,
Jacob van Etten [ctb, ths] ,
Svein Ø. Solberg [ctb, ths] | Title: | Climate Variability Indices for Ecological Modelling |
|---|---|
| Description: | Supports analysis of trends in climate change, ecological and crop modelling. |
| Authors: | Kauê de Sousa [aut, cre] ,
Jacob van Etten [ctb, ths] ,
Svein Ø. Solberg [ctb, ths] |
| Maintainer: | Kauê de Sousa <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0 |
| Built: | 2025-02-09 04:45:55 UTC |
| Source: | https://github.com/agrdatasci/climatrends |
Input example from disk data. See details
an array with two layers (temp_dat) a matrix (rain_dat), a data.frame (innlandet), a sf object (lonlatsf), a list ("commonbean")
commonbean: a list with three data sets 1) on-farm variety performance data,
2) chirps data, and 3) modis data.
rain_dat: matrix with precipitation from CHIRPS. An excerpt to represent
an example of the input data in rainfall() or
get_timeseries() when a matrix is provided.
rain_dat: matrix with precipitation from CHIRPS. An excerpt to represent
an example of the input data in rainfall() or
get_timeseries() when a matrix is provided.
innlandet: a data.frame with maximum and minimum temperature for a random point in
the Innlandet county in Norway, spanning from "2019-01-01" to "2019-07-01"
lonlatsf: a 'sf' object with 'POINT' geometry with five random points around
the municipality of Sinop, Brazil
temp_dat and rain_dat, rows represents the coordinates for the
given lonlat provided in raster::extract() and colunms represents the dates
from the observed precipitation/temperature.
Funk, C. et al. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2, 150066. doi:10.1038/sdata.2015.66
van Etten, J. et. al. (2019). PNAS 116(10) 4194-4199 doi:10.1073/pnas.1813720116
Wan Z, Hook S, Hulley G (2015) MYD11A1 MODIS/Aqua Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V006 doi:10.5067/MODIS/MYD11A2.006.
Supports analysis of trends in climate change, ecological and crop modelling.
Kauê de Sousa and Jacob van Etten and Svein Ø. Solberg
Useful links:
Development repository: https://github.com/agrdatasci/climatrends
Static documentation: https://agrdatasci.github.io/climatrends/
Report bugs: https://github.com/agrdatasci/climatrends/issues
Compute crop sensitive indices. These indices are designed to capture the changes in temperature extremes during key phenological stages (e.g. anthesis), but can also be applied to other phenological stages.
crop_sensitive(object, ...) ## Default S3 method: crop_sensitive(object, tmin, ...) ## S3 method for class 'data.frame' crop_sensitive(object, day.one, ...) ## S3 method for class 'array' crop_sensitive(object, day.one, ...) ## S3 method for class 'sf' crop_sensitive(object, day.one, ..., as.sf = TRUE)crop_sensitive(object, ...) ## Default S3 method: crop_sensitive(object, tmin, ...) ## S3 method for class 'data.frame' crop_sensitive(object, day.one, ...) ## S3 method for class 'array' crop_sensitive(object, day.one, ...) ## S3 method for class 'sf' crop_sensitive(object, day.one, ..., as.sf = TRUE)
object |
a numeric vector with the maximum temperature,
or a data.frame with geographical coordinates (lonlat),
or an object of class |
... |
additional arguments passed to methods. See details |
tmin |
a numeric vector with the minimum temperature |
day.one |
a vector of class |
as.sf |
logical, to return an object of class 'sf' |
The function uses pre-defined threshold to compute the indices. For hts_mean (32, 35, 38 Celsius), for hts_max (36, 39, 42 Celsius), for hse (31 Celsius), for cdi_mean (22, 23, 24 Celsius), for cdi_max (27, 28, 29 Celsius) and for lethal (43, 46, 49 Celsius).
Additional arguments:
The thresholds can be adjusted using the arguments hts_mean.threshold,
hts_max.threshold, hse.threshold, cdi_mean.threshold,
cdi_max.threshold and lethal.threshold which are a numeric (or
vector of numeric)
last.day: an object (optional to span) of class Date or
any other object that can be coerced to Date (e.g. integer, character
YYYY-MM-DD) for the last day of the time series. For data.frame, array
and sf methods
span: an integer (optional to last.day) or a vector with
integers (optional if last.day is given) for the length of
the time series to be captured. For data.frame, array
and sf methods
A dataframe with crop sensitive indices with n colunms depending on the number of thresholds passed to each index:
hts_mean |
high temperature stress using daily MEAN temperature, and given as percentage number of days a certain threshold is exceeded |
hts_max |
high temperature stress using daily MAX temperature, and given as percentage number of days a certain threshold is exceeded |
hse |
heat stress event, and given as percentage number of days a a certain threshold is exceeded for at least two consecutive days |
hse_ms |
heat stress event, and given the maximum number of days a certain threshold is exceeded for at least two consecutive days |
cdi_mean |
crop duration index using daily MEAN temperature, and given as max(Tmean - threshold, 0) |
cdi_max |
crop duration index using daily MAX temperature, and given as max(Tmax - threshold, 0) |
lethal |
lethal temperatures, defined as percentage of days during the timeseries where daily MEAN temperature exceeds a given threshold |
Challinor et al. (2016). Nature Climate Change 6(10):6954-958 doi:10.1038/nclimate3061
Trnka et al. (2014). Nature Climate Change 4(7):637–43. doi:10.1038/nclimate2242
Other temperature functions:
ETo(),
GDD(),
temperature()
# the default method set.seed(78) tmax <- runif(50, 37, 47) set.seed(79) tmin <- runif(50, 31, 34) crop_sensitive(tmax, tmin) ############################################### # the array method data("temp_dat", package = "climatrends") # use the default thresholds crop_sensitive(temp_dat, day.one = "2013-10-27", last.day = "2013-11-04") # or change the thresholds based on the crop physiology crop_sensitive(temp_dat, day.one = "2013-10-27", last.day = "2013-11-04", hts_mean.threshold = c(24), hts_max.threshold = c(31, 33))# the default method set.seed(78) tmax <- runif(50, 37, 47) set.seed(79) tmin <- runif(50, 31, 34) crop_sensitive(tmax, tmin) ############################################### # the array method data("temp_dat", package = "climatrends") # use the default thresholds crop_sensitive(temp_dat, day.one = "2013-10-27", last.day = "2013-11-04") # or change the thresholds based on the crop physiology crop_sensitive(temp_dat, day.one = "2013-10-27", last.day = "2013-11-04", hts_mean.threshold = c(24), hts_max.threshold = c(31, 33))
Returns a vector with the cumulative sum of the maximum length of dry spell (MLDS)
cumdrought(x)cumdrought(x)
x |
a numeric vector |
a vector with the cumulative sum of MLDS
rain <- c(0,0.2,0.4,0.1,0.4,5.1,1.5,1.6,0.1,0,7,6,4,6,0.1,1.2,0.5,0) cumdrought(rain)rain <- c(0,0.2,0.4,0.1,0.4,5.1,1.5,1.6,0.1,0,7,6,4,6,0.1,1.2,0.5,0) cumdrought(rain)
Returns a vector with the cumulative sum of the maximum length of wet spell (MLWS)
cumrain(x)cumrain(x)
x |
a numeric vector |
a vector with the cumulative sum of MLWS
# Example 1 rain <- c(0,0.2,1.4,6.1,1.4,5.1,1.5,1.6,0.1,0,7,6,4,6,1.1,1.2,1.5,0) cumrain(rain) # should return this vector # raincum <- c(0,0,1,2,3,4,5,6,6,6,6,6,6,6,6,6,7,7) # Example 2 rain2 <- c(1,0,1,1,0,0,1,1,1,0,0,1,1) cumrain(rain2) # should return this # raincum2 <- c(1,1,1,2,2,2,2,2,3,3,3,3,3)# Example 1 rain <- c(0,0.2,1.4,6.1,1.4,5.1,1.5,1.6,0.1,0,7,6,4,6,1.1,1.2,1.5,0) cumrain(rain) # should return this vector # raincum <- c(0,0,1,2,3,4,5,6,6,6,6,6,6,6,6,6,7,7) # Example 2 rain2 <- c(1,0,1,1,0,0,1,1,1,0,0,1,1) cumrain(rain2) # should return this # raincum2 <- c(1,1,1,2,2,2,2,2,3,3,3,3,3)
Reference evapotranspiration using the Blaney-Criddle method. This is general theoretical method used when no measured data on pan evaporation is available locally.
ETo(object, ..., Kc = 1) ## Default S3 method: ETo(object, tmin, ..., Kc = 1, lat = NULL, month = NULL) ## S3 method for class 'data.frame' ETo(object, day.one, ..., Kc = 1) ## S3 method for class 'array' ETo(object, day.one, ..., Kc = 1, lat = NULL, p = 0.27) ## S3 method for class 'sf' ETo(object, day.one, ..., Kc = 1, as.sf = TRUE)ETo(object, ..., Kc = 1) ## Default S3 method: ETo(object, tmin, ..., Kc = 1, lat = NULL, month = NULL) ## S3 method for class 'data.frame' ETo(object, day.one, ..., Kc = 1) ## S3 method for class 'array' ETo(object, day.one, ..., Kc = 1, lat = NULL, p = 0.27) ## S3 method for class 'sf' ETo(object, day.one, ..., Kc = 1, as.sf = TRUE)
object |
a numeric vector with the maximum temperature,
or a data.frame with geographical coordinates (lonlat),
or an object of class |
... |
additional arguments passed to methods. See details |
Kc |
a numeric value for the crop factor for water requirement |
tmin |
a numeric vector with the minimum temperature |
lat |
a vector for the latitude (in Decimal degrees), used to compute
mean daily percentage of annual daytime hours based on the latitude and month.
This is extracted automatically in the |
month |
an integer for the reference month of daylight percentage |
day.one |
a vector of class |
p |
optional if lat is given, a numeric for the mean daily percentage of annual daytime hours (p = 0.27 by default) |
as.sf |
logical, to return an object of class 'sf' |
When lat is provided, it is combined with the month provided in
day.one to call for the system data daylight to find
the correct value for p which represents the daily percentage
of daytime hours in the given month and latitude. Otherwise p is set
to 0.27 as default.
The array method assumes that object contains climate data available
in your R section; this requires an array with two dimensions, 1st dimension
contains the day temperature and 2nd dimension the night temperature,
see help("temp_dat", package = "climatrends") for an example on input structure.
The data.frame method and the sf method assumes that the climate data
will be fetched from a remote (cloud) source that be adjusted using the argument
data.from.
Additional arguments:
last.day: an object (optional to span) of class Date or
any other object that can be coerced to Date (e.g. integer, character
YYYY-MM-DD) for the last day of the time series
span: an integer (optional to last.day) or a vector with
integers (optional if last.day is given) for the length of
the time series to be captured
data.from: character for the source of climate data. Current remote data
is: 'nasapower'
pars: character vector for the temperature data to be fetched. If
data.from is 'nasapower'. The temperature can be adjusted to 2 m, the default,
c("T2M_MAX", "T2M_MIN") or 10 m c("T10M_MAX", "T10M_MIN")
days.before: optional, an integer for the number of days before
day.one to be included in the timespan.
The evapotranspiration in mm/day
Brouwer C. & Heibloem M. (1986). Irrigation water management: Irrigation water needs. Food and Agriculture Organization of The United Nations, Rome, Italy. https://www.fao.org/3/S2022E/s2022e00.htm
Other temperature functions:
GDD(),
crop_sensitive(),
temperature()
# the default method set.seed(78) tmax <- runif(50, 37, 47) set.seed(79) tmin <- runif(50, 31, 34) ETo(tmax, tmin, lat = 22, month = 10) ############################################### # the array method data("temp_dat", package = "climatrends") ETo(temp_dat, day.one = "2013-10-28", span = 10, Kc = 0.92)# the default method set.seed(78) tmax <- runif(50, 37, 47) set.seed(79) tmin <- runif(50, 31, 34) ETo(tmax, tmin, lat = 22, month = 10) ############################################### # the array method data("temp_dat", package = "climatrends") ETo(temp_dat, day.one = "2013-10-28", span = 10, Kc = 0.92)
This a heuristic tool in phenology that measures heat accumulation and is used to predict plant and animal development rates. Growing degree-days are calculated by taking the integral of warmth above a base temperature.
GDD(object, ..., tbase = 10) ## Default S3 method: GDD(object, tmin, ..., tbase = 10) ## S3 method for class 'data.frame' GDD(object, day.one, ..., tbase = 10) ## S3 method for class 'array' GDD(object, day.one, ..., tbase = 10) ## S3 method for class 'sf' GDD(object, day.one, ..., tbase = 10, as.sf = TRUE)GDD(object, ..., tbase = 10) ## Default S3 method: GDD(object, tmin, ..., tbase = 10) ## S3 method for class 'data.frame' GDD(object, day.one, ..., tbase = 10) ## S3 method for class 'array' GDD(object, day.one, ..., tbase = 10) ## S3 method for class 'sf' GDD(object, day.one, ..., tbase = 10, as.sf = TRUE)
object |
a numeric vector with the maximum temperature,
or a data.frame with geographical coordinates (lonlat),
or an object of class |
... |
additional arguments passed to methods. See details |
tbase |
an integer for the minimum temperature for growth |
tmin |
a numeric vector with the minimum temperature |
day.one |
a vector of class |
as.sf |
logical, to return an object of class 'sf' |
Additional arguments:
equation character to specify the equation to be used, one of "default",
"a", "b" or "c". See Equations below
tbase_max optional, the maximum tbase temperature,
required if equation = "c"
return.as character (one of, the default, "acc" or "daily",
"ndays") to select if the function returns the accumulated gdd, or the
daily values of gdd across the series, or the number of days required to reach a
certain number of degree.days
degree.days an integer for the accumulated degree-days required by the
organism. Optional if return.as = "daily" or return.as = "acc"
last.day: an object (optional to span) of class Date or
any other object that can be coerced to Date (e.g. integer, character
YYYY-MM-DD) for the last day of the time series. For data.frame,
array and sf methods
span: an integer (optional to last.day) or a vector with
integers (optional if last.day is given) for the length of
the time series to be captured. For data.frame, array
and sf methods
S3 Methods:
The array method assumes that object contains climate data available
in your R section; this requires an array with two dimensions, 1st dimension
contains the day temperature and 2nd dimension the night temperature,
see help("temp_dat", package = "climatrends") for an example on input structure.
The data.frame and sf methods assumes that the climate data
will e fetched from a remote (cloud) source that be adjusted using the argument
data.from
Equations:
"default": GDD = ((tmax + tmin) / 2) - tbase
"a": adjust tmean = tbase if tmeam < tbase
"b": adjust tmin = tbase if tmin < tbase,
adjust tmax = tbase if tmax < tbase
"c": adjust tmin = tbase if tmin < tbase,
adjust tmax = tbase_max if tmax < tbase_max
The number of days to reach the accumulated degree.days or the daily degree-days as defined with the argument return.as
Prentice I. C., et al. (1992) Journal of Biogeography, 19(2), 117.
Baskerville, G., & Emin, P. (1969). Ecology, 50(3), 514-517. doi:10.2307/1933912
Other temperature functions:
ETo(),
crop_sensitive(),
temperature()
Other GDD functions:
late_frost()
data("innlandet", package = "climatrends") # use the default equation GDD(innlandet$tmax, innlandet$tmin, tbase = 2) # set the equation "b", which is a better option for this case # tmin = tbase if tmin < tbase # tmax = tbase if tmax < tbase GDD(innlandet$tmax, innlandet$tmin, tbase = 2, equation = "b") ##################################################### # return as the number of days required to reach a certain accumulated GDD # use equation "c", which adjusts tmax base on a tbase_max data("temp_dat", package = "climatrends") GDD(temp_dat, day.one = "2013-10-27", degree.days = 90, return.as = "ndays", tbase_max = 32, equation = "c")data("innlandet", package = "climatrends") # use the default equation GDD(innlandet$tmax, innlandet$tmin, tbase = 2) # set the equation "b", which is a better option for this case # tmin = tbase if tmin < tbase # tmax = tbase if tmax < tbase GDD(innlandet$tmax, innlandet$tmin, tbase = 2, equation = "b") ##################################################### # return as the number of days required to reach a certain accumulated GDD # use equation "c", which adjusts tmax base on a tbase_max data("temp_dat", package = "climatrends") GDD(temp_dat, day.one = "2013-10-27", degree.days = 90, return.as = "ndays", tbase_max = 32, equation = "c")
General functions and methods to concatenate climate data across a time series
get_timeseries(object, day.one, ...) ## Default S3 method: get_timeseries( object, day.one, span = NULL, last.day = NULL, as.matrix = FALSE, data.from = "nasapower", ... ) ## S3 method for class 'matrix' get_timeseries(object, day.one, span = NULL, last.day = NULL, ...) ## S3 method for class 'array' get_timeseries(object, day.one, span = NULL, last.day = NULL, ...)get_timeseries(object, day.one, ...) ## Default S3 method: get_timeseries( object, day.one, span = NULL, last.day = NULL, as.matrix = FALSE, data.from = "nasapower", ... ) ## S3 method for class 'matrix' get_timeseries(object, day.one, span = NULL, last.day = NULL, ...) ## S3 method for class 'array' get_timeseries(object, day.one, span = NULL, last.day = NULL, ...)
object |
a |
day.one |
a vector of class |
... |
additional arguments passed to methods. See details. |
span |
an integer or a vector with integers (optional if last.day is given) for the length of the time series to be captured |
last.day |
optional to span, an object of class |
as.matrix |
logical, optional, to return a matrix or array instead of a data.frame |
data.from |
character, for the source of climate data. See details. |
The default method and the sf method assumes that the climate
data will be fetched from an remote (cloud) data.from.
The matrix method assumes that the climate data was previously handled
and will be inputted in the format of a named matrix.
See help("modis", "climatrends") for examples.
Available remote sources to pass data.from: "nasapower"
Additional arguments:
pars: character vector of solar, meteorological or climatology parameters
to download. See help("parameters", "nasapower") when data.from = "nasapower".
days.before: an integer for the number of days before day.one to be
included in the timespan.
A list with class clima_ls with data.frame(s) with
the class clima_df
# Using local sources # an array with temperature data data("temp_dat", package = "climatrends") set.seed(9271) span <- as.integer(runif(10, 6, 15)) get_timeseries(temp_dat, "2013-10-28", span = span) # matrix with precipitation data data("rain_dat", package = "climatrends") get_timeseries(rain_dat, "2013-10-28", span = span) # data can be returned as matrix library("sf") # Fetch data from NASA POWER using 'sf' method data("lonlatsf", package = "climatrends") g <- get_timeseries(object = lonlatsf, day.one = "2018-05-16", last.day = "2018-05-30", pars = c("PRECTOTCORR", "T2M"), as.matrix = TRUE)# Using local sources # an array with temperature data data("temp_dat", package = "climatrends") set.seed(9271) span <- as.integer(runif(10, 6, 15)) get_timeseries(temp_dat, "2013-10-28", span = span) # matrix with precipitation data data("rain_dat", package = "climatrends") get_timeseries(rain_dat, "2013-10-28", span = span) # data can be returned as matrix library("sf") # Fetch data from NASA POWER using 'sf' method data("lonlatsf", package = "climatrends") g <- get_timeseries(object = lonlatsf, day.one = "2018-05-16", last.day = "2018-05-30", pars = c("PRECTOTCORR", "T2M"), as.matrix = TRUE)
Compute late spring frost, which is a freezing event occurring after a substantial accumulation of warmth
late_frost(object, ..., tbase = 4, tfrost = -2) ## Default S3 method: late_frost(object, tmin, ..., tbase = 4, tfrost = -2) ## S3 method for class 'data.frame' late_frost(object, day.one, ..., tbase = 4, tfrost = -2) ## S3 method for class 'array' late_frost(object, day.one, ..., tbase = 4, tfrost = -2) ## S3 method for class 'sf' late_frost(object, day.one, ..., tbase = 4, tfrost = -2)late_frost(object, ..., tbase = 4, tfrost = -2) ## Default S3 method: late_frost(object, tmin, ..., tbase = 4, tfrost = -2) ## S3 method for class 'data.frame' late_frost(object, day.one, ..., tbase = 4, tfrost = -2) ## S3 method for class 'array' late_frost(object, day.one, ..., tbase = 4, tfrost = -2) ## S3 method for class 'sf' late_frost(object, day.one, ..., tbase = 4, tfrost = -2)
object |
a numeric vector with the maximum temperature,
or a data.frame with geographical coordinates (lonlat),
or an object of class |
... |
additional arguments passed to methods. See details |
tbase |
an integer for the minimum temperature for growth |
tfrost |
an integer for the freezing threshold |
tmin |
a numeric vector with the minimum temperature |
day.one |
a vector of class |
Additional arguments:
equation: character to specify the equation to be used, "b"
is set by default. See GDD()
dates: a character (or Date or numeric) vector for the dates of tmax and tmin
in the default method
last.day: an object (optional to span) of class Date or
any other object that can be coerced to Date (e.g. integer, character
YYYY-MM-DD) for the last day of the time series
span: an integer (optional to last.day) or a vector with
integers (optional if last.day is given) for the length of
the time series to be captured
A data.frame with the late frost events
id |
the id generated using the indices for the rows in object |
date |
the first day of the event |
gdd |
the growing degree-days accumulated during the event |
event |
a factor for the observed event, frost, latent (where there is no frost event, but also there is no GDD), and warming (where GDD is accumulated) |
duration |
the number of days the event spanned |
Trnka et al. (2014). Nature Climate Change 4(7):637–43. doi:10.1038/nclimate2242
Zohner et al. (2020). PNAS. doi:10.1073/pnas.1920816117
Other GDD functions:
GDD()
# default method data("innlandet", package = "climatrends") # equation b is set by default # where tmin and tmax are adjusted if below tbase late_frost(innlandet$tmax, innlandet$tmin, dates = innlandet$date, tbase = 2, tfrost = -2) # slightly different series if equation a is used late_frost(innlandet$tmax, innlandet$tmin, dates = innlandet$date, tbase = 2, tfrost = -2, equation = "a") ##################################################### # demo of the array method but no frost event is returned # because the data comes from the tropics data("temp_dat", package = "climatrends") late_frost(temp_dat, day.one = "2013-10-27")# default method data("innlandet", package = "climatrends") # equation b is set by default # where tmin and tmax are adjusted if below tbase late_frost(innlandet$tmax, innlandet$tmin, dates = innlandet$date, tbase = 2, tfrost = -2) # slightly different series if equation a is used late_frost(innlandet$tmax, innlandet$tmin, dates = innlandet$date, tbase = 2, tfrost = -2, equation = "a") ##################################################### # demo of the array method but no frost event is returned # because the data comes from the tropics data("temp_dat", package = "climatrends") late_frost(temp_dat, day.one = "2013-10-27")
Methods to compute rainfall indices over a time series
rainfall(object, ...) ## Default S3 method: rainfall(object, ..., timeseries = FALSE) ## S3 method for class 'data.frame' rainfall(object, day.one, span = NULL, ..., timeseries = FALSE) ## S3 method for class 'matrix' rainfall(object, day.one, span = NULL, ..., timeseries = FALSE) ## S3 method for class 'sf' rainfall(object, day.one, span = NULL, ..., timeseries = FALSE, as.sf = TRUE)rainfall(object, ...) ## Default S3 method: rainfall(object, ..., timeseries = FALSE) ## S3 method for class 'data.frame' rainfall(object, day.one, span = NULL, ..., timeseries = FALSE) ## S3 method for class 'matrix' rainfall(object, day.one, span = NULL, ..., timeseries = FALSE) ## S3 method for class 'sf' rainfall(object, day.one, span = NULL, ..., timeseries = FALSE, as.sf = TRUE)
object |
a numeric vector with precipitation data or a |
... |
additional arguments passed to methods. See details. |
timeseries |
logical, |
day.one |
a vector of class |
span |
an integer or a vector with integers (optional if last.day is given) for the length of the time series to be captured |
as.sf |
logical, to return an object of class 'sf' |
#' Additional arguments:
intervals: an integer (no lower than 5), for the days intervals when
timeseries = TRUE
last.day: optional to span, an object of class Date or
any other object that can be coerced to Date (e.g. integer, character
YYYY-MM-DD) for the last day of the time series
dates: a character (or Date or numeric) vector for the dates of tmax and tmin
in the default method
data.from: character for the source of remote data. Current remote source
is: 'nasapower'
pars: character vector for the precipitation data to be fetched. If
data.from is 'nasapower', the default precipitation parameter is "PRECTOTCORR".
days.before: optional, an integer for the number of days before
day.one to be included in the timespan.
# S3 Methods
The matrix method assumes that object contains climate data available in
your R section; see help("rain_dat", package = "climatrends") for an example on input
structure.
The data.frame and the sf methods assumes that the climate data
will e fetched from a remote (cloud) source that be adjusted using the argument
data.from.
When timeseries = TRUE, an id is created,
which is the index for the rownames of the inputted object.
A dataframe with rainfall indices:
MLDS |
maximum length of consecutive dry day, rain < 1 mm (days) |
MLWS |
maximum length of consecutive wet days, rain >= 1 mm (days) |
R10mm |
number of heavy precipitation days 10 >= rain < 20 mm (days) |
R20mm |
number of very heavy precipitation days rain >= 20 (days) |
Rx1day |
maximum 1-day precipitation (mm) |
Rx5day |
maximum 5-day precipitation (mm) |
R95p |
total precipitation when rain > 95th percentile (mm) |
R99p |
total precipitation when rain > 99th percentile (mm) |
Rtotal |
total precipitation (mm) in wet days, rain >= 1 (mm) |
SDII |
simple daily intensity index, total precipitation divided by the number of wet days (mm/days) |
Aguilar E., et al. (2005). Journal of Geophysical Research, 110(D23), D23107. doi:10.1029/2005JD006119
# A vector with precipitation data set.seed(987219) rain <- runif(50, min = 0, max = 6) rainfall(rain) # Return as timeseries with intervals of 7 days dates <- 17650:17699 rainfall(rain, dates = dates, timeseries = TRUE, intervals = 7) ###################################################### # the matrix method data("rain_dat", package = "climatrends") rainfall(rain_dat, day.one = "2013-10-28", span = 12)# A vector with precipitation data set.seed(987219) rain <- runif(50, min = 0, max = 6) rainfall(rain) # Return as timeseries with intervals of 7 days dates <- 17650:17699 rainfall(rain, dates = dates, timeseries = TRUE, intervals = 7) ###################################################### # the matrix method data("rain_dat", package = "climatrends") rainfall(rain_dat, day.one = "2013-10-28", span = 12)
Methods to compute temperature indices over a time series
temperature(object, ...) ## Default S3 method: temperature(object, tmin, ..., timeseries = FALSE) ## S3 method for class 'data.frame' temperature(object, day.one, span = NULL, ..., timeseries = FALSE) ## S3 method for class 'array' temperature(object, day.one, span = NULL, ..., timeseries = FALSE) ## S3 method for class 'sf' temperature( object, day.one, span = NULL, ..., timeseries = FALSE, as.sf = TRUE )temperature(object, ...) ## Default S3 method: temperature(object, tmin, ..., timeseries = FALSE) ## S3 method for class 'data.frame' temperature(object, day.one, span = NULL, ..., timeseries = FALSE) ## S3 method for class 'array' temperature(object, day.one, span = NULL, ..., timeseries = FALSE) ## S3 method for class 'sf' temperature( object, day.one, span = NULL, ..., timeseries = FALSE, as.sf = TRUE )
object |
a numeric vector with the maximum temperature,
or a data.frame with geographical coordinates (lonlat),
or an object of class |
... |
additional arguments passed to methods. See details |
tmin |
a numeric vector with the minimum temperature |
timeseries |
logical, |
day.one |
a vector of class |
span |
an integer or a vector with integers (optional if last.day is given) for the length of the time series to be captured |
as.sf |
logical, to return an object of class 'sf' |
Additional arguments:
intervals: an integer (no lower than 5), for the days intervals when
timeseries = TRUE
last.day: optional to span, an object of class Date or
any other object that can be coerced to Date (e.g. integer, character
YYYY-MM-DD) for the last day of the time series. For data.frame, array
and sf methods
dates: a character (or Date or numeric) vector for the dates of tmax and tmin
in the default method
data.from: character for the source of remote data. Current remote source
is: 'nasapower'
pars: character vector for the temperature data to be fetched. If
data.from is 'nasapower', the temperature can be adjusted to 2 m, the default,
c("T2M_MAX", "T2M_MIN") or 10 m c("T10M_MAX", "T10M_MIN")
days.before: optional, an integer for the number of days before
day.one to be included in the timespan.
# S3 Methods
The array method assumes that object contains climate data available
in your R section; this requires an array with two dimensions, 1st dimension
contains the day temperature and 2nd dimension the night temperature,
see help("temp_dat", package = "climatrends") for an example on input structure.
The data.frame and the sf methods assumes that the climate data
will be fetched from a remote (cloud) source that be adjusted using the argument
data.from.
When timeseries = TRUE, an id is created, which is the index for the rownames of the inputted object.
A dataframe with temperature indices:
maxDT |
maximun day temperature (degree Celsius) |
minDT |
minimum day temperature (degree Celsius) |
maxNT |
maximun night temperature (degree Celsius) |
minNT |
minimum night temperature (degree Celsius) |
DTR |
diurnal temperature range (mean difference between DT and NT (degree Celsius)) |
SU |
summer days, number of days with maximum temperature > 30 (degree Celsius) |
TR |
tropical nights, number of nights with maximum temperature > 25 (degree Celsius) |
CFD |
consecutive frosty days, number of days with temperature bellow 0 degree Celsius |
WSDI |
maximum warm spell duration, consecutive days with temperature > 90th percentile |
CSDI |
maximum cold spell duration, consecutive nights with temperature < 10th percentile |
T10p |
the 10th percentile of night tempeture (degree Celsius) |
T90p |
the 90th percentile of day tempeture (degree Celsius) |
Aguilar E., et al. (2005). Journal of Geophysical Research, 110(D23), D23107. doi:10.1029/2005JD006119
Other temperature functions:
ETo(),
GDD(),
crop_sensitive()
# the default method data("innlandet", package = "climatrends") # a single temporal observation temperature(innlandet$tmax, innlandet$tmin) # return as timeseries with 30-day intervals temperature(innlandet$tmax, innlandet$tmin, dates = innlandet$dates, timeseries = TRUE, intervals = 30) ##################################################### # array method data("temp_dat", package = "climatrends") temperature(temp_dat, day.one = "2013-10-28", span = 12)# the default method data("innlandet", package = "climatrends") # a single temporal observation temperature(innlandet$tmax, innlandet$tmin) # return as timeseries with 30-day intervals temperature(innlandet$tmax, innlandet$tmin, dates = innlandet$dates, timeseries = TRUE, intervals = 30) ##################################################### # array method data("temp_dat", package = "climatrends") temperature(temp_dat, day.one = "2013-10-28", span = 12)