, baseline = c(2001, 2003))

varunayan documentation built on Oct. 30, 2026, 1:07 a.m.