| Title: | Weather and Climate Inputs for 'SWAT' |
| Version: | 0.1.1 |
| Description: | Provides workflows to prepare weather and climate time series from gridded and station data for 'SWAT' ('Soil and Water Assessment Tool'). Supports data extraction, aggregation, interpolation, quality control, unit conversion, and export of per-location model input files. For the underlying model, see Arnold et al. (1998) "Large Area Hydrologic Modeling and Assessment Part I: Model Development" <doi:10.1111/j.1752-1688.1998.tb05961.x>. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| Depends: | R (≥ 4.1.0) |
| Suggests: | future, knitr, rmarkdown, testthat (≥ 3.0.0), withr |
| Config/testthat/edition: | 3 |
| Imports: | dplyr, tidyr, raster, ncdf4, data.table, glue, stringr, sf, lubridate, hyfo, ggplot2, terra, future.apply, progressr, methods |
| URL: | https://github.com/reginalexavier/wcswatin, https://reginalexavier.github.io/wcswatin/ |
| BugReports: | https://github.com/reginalexavier/wcswatin/issues |
| VignetteBuilder: | knitr |
| Config/roxygen2/version: | 8.0.0 |
| NeedsCompilation: | no |
| Packaged: | 2026-07-10 16:55:33 UTC; tredgi |
| Author: | Réginal Exavier 
[aut, cre],
Fernando Shinji Kawakubo
[aut],
Peter Zeilhofer
[aut] |
| Maintainer: | Réginal Exavier <reginalexavier@rocketmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2026-07-19 13:10:02 UTC |
wcswatin: Climate & Weather SWAT Input.
Description
wcswatin (Climate & Weather SWAT input) is an open-source R package for preparing weather and climate data from different sources for input in the Soil & Water Assessment Tool (SWAT), funded by the Critical Ecosystem Partnership Fund (CEPF). Currently two blocks of processing routines are implemented, one for the pre-processing of NetCDF and tif raster files as made available from a increasing number of data-providing institutions around the globe and a second one for the upscaling of physical station data by interpolation methods. For processing all used datasets MUST have geographic coordinates using WGS 84 as datum.
Author(s)
Reginal Exavier reginalexavier@rocketmail.com, Fernando Shinji Kawakubo fskgeo@gmail.com, Peter Zeilhofer zeilhoferpeter@gmail.com
Clean a directory if it exists This function removes files and/or sub-directory within the directory if exists.
Description
Clean a directory if it exists This function removes files and/or sub-directory within the directory if exists.
Usage
clean_dir(folder_path)
Arguments
folder_path |
A character string with the path of the directory to be cleaned. |
Value
NULL. Only for side effects.
Count the amount or percentage of NA in a table by column
Description
Count the amount or percentage of NA in a table by column
Usage
count_na(dataset, percent = FALSE)
Arguments
dataset |
A dataframe containing rainfall data from different gauges in its columns. |
percent |
logical, controls whether to calculate the amount or
percentage of |
Value
A dataframe.
Convert a Cube format data into a Table format
Description
The function extracts the values of a NetCDF/raster layer and converts it to
a table format containing the values of the pixels and the layer name as two
columns. The pixel is identified by the ID (row and col), and the layer
name represents the date of the data collected. All layers are stacked in a
single table, each layer is differentiated by the column layer_name
containing the date of the data collected. The function, due to the large
amount of data, counts with the structure of parallel processing based on the
future package to speed up the process. By default, the computation is done
in sequential mode future::plan(future::sequential), for parallel
processing, the user must change to the desired mode (ex:
future::plan(future::multisession, workers = 6)).
Usage
cube2table(
input_path,
var = NA,
n_layers,
study_area,
future_scheduling = 1,
missing_value = -99,
final_dir = NULL,
side_effect = "only",
temp_dir = NULL,
clean_after = FALSE
)
Arguments
input_path |
Path to the NetCDF or raster file. |
var |
The variable to be extracted. The default is NA. For NetCDF files containing multiple variables, the user must provide the name of the variable to be extracted. If the file contains only one variable, the user can leave this argument as NA. |
n_layers |
Number of layers in the raster file to be extracted |
study_area |
The table from 'study_area_records' |
future_scheduling |
Controling how the future will be scheduled and
distributed between the workers. The default is 1, which means that the
future will be scheduled by core. See the documentation of future package
for more details |
missing_value |
The value to be used when the data is missing |
final_dir |
The directory to save the final table. If NULL, the final table will not be saved. |
side_effect |
The side effect of the function. The default is "only", which means that the function will only save the final table in disk (if final_dir is provided). The other options are "both" and "none". If "both", the function will save the final table in disk and return it within the R environment. If "none", the function will only return the final table whithin the R environment. |
temp_dir |
The directory to save the intermediate tables. If the directory already exists, the tables will be saved in the existing directory. If the directory does not exist, it will be created. If NULL, the tables will be saved in a temporary directory. |
clean_after |
Logical. If TRUE, the directory with the intermediate tables will be deleted after the process is finished. If FALSE, the directory will be kept. The default is FALSE. And when the temp_dir is NULL, what implies that the tables will be saved in a temporary directory, the temp_dir will be deleted after the process is finished. |
Value
A table containing the:
ID: The pixel ID (row and col);
values: The values of the pixel;
layer_name: The layer name (date of the data collected).
Examples
cube_file <- tempfile("wcswatin-cube-", fileext = ".tif")
cube <- terra::rast(
nrows = 1,
ncols = 2,
nlyrs = 2,
vals = c(10, 20, 11, 21)
)
names(cube) <- c("X20200101", "X20200102")
terra::writeRaster(cube, cube_file, overwrite = TRUE)
cube2table(
input_path = cube_file,
var = NULL,
n_layers = 2,
study_area = data.frame(ID = 1:2),
side_effect = "none"
)
unlink(cube_file)
Create a daily aggregation from an hourly dataset
Description
https://confluence.ecmwf.int/display/CKB/ERA5+family+post-processed+daily+statistics+documentation # nolint: line_length_linter
Usage
daily_aggregation(
folder_in,
folder_out,
pattern = ".txt$",
from = "2002-01-01 00",
to = "2021-05-31 23",
drop_first_record = TRUE,
aggregation_function = mean,
mode = c("agg_fun", "max_min", "value_at_hour")[1],
value_hour = 0,
na.rm = FALSE
)
Arguments
folder_in |
Path of the input files |
folder_out |
Path where to save the transformed files |
pattern |
an optional |
from |
The first date of the series, including the hour part. |
to |
The last date of the series, including the hour part. |
drop_first_record |
Logical. If TRUE, the first row of each input
file is removed before the date sequence is assigned. This is useful when
the input file contains a leading 00:00 record that belongs to the previous
day and should not be part of the requested |
aggregation_function |
The function to use on the hourly groups like mean, sum, mode, etc |
mode |
The mode of aggregation. The options are |
value_hour |
Integer hour between 0 and 23 used when
|
na.rm |
a logical value indicating whether NA values should be removed before the computation proceeds. |
Details
This function allows to aggregate hourly observations to daily time series.
The function for aggregation can be informed in the
aggregation_function parameter, this parameter takes a function as
argument. The default function is mean, so a daily
average is returned.
The function will create a daily aggregation from an hourly dataset.
The function for aggregation can be informed in the
aggregation_function parameter, this parameter takes a function as
argument. The default function is mean, so a daily
average is returned. Alternatively, the user can choose the mode
parameter to inform the function to use choosing between the agg_fun,
max_min, and value_at_hour. The agg_fun will use the function
informed in the aggregation_function parameter. The max_min
will return the maximum and minimum values of the day. The
value_at_hour mode will return the value timestamped at
value_hour. For daily accumulated products timestamped at 00:00,
set mode = "value_at_hour", keep value_hour = 0, and define
from/to so that the 00:00 record ending the accumulation
period is included. Use drop_first_record = TRUE only when the file
also contains an extra leading row that must be discarded before assigning
this date range.
Value
Files with a daily resolution
Create a daily aggregation from an hourly datacube
Description
datacube_aggregation() aggregates a datacube by a given function. The
function can either apply an aggregation function to each day of the datacube
or select the layer timestamped at a given hour.
Usage
datacube_aggregation(
input_path,
output_filename = "",
fun = mean,
cores = 1,
mode = c("agg_fun", "value_at_hour")[1],
value_hour = 0,
date_shift_days = 0,
drop_first_layer = FALSE,
...
)
Arguments
input_path |
Path to the datasetcube |
output_filename |
Path to the output file |
fun |
Function to be applied to the datasetcube (default is mean). The
function must be a function that takes a vector as input and returns a
single value. The main functions to be used are: Sum, Mean, Min, Max, First
and Last. For last, use |
cores |
Number of cores to use for the aggregation. Default is 1. See
|
mode |
The mode of aggregation. The options are |
value_hour |
Integer hour between 0 and 23 used when
|
date_shift_days |
Whole number of days added to the output layer dates
when |
drop_first_layer |
Logical. If TRUE and
|
... |
Additional arguments to pass to |
Value
A raster object with the aggregated data
See Also
terra::tapp(), names_to_date(), ERA5 family post-processed daily statistics documentation # nolint: line_length_linter
Extract the file name
Description
This function extracts the file name without the extension from a file path.
It is an internal function used in the unit_converter function.
Usage
file_name(path)
Arguments
path |
A character string with the file path. |
Value
A character string with the file name without the extension.
Turns multiple time series files into a single table
Description
Turns multiple time series files into a single table
Usage
files_to_table(
files_path,
files_pattern,
start_date = "1970-12-31",
end_date = "1980-12-31",
interval = "day",
na_value = NA,
neg_to_zero = FALSE
)
Arguments
files_path |
path where the files are. |
files_pattern |
pattern for the observation/station points name. |
start_date |
Inform the start date of the series in the format %Y-%m-%d. |
end_date |
Inform the end date of the series in the format %Y-%m-%d. |
interval |
Inform the interval between two observations. See the function x |
na_value |
Value encoded as not available, use |
neg_to_zero |
logical. inform whether negative values should be corrected to zero. |
Value
A dataframe.
Examples
series_dir <- tempfile("wcswatin-series-")
dir.create(series_dir)
utils::write.csv(
data.frame(value = c(1, 2)),
file.path(series_dir, "station_a.csv"),
row.names = FALSE
)
utils::write.csv(
data.frame(value = c(3, 4)),
file.path(series_dir, "station_b.csv"),
row.names = FALSE
)
files_to_table(
files_path = series_dir,
files_pattern = "station",
start_date = "2020-01-01",
end_date = "2020-01-02"
)
unlink(series_dir, recursive = TRUE)
A wrapper function for filling gaps in the rainfall time series
Description
This function is a wrapper for the fillGap in the
hyfo hyfo package. The main idea here for this wrapping function
is to preserve the column names as they are in the dataset input.
Usage
fill_gap(dataset, corPeriod = "daily")
Arguments
dataset |
A dataframe with first column the time, the rest columns are rainfall data of different gauges. |
corPeriod |
A string showing the period used in the correlation computing, e.g. daily, monthly, yearly. |
Value
A dataframe.
See Also
For more detail about the algorithm used to fill the gaps, please
see fillGap.
Input Raster
Description
Method to load ncdf or tiff file and convert them into a SpatRaster object.
Usage
input_raster(x, ...)
## S4 method for signature 'character'
input_raster(x, ...)
## S4 method for signature 'SpatRaster'
input_raster(x, ...)
## S4 method for signature 'RasterLayer'
input_raster(x, ...)
## S4 method for signature 'RasterBrick'
input_raster(x, ...)
## S4 method for signature 'RasterStack'
input_raster(x, ...)
Arguments
x |
path (character) to the file or a SpatRaster object. |
... |
additional arguments to |
Value
SpatRaster
See Also
terra::rast
Input Table
Description
Method to load a table file and convert it into a data.table object.
Usage
input_table(x, ...)
## S4 method for signature 'character'
input_table(x, ...)
## S4 method for signature 'data.table'
input_table(x, ...)
## S4 method for signature 'data.frame'
input_table(x, ...)
Arguments
x |
path (character) to the file or a data.table object. |
... |
additional arguments to |
Value
data.table
See Also
data.table::fread
Input Vector
Description
Method to load a vector file and convert it into a SpatVector object.
Usage
input_vector(x, ...)
## S4 method for signature 'character'
input_vector(x, ...)
## S4 method for signature 'SpatVector'
input_vector(x, ...)
## S4 method for signature 'sf'
input_vector(x, ...)
Arguments
x |
path (character) to the file or a SpatVector object. |
... |
additional arguments to |
Value
SpatVector
See Also
terra::vect
Series of Pixel Values
Description
Converts layer-wise values from cube2table() into SWAT-style input:
a time series for each pixel in the study area.
Usage
layervalues2pixel(
layer_values,
main_tbl,
col_name = "20220101",
inline_output = TRUE,
path_output = NULL,
append = FALSE
)
Arguments
layer_values |
List. Values extracted per raster layer (from
|
main_tbl |
A table with pixel metadata (e.g., from
|
col_name |
Column name for each SWAT input table. Typically the first date in the time series (e.g., "20220101"). |
inline_output |
Logical. If TRUE, returns a list of data.tables. |
path_output |
Directory to write one file per pixel when
|
append |
Logical. If TRUE, append to existing files; otherwise overwrite. |
Value
A list of tables (when inline_output = TRUE) or a set of files in
path_output (one for each pixel).
Examples
layer_values <- data.frame(
ID = c(1, 2, 1, 2),
values = c(10, 20, 11, 21),
layer_name = c("day_1", "day_1", "day_2", "day_2")
)
main_tbl <- data.frame(NAME = c("tmin_1", "tmin_2"))
layervalues2pixel(
layer_values = layer_values,
main_tbl = main_tbl,
col_name = "20200101"
)
Main table constructor by Variable
Description
Construct a main table needed for the input in SWAT
Usage
main_input_var(study_area, var_name = "temp")
Arguments
study_area |
The object from 'study_area_records' |
var_name |
The name of the variable to be extracted |
Value
A table
Examples
study_area <- data.frame(
ID = 1:2,
LAT = c(-15.0, -15.5),
LON = c(-56.0, -56.5),
ELEVATION = c(100, 120)
)
main_input_var(study_area, var_name = "tmin")
Extract the date from the layer names This function extracts the date from the layer names of a raster object.
Description
Extract the date from the layer names This function extracts the date from the layer names of a raster object.
Usage
names_to_date(
raster_cube,
origin = "1970-01-01",
tz = "UTC",
regex = ".*=(\\d+)"
)
Arguments
raster_cube |
A raster object. |
origin |
A character string with the origin date. The default is "1970-01-01". |
tz |
A character string with the time zone. The default is "UTC". |
regex |
A character string with the regular expression to extract the date. |
Value
A POSIXct object.
Transforms the raw value from point perspective to a daily perspective
Description
Having the collected observation from a point perspective, this function transform the input to a vertical perspective, like a a daily perspective.
Usage
point_to_daily(
my_folder,
var_pattern = "p-",
main_pattern = "pcp",
start_date = "20170301",
end_date = "20170331",
interval = "day",
na_value = -99,
neg_to_zero = TRUE,
prefix = "day_"
)
Arguments
my_folder |
character. The path to the raw files. |
var_pattern |
character. A pattern for the observation/station points name. |
main_pattern |
character. A pattern for the main file containing all the points with ID, NAME, LAT, LON and ELEVATION. |
start_date |
character. Inform the start date of the serie in the format yyyymmdd. |
end_date |
character. Inform the end date of the serie in the format yyyymmdd. |
interval |
charactere. Inform the inteval betwenn two observations. See the function x |
na_value |
numeric. Value encoded as not available, use |
neg_to_zero |
logical. Inform whether negative values should be
corrected to zero after applying |
prefix |
character. A prefix for naming the table in the format of "prefix+date". for more detail. |
Value
A list of table
Examples
folder <- system.file("extdata/pcp_stations", package = "wcswatin")
test01 <- point_to_daily(my_folder = folder)
Summarize raster file metadata
Description
Summarize raster file metadata
Usage
raster_info(path)
Arguments
path |
Path to one or more raster files. |
Value
A data.table with one row per raster variable and the columns: file, variable, long_name, unit, n_layers, n_rows, n_cols, x_min, x_max, y_min, y_max, crs, has_time, time_start, time_end, time_step and time_resolution. The file and CRS columns are short labels intended for console inspection.
Calculate the Relative Humidity from dewpoint and ambient temperature
Description
This function performs the calculation of a relative humidity with dewpoint
and ambient temperature as input applying the formula:
RH = 100*10^(m*[(Td/(Td+Tn)) - (Tambient/(Tambient+Tn)])). Where
m and Tn are constants (Vaisala, 2013).
Usage
rh_calculator(
folder_dpt,
folder_tas,
folder_out,
file_name_output = "rh",
m_value = 7.591386,
Tn_value = 240.7263,
pattern = ".txt$"
)
Arguments
folder_dpt |
Path of the input 2m dewpoint temperature files as
|
folder_tas |
Path of the input Near-Surface Air Temperature files as
|
folder_out |
Path where to save the transformed files |
file_name_output |
Character string for the Relative humidity files on output. |
m_value |
The value for the constant |
Tn_value |
The value for |
pattern |
an optional regular expression. Only file names which match the regular expression will be returned. |
Value
Files with the same temporal resolution as the input
References
VAISALA (2013) HUMIDITY CONVERSION FORMULAS, Calculation formulas for humidity.
Save the csv files after transformation to daily form
Description
Save the csv files after transformation to daily form
Usage
save_daily_tbl(tbl_list, path)
Arguments
tbl_list |
list. A list, the output of the x function |
path |
The path where the files must be saved |
Value
No return value (NULL), called for side effects. Writes one CSV
file for every named element of tbl_list to path; output files are
named <element-name>.csv and retain the element table structure.
Examples
daily_dir <- tempfile("wcswatin-daily-")
dir.create(daily_dir)
daily_tables <- list(
day_20200101 = data.frame(ID = 1:2, pcp = c(1.2, 0))
)
save_daily_tbl(daily_tables, daily_dir)
list.files(daily_dir)
unlink(daily_dir, recursive = TRUE)
Study Area Records
Description
This function extracts the records position grid for the study area combining with the DEM for every point
Usage
study_area_records(raster_model, roi, dem)
Arguments
raster_model |
One layer representing where the data wile be extracted |
roi |
A shapefile delimiting the study area |
dem |
An elevation raster for the study area |
Value
A table
Plot a summary of the data
Description
This function creates a graph of daily values observed from several points on a monthly basis, using a boxplot. You can choose the amount of points to be randomly computed in the total point set.
Usage
summary_plot(
var_folder,
sample = 5,
percent = FALSE,
from = "2002-01-01",
to = "2021-05-31",
x_lab = "Months of observation",
y_lab = "Vriable name and unit",
pattern = ".txt$"
)
Arguments
var_folder |
Path of the input files |
sample |
Numeric value, informing the number of files to be used. Until the total amount is informed, the choice of points to be computed is random. |
percent |
When TRUE, the values passed on |
from |
The first date of the series. |
to |
The last date of the series. |
x_lab |
Character. Title for the x, see |
y_lab |
Character. Title for the y, see
|
pattern |
an optional regular expression. Only file names which match the regular expression will be returned. |
Value
A summary plot
Table summary of the data
Description
This function creates a table summary containing the min, the
max, the mean, the sd (standard deviation) and
n (number of value) of daily values observed from several
points on a monthly basis. When the parameter by_month is FALSE
the summary return is general, i.e., not on a monthly basis, so the table
contains only one row with the same columns (min, max, mean, sd, and
n). You can choose the amount of points to be randomly computed in the total
point set.
Usage
summary_table(
var_folder,
sample = 5,
percent = FALSE,
by_month = TRUE,
from = "2002-01-01",
to = "2021-05-31",
pattern = ".txt$"
)
Arguments
var_folder |
Path of the input files |
sample |
Numeric value, informing the number of files to be used. Until the total amount is informed, the choice of points to be computed is random. |
percent |
When TRUE, the values passed on |
by_month |
Either the summary should be done per month or in general.
When true, the parameters |
from |
The first date of the series when |
to |
The last date of the series when |
pattern |
an optional regular expression. Only file names which match the regular expression will be returned. |
Value
A summary table.
Export tables to txt or csv files
Description
Export tables to txt or csv files
Usage
table_to_files(table, folder_path, first_date, file_extension = "txt")
Arguments
table |
A table |
folder_path |
Character string of the folder where the file must be saved. |
first_date |
Character string of the first date for the time series.
This value is used to renaming the columns on every single file while
saving. The actual name is used as the file names. The suggested format is
|
file_extension |
Character. |
Value
No return value (NULL), called for side effects. Writes one
single-column txt or csv file per column in table to folder_path.
Each file is named after its input column and uses first_date as its
column name.
Extract gridded values at station or reference points
Description
Extract values from a raster layer, stack or brick at reference point locations and return a wide table with one column per point. This prepares gridded or simulated data for point-based validation against station observations.
Usage
tbl_from_references(raster_file, ref_points, prefix_colname = NULL, ...)
Arguments
raster_file |
Raster object accepted by |
ref_points |
Reference point locations. This input can be a
data.frame-like table with NAME, LAT and LON columns, a .txt or .csv file
path, an sf object, a SpatVector object or a vector file path accepted by
|
prefix_colname |
If not null, a character string used to prefix the station column names. |
... |
further arguments passed to |
Details
This function does not calculate validation metrics. Instead, it prepares the
extracted raster values in a table shape that can be combined with observed
station data and then passed to validation tools such as
hydroGOF::gof(), hydroGOF::ggof() or any other function that
compares observed and simulated series.
The returned table has one row per raster layer and one column per reference
point. Column names are taken from the NAME field in ref_points,
optionally prefixed with prefix_colname. When combining this output
with observed data, make sure raster layers and observation rows represent
the same dates or time steps in the same order.
Additional arguments passed through ... are forwarded to
extract, allowing options such as method,
buffer and fun.
Value
A data.frame with one row per raster layer and one column per reference point.
Examples
raster_layer <- terra::rast(
nrows = 2,
ncols = 2,
vals = 1:4,
crs = "EPSG:4326",
extent = c(-1, 1, -1, 1)
)
raster_stack <- c(raster_layer, raster_layer + 10)
stations <- data.frame(
NAME = c("station_a", "station_b"),
LAT = c(-0.5, 0.5),
LON = c(-0.5, 0.5)
)
simulated <- tbl_from_references(
raster_file = raster_stack,
ref_points = stations,
prefix_colname = "sim"
)
simulated
Create a directory if it does not exist
Description
This function creates a directory if it does not exist.
It is an internal function used in the unit_converter function.
Usage
touch_dir(folder_path, return_path = FALSE)
Arguments
folder_path |
A character string with the path of the directory to be created. |
return_path |
logical. If |
Value
NULL. Only for side effects.
Save trend-surface point time series to files
Description
Save the list returned by ts_to_point() as one file per target point.
Each output file has a single column named with the first date of the time
series in YYYYMMDD format.
Usage
ts_point_to_files(
points_list,
output_folder,
file_prefix = "pcp",
start_date = NULL
)
Arguments
points_list |
A list of tables returned by |
output_folder |
Path where output files will be saved. |
file_prefix |
Prefix used in output file names. It is separated from the point ID by an underscore. |
start_date |
Optional column name to use in all output files. If
|
Value
NULL. Called for side effects.
Trend Surface Interpolation into Raster
Description
This function make an interpolation whith the trend surface method where the user have to inform the polynome degree. The interpolation is made over the tageded points for all the serie on the input.
Usage
ts_to_area(my_folder, bassin_limit_path, poly_degree = 2, resolution = 0.01)
Arguments
my_folder |
Folder containing the ts input files. |
bassin_limit_path |
A shapefile containing the bassin limit where the trend suface function have to be predicted. |
poly_degree |
The degree to be used in the polynomial function for the trend surface. |
resolution |
The resolution for the output raster in degree. |
Value
A rasterbrick
Examples
ts_to_area(
my_folder = system.file("extdata/ts_input", package = "wcswatin"),
bassin_limit_path = system.file("extdata/sl_bassin/sl_bassin_limit.shp",
package = "wcswatin"
),
poly_degree = 2,
resolution = 0.5
)
Trend Surface Interpolation into Targeded Points
Description
This function make an interpolation whith the trend surface method where the user have to inform the polynome degree. The interpolation is made over the tageded points for all the serie on the input.
Usage
ts_to_point(my_folder, targeted_points_path, poly_degree = 2)
Arguments
my_folder |
Folder containing the ts input files. |
targeted_points_path |
A shapefile containing the targeted points where the trend suface function have to predict values. |
poly_degree |
The degree to be used in the polynomial function for the trend surface. |
Value
A list of tibles by day
Unit Converter
Description
This function performs the same calculation on each observation of a time
series, the time resolution is the same on input as on output. This feature
allows you to convert one unit to another. Just inform the conversion
function in the FUN parameter. The standard function performs the
conversion from Kelvin temperatures to degrees Celsius.
Usage
unit_converter(
folder_in,
folder_out,
pattern = ".txt$",
FUN = function(x) (x - 273.15)
)
Arguments
folder_in |
Path of the input files |
folder_out |
Path where to save the transformed files |
pattern |
an optional regular expression. Only file names which match the regular expression will be returned. |
FUN |
The function to use for transforming the unit of the variable on input. |
Value
Files with the same temporal resolution as the input
Main table creator for SWAT Input from Trend SUrface Interpolation
Description
This function is to create the main table for the input table for SWAT.
Usage
var_main_creator(targeted_points_path, var_name = "pcp", col_elev = "Elev")
Arguments
targeted_points_path |
Shapefile path |
var_name |
The variable name |
col_elev |
The column contain the elevation values |
Value
A table
Examples
var_main_creator(targeted_points_path = system.file("extdata/sl_centroides",
"Centroide_watershed_grau.shp",
package = "wcswatin"
))
Shows raster variables present in a NetCDF
Description
Shows raster variables present in a NetCDF
Usage
var_names(path)
Arguments
path |
Path where the netcdf file is located |
Value
Character vector of raster variable names
Calculate the wind speed from Eastward and Northward Near-Surface Wind
Description
This function performs the calculation of the wind speed from Eastward and
Northward Near-Surface Wind as input applying the formula: ws =
\sqrt(u^2 + v^2).
Usage
windspeed_calculator(
folder_uas,
folder_vas,
folder_out,
col_name = "20020101",
file_name_output = "ws",
pattern = ".txt$"
)
Arguments
folder_uas |
Path of the input Eastward Near-Surface Wind files (as u component). |
folder_vas |
Path of the input Northward Near-Surface Wind files (as v component). |
folder_out |
Path where to save the transformed files |
col_name |
The column name for the tables on the output. Usually, the first date of the time series. |
file_name_output |
Character string for the Wind speed files on output. |
pattern |
an optional regular expression. Only file names which match the regular expression will be returned. |
Value
Files with the same temporal resolution as the input.