Package 'airGRccia'

Calculate hypsometric curves for sub-basins

Description

This function computes the hypsometric curve for each sub-basin based on the provided DEM. The hypsometric curve represents the distribution of elevation within a basin and is mandatory for using the CemaNeige module in airGR.

Usage

calc_hypso(sb, dem)

Arguments

sb	An sf object representing the sub-basins with an "id" field (See output of extract_sub_basins).
dem	A raster object (e.g., SpatRaster, RasterLayer, or file path) representing the Digital Elevation Model (DEM) (See output of get_ign_dem).

Value

A matrix where each column corresponds to a sub-basin and each row represents the elevation quantiles (from 0% to 100%). This corresponds to the format required by airGRiwrm::CreateInputsModel.GRiwrm for the HypsoData argument.

---

Create a BasinsObs object (Hydroclimatic time series)

Description

Create a BasinsObs object (Hydroclimatic time series)

Usage

CreateBasinsObs(
  s_sb,
  ids,
  Qobs = NULL,
  Qinf = NULL,
  Qrelease = NULL,
  measures = c("Precip", "PotEvap", "TempMean")
)

Arguments

s_sb	Climate data (See get_climate_safran)
ids	Vector of sub-basin ids
Qobs	Observed flows (See get_hubeau_flows)
Qinf	(optional) matrix or data.frame of numeric containing observed flows. It must be provided only for nodes of type "Direct injection" and "Diversion". See CreateGRiwrm for details about these node types. Unit is [mm per time step] for nodes with an area, and [m³ per time step] for nodes with area=NA. Column names correspond to node IDs. Negative flows are abstracted from the model and positive flows are injected to the model
Qrelease	(optional) matrix or data.frame of numeric containing release flows by nodes using the model RunModel_Reservoir [m3 per time step]
measures

Value

A list containing an itemDatesR, vector of dates corresponding to the time dimension of the data, following by data.frames for each measure including climate data ("Precip", "PotEvap", "TempMean"), and observed flows ("Q").

---

Extract River Reaches by Sub-basin from a DEM

Description

This function extracts individual river reaches by tracing flow paths from each sub-basin outlet to the next downstream outlet using a D8 flow direction raster and a stream network.

Usage

extract_reaches(
  sb,
  d8fd = attr(sb, "files")["d8"],
  streams = attr(sb, "files")["streams"],
  max_tries = 1e+05,
  eps = 1,
  max_step_back = 10,
  max_azimut = 4,
  max_buffer = 3,
  dTolerance = 2 * terra::res(d8fd),
  future_plan = future::multisession,
  future_workers = get_future_workers(),
  filename = getCachePath(sb, "reaches", "gpkg"),
  overwrite = FALSE
)

Arguments

sb	An sf object representing the perimeter of sub-basins. Typically the output of extract_sub_basins().
d8fd	A SpatRaster or character path to the file containing D8 flow directions. Can also be obtained from extract_sub_basins().
streams	An sf object or character path to the file containing the stream network (e.g., output of get_ign_streams()).
max_tries	integer. Maximum cells tested for retrieving a reach.
eps	numeric. Minimum acceptable distance between the downstream reach and the downstream outlet in cells.
max_step_back	integer Maximum contiguous back steps in stream cell research
max_azimut	integer Maximum change of orientation in cell research (See details)
max_buffer	integer Maximum number of cells matching around stream cells
dTolerance	numeric. The tolerance for simplifying the reach geometry ( Used in sf::st_simplify).
future_plan	function One of available future plan: future::sequential, future::multisession, future::multicore, future::cluster
future_workers	integer Number of workers to use for parallel processing.
filename	character Optional. If provided, the streams geometry will be saved to this file and can be used as cache if overwrite = FALSE.
overwrite	logical Whether to overwrite the cached file if it exists.

Details

Type logger::log_level(logger::DEBUG) or logger::log_level(logger::TRACE) before running the function for getting details on stream cells search. In this mode, maps zooming on issue zones are recorded in the working directory (names "⁠debug_extract_reaches_bvi*_step*_row*_col*.png⁠").

max_azimut correspond to the maximum change of orientation when searching the next stream cell (0, for no change; 2 for 45°; 4 for 90°, 6 for 135° and 7 for 90°).

Value

An sf object containing one river reach per sub-basin, from the sub-basin outlet to the next downstream outlet.

---

Extract sub-basins from DEM

Description

Extract sub-basins from DEM

Usage

extract_sub_basins(
  outlets,
  dem = NULL,
  streams,
  crs = NULL,
  threshold = 1000,
  snap_dist = 100,
  area_tolerance = 0.05,
  max_radius = 50,
  filename = TRUE,
  overwrite = FALSE,
  verbose_mode = NULL
)

watershed_get_from_outlet(
  i,
  outlets,
  file_d8,
  r_flow_accum,
  crs,
  area_tolerance = 0.05,
  max_radius = 50,
  verbose_mode = NULL,
  overwrite = FALSE
)

find_near_target(
  r_flow_accum,
  row0,
  col0,
  target,
  area_tolerance,
  max_radius = 5
)

Arguments

outlets	data.frame with columns "id", "x", and "y" and optionnaly "area" containing respectively the outlet ID, X and Y coordinates (in the CRS of the DEM) and the a priori area of the sub-basin (in square meters) (See Details)
dem	character containing the filename of the DEM raster, or a SpatRaster object
streams	A sf object containing the streams.
crs	The CRS used for outlets coordinates. If NULL, the CRS of dem is used
threshold	Minimum threshold of contributing area to extract streams (unit: square meters)
snap_dist	Maximum distance between outlet and a stream vector for relocating outlet in the stream (in meters)
area_tolerance	Maximum relative error between the a priori area and the computed area of the sub-basin
max_radius	Maximum search radius (in number of cells) to find a closer point
filename	character Optional. If provided, the sub-basins geometry will be saved to this file and can be used as cache if overwrite = FALSE. If TRUE (default), a filename will be automatically generated with getCachePath based on the input parameters.
overwrite	logical Whether to overwrite the cached file if it exists.
verbose_mode	Sets verbose mode. If verbose mode is FALSE, tools will not print output messages. Default: NULL will use the value in WhiteboxTools settings, see wbt_verbose() for details.
i	integer row number to process in outlets
file_d8	Path of raster D8
r_flow_accum	Raster of flow accumulation
row0, col0	respectively row and column number of the current coordinates of the outlet in the raster
target	Target area to reach (in square meters)

Details

extract_sub_basins delineate the watershed from the DEM for each outlet, then it computes the sub-basins contour by remove overlapping basin areas and compute which sub-basin is downstream of which other sub-basin.

watershed_get_from_outlet delineate one basin given outlet coordinates. One can provide an a priori area in outlets$area which is used to control the area of the resulting polygon. If the resulting area is outside the tolerance (area_tolerance), the algorithm search for a closer point (at a maximum distance of max_radius cells of the DEM raster) on the stream network and delineate again the basin. This is repeated until the area is within the tolerance or max_tries is reached.

Value

extract_sub_basins returns a sf object with the polygons representing the sub-basins and various attributes detailling the sub-basin up-down order, total basin and sub-basin areas, coordinates of outlets after move.

Functions

• extract_sub_basins(): extracts sub-basins from a DEM using whitebox tools.

• watershed_get_from_outlet(): Extract one watershed for one outlet

• find_near_target(): Find a cell near (row0, col0) in the flow accumulation raster cells with a value close to a target area.

---

Extract and Aggregate SIM2 Daily Climate Data from RADIS

Description

This function retrieves selected daily climate variables from the SIM2 dataset via the RADIS::get_sim2_daily() function, renames the fields for compatibility with GR models (e.g., airGR), and optionally aggregates multiple related source fields (e.g., solid and liquid precipitation).

Usage

get_climate_safran(
  x,
  fields = list(Precip = c("PRENEI_Q", "PRELIQ_Q"), PotEvap = "ETP_Q", TempMean = "T_Q"),
  date_start,
  date_end,
  overlay_mode = "aggregate",
  filename = getCachePath(list(x, fields, date_start, date_end, overlay_mode),
    "climate_safran", ifelse(overlay_mode %in% c("intersect_geometry", "aggregate"),
    "nc", "RDS")),
  overwrite = FALSE,
  ...
)

Arguments

x	An sf object or geometry supported by RADIS::get_sim2_daily(), typically representing a spatial feature (e.g., watershed or point).
fields	A named list where each element corresponds to a GR-compatible variable name (e.g., Precip, PotEvap, TempMean) and contains one or more SIM2 field names to be retrieved and optionally summed.
date_start, date_end	A Date object or a character in format "%Y-%m-%d" specifying start and end dates of the extraction period.
overlay_mode	A character string passed to RADIS::get_sim2_daily(), typically "aggregate" for spatial mean over polygons or "raw" for full raster.
filename	If not NULL, path of cached data (See details).
overwrite	logical Whether to overwrite the cached file if it exists.
...	Additional arguments passed to RADIS::get_sim2_daily().

Details

Default value for filename depends on the parameter overlay_mode which cache the data as a netCDF file for overlay_mode = "intersect_geometry" and in RDS format for overlay_mode = "intersect_geometry".

Value

A named list where each element is a 3D array (or similar structure) containing the extracted and aggregated climate variables, named using the keys of fields (e.g., "Precip", "PotEvap", etc.).

See Also

RADIS::get_sim2_daily()

Examples

## Not run: 
library(sf)
# Example with a watershed polygon
basin <- sf::st_read("my_watershed.gpkg")
data <- get_climate_safran(
  x = basin,
  date_start = as.Date("2000-01-01"),
  date_end = as.Date("2005-12-31")
)

## End(Not run)

---

Compute number of workers given available memory and CPUs

Description

This function computes the number of workers to use for parallel processing taking into account both constraints that are available RAM and CPUs.

Usage

get_future_workers(mem_share = 0.8, cpu_share = 0.8)

Arguments

mem_share	numeric Share of RAM to use given total RAM and currently used RAM by this R session.
cpu_share	numeric Share of CPUs to use given total available CPUs.

Value

integer Number of workers to use.

Examples

get_future_workers()

---

Retrieve river flow observation from the Hydrometrie Hub'Eau API

Description

Flow observations are retrieved with hubeau::get_hydrometrie_obs_elab and formatted for airGRiwrm usage. Station identifiers are used as code_entite filter parameter on the Hydrometrie Hub"Eau API and can be either a site (8 characters-length) or a measuring station (10 characters-length) code.

Usage

get_hubeau_flows(x, ...)

## Default S3 method:
get_hubeau_flows(x, ...)

## S3 method for class 'SubBasins'
get_hubeau_flows(x, ...)

## S3 method for class 'character'
get_hubeau_flows(
  x,
  date_start,
  date_end,
  grandeur_hydro_elab = "QmnJ",
  max_days = 10000,
  max_retries = 3,
  wait_seconds = 2,
  path_cache = getCachePath(list(date_start, date_end, grandeur_hydro_elab),
    "hubeau_flows"),
  overwrite = FALSE,
  ...
)

Arguments

x	Either a character vector of station codes or a SubBasins object.
...	Parameters passed to methods or to hubeau::get_hydrometrie_obs_elab
date_start	Date of starting period
date_end	Date of ending period
grandeur_hydro_elab	Processed hydrometric variable type ("HIXM", "HIXnJ", "QINM", "QINnJ", "QixM", "QIXnJ", "QmM", or "QmnJ")
max_days	Maximum number of days in one API call
max_retries	Maximum number of retries for failed API calls
wait_seconds	Initial waiting time between retries (will be doubled at each retry)
path_cache	If not NULL, folder path of cached data
overwrite	logical Whether to overwrite the cached file if it exists.

Details

Hub'Eau APIs are currently limited to 20000 rows so this function cut the time period in max_days chunks for the API calls.

Value

A data.frame with a first column DatesR containing the dates and one column by station containing observed flows in m3/s. The returned object contains an attribute "codes_site_station" listing the correspondance between code_entite and station codes.

---

Extract DEM from IGN API

Description

This function download DEM from IGN API and perform correction on outliers.

Usage

get_ign_dem(x, ...)

## S3 method for class 'sf'
get_ign_dem(x, ...)

## S3 method for class 'bbox'
get_ign_dem(x, ...)

## S3 method for class 'sfc'
get_ign_dem(
  x,
  layer = "ELEVATION.ELEVATIONGRIDCOVERAGE",
  res = 25,
  crs = sf::st_crs(x),
  neg_outlier_threshold = 0,
  ...
)

Arguments

x	A bounding box, a simple feature collection or a simple feature geometry.
...	Further parameters passed to methods and to happign::get_wms_raster
layer	character; layer name obtained from get_layers_metadata("wms-r") or the IGN website.
res	numeric; resolution specified in the units of the coordinate system (e.g., meters for EPSG:2154, degrees for EPSG:4326). See details for more information.
crs	numeric, character, or object of class sf or sfc; defaults to EPSG:2154. See sf::st_crs() for more details.
neg_outlier_threshold	Threshold under which data is rejected as outlier and replaced by the mean of neighbour cells

Details

Default value of layer correspond to the BD Alti with 25 m of resolution. Available DEM on IGN API can be explored with the instruction: happign::get_layers_metadata("wms-r", "altimetrie").

Value

A SpatRaster object containing the DEM data.

Examples

# Basic usage with default DEM
bbox <- c(xmin = 325522.8, ymin = 6250949.2, xmax = 347137.1, ymax = 6270285.1)
dem <- get_ign_dem(sf::st_bbox(bbox, crs = 2154))
dem
terra::plot(dem)

---

Get streams from IGN

Description

This function retrieves hydrographic streams from the IGN WFS service and filter them based on specified criteria.

Usage

get_ign_streams(x, ...)

## S3 method for class 'sf'
get_ign_streams(x, ...)

## S3 method for class 'bbox'
get_ign_streams(x, ...)

## S3 method for class 'sfc'
get_ign_streams(
  x,
  layer = "BDCARTO_V5:troncon_hydrographique",
  crs = sf::st_crs(x),
  filters = NULL,
  filename = NULL,
  overwrite = FALSE,
  ...
)

Arguments

x	A bounding box, a simple feature collection or a simple feature geometry.
...	Further parameters passed to methods and to happign::get_wfs
layer	character; name of the WFS layer. Must correspond to a layer available on the IGN WFS service (see get_layers_metadata()).
crs	The CRS to use for the output streams (the CRS of x is used by default).
filters	A list of filters to apply to the streams data (See details)
filename	character Optional. If provided, the streams geometry will be saved to this file and can be used as cache if overwrite = FALSE.
overwrite	logical Whether to overwrite the cached file if it exists.

Details

List of filters is structured as follow: name correspond to the column names in the streams data, and values can be:

• A character vector to filter by specific values

• A function that takes a vector and returns a logical vector (e.g., not.is.na) not.is.na is equivalent of !is.na().

Value

A sf object containing the streams data.

Examples

bbox <- c(xmin = 325522.8, ymin = 6250949.2, xmax = 347137.1, ymax = 6270285.1)
# Retrieve only river streams that have a name and of specific nature
streams <- get_ign_streams(
  sf::st_bbox(bbox, crs = 2154),
  filters = list(
    nature = c("Ecoulement naturel", "Ecoulement canalis\u00E9"),
    cpx_toponyme_de_cours_d_eau = not.is.na
  )
)
streams
mapview::mapview(streams)

---

Get the amount of RAM

Description

Attempt to extract the amount of RAM on the current machine. This is OS specific:

• Linux: proc/meminfo

• Apple: system_profiler -detailLevel mini

• Windows: First tries grep MemTotal /proc/meminfo then falls back to wmic MemoryChip get Capacity

• Solaris: prtconf

A value of NA is return if it isn't possible to determine the amount of RAM.

Usage

get_ram()

References

This function and associated codes were taken from the benchmarkme package.

Examples

# Return (and pretty print) the amount of RAM
get_ram()
# In raw format
str(get_ram)

---

Get units of the variables available in SAFRAN database

Description

Get units of the variables available in SAFRAN database

Usage

get_safran_var_units()

Value

named character vector containing units for each climatic variable

Examples

get_safran_var_units()

---

Get cache path

Description

Build a path in a cache directory and a file name build from MD5 hash.

Usage

getCacheDir(
  path = Sys.getenv("CACHE_DIR_AIRGRCCIA", file.path(basepath, pkg)),
  pkg = utils::packageName(),
  basepath = rappdirs::user_cache_dir()
)

getCachePath(
  x,
  prefix = as.character(substitute(x)),
  fileext = NULL,
  cache_dir = getCacheDir()
)

clearCache(cache_dir = getCacheDir())

Arguments

path	Path to the cache directory (default from environment variable CACHE_DIR_AIRGRCCIA or package cache directory).
pkg	Package name (default current package)
basepath	Folder in which to add package cache directory (by default OS dependant cache folder)
x	Object to save (or property of object that can be used as a signature)
prefix	Optional prefix to add to the file name (default variable name)
fileext	Optional file extension to use for the file (e.g., "rds", "csv", etc.)
cache_dir	Path to the cache directory (default from getCacheDir())

Value

The cache path.

Functions

• getCacheDir(): Returns cache path root and create the folder if it does not exist.

• getCachePath(): Returns cache path for a specific file.

• clearCache(): Clear the cache directory

Examples

getCacheDir()
path_cars <- getCachePath(cars, fileext = "rds")
path_cars
## Not run: 
saveRDS(cars, path_cars)

## End(Not run)

---

Get info on hydrometric station

Description

Get info on hydrometric station

Usage

hydro_get_station(x)

hydro_station_as_sf(x)

Arguments

x

code site

Value

A data.frame or a SpatVector. See hubeau::get_hydrometrie_sites.

Examples

hydro_get_station("Y2300020")
hydro_station_as_sf("Y2300020")

---

Check if the CRS is projected

Description

Check if the CRS is projected

Usage

is_projected_crs(x)

Arguments

x	sf, sf geometry, or bbox.

Value

TRUE if the CRS is projected, FALSE otherwise.

Examples

# Define the bounding box coordinates in WGS84
bounding_box <- c(xmin = 3.211086, ymin = 43.24153, xmax = 3.904615, ymax = 44.28818)

# Create the bounding box using st_bbox
bbox <- sf::st_bbox(bounding_box, crs = 4326)

# Is it in projected CRS?
is_projected_crs(bbox)

# Convert to projected CRS
bbox_projected <- sf::st_transform(bbox, 2154)
is_projected_crs(bbox_projected)

---

Shortcut for !is.na

Description

Shortcut for !is.na

Usage

not.is.na(x)

Arguments

x	an R object to be tested

Value

logical See is.na.

---

Predictive Uncertainty functions

Description

Predictive Uncertainty functions

Usage

predict_uncertainty_calibration(
  Qobs,
  Qsim,
  bind_size = predict_uncertainty_bind_size(Qobs, obs_by_bind, min_bind_size),
  bind_step = 0.1 * bind_size,
  min_bind_size = 0.1,
  obs_by_bind = 1000,
  confidence_interval = 0.9,
  probs = c((1 - confidence_interval)/2, 1 - (1 - confidence_interval)/2)
)

predict_uncertainty(Qsim, uncertainty_table)

plot_uncertainty_time_series(
  Qsim_uncertain,
  DatesR,
  Qobs = NULL,
  bounds = names(Qsim_uncertain)[c(2, ncol(Qsim_uncertain))],
  rubbon_fill = "darkblue",
  rubbon_alpha = 0.2,
  line_colors = c(Simulated = "orangered", Observed = "black"),
  labels = list(title = "Predicted Discharge with Uncertainty Intervals", x = "Date", y =
    "Discharge (mm)", color = "Flow type")
)

plot_uncertainty_calibration(
  Qsim_uncertain,
  Qobs,
  bounds = names(Qsim_uncertain)[c(2, ncol(Qsim_uncertain))],
  rubbon_fill = "darkblue",
  rubbon_alpha = 0.2,
  outliers_quantile = 0.999,
  labels = list(title =
    "Relative Error vs. Simulated Discharge with Uncertainty Intervals", x =
    "Simulated Discharge (mm)", y = "Relative Error Qobs/Qsim (-)")
)

predict_uncertainty_bind_size(Qobs, obs_by_bind = 1000, min_bind_size = 0.1)

transfer_uncertainty(Qsim, uncertainty_table)

Arguments

Qobs	Observed discharge values (numeric vector).
Qsim	Simulated discharge values (numeric vector).
bind_size	Size of the moving bin as a fraction of the data range (default is 0.1).
bind_step	Step size for moving the bin as a fraction of the data range.
min_bind_size	Minimum bind size (default is 0.1).
obs_by_bind	Number of observations per bind (default is 1000).
confidence_interval	Confidence interval level (default is 0.90).
probs	Probabilities for quantile calculation (default is c(0.05, 0.5, 0.95)) for a 90% prediction interval.
uncertainty_table	Data frame returned by predict_uncertainty_calibration.
Qsim_uncertain	Data frame returned by predict_uncertainty, containing simulated discharge and uncertainty quantiles.
DatesR	Dates corresponding to the discharge values.
bounds	Names of the columns in QSim_uncertain representing the uncertainty bounds (default is the first and last error quantile).
rubbon_fill	Fill color for the uncertainty ribbon.
rubbon_alpha	Alpha transparency for the uncertainty ribbon.
line_colors	Named vector specifying colors for the simulated and observed discharge lines.
labels	List of labels for the plot (title, x, y, ...) used in ggplot2::labs().
outliers_quantile	Quantile threshold to filter out extreme relative errors (default is 0.999).

Details

This method is based on the approach described in Bourgin (2014) pages 201-203 with a variant consisting in moving a bin along the distribution of simulated discharge instead of using fixed discharge classes. Morever, the size of the bin is defined with respect to the number of observations which allows to refine the bin size when a large dataset is used.

References:

Bourgin, François. 2014. « Comment quantifier l’incertitude prédictive en modélisation hydrologique ? : Travail exploratoire sur un grand échantillon de bassins versants ». Phdthesis, AgroParisTech. https://pastel.archives-ouvertes.fr/tel-01130084.

Value

A data frame with columns:

• Qmin: Minimum discharge value of the bin.

• Qmax: Maximum discharge value of the bin.

• Quantiles of the relative error (Qsim / Qobs) for the specified probabilities.

Each row corresponds to a bin defined by bind_size and bind_step.

Functions

• predict_uncertainty_calibration(): Predictive Uncertainty Calibration

  Model of predictive uncertainty based on quantiles of the empirical distribution of relative errors for different bins of simulated discharge values using a moving bin approach.

• predict_uncertainty(): Predictive Uncertainty Prediction Using the uncertainty table generated by predict_uncertainty_calibration, this function predicts the relative uncertainty for new simulated discharge values.

• plot_uncertainty_time_series(): Plot Predicted Uncertainty Time Series. This function visualizes the predicted discharge values along with their uncertainty intervals.

• plot_uncertainty_calibration(): Plot Predicted Uncertainty Calibration. This function visualizes the relative error between observed and simulated discharge values, along with the predicted uncertainty intervals.

• predict_uncertainty_bind_size(): Predict Bind Size for Uncertainty Calibration. This function suggests an appropriate bind size for the uncertainty calibration based on the number of observations.

• transfer_uncertainty(): Transfer Uncertainty Quantiles.

Examples

library(ggplot2)
library(dplyr)
library(tidyr)

data(L0123001, package = "airGR")
InputsModel <- CreateInputsModel(
  RunModel_GR4J,
  DatesR = BasinObs$DatesR,
  Precip = BasinObs$P,
  PotEvap = BasinObs$E
)
Ind_Run <- seq(
  which(format(BasinObs$DatesR, format = "%Y-%m-%d") == "1985-01-01"),
  which(format(BasinObs$DatesR, format = "%Y-%m-%d") == "2012-12-31")
)
RunOptions <- CreateRunOptions(
  RunModel_GR4J,
  InputsModel = InputsModel,
  IndPeriod_Run = Ind_Run,
  IndPeriod_WarmUp = (Ind_Run[1] - 365):(Ind_Run[1] - 1)
)
Param <- c(257.237556, 1.012237, 88.234673, 2.207958)
OutputsModel <- RunModel_GR4J(
  InputsModel = InputsModel,
  RunOptions = RunOptions,
  Param = Param
)
Qsim <- OutputsModel$Qsim
Qobs <- BasinObs$Qmm[Ind_Run]

uncertainty_table <- predict_uncertainty_calibration(Qobs, Qsim)

Qsim_uncertain <- predict_uncertainty(Qsim, uncertainty_table)

# Plot the result of uncertainty estimation
plot_uncertainty_calibration(Qsim_uncertain, Qobs)

# Plot the time series with uncertainty intervals
ts_selection <- 1:365
plot_uncertainty_time_series(
  Qsim_uncertain[ts_selection, ],
  BasinObs$DatesR[Ind_Run[ts_selection]]
)

# Plot the time series with uncertainty intervals compared with observations
plot_uncertainty_time_series(
  Qsim_uncertain[ts_selection, ],
  BasinObs$DatesR[Ind_Run[ts_selection]],
  Qobs[ts_selection]
)

# Transfer uncertainty estimation to another simulated station
# Here we artificially modify Qsim to simulate another station
Qsim_new <- Qsim * 1.2 + 0.5
uncertainty_table_new <- transfer_uncertainty(Qsim_new, uncertainty_table)

Qsim_uncertain_new <- predict_uncertainty(Qsim_new, uncertainty_table_new)

plot_uncertainty_time_series(
  Qsim_uncertain[ts_selection, ],
  BasinObs$DatesR[Ind_Run[ts_selection]]
)

---

Title

Description

Title

Usage

sb_to_outlets(sb)

Arguments

sb	Output of extract_sub_basins.

Value

A simple feature with points representing sub-basins outlets.

---