Commit 8632a439 authored by Dorchies David's avatar Dorchies David
Browse files

feat: compute and record monthly mean on climatic data

Refs #12
parent 59daa6b3
Pipeline #32206 failed with stages
in 55 minutes and 13 seconds
......@@ -18,6 +18,10 @@ S3method(calcQMNAn,data.frame)
S3method(calcVCN,matrix)
S3method(calcVCN,numeric)
S3method(calcVCNn,data.frame)
S3method(convertMeteoBVI2BV,GRiwrm)
S3method(convertMeteoBVI2BV,character)
S3method(convertMeteoBVI2BV,data.frame)
S3method(convertMeteoBVI2BV,matrix)
S3method(convertUnitQ,data.frame)
export(addReservoirsGRiwrm)
export(addReservoirsQ)
......@@ -46,6 +50,7 @@ export(calcVCN30_10)
export(calcVCN30_2)
export(calcVCN30_5)
export(calcVCNn)
export(convertMeteoBVI2BV)
export(convertUnitQ)
export(createBasinsObs)
export(getAprioriIds)
......@@ -62,12 +67,15 @@ export(max_na_rm)
export(mean_na_rm)
export(min_na_rm)
export(not_na_count)
export(plot_monthly_mean)
export(plot_seine_map)
export(pmedian)
export(readDrias2020)
export(readQsim)
export(saveFlowDB)
export(selectDriasPeriod)
import(airGRiwrm)
import(ggplot2)
import(magrittr)
import(stats)
import(utils)
#' Conversion of meteorological data from sub-basin scale to basin scale
#'
#' @details
#' If `x` is a [data.frame], the first column should a [POSIXct] containing the date and the other columns the time series data.
#'
#' @param x either a `GRiwrm` network description (See [CreateGRiwrm]), a [character] id of a node, or a [matrix] or a [data.frame] containing meteorological data (See details)
#' @param ... Parameters passed to the methods
#'
#' @return [matrix] a matrix containing the converted meteorological data or a [data.frame] if `x` is a [data.frame].
#' @export
#' @rdname convertMeteoBVI2BV
#'
convertMeteoBVI2BV <- function(x, ...) {
UseMethod("convertMeteoBVI2BV", x)
}
#' @export
#' @rdname convertMeteoBVI2BV
convertMeteoBVI2BV.data.frame <- function(x, griwrm, ...) {
if (!inherits(x[[1L]], "POSIXt")) {
stop("'x' first column must be a vector of class 'POSIXlt' or 'POSIXct'")
}
DatesR <- x[[1L]]
m <- convertMeteoBVI2BV(griwrm, as.matrix(x[, -1]))
df <- cbind(DatesR, as.data.frame(m))
names(df) <- names(x)
return(df)
}
#' @param meteo [matrix] or [data.frame] containing meteorological data. Its [colnames] should be equal to the ID of the basins
#' @export
#' @rdname convertMeteoBVI2BV
convertMeteoBVI2BV.GRiwrm <- function(x, meteo, ...) {
meteo <- as.matrix(meteo)
output <- lapply(colnames(meteo), convertMeteoBVI2BV , griwrm = x, meteo = meteo)
meteoOut <- do.call(cbind,output)
dimnames(meteoOut)[[2]] <- colnames(meteo)
return(meteoOut)
}
#' @param griwrm `GRiwrm` object describing the semi-distributed network (See [CreateGRiwrm])
#' @export
#' @rdname convertMeteoBVI2BV
convertMeteoBVI2BV.character <- function(x, griwrm, meteo, ...) {
upperBasins <- !is.na(griwrm$down) & griwrm$down == x
if(all(!upperBasins)) {
return(meteo[,x])
}
upperIDs <- griwrm$id[upperBasins]
areas <- griwrm$area[match(c(x, upperIDs), griwrm$id)]
output <- convertMeteoBVI2BV(
meteo[,c(x, upperIDs), drop = FALSE],
areas = areas
)
return(output)
}
#' @param areas [numeric] vector with the total area of the basin followed by the areas of the upstream basins in km2
#' @param temperature [logical] `TRUE` if the meteorological data contain air temperature. If `FALSE` minimum output values are bounded to zero
#' @export
#' @rdname convertMeteoBVI2BV
convertMeteoBVI2BV.matrix <- function(x, areas, temperature = FALSE, ...) {
# Check arguments
if(nrow(x) < 2) {
stop("Meteorological data matrix should contain more than one row")
}
if(length(areas) != ncol(x)) {
stop("'areas' length and meteo data matrix number of columns should be equal")
}
if(areas[1] <= sum(areas[-1])) {
stop("Basin area 'areas[1]' should be greater than the sum of the upstream sub-basin areas")
}
if(ncol(x) == 1) {
return(x)
}
# Convert mm to 1E3 m3 and weighted temperatures
V <- x * rep(areas, each = nrow(x))
# Sum all weighted data and convert back to mm or °C
meteoBV <- rowSums(V) / sum(areas)
return(as.matrix(meteoBV, ncol = 1))
}
#' Plot monthly mean at one gauging station
#'
#' Used for simulated flows and climatic inputs.
#'
#' @details
#' Select time steps corresponding to one of the DRIAS 2020 periods:
#' "ref" for reference (1976-2005), "near" for near future (2021-2050),
#' "middle" for middle of the century (2041-2070), "end" for end of the century (2071-2100).
#'
#' @param rcp [character], "rcp4.5" or "rcp8.5"
#' @param period [character], see details
#' @param station [character], id of gauging station
#' @param drias [list], monthly data of each GCM/RCM projection and each period
#' @param hist [list], monthly data coming from observations
#' @param y_label [character], argument passed to [ggplot2::ylab]
#'
#' @return A ggplot object.
#' @import ggplot2
#' @export
#'
plot_monthly_mean <- function(rcp, period, station, drias, hist, y_label = "Flow (m³/s)") {
scenarios <- names(drias)
names(scenarios) <- scenarios
drias <- lapply(scenarios, function(x) {
if (grepl(rcp, x, fixed = TRUE)) {
drias[[x]][[period]][station, ]
} else {
NULL
}
})
drias <- drias[!sapply(drias,is.null)]
df <- as.data.frame(do.call(cbind, drias))
df$Mois <- seq.int(12)
dfHist <- as.data.frame(do.call(cbind, lapply(hist, function(x) x[station, ])))
dfHist$Mois <- seq.int(12)
ggplot(tidyr::gather(df, key = "scenario", value = "Q", -Mois)) +
geom_line(aes(x = Mois, y = Q, color = scenario), size = 1) +
geom_line(data = tidyr::gather(dfHist, key = "historique", value = "Q", -Mois),
aes(x = Mois, y = Q, linetype = historique),
size = 1) +
scale_x_continuous(breaks = seq.int(12),
labels = strsplit("JFMAMJJASOND", "")[[1]]) +
ylab(y_label) + xlab("")
}
#' Select a period in a time series
#'
#' @details
#' Select time steps corresponding to one of the DRIAS 2020 periods:
#' "ref" for reference (1976-2005), "near" for near future (2021-2050),
#' "middle" for middle of the century (2041-2070), "end" for end of the century (2071-2100).
#'
#' @param df [data.frame] with a first column of [POSIXt] dates and [numeric] in the other columns
#' @param period [character], see details
#' @template param_cfg
#'
#' @return [data.frame] with a first column of [POSIXt] dates and [numeric] in the other columns with selected rows according to the chosen period
#' @export
#'
selectDriasPeriod <- function(df, period, cfg = loadConfig()) {
periods <- lapply(cfg$hydroclim$drias$periods, as.POSIXct, tz = "UTC")
if (!period %in% names(periods)) {
stop("`period`should be in ",
paste(sprintf("\"%s\"", names(periods)), collapse = ", "))
}
selectDates <- df$DatesR >= periods[[period]][1] &
df$DatesR <= periods[[period]][2]
return(df[selectDates, ])
}
......@@ -28,13 +28,16 @@ plot_safran_bvi <- function(gis_safran, gis_bvi) {
prettymapr::addscalebar()
prettymapr::addnortharrow(pos = "topleft", scale = 0.5)
}
```
```{r, fig.cap = "Carte de superposition des mailles SAFRAN avec le contour du bassin versant de la Seine à Vernon"}
plot_safran_bvi(gis_safran = gis_safran, gis_bvi = gis_bvi)
title("Superposition des mailles SAFRAN avec le contour du BV")
```
## Calcul de l'intersection entre les couches mailles SAFRAN et BVI
```{r}
```{r, fig.cap="Fusion de la couche SAFRAN avec celle des bassins versant intermédiaire du bassin versant de la Seine à Vernon"}
spMailles <- raster::intersect(gis_safran, gis_bvi)
plot(gis_bvi, col = "#33333330")
plot(spMailles, add = TRUE)
......@@ -77,7 +80,7 @@ La liste des scénarios sélectionnés pour l'étude est la suivante : `r paste(
### Aggrégation des données DRIAS 2020
```{r, eval=!cfg$data$cloud}
```{r, eval=!cfg$data$write_results}
driasPath <- getDataPath(cfg$hydroclim$path)
saveBasinsObs <- function(rcp, scenario) {
......@@ -99,3 +102,113 @@ mapply(saveBasinsObs,
rcp = cfg$hydroclim$drias$rcp[-1],
scenario = rep(colnames(scenarioDriasFiles), 2))
```
## Analyse des données climatiques des GCM/RCM
```{r}
rcps <- cfg$hydroclim$drias$rcp[-1]
scenarios <- gsub("/", "_", cfg$hydroclim$drias$scenarios)
scenariosX <- rep(scenarios, length(rcps))
rcpsX <- rep(rcps, each = length(scenarios))
```
## Lecture des données climatiques
```{r}
# Load all climatic data scenarios
loadAllBasinsObs <- function(rcp, scenario) {
message("Processing ", rcp, " scenario ", scenario, "...")
file <- paste0(paste("BasinObs", rcp, scenario, sep = "_"), ".RDS")
loadBasinsObs(file, cfg = cfg)
}
AllBasinsObs <- mapply(loadAllBasinsObs,
rcp = rcpsX,
scenario = scenariosX,
SIMPLIFY = FALSE)
names(AllBasinsObs) <- paste(rcpsX, scenariosX, sep = " - ")
```
```{r}
# arrange data by climat variable
formatObs <- function(BasinsObs, item) {
cbind(DatesR = BasinsObs$DatesR, as.data.frame(BasinsObs[[item]]))
}
P_drias_BVI <- lapply(AllBasinsObs, formatObs, item = "P")
E_drias_BVI <- lapply(AllBasinsObs, formatObs, item = "E")
T_drias_BVI <- lapply(AllBasinsObs, formatObs, item = "Temp")
rm(AllBasinsObs)
```
```{r}
# Convert BVI to BV
data("griwrm")
P_drias <- lapply(P_drias_BVI, convertMeteoBVI2BV, griwrm = griwrm)
E_drias <- lapply(E_drias_BVI, convertMeteoBVI2BV, griwrm = griwrm)
T_drias <- lapply(T_drias_BVI, convertMeteoBVI2BV, griwrm = griwrm)
# Calcul pour tous les scénarios et périodes
calcPeriod <- function(period, df, calcFUN) {
ind <- calcFUN(selectDriasPeriod(df, period))
t(ind)
}
calcAll <- function(data, calcFUN, periods = names(cfg$hydroclim$drias$periods)) {
names(periods) <- periods
lapply(data, function(df) {
lapply(as.list(periods), calcPeriod, df = df, calcFUN = calcFUN)
})
}
P_drias_month <- calcAll(P_drias, calcMonthlyInterannualSum)
T_drias_month <- calcAll(T_drias, calcMonthlyInterannualMean)
E_drias_month <- calcAll(E_drias, calcMonthlyInterannualSum)
```
```{r}
# Calculs pour les données observées sur la période de référence
BasinsObs <- loadBasinsObs("BasinsObs_observations_day_1958-2019.RDS", cfg = cfg)
P_obs <- formatObs(BasinsObs, item = "P")
T_obs <- formatObs(BasinsObs, item = "Temp")
E_obs <- formatObs(BasinsObs, item = "E")
P_obs_month <- t(calcMonthlyInterannualSum(P_obs))
T_obs_month <- t(calcMonthlyInterannualMean(T_obs))
E_obs_month <- t(calcMonthlyInterannualSum(E_obs))
```
```{r, fig.cap="Précipitation moyenne mensuelle du bassin versant à Paris Austerlitz (H5920010) entre 1976 et 2005 pour le climat observé et 5 couples GCM/RCM (DRIAS 2020)"}
plot_monthly_mean("rcp4.5", "ref", "H5920010", P_drias_month, list(obs = P_obs_month), "Precipitation (mm)")
```
```{r, fig.cap="Température moyenne mensuelle du bassin versant à Paris Austerlitz (H5920010) entre 1976 et 2005 pour le climat observé et 5 couples GCM/RCM (DRIAS 2020)"}
plot_monthly_mean("rcp4.5", "ref", "H5920010", T_drias_month, list(obs = T_obs_month), "Temperature (°C)")
```
```{r, fig.cap="ETP moyenne mensuelle du bassin versant à Paris Austerlitz (H5920010) entre 1976 et 2005 pour le climat observé et 5 couples GCM/RCM (DRIAS 2020)"}
plot_monthly_mean("rcp4.5", "ref", "H5920010", E_drias_month, list(obs = E_obs_month), "PE (mm)")
```
```{r, eval=cfg$data$write_results}
# Sauvegarde des données mensuelles dans le cloud
saveClimaticData <- function(obs, drias, path, name) {
drias$obs <- list(ref = obs)
lapply(names(drias), function(scenario) {
lapply(names(drias[[scenario]]), function(period) {
file <- paste0(name, "_monthly_",
gsub(" ", "", scenario),"_",
paste(lubridate::year(cfg$hydroclim$drias$periods[[period]]), collapse = "-"),
".tsv")
m <- drias[[scenario]][[period]]
df <- cbind(Id = rownames(m), m)
readr::write_tsv(as.data.frame(df),
file.path(path, file))
})
})
}
path <- getDataPath(cfg$hydroclim$path, "Analyses")
saveClimaticData(P_obs_month, P_drias_month, path, "P")
saveClimaticData(T_obs_month, T_drias_month, path, "T")
saveClimaticData(E_obs_month, E_drias_month, path, "E")
```
......@@ -13,20 +13,6 @@ L'évolution est représentée sous la forme du rapport entre l'indicateur calcu
La période de référence correspond à la période 1976-2005 et la période future choisie ici correspond à la période "fin de siècle" 2071-2100.
```{r}
selectPeriod <- function(df, period) {
periods <- lapply(cfg$hydroclim$drias$periods, as.POSIXct, tz = "UTC")
if (!period %in% names(periods)) {
stop("`period`should be in ",
paste(sprintf("\"%s\"", names(periods)), collapse = ", "))
}
selectDates <- df$DatesR >= periods[[period]][1] &
df$DatesR <= periods[[period]][2]
return(df[selectDates, ])
}
```
Les indicateurs sont calculés séparément pour les deux scénarios d'émission RCP 4.5 et RCP 8.5 et une synthèse des différents couples GCM/RCM est réalisée.
```{r}
......@@ -36,45 +22,6 @@ scenariosX <- rep(scenarios, length(rcps))
rcpsX <- rep(rcps, each = length(scenarios))
```
## Lecture des données climatiques
Les données climatiques sont stockées dans les objets `BasinsObs` (Voir `?loadBasinsObs` pour le chargement). Le nom des fichiers contenant ces données est formé comme suit&nbsp;: `BasinObs_[RCP]_[GCM_RCM].RDS`. Ce qui se traduit en R par&nbsp:
```r
file <- paste0(paste("BasinObs", rcp, scenario, sep = "_"), ".RDS")
BasinsObs <- loadBasinsObs(file, cfg = cfg)
```
On peut charger toutes les données climatiques de tous les scénarios GCM/RCM dans une liste avec le script suivant&nbsp;:
```{r}
loadAllBasinsObs <- function(rcp, scenario) {
message("Processing ", rcp, " scenario ", scenario, "...")
file <- paste0(paste("BasinObs", rcp, scenario, sep = "_"), ".RDS")
loadBasinsObs(file, cfg = cfg)
}
AllBasinsObs <- mapply(loadAllBasinsObs,
rcp = rcpsX,
scenario = scenariosX,
SIMPLIFY = FALSE)
names(AllBasinsObs) <- paste(rcpsX, scenariosX, sep = " - ")
```
On réarrange les données pour avoir une liste de data.frame pour chaque variable étudiée:
```{r}
formatObs <- function(BasinObs, item) {
cbind(DatesR = BasinObs$DatesR, as.data.frame(BasinObs[[item]]))
}
P_drias <- lapply(AllBasinsObs, formatObs, item = "P")
E_drias <- lapply(AllBasinsObs, formatObs, item = "E")
T_drias <- lapply(AllBasinsObs, formatObs, item = "Temp")
rm(AllBasinsObs)
```
Chaque élément de la liste `AllBasinsObs` est une liste structurée de classe *BasinsObs* (Voir `?createBasinsObs`).
## Listes des indicateurs
### Indicateurs mensuels
......@@ -86,24 +33,13 @@ Pour les données climatiques&nbsp;:
- Cumul ETP (mm)
```{r}
# Précipitation moyenne mensuelle pour la période de référence
calcMonthlyInterannualSum(selectPeriod(P_drias[[1]][, 1:3], "ref"))
# Température moyenne mensuelle pour l'horizon fin de siècle
calcMonthlyInterannualMean(selectPeriod(T_drias[[1]][, 1:3], "end"))
# Calcul pour tous les scénarios et périodes
calcPeriod <- function(period, df, calcFUN) {
ind <- calcFUN(selectPeriod(df, period))
t(ind)
}
calcAll <- function(data, calcFUN) {
lapply(data, function(df) {
periods <- names(cfg$hydroclim$drias$periods)
names(periods) <- periods
lapply(as.list(periods), calcPeriod, df = df, calcFUN = calcFUN)
})
readTsvMatrix <- function(path) {
df <- read.csv(path, sep = "\t")
m <- as.matrix(df[,-1])
rownames(m) <- df[, 1]
m
}
P_month <- calcAll(P_drias, calcMonthlyInterannualSum)
T_month <- calcAll(T_drias, calcMonthlyInterannualMean)
E_month <- calcAll(E_drias, calcMonthlyInterannualSum)
......@@ -117,12 +53,6 @@ Pour les débits&nbsp;:
Ces données ont été calculées et enregistrées lors de la simulation des débits.
```{r}
readTsvMatrix <- function(path) {
df <- read.csv(path, sep = "\t")
m <- as.matrix(df[,-1])
rownames(m) <- df[, 1]
m
}
loadIndicators <- function(rcp, scenario, indicator) {
periods <- names(cfg$hydroclim$drias$periods)
names(periods) <- periods
......@@ -204,45 +134,16 @@ Qhist_month5 <- lapply(historiQ, loadHistoriQ, indicator = "Q_monthly_5years")
Qhist_indicators <- lapply(historiQ, loadHistoriQ, indicator = "Q_indicators")
```
```{r}
plotDataMonth <- function(rcp, period, station, drias, hist) {
scenarios <- names(drias)
names(scenarios) <- scenarios
drias <- lapply(scenarios, function(x) {
if (grepl(rcp, x, fixed = TRUE)) {
drias[[x]][[period]][station, ]
} else {
NULL
}
})
drias <- drias[!sapply(drias,is.null)]
df <- as.data.frame(do.call(cbind, drias))
df$Mois <- seq.int(12)
dfHist <- as.data.frame(do.call(cbind, lapply(hist, function(x) x[station, ])))
dfHist$Mois <- seq.int(12)
library(ggplot2)
ggplot(tidyr::gather(df, key = "scenario", value = "Q", -Mois)) +
geom_line(aes(x = Mois, y = Q, color = scenario), size = 1) +
geom_line(data = tidyr::gather(dfHist, key = "historique", value = "Q", -Mois),
aes(x = Mois, y = Q, linetype = historique),
size = 1) +
scale_x_continuous(breaks = seq.int(12),
labels = strsplit("JFMAMJJASOND", "")[[1]])
}
```
Débits moyens mensuels à Paris sur la période de référence:
```{r}
plotDataMonth("rcp4.5", "ref", "H5920010", Q_month, Qhist_month)
```{r, fig.asp = 0.7}
plot_monthly_mean("rcp4.5", "ref", "H5920010", Q_month, Qhist_month)
```
Débits moyens à Paris sur la période 2071-2100 avec le scénario d'émission RCP4.5:
```{r}
plotDataMonth("rcp4.5", "end", "H5920010", Q_month, Qhist_month)
```{r, fig.asp = 0.7}
plot_monthly_mean("rcp4.5", "end", "H5920010", Q_month, Qhist_month)
```
......
......@@ -85,6 +85,17 @@
file = {C\:\\Users\\david.dorchies\\Zotero\\storage\\9ENK4C92\\Oudin et al. - 2005 - Which potential evapotranspiration input for a lum.pdf}
}
@report{soubeyrouxNouvellesProjectionsClimatiques2020,
title = {Les nouvelles projections climatiques de référence DRIAS 2020 pour la métropole},
author = {Soubeyroux, Jean-Michel and Bernus, Sébastien and Corre, Lola and Drouin, Agathe and Dubuisson, Brigitte and Etchevers, Pierre and Gouget, Viviane and Josse, Patrick and Kerdoncuff, Maryvonne and Samacoits, Raphaëlle and Tocquer, Flore},
date = {2020},
pages = {98},
institution = {{Météo France}},
url = {http://www.drias-climat.fr/document/rapport-DRIAS-2020-red3-2.pdf},
langid = {french},
file = {C\:\\Users\\david.dorchies\\Zotero\\storage\\D7FZ2YZ4\\Soubeyroux et al. - 2020 - Les nouvelles projections climatiques de référence.pdf}
}
@article{terrierStreamflowNaturalizationMethods2020,
title = {Streamflow Naturalization Methods: A Review},
shorttitle = {Streamflow Naturalization Methods},
......
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/convertMeteoBVI2BV.R
\name{convertMeteoBVI2BV}
\alias{convertMeteoBVI2BV}
\alias{convertMeteoBVI2BV.data.frame}
\alias{convertMeteoBVI2BV.GRiwrm}
\alias{convertMeteoBVI2BV.character}
\alias{convertMeteoBVI2BV.matrix}
\title{Conversion of meteorological data from sub-basin scale to basin scale}
\usage{
convertMeteoBVI2BV(x, ...)
\method{convertMeteoBVI2BV}{data.frame}(x, griwrm, ...)
\method{convertMeteoBVI2BV}{GRiwrm}(x, meteo, ...)
\method{convertMeteoBVI2BV}{character}(x, griwrm, meteo, ...)
\method{convertMeteoBVI2BV}{matrix}(x, areas, temperature = FALSE, ...)
}
\arguments{
\item{x}{either a \code{GRiwrm} network description (See \link{CreateGRiwrm}), a \link{character} id of a node, or a \link{matrix} or a \link{data.frame} containing meteorological data (See details)}
\item{...}{Parameters passed to the methods}
\item{griwrm}{\code{GRiwrm} object describing the semi-distributed network (See \link{CreateGRiwrm})}
\item{meteo}{\link{matrix} or \link{data.frame} containing meteorological data. Its \link{colnames} should be equal to the ID of the basins}
\item{areas}{\link{numeric} vector with the total area of the basin followed by the areas of the upstream basins in km2}
\item{temperature}{\link{logical} \code{TRUE} if the meteorological data contain air temperature. If \code{FALSE} minimum output values are bounded to zero}
}
\value{
\link{matrix} a matrix containing the converted meteorological data or a \link{data.frame} if \code{x} is a \link{data.frame}.
}
\description{
Conversion of meteorological data from sub-basin scale to basin scale
}
\details{
If \code{x} is a \link{data.frame}, the first column should a \link{POSIXct} containing the date and the other columns the time series data.
}
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_monthly_mean.R
\name{plot_monthly_mean}
\alias{plot_monthly_mean}
\title{Plot monthly mean at one gauging station}
\usage{
plot_monthly_mean(rcp, period, station, drias, hist, y_label = "Flow (m³/s)")
}
\arguments{
\item{rcp}{\link{character}, "rcp4.5" or "rcp8.5"}
\item{period}{\link{character}, see details}
\item{station}{\link{character}, id of gauging station}
\item{drias}{\link{list}, monthly data of each GCM/RCM projection and each period}
\item{hist}{\link{list}, monthly data coming from observations}
\item{y_label}{\link{character}, argument passed to \link[ggplot2:labs]{ggplot2::ylab}}
}
\value{
A ggplot object.
}
\description{
Used for simulated flows and climatic inputs.
}
\details{
Select time steps corresponding to one of the DRIAS 2020 periods:
"ref" for reference (1976-2005), "near" for near future (2021-2050),
"middle" for middle of the century (2041-2070), "end" for end of the century (2071-2100).
}
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/selectDriasPeriod.R
\name{selectDriasPeriod}
\alias{selectDriasPeriod}
\title{Select a period in a time series}
\usage{
selectDriasPeriod(df, period, cfg = loadConfig())
}
\arguments{
\item{df}{\link{data.frame} with a first column of \link{POSIXt} dates and \link{numeric} in the other columns}
\item{period}{\link{character}, see details}
\item{cfg}{a config object. Configuration to use. See \link{loadConfig} for details}
}
\value{
\link{data.frame} with a first column of \link{POSIXt} dates and \link{numeric} in the other columns with selected rows according to the chosen period
}
\description{
Select a period in a time series
}
\details{
Select time steps corresponding to one of the DRIAS 2020 periods:
"ref" for reference (1976-2005), "near" for near future (2021-2050),
"middle" for middle of the century (2041-2070), "end" for end of the century (2071-2100).
}
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