Type:	Package
Title:	Easy Handling of the CAMELS-CL Dataset
Version:	0.1-10
Maintainer:	Hector Garces-Figueroa <hegarcesf@gmail.com>
Description:	Download and handle spatial and temporal data from the CAMELS-CL dataset (Catchment Attributes and Meteorology for Large Sample Studies, Chile) https://camels.cr2.cl/, developed by Alvarez-Garreton et al. (2018) <doi:10.5194/hess-22-5817-2018>. The package does not generate new data, it only facilitates direct access to the original dataset for hydrological analyses.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Depends:	R (≥ 4.0.0)
Imports:	httr, zoo (≥ 1.7-2), hydroTSM (≥ 0.5-0), terra (≥ 1.7.78)
Suggests:	rmarkdown
URL:	https://gitlab.com/hgarcesf/camelsCL
MailingList:	https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
BugReports:	https://gitlab.com/hgarcesf/camelsCL/-/issues
LazyLoad:	yes
NeedsCompilation:	no
Repository:	CRAN
Packaged:	2025-09-07 01:48:11 UTC; hector
Author:	Hector Garces-Figueroa [aut, cph, cre], Mauricio Zambrano-Bigiarini [aut, cph]
Date/Publication:	2025-09-10 09:10:02 UTC

getData

Description

Download data for a specific catchment from the CAMELS-CL dataset.

Usage

getData(x, tscale, from, to, na.rm, na.rm.max, include.shp, include.attr,
        include.extra.P, verbose)

## Default S3 method:
getData(x, tscale=c("daily", "monthly", "annual"),
           from="auto", to="auto", na.rm=TRUE, na.rm.max=0, include.shp=FALSE,
           include.attr=FALSE, include.extra.P=FALSE, verbose=TRUE)

Arguments

Details

Currently it works with CAMELS-CL version '2022 enero'.

Value

The daily time series output contains a single zoo object with the following columns:

1) P_mm: precipitation obtained fromt the CR2MET dataset, [mm/day].

2) Tmin_degC: minimum air temperature obtained from CR2METv2 dataset, [mm/day].

3) Tavg_degC: average air temperature obtained from CR2METv2 dataset, [mm/day].

4) Tmax_degC: maximum air temperature obtained from CR2METv2 dataset, [mm/day].

5) PET_mm: potential evapotranspiration computed with the Hargreaves-Samani equation, [mm/day].

6) Qobs_mm: streamflow obtained from 'Direccion General de Aguas (DGA)', [mm/day].

7) Qobs_m3s: streamflow obtained from 'Direccion General de Aguas (DGA)', [m3/s].

If include.extra.P = TRUE, the following columns will also be included:

8) P_CHIRPSv2_mm: precipitation obtained from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) version 2 dataset, [mm/day].

9) P_MSWEP_mm: precipitation obtained from Multi-Source Weighted-Ensemble Precipitation (MSWEP) v1.1 dataset, [mm/day].

10) P_TMPA_mm: precipitation obtained from TMPA 3B42v7 dataset, [mm/day].

Author(s)

Hector Garces-Figueroa <hegarcesf@gmail.com> and Mauricio Zambrano-Bigiarini <mzb.devel@gmail.com>

References

https://camels.cr2.cl

Alvarez-Garreton, C.; Mendoza, P. A.; Boisier, J. P.; Addor, N.; Galleguillos, M.; Zambrano-Bigiarini, M.; Lara, A.; Puelma, C.; Cortes, G.; Garreaud, R.; McPhee, J.; and Ayala, A. (2018). The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies - Chile dataset, Hydrology and Earth System Sciences 22, 5817-5846. doi:10.5194/hess-22-5817-2018.

Examples

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

# Example 1: Getting only daily time series data for the 'Rio Trancura Antes Rio Llafenco'
# catchment
x1 <- getData(9414001) 
head(x1)

# Example 2: Getting only daily time series of P [mm/day], Tmin [degC], Tavg [degC],
# Tmax [degC], PET [mm/day] and Q [mm/day] data for the 'Rio Trancura Antes Rio Llafenco'
# catchment
var.names <- c("P_mm_day", "Tmin_degC_day", "Tavg_degC_day", 
               "Tmax_degC_day", "PET_mm_day", "Qobs_mm_day")
x2 <- getData(9414001)
x2 <- x2[, var.names]
names(x2) <- c("P", "Tmin", "Tavg", "Tmax", "PET", "Q")

# Example 3: Getting only daily time series and catchment borders data for the 
# 'Rio Trancura Antes Rio Llafenco' catchment
x3 <- getData(9414001, include.shp=TRUE)
plot(x3[["CatchmentBorders"]]) 

# Example 4: Getting only daily time series and catchment attributes data for the 
# 'Rio Trancura Antes Rio Llafenco' catchment
x4 <- getData(9414001, include.attr=TRUE) 
head(x4[["CatchmentAttributes"]])

# Example 5: Getting daily time series, catchment borders and catchment attributes data for the 
# 'Rio Trancura Antes Rio Llafenco' catchment
x5 <- getData(9414001, include.shp=TRUE, include.attr=TRUE) 
head(x5[["CatchmentTS"]])
plot(x5[["CatchmentBorders"]]) 
head(x5[["CatchmentAttributes"]])

# Example 6: Getting daily time series, catchment borders, catchment attributes and additional
# precipitation datasets for the 'Rio Trancura Antes Rio Llafenco' catchment
x6 <- getData(9414001, include.shp=TRUE, include.attr=TRUE,
              include.extra.P=TRUE)
head(x6[["CatchmentTS"]])
plot(x6[["CatchmentBorders"]]) 
head(x6[["CatchmentAttributes"]])

# Example 7: Getting only monthly time series data for the 'Rio Trancura Antes Rio Llafenco'
# catchment
x7 <- getData(9414001, tscale="monthly") 
head(x7)

# Example 8: Getting only annual time series data for the 'Rio Trancura Antes Rio Llafenco'
# catchment
x8 <- getData(9414001, tscale="annual")
head(x8)

# Example 9: Getting only annual time series data between 2008 and 2015 for the
# 'Rio Trancura Antes Rio Llafenco' catchment
x9 <- getData(9414001, tscale="annual", from = as.Date("2008-01-01"),
              to = as.Date("2015-12-31"))
head(x9)

# Example 10: Getting only monthly time series data for the 'Rio Mapocho en Los Almendros'
# and 'Rio Trancura Antes Rio Llafenco' catchments

target.ids <- c(5722002, 9414001)
nelements  <- length(target.ids)
x10 <- vector(mode = "list", length = nelements)

for (i in seq_len(nelements)) {
  x10[[i]] <- getData(target.ids[[i]], tscale="monthly") 
}
str(x10)