Obtener valores normales climáticos mensuales del departamento del Casanare con ClimateR
Anyi Lorena Lopez Guevara
30/11/2022
Introduccion
En este cuaderno se mostrara de manera sencilla como obtener valores mensuales de precipitacion y temperatura “normales” en el periodo comprendido entre 1981 al 2010 colocando en practica la libreria climateR del departamento del Casanare. Cuando se habla de una precipitacion “normal” no hace referencia a un valor que se esperaria, se refiere a un promedio de los valores de precipitacion durante un periodo de 30 años y de esta manera la precipitacion real en un año determinado puede encontrarse por encima o por debajo del promedio estacional o tambien podria ser “normal”.
Setup
Primero se realiza una limpieza de la memoria de R mediante el siguiente codigo:
rm(list=ls())# install.packages("remotes")Ahora se cargaran las librerias
library(AOI)
library(climateR)
library(sf)## Linking to GEOS 3.9.1, GDAL 3.4.3, PROJ 7.2.1; sf_use_s2() is TRUElibrary(raster)## Loading required package: splibrary(rasterVis)## Warning: package 'rasterVis' was built under R version 4.2.2## Loading required package: latticelibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:raster':
##
## intersect, select, union## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(leaflet)
library(leafem)
library(RColorBrewer)
library(stars)## Loading required package: abindLeer el área de estudio: Casanare
list.files()## [1] "INPUTS" "NotebookFinal.html" "NotebookFinal.Rmd"Se lee un archivo previo con la division politica del departamento
(casanare <- st_read("./INPUTS/CasanareCitites.shp"))## Reading layer `CasanareCitites' from data source
## `C:\Users\USER\OneDrive\Documentos\GEOMATICA\GB\PF\INPUTS\CasanareCitites.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 19 features and 13 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -73.0989 ymin: 4.246699 xmax: -69.84787 ymax: 6.250501
## Geodetic CRS: WGS 84## Simple feature collection with 19 features and 13 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -73.0989 ymin: 4.246699 xmax: -69.84787 ymax: 6.250501
## Geodetic CRS: WGS 84
## First 10 features:
## fid ID_0 ISO NAME_0 ID_1 NAME_1 ID_2 NAME_2 TYPE_2
## 1 1 53 COL Colombia 10 Casanare 388 Aguazul Municipio
## 2 2 53 COL Colombia 10 Casanare 389 Chameza Municipio
## 3 3 53 COL Colombia 10 Casanare 390 Hato Corozal Municipio
## 4 4 53 COL Colombia 10 Casanare 391 La Salina Municipio
## 5 5 53 COL Colombia 10 Casanare 392 Mani Municipio
## 6 6 53 COL Colombia 10 Casanare 393 Monterrey Municipio
## 7 7 53 COL Colombia 10 Casanare 394 Nunchia Municipio
## 8 8 53 COL Colombia 10 Casanare 395 Orocue Municipio
## 9 9 53 COL Colombia 10 Casanare 396 Paz de Ariporo Municipio
## 10 10 53 COL Colombia 10 Casanare 397 Pore Municipio
## ENGTYPE_2 NL_NAME_2 VARNAME_2 AREA geometry
## 1 Municipality <NA> <NA> 1433.3676 POLYGON ((-72.5075 4.834401...
## 2 Municipality <NA> <NA> 328.6694 POLYGON ((-72.7872 5.140501...
## 3 Municipality <NA> <NA> 5063.0322 POLYGON ((-72.0873 6.0335, ...
## 4 Municipality <NA> <NA> 152.3924 POLYGON ((-72.4195 6.2267, ...
## 5 Municipality <NA> <NA> 3862.1055 POLYGON ((-72.2622 4.3903, ...
## 6 Municipality <NA> <NA> 720.9689 POLYGON ((-72.93552 5.01439...
## 7 Municipality <NA> <NA> 1046.8378 POLYGON ((-72.0023 5.553099...
## 8 Municipality <NA> <NA> 4742.7239 POLYGON ((-71.0854 4.860201...
## 9 Municipality <NA> <NA> 12435.9210 POLYGON ((-69.84787 6.00488...
## 10 Municipality <NA> <NA> 784.1395 POLYGON ((-71.9291 5.521599...Obtener valores normales de precipitación mensuales en Casanare
Un año abarca doce meses:
oneyear = seq(1,12)Ahora se descarga la precipitación mensual “normal” de 1981 a 2010:
precip = getTerraClimNormals(casanare, param = "prcp", period = "19812010", month=oneyear)## Spherical geometry (s2) switched off## Spherical geometry (s2) switched onAhora se observa lo que se obtuvo:
precip## $terraclim_19812010_prcp
## class : RasterStack
## dimensions : 50, 79, 3950, 12 (nrow, ncol, ncell, nlayers)
## resolution : 0.04166667, 0.04166667 (x, y)
## extent : -73.125, -69.83333, 4.208333, 6.291667 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## names : X01, X02, X03, X04, X05, X06, X07, X08, X09, X10, X11, X12
## min values : 1.0, 11.4, 39.4, 111.5, 105.9, 95.0, 51.4, 53.4, 61.6, 102.8, 82.5, 11.8
## max values : 123.1, 162.0, 256.4, 406.7, 560.6, 624.4, 583.0, 490.2, 366.5, 381.6, 259.7, 210.1Tenemos que tener en cuenta que el objeto “precip” tiene doce capas
capas <-names(precip)Ahora obtendremos un histograma por cada una de las capas:
par(mfcol=c(12,12), mfrow=c(4,3), oma=c(1,1,0,0),
mar=c(1,1,1,0), tcl=-0.1, mgp=c(0,0,0))
for (penta in capas) {
hist(precip[[penta]], ylab=NA, cex.axis=0.5, font.main=1, cex.main=0.8)
}
Ahora se calcula la precicpitacion normal para todo el año:
year_precip <- sum(precip$terraclim_19812010_prcp)Y obtenemos: una precipitacion minima de 960,4 y maxima de 3585,9
year_precip## class : RasterLayer
## dimensions : 50, 79, 3950 (nrow, ncol, ncell)
## resolution : 0.04166667, 0.04166667 (x, y)
## extent : -73.125, -69.83333, 4.208333, 6.291667 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## source : memory
## names : layer
## values : 960.4, 3585.9 (min, max)Ahora se creara una paleta de colores para la precipitacion:
ppal <- colorNumeric(c("orange", "yellow", "#a1d99b", "#deebf7", "#9ecae1","#3182bd", "darkblue"), values(year_precip$layer),
na.color = "transparent")Visualizar la precipitacion acumulada:
leaflet() %>% addTiles() %>%
addRasterImage(year_precip, colors = ppal, opacity = 0.8) %>%
addLegend(pal = ppal, values = values(year_precip$layer),
title = "Annual Precipitation")## Warning in colors(.): Some values were outside the color scale and will be
## treated as NAObtener valores normales mensuales de temperatura
Temperatura maxima
tmax = getTerraClimNormals(casanare, param = "tmax", period = "19812010", month=oneyear)## Spherical geometry (s2) switched off## Spherical geometry (s2) switched onVisualizamos la temperatura maxima
tmax## $terraclim_19812010_tmax
## class : RasterStack
## dimensions : 50, 79, 3950, 12 (nrow, ncol, ncell, nlayers)
## resolution : 0.04166667, 0.04166667 (x, y)
## extent : -73.125, -69.83333, 4.208333, 6.291667 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## names : X01, X02, X03, X04, X05, X06, X07, X08, X09, X10, X11, X12
## min values : 9.9, 10.0, 9.9, 9.1, 8.7, 7.2, 6.5, 7.4, 8.1, 8.4, 9.0, 9.4
## max values : 34.1, 35.3, 34.9, 32.9, 31.0, 29.7, 29.8, 30.7, 31.5, 31.7, 31.9, 32.4Temperatura maxima anual
(year_tmax = max(tmax$terraclim_19812010_tmax))## class : RasterLayer
## dimensions : 50, 79, 3950 (nrow, ncol, ncell)
## resolution : 0.04166667, 0.04166667 (x, y)
## extent : -73.125, -69.83333, 4.208333, 6.291667 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## source : memory
## names : layer
## values : 10, 35.3 (min, max)Temperatura minima
tmin = getTerraClimNormals(casanare, param = "tmin", period = "19812010", month=oneyear)## Spherical geometry (s2) switched off## Spherical geometry (s2) switched onVisualizamos la temperatura minima
tmin## $terraclim_19812010_tmin
## class : RasterStack
## dimensions : 50, 79, 3950, 12 (nrow, ncol, ncell, nlayers)
## resolution : 0.04166667, 0.04166667 (x, y)
## extent : -73.125, -69.83333, 4.208333, 6.291667 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## names : X01, X02, X03, X04, X05, X06, X07, X08, X09, X10, X11, X12
## min values : 0.0, -0.1, 1.1, 1.8, 1.4, 1.3, 0.7, 0.8, 0.7, 1.2, 1.3, 0.9
## max values : 22.8, 23.4, 24.1, 23.9, 23.1, 22.8, 22.5, 22.7, 22.9, 23.2, 23.5, 23.2Temperarura minima anual
(year_tmin = min(tmin$terraclim_19812010_tmin))## class : RasterLayer
## dimensions : 50, 79, 3950 (nrow, ncol, ncell)
## resolution : 0.04166667, 0.04166667 (x, y)
## extent : -73.125, -69.83333, 4.208333, 6.291667 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## source : memory
## names : layer
## values : -0.1, 22.5 (min, max)Ahora crearemos una copia
(tempera <- stack(year_tmin, year_tmax))## class : RasterStack
## dimensions : 50, 79, 3950, 2 (nrow, ncol, ncell, nlayers)
## resolution : 0.04166667, 0.04166667 (x, y)
## extent : -73.125, -69.83333, 4.208333, 6.291667 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## names : layer.1, layer.2
## min values : -0.1, 10.0
## max values : 22.5, 35.3Ahora cambiamos los nombres de las capas
names(tempera) <- c("tmin", "tmax")Ahora definiremos los valores para la visualizacion
tvals <- sort(c(values(tempera$tmin),values(tempera$tmax)))Crearemos la paleta de colores
pal <- colorNumeric(rev(c("red", "orange", "#fff9ae",
"#77ab59", "#dcf3ff", "#a2d2df", "#257ca3")),
tvals, na.color = "transparent")Ahora podremos visualizar:
capas <- names(tempera)Crearemos la visualizacion del mapa:
# Create leaflet widget --------------------------------------------------------
m <- leaflet() %>%
addTiles(group = "OSM (default)") %>%
addRasterImage(year_tmin, colors = pal, opacity = 0.8, group= capas[1]) %>%
addRasterImage(year_tmax, colors = pal, opacity = 0.8, group= capas[2]) ## Warning in colors(.): Some values were outside the color scale and will be
## treated as NA
## Warning in colors(.): Some values were outside the color scale and will be
## treated as NA#
# Additional leaflet options
m <- m %>%
# Add layers controls
addLayersControl(
overlayGroups = rev(capas),
options = layersControlOptions(collapsed = FALSE)
) %>%
# Add common legend
addLegend(pal = pal, values = tvals,
title = "Annual Temperature")Y lo visualizamos
mReproduccion
Citar este trabajo como: López, A., 2022. Obtener valores normales climáticos mensuales del departamento del Casanare con ClimateR.
sessionInfo()## R version 4.2.1 (2022-06-23 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Colombia.utf8 LC_CTYPE=Spanish_Colombia.utf8
## [3] LC_MONETARY=Spanish_Colombia.utf8 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Colombia.utf8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] stars_0.5-6 abind_1.4-5 RColorBrewer_1.1-3 leafem_0.2.0
## [5] leaflet_2.1.1 dplyr_1.0.10 rasterVis_0.51.4 lattice_0.20-45
## [9] raster_3.6-3 sp_1.5-0 sf_1.0-8 climateR_0.1.0
## [13] AOI_0.2.1
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.4 rnaturalearth_0.1.0 sass_0.4.2
## [4] jsonlite_1.8.0 viridisLite_0.4.1 foreach_1.5.2
## [7] bslib_0.4.0 assertthat_0.2.1 highr_0.9
## [10] latticeExtra_0.6-30 yaml_2.3.5 pillar_1.8.1
## [13] glue_1.6.2 digest_0.6.29 rvest_1.0.3
## [16] colorspace_2.0-3 htmltools_0.5.3 pkgconfig_2.0.3
## [19] s2_1.1.1 purrr_0.3.4 scales_1.2.1
## [22] terra_1.6-17 jpeg_0.1-9 tibble_3.1.8
## [25] proxy_0.4-27 farver_2.1.1 generics_0.1.3
## [28] cachem_1.0.6 hexbin_1.28.2 cli_3.4.0
## [31] fipio_1.1.1 magrittr_2.0.3 deldir_1.0-6
## [34] evaluate_0.16 fansi_1.0.3 doParallel_1.0.17
## [37] xml2_1.3.3 lwgeom_0.2-9 class_7.3-20
## [40] tools_4.2.1 lifecycle_1.0.2 stringr_1.4.1
## [43] interp_1.1-3 munsell_0.5.0 compiler_4.2.1
## [46] jquerylib_0.1.4 e1071_1.7-11 RNetCDF_2.6-1
## [49] rlang_1.0.5 classInt_0.4-8 units_0.8-0
## [52] grid_4.2.1 iterators_1.0.14 rstudioapi_0.14
## [55] htmlwidgets_1.5.4 crosstalk_1.2.0 base64enc_0.1-3
## [58] rmarkdown_2.16 wk_0.7.0 codetools_0.2-18
## [61] DBI_1.1.3 R6_2.5.1 zoo_1.8-11
## [64] knitr_1.40 fastmap_1.1.0 utf8_1.2.2
## [67] KernSmooth_2.23-20 stringi_1.7.8 parallel_4.2.1
## [70] Rcpp_1.0.9 vctrs_0.4.1 png_0.1-7
## [73] tidyselect_1.1.2 xfun_0.33