Package 'chirps' reference manual

Title:	API Client for CHIRPS and CHIRTS
Description:	API Client for the Climate Hazards Center 'CHIRPS' and 'CHIRTS'. The 'CHIRPS' data is a quasi-global (50°S – 50°N) high-resolution (0.05 arc-degrees) rainfall data set, which incorporates satellite imagery and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. 'CHIRTS' is a quasi-global (60°S – 70°N), high-resolution data set of daily maximum and minimum temperatures. For more details on 'CHIRPS' and 'CHIRTS' data please visit its official home page <https://www.chc.ucsb.edu/data>.
Authors:	Kauê de Sousa [aut, cre] , Adam H. Sparks [aut] , Aniruddha Ghosh [aut] , Pete Peterson [ctb] (API Client implementation), William Ashmall [ctb] (API Client implementation), Jacob van Etten [ths] , Svein Ø. Solberg [ths]
Maintainer:	Kauê de Sousa <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.5
Built:	2024-11-28 05:56:24 UTC
Source:	https://github.com/ropensci/chirps

Methods to coerce geographical coordinates into a geojson polygon

Description

Take single points from geographical coordinates and coerce into a geojson of geometry 'Polygon'

Usage

as.geojson(lonlat, dist = 0.00001, nQuadSegs = 2L, ...)

## Default S3 method:
as.geojson(lonlat, dist = 0.00001, nQuadSegs = 2L, ...)

## S3 method for class 'sf'
as.geojson(lonlat, dist = 0.00001, nQuadSegs = 2L, ...)
as.geojson(lonlat, dist = 0.00001, nQuadSegs = 2L, ...)

## Default S3 method:
as.geojson(lonlat, dist = 0.00001, nQuadSegs = 2L, ...)

## S3 method for class 'sf'
as.geojson(lonlat, dist = 0.00001, nQuadSegs = 2L, ...)

Arguments

`lonlat`	a `data.frame` or matrix with geographical coordinates 'lonlat', in that order, or an object of class `sf` with geometry type 'POINT' or 'POLYGON'
`dist`	numeric, buffer distance for all `lonlat`
`nQuadSegs`	integer, number of segments per quadrant
`...`	further arguments passed to `sf` methods

Value

An object of class 'geosjon' for each row in lonlat

Examples


# Default S3 Method
# random geographic points within bbox(10, 12, 45, 47)
library("sf")

set.seed(123)
lonlat <- data.frame(lon = runif(1, 10, 12),
                     lat = runif(1, 45, 47))

gjson <- as.geojson(lonlat)

#################

# S3 Method for objects of class 'sf'
# random geographic points within bbox(10, 12, 45, 47)
library("sf")

set.seed(123)
lonlat <- data.frame(lon = runif(5, 10, 12),
                     lat = runif(5, 45, 47))

lonlat <- st_as_sf(lonlat, coords = c("lon","lat"))

gjson <- as.geojson(lonlat)

# Default S3 Method
# random geographic points within bbox(10, 12, 45, 47)
library("sf")

set.seed(123)
lonlat <- data.frame(lon = runif(1, 10, 12),
                     lat = runif(1, 45, 47))

gjson <- as.geojson(lonlat)

#################

# S3 Method for objects of class 'sf'
# random geographic points within bbox(10, 12, 45, 47)
library("sf")

set.seed(123)
lonlat <- data.frame(lon = runif(5, 10, 12),
                     lat = runif(5, 45, 47))

lonlat <- st_as_sf(lonlat, coords = c("lon","lat"))

gjson <- as.geojson(lonlat)

API Client for CHIRPS and CHIRTS

Description

API Client for the Climate Hazards Center 'CHIRPS' and 'CHIRTS'. The 'CHIRPS' data is a quasi-global (50°S – 50°N) high-resolution (0.05 arc-degrees) rainfall data set, which incorporates satellite imagery and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. 'CHIRTS' is a quasi-global (60°S – 70°N), high-resolution data set of daily maximum and minimum temperatures. For more details on 'CHIRPS' and 'CHIRTS' data please visit its official home page https://www.chc.ucsb.edu/data.

Note

While chirps does not redistribute the data or provide it in any way, we encourage users to cite Funk et al. (2015) when using CHIRPS and Funk et al. (2019) when using CHIRTS.

Funk et al. (2015). Scientific Data, 2, 150066. doi:10.1038/sdata.2015.66

Funk et al. (2019). Journal of Climate, 32(17), 5639–5658. doi:10.1175/JCLI-D-18-0698.1

Author(s)

Kauê de Sousa and Adam H. Sparks and Aniruddha Ghosh

Get CHIRPS precipitation data

Description

Get daily precipitation data from the "Climate Hazards Group". Two server sources are available. The first, "CHC" (default) is recommended for multiple data-points, while "ClimateSERV" is recommended when few data-points are required (~ 50).

Usage

get_chirps(object, dates, server, ...)

## Default S3 method:
get_chirps(object, dates, server, as.matrix = FALSE, ...)

## S3 method for class 'SpatVector'
get_chirps(object, dates, server = "CHC", as.raster = TRUE, ...)

## S3 method for class 'SpatRaster'
get_chirps(
  object,
  dates,
  server = "CHC",
  as.matrix = TRUE,
  as.raster = FALSE,
  ...
)

## S3 method for class 'SpatExtent'
get_chirps(object, dates, server = "CHC", as.raster = TRUE, ...)

## S3 method for class 'sf'
get_chirps(object, dates, server, as.sf = FALSE, ...)

## S3 method for class 'geojson'
get_chirps(object, dates, server, as.geojson = FALSE, ...)

## S3 method for class 'SpatExtent'
get_chirps(object, dates, server = "CHC", as.raster = TRUE, ...)
get_chirps(object, dates, server, ...)

## Default S3 method:
get_chirps(object, dates, server, as.matrix = FALSE, ...)

## S3 method for class 'SpatVector'
get_chirps(object, dates, server = "CHC", as.raster = TRUE, ...)

## S3 method for class 'SpatRaster'
get_chirps(
  object,
  dates,
  server = "CHC",
  as.matrix = TRUE,
  as.raster = FALSE,
  ...
)

## S3 method for class 'SpatExtent'
get_chirps(object, dates, server = "CHC", as.raster = TRUE, ...)

## S3 method for class 'sf'
get_chirps(object, dates, server, as.sf = FALSE, ...)

## S3 method for class 'geojson'
get_chirps(object, dates, server, as.geojson = FALSE, ...)

## S3 method for class 'SpatExtent'
get_chirps(object, dates, server = "CHC", as.raster = TRUE, ...)

Arguments

`object`	input, an object of class `data.frame` (or any other object that can be coerced to `data.frame`), `SpatVector`, `SpatRaster`, `sf` or `geojson`
`dates`	a character of start and end dates in that order in the format "YYYY-MM-DD"
`server`	a character that represents the server source "CHC" or "ClimateSERV"
`...`	additional arguments passed to `terra` or `sf` methods See details
`as.matrix`	logical, returns an object of class `matrix`
`as.raster`	logical, returns an object of class `SpatRaster`
`as.sf`	logical, returns an object of class `sf`
`as.geojson`	logical, returns an object of class `geojson`

Details

Data description at https://data.chc.ucsb.edu/products/CHIRPS-2.0/README-CHIRPS.txt

Additional arguments when using server = "CHC"

resolution: numeric, resolution of CHIRPS tiles either 0.05 (default) or 0.25 degrees

Additional arguments when using server = "ClimateSERV"

dist: numeric, buffer distance for each object coordinate

nQuadSegs: integer, number of segments per buffer quadrant

operation: supported operations for ClimateSERV are:

operation		value
max	=	0
min	=	1
median	=	2
sum	=	4
average	=	5 (default value)

Value

A matrix, raster or a data frame of CHIRPS data:

id: the index for the rows in object
dates: the dates from which CHIRPS was requested
lon: the longitude as provided in object
lat: the latitude as provided in object
chirps: the CHIRPS value in mm

Note

get_chirps() may return some warning messages given by sf, please look sf documentation for possible issues.

References

Funk C. et al. (2015). Scientific Data, 2, 150066.
doi:10.1038/sdata.2015.66

Examples


library("chirps")
library("terra")

# Case 1: return as a data.frame
dates <- c("2017-12-15","2017-12-31")
lonlat <- data.frame(lon = c(-55.0281,-54.9857), lat = c(-2.8094, -2.8756))

r1 <- get_chirps(lonlat, dates, server = "CHC")

# Case 2: return a matrix
r2 <- get_chirps(lonlat, dates, server = "CHC", as.matrix = TRUE)

# Case 3: input SpatVector and return raster
f <- system.file("ex/lux.shp", package = "terra")
v <- vect(f)
r3 <- get_chirps(v, dates, server = "CHC", as.raster = TRUE)

# Case 4: input SpatExtent and return a raster within the extent
area <- ext(c(-66, -64, -6, -4))

dates <- c("2017-12-15", "2017-12-31")

r4 <- get_chirps(area, dates, server = "CHC")

# Case 5: using the server "ClimateSERV"
r5 <- get_chirps(lonlat, dates, server = "ClimateSERV")

# Case 6: from "ClimateSERV" and return as a matrix
r6 <- get_chirps(lonlat, dates, server = "ClimateSERV", as.matrix = TRUE)


library("chirps")
library("terra")

# Case 1: return as a data.frame
dates <- c("2017-12-15","2017-12-31")
lonlat <- data.frame(lon = c(-55.0281,-54.9857), lat = c(-2.8094, -2.8756))

r1 <- get_chirps(lonlat, dates, server = "CHC")

# Case 2: return a matrix
r2 <- get_chirps(lonlat, dates, server = "CHC", as.matrix = TRUE)

# Case 3: input SpatVector and return raster
f <- system.file("ex/lux.shp", package = "terra")
v <- vect(f)
r3 <- get_chirps(v, dates, server = "CHC", as.raster = TRUE)

# Case 4: input SpatExtent and return a raster within the extent
area <- ext(c(-66, -64, -6, -4))

dates <- c("2017-12-15", "2017-12-31")

r4 <- get_chirps(area, dates, server = "CHC")

# Case 5: using the server "ClimateSERV"
r5 <- get_chirps(lonlat, dates, server = "ClimateSERV")

# Case 6: from "ClimateSERV" and return as a matrix
r6 <- get_chirps(lonlat, dates, server = "ClimateSERV", as.matrix = TRUE)

Get CHIRTS temperature data

Description

Get daily maximum and minimum temperature data from the "Climate Hazards Group". CHIRTS-daily is a global 2-m temperature product that combines the monthly CHIRTSmax data set with the ERA5 reanalysis to produce routinely updated data to support the monitoring of temperature extreme. Data is currently available from 1983 to 2016. Soon available to near-present.

Usage

get_chirts(object, dates, var, ...)

## Default S3 method:
get_chirts(object, dates, var, as.matrix = FALSE, ...)

## S3 method for class 'SpatVector'
get_chirts(object, dates, var, as.raster = TRUE, ...)

## S3 method for class 'SpatRaster'
get_chirts(object, dates, var, as.raster = TRUE, ...)

## S3 method for class 'SpatExtent'
get_chirts(object, dates, var, as.raster = TRUE, ...)
get_chirts(object, dates, var, ...)

## Default S3 method:
get_chirts(object, dates, var, as.matrix = FALSE, ...)

## S3 method for class 'SpatVector'
get_chirts(object, dates, var, as.raster = TRUE, ...)

## S3 method for class 'SpatRaster'
get_chirts(object, dates, var, as.raster = TRUE, ...)

## S3 method for class 'SpatExtent'
get_chirts(object, dates, var, as.raster = TRUE, ...)

Arguments

`object`	an object of class `data.frame` (or any other object that can be coerced to a `data.frame`), `SpatVector`, or `SpatRaster`
`dates`	a character of start and end dates in that order in the format "YYYY-MM-DD"
`var`	character, A valid variable from the options: “Tmax”, “Tmin”, “RHum” and “HeatIndex”
`...`	further arguments passed to `terra`
`as.matrix`	logical, returns an object of class `matrix`
`as.raster`	logical, returns an object of class `SpatRaster`

Details

Variable description from https://data.chc.ucsb.edu/products/CHIRTSdaily/aaa.Readme.txt

Tmax: Daily average maximum air temperature at 2 m above ground
Tmin: Daily average minimum air temperature at 2 m above ground
RHum: Daily average relative humidity
HeatIndex: Daily average heat index

Value

A SpatRaster object if as.raster=TRUE, else matrix, list, or data.frame

Additional arguments

interval: supported intervals are “daily”, “pentad”, “dekad”, “monthly”, “2-monthly”, “3-monthly”, and “annual”. Currently hard coded to “daily”.

Examples


library("chirps")
library("terra")

# Case 1: input a data frame return a data frame in the long format
dates <- c("2010-12-15","2010-12-31")
lonlat <- data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

temp1 <- get_chirts(lonlat, dates, var = "Tmax")

# Case 2: input a data frame return a matrix
temp2 <- get_chirts(lonlat, dates, "Tmax", as.matrix = TRUE)

# Case 3: input a raster and return raster
f <- system.file("ex/lux.shp", package="terra")
v <- vect(f)
temp3 <- get_chirts(v, dates, var = "Tmax", as.raster = TRUE)

# Case 4: input a raster and return raster
temp4 <- get_chirts(v, dates, var = "Tmax", as.matrix = TRUE)

library("chirps")
library("terra")

# Case 1: input a data frame return a data frame in the long format
dates <- c("2010-12-15","2010-12-31")
lonlat <- data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

temp1 <- get_chirts(lonlat, dates, var = "Tmax")

# Case 2: input a data frame return a matrix
temp2 <- get_chirts(lonlat, dates, "Tmax", as.matrix = TRUE)

# Case 3: input a raster and return raster
f <- system.file("ex/lux.shp", package="terra")
v <- vect(f)
temp3 <- get_chirts(v, dates, var = "Tmax", as.raster = TRUE)

# Case 4: input a raster and return raster
temp4 <- get_chirts(v, dates, var = "Tmax", as.matrix = TRUE)

Get evaporative stress index (ESI) data

Description

Get evaporative stress index (ESI) from SERVIR Global via ClimateSERV API Client. ESI is available every four (or twelve) weeks from 2001 to present. The dataset may contain cloudy data which is returned as NAs. ClimateSERV works with 'geojson' of type 'Polygon'. The input object is then transformed into polygons with a small buffer area around the point.

Usage

get_esi(object, dates, operation = 5, period = 1, ...)

## Default S3 method:
get_esi(object, dates, operation = 5, period = 1, ...)

## S3 method for class 'sf'
get_esi(object, dates, operation = 5, period = 1, as.sf = FALSE, ...)

## S3 method for class 'geojson'
get_esi(object, dates, operation = 5, period = 1, as.geojson = FALSE, ...)
get_esi(object, dates, operation = 5, period = 1, ...)

## Default S3 method:
get_esi(object, dates, operation = 5, period = 1, ...)

## S3 method for class 'sf'
get_esi(object, dates, operation = 5, period = 1, as.sf = FALSE, ...)

## S3 method for class 'geojson'
get_esi(object, dates, operation = 5, period = 1, as.geojson = FALSE, ...)

Arguments

`object`	input, an object of class `data.frame` (or any other object that can be coerced to `data.frame`), `SpatVector`, `SpatRaster`, `sf` or `geojson`
`dates`	a character of start and end dates in that order in the format "YYYY-MM-DD"
`operation`	optional, an integer that represents which type of statistical operation to perform on the dataset
`period`	an integer value for the period of ESI data, four weeks period = 1, twelve weeks = 2
`...`	additional arguments passed to `terra` or `sf` methods See details
`as.sf`	logical, returns an object of class `sf`
`as.geojson`	logical, returns an object of class `geojson`

Details

operation: supported operations are:

operation		value
max	=	0
min	=	1
median	=	2
sum	=	4
average	=	5 (default value)

dist: numeric, buffer distance for each object coordinate

nQuadSegs: integer, number of segments per buffer quadrant

Value

A data frame of ESI data:

`id`	the index for the rows in `object`
`dates`	the dates from which ESI was requested
`lon`	the longitude as provided in `object`
`lat`	the latitude as provided in `object`
`esi`	the ESI value

Note

get_esi() may return some warning messages given by sf, please check the sf documentation for possible issues.

Examples



lonlat <- data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates <- c("2017-12-15","2018-06-20")

# by default the function sets a very small buffer around the points which
# can return NAs due to cloudiness in ESI data

dt <- get_esi(lonlat, dates = dates)

# the argument dist passed through sf increase the buffer area

dt <- get_esi(lonlat, dates = dates, dist = 0.1)

lonlat <- data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates <- c("2017-12-15","2018-06-20")

# by default the function sets a very small buffer around the points which
# can return NAs due to cloudiness in ESI data

dt <- get_esi(lonlat, dates = dates)

# the argument dist passed through sf increase the buffer area

dt <- get_esi(lonlat, dates = dates, dist = 0.1)

Get Integrated Multisatellite Retrievals for GPM (IMERG) data

Description

The IMERG dataset provides near-real time global observations of rainfall at 10km resolution, which can be used to estimate total rainfall accumulation from storm systems and quantify the intensity of rainfall and flood impacts from tropical cyclones and other storm systems. IMERG is a daily precipitation dataset available from 2015 to present within the latitudes 70 and -70 degrees.

Usage

get_imerg(object, dates, operation = 5, ...)

## Default S3 method:
get_imerg(object, dates, operation = 5, ...)

## S3 method for class 'sf'
get_imerg(object, dates, operation = 5, as.sf = FALSE, ...)

## S3 method for class 'geojson'
get_imerg(object, dates, operation = 5, as.geojson = FALSE, ...)
get_imerg(object, dates, operation = 5, ...)

## Default S3 method:
get_imerg(object, dates, operation = 5, ...)

## S3 method for class 'sf'
get_imerg(object, dates, operation = 5, as.sf = FALSE, ...)

## S3 method for class 'geojson'
get_imerg(object, dates, operation = 5, as.geojson = FALSE, ...)

Arguments

`object`	input, an object of class `data.frame` (or any other object that can be coerced to `data.frame`), `SpatVector`, `SpatRaster`, `sf` or `geojson`
`dates`	a character of start and end dates in that order in the format "YYYY-MM-DD"
`operation`	optional, an integer that represents which type of statistical operation to perform on the dataset
`...`	additional arguments passed to `terra` or `sf` methods See details
`as.sf`	logical, returns an object of class `sf`
`as.geojson`	logical, returns an object of class `geojson`

Details

operation: supported operations are:

operation		value
max	=	0
min	=	1
median	=	2
sum	=	4
average	=	5 (default value)

dist: numeric, buffer distance for each object coordinate

nQuadSegs: integer, number of segments per buffer quadrant

Value

A data frame of imerg data:

`id`	the index for the rows in `object`
`dates`	the dates from which imerg was requested
`lon`	the longitude as provided in `object`
`lat`	the latitude as provided in `object`
`imerg`	the IMERG value

Examples


lonlat <- data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates <- c("2017-12-15", "2017-12-31")

dt <- get_imerg(lonlat, dates)

dt

lonlat <- data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates <- c("2017-12-15", "2017-12-31")

dt <- get_imerg(lonlat, dates)

dt

Compute precipitation indices over a time series

Description

Compute precipitation indices over a time series

Usage

precip_indices(object, timeseries = FALSE, intervals = NULL)
precip_indices(object, timeseries = FALSE, intervals = NULL)

Arguments

`object`	an object of class `chirps` as provided by `get_chirps`
`timeseries`	logical, `FALSE` for a single point time series observation or `TRUE` for a time series based on `intervals`
`intervals`	integer no lower than 5, for the days intervals when `timeseries` = `TRUE`

Value

A data frame with precipitation 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)

References

Aguilar E., et al. (2005). Journal of Geophysical Research, 110(D23), D23107.

Kehel Z., et al. (2016). In: Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits (eds Bari A., Damania A. B., Mackay M., Dayanandan S.), pp. 151–174. CRC Press.

Examples


lonlat <- data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates <- c("2017-12-15", "2017-12-31")

dt <- get_chirps(lonlat, dates, server = "ClimateSERV")

# take the indices for the entire period
precip_indices(dt, timeseries = FALSE)

# take the indices for periods of 7 days
precip_indices(dt, timeseries = TRUE, intervals = 7)

lonlat <- data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates <- c("2017-12-15", "2017-12-31")

dt <- get_chirps(lonlat, dates, server = "ClimateSERV")

# take the indices for the entire period
precip_indices(dt, timeseries = FALSE)

# take the indices for periods of 7 days
precip_indices(dt, timeseries = TRUE, intervals = 7)

Tapajos National Forest

Description

Geometries for the Tapajos National Forest, a protected area in the Brazilian Amazon

Usage

tapajos
tapajos

Format

An object of class 'sfc_POLYGON' within the bounding box xmin: -55.41127 ymin: -4.114584 xmax: -54.7973 ymax: -2.751706

Source

The data was provided by the Chico Mendes Institute via https://www.protectedplanet.net/en

Package 'chirps'

Help Index

Methods to coerce geographical coordinates into a geojson polygon

Description

Usage

Arguments

Value

Examples

API Client for CHIRPS and CHIRTS

Description

Note

Author(s)

See Also

Get CHIRPS precipitation data

Description

Usage

Arguments

Details

Value

Note

References

Examples

Get CHIRTS temperature data

Description

Usage

Arguments

Details

Value

Additional arguments

Examples

Get evaporative stress index (ESI) data

Description

Usage

Arguments

Details

Value

Note

Examples

Get Integrated Multisatellite Retrievals for GPM (IMERG) data

Description

Usage

Arguments

Details

Value

Examples

Compute precipitation indices over a time series

Description

Usage

Arguments

Value

References

Examples

Tapajos National Forest

Description

Usage

Format

Source