README.md
In lhmet-ped/HEgis: Functions To Import GIS Data Needed For FUSE
HEgis
The goal of HEgis is to prepare GIS data for use in
fuse.prep. The main functions
of are:
-
import_bhs_ons(): use it to import the shapefile with basins of
major hydroelectric power dams.
-
info_station(): it is useful to get basic information from a ONS
station.
-
extract_poly(): to extract a specific watershed polygon from the
data set.
-
extract_condem(): to crop and mask a geographic subset of a
raster. In this specific case the raster is a hydrologically
conditioned elevation model.
Installation
You can install {HEgis} from
github with:
library(devtools)
install_github("lhmet-ped/HEgis")
We load the required packages using:
library(sf)
#> Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
library(spData)
#> To access larger datasets in this package, install the spDataLarge
#> package with: `install.packages('spDataLarge',
#> repos='https://nowosad.github.io/drat/', type='source')`
library(HEgis)
library(raster)
#> Loading required package: sp
library(lhmetools)
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
#> ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
#> ✓ tibble 3.0.4 ✓ dplyr 1.0.2
#> ✓ tidyr 1.1.2 ✓ stringr 1.4.0
#> ✓ readr 1.4.0 ✓ forcats 0.5.0
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> x tidyr::extract() masks raster::extract()
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x dplyr::select() masks raster::select()
Watersheds
A shapefile with the watersheds of major Hydroelectric Power plants from
ONS is available with {HEgis} package automatically after you
install it. Import this data simply using:
(bhs_pols <- import_bhs_ons())
#> Using previously extracted data.
#> reading file from:
#> /home/hidrometeorologista/.R/libs/HEgis/extdata/BaciasHidrograficasONS_JUNTOS/BaciasHidrograifcasUHEsONS.shp
#> Simple feature collection with 87 features and 9 fields
#> geometry type: POLYGON
#> dimension: XY
#> bbox: xmin: -72.41788 ymin: -29.41284 xmax: -38.93867 ymax: -2.495375
#> geographic CRS: SIRGAS 2000
#> First 10 features:
#> codONS codANA nome nomeOri adkm2 volhm3 rio
#> 1 266 11735 ITAIPU UHE Itaipu 822904.333 29403.91 Rio Paraná
#> 2 291 62833 DARDANELOS <NA> 15332.607 NA Rio Aripuanã
#> 3 211 3581 FUNIL-GRANDE UHE Funil 15720.006 268.93 Rio Grande
#> 4 134 3875 SALTO GRANDE UHE Salto Grande 2476.716 78.00 Rio Guanhães
#> 5 245 8124 JUPIA UHE Jupiá 476527.733 3354.00 Rio Paraná
#> 6 197 3626 PICADA UHE Picada 1725.833 7.09 Rio do Peixe
#> 7 295 14588 JAURU UHE Jauru 2245.733 17.15 <NA>
#> 8 296 14589 GUAPORE UHE Guaporé 1344.294 21.00 Rio Guaporé
#> 9 240 8013 PROMISSAO UHE Promissão 57841.510 7408.00 Rio Tietê
#> 10 216 7607 CAMPOS NOVOS UHE Campos Novos 14445.832 1477.00 Rio Canoas
#> cobacia tpopera geometry
#> 1 8631311 Fio d'água POLYGON ((-43.60082 -21.168...
#> 2 46293331 <NA> POLYGON ((-59.35952 -11.975...
#> 3 86895773 Fio d'água POLYGON ((-44.50861 -22.224...
#> 4 7766211 Fio d'água POLYGON ((-43.04034 -18.603...
#> 5 865775 Fio d'água POLYGON ((-46.35946 -23.273...
#> 6 7788753 Fio d'água POLYGON ((-43.89604 -21.974...
#> 7 89969715 Fio d'água POLYGON ((-58.76821 -14.676...
#> 8 4699693 Fio d'água POLYGON ((-58.90869 -14.595...
#> 9 8661373 Regulariza_ONS POLYGON ((-46.10237 -22.999...
#> 10 829173 Regulariza_ONS POLYGON ((-49.44034 -28.132...
#> Simple feature collection with 87 features and 9 fields
#> geometry type: POLYGON
#> dimension: XY
#> bbox: xmin: -72.41788 ymin: -29.41284 xmax: -38.93867 ymax: -2.495375
#> geographic CRS: SIRGAS 2000
#> First 10 features:
#> codONS codANA nome nomeOri adkm2 volhm3 rio
#> 1 266 11735 ITAIPU UHE Itaipu 822904.333 29403.91 Rio Paraná
#> 2 291 62833 DARDANELOS <NA> 15332.607 NA Rio Aripuanã
#> 3 211 3581 FUNIL-GRANDE UHE Funil 15720.006 268.93 Rio Grande
#> 4 134 3875 SALTO GRANDE UHE Salto Grande 2476.716 78.00 Rio Guanhães
#> 5 245 8124 JUPIA UHE Jupiá 476527.733 3354.00 Rio Paraná
#> 6 197 3626 PICADA UHE Picada 1725.833 7.09 Rio do Peixe
#> 7 295 14588 JAURU UHE Jauru 2245.733 17.15 <NA>
#> 8 296 14589 GUAPORE UHE Guaporé 1344.294 21.00 Rio Guaporé
#> 9 240 8013 PROMISSAO UHE Promissão 57841.510 7408.00 Rio Tietê
#> 10 216 7607 CAMPOS NOVOS UHE Campos Novos 14445.832 1477.00 Rio Canoas
#> cobacia tpopera geometry
#> 1 8631311 Fio d'água POLYGON ((-43.60082 -21.168...
#> 2 46293331 <NA> POLYGON ((-59.35952 -11.975...
#> 3 86895773 Fio d'água POLYGON ((-44.50861 -22.224...
#> 4 7766211 Fio d'água POLYGON ((-43.04034 -18.603...
#> 5 865775 Fio d'água POLYGON ((-46.35946 -23.273...
#> 6 7788753 Fio d'água POLYGON ((-43.89604 -21.974...
#> 7 89969715 Fio d'água POLYGON ((-58.76821 -14.676...
#> 8 4699693 Fio d'água POLYGON ((-58.90869 -14.595...
#> 9 8661373 Regulariza_ONS POLYGON ((-46.10237 -22.999...
#> 10 829173 Regulariza_ONS POLYGON ((-49.44034 -28.132...
We can view the major watersheds (87) with:
sa <- world[world$continent == "South America", ]
br <- world[world$name_long == "Brazil", ]
set.seed(12)
ord_areas <- order(bhs_pols$adkm2, decreasing = TRUE)
cols <- sample(colors(), size = nrow(bhs_pols))
plot(st_geometry(sa),
axes = TRUE,
border = "grey",
xlim = c(-80, -30),
ylim = c(-40, 10)
)
plot(st_geometry(br), axes = TRUE, border = "grey60", lwd = 2, add = TRUE)
# from highest to lower drainage areas
for (i in ord_areas) {
# i <- ord_areas[1]
plot(st_geometry(bhs_pols)[i],
add = TRUE,
col = cols[i],
border = 1,
lwd = 0.7
)
}
Extraction from a specific watershed polygon
To extract a watershed polygon we first need the "station id" (posto).
We can get this information with the function info_station(), for
example for the HPP station of G.B. MUNHOZ we can use the following
code:
info_posto <- info_station(name_regex = "MUNHOZ")
info_posto
#> # A tibble: 1 x 10
#> num nome posto jus ree v_inic u_exis modif inic_hist fim_hist
#> <int> <chr> <int> <int> <int> <dbl> <chr> <int> <int> <int>
#> 1 74 G.B. MUNHOZ 74 76 11 29.2 EX 0 1931 2018
We now use the "station id" (posto) to select the polygon of interest.
(poly_posto <- extract_poly(station = info_posto$posto))
#> The data is not projected. We are taking CRS as SIRGAS 2000 (EPSG: 4674),
#> the same as that of BHO-ANA on which the provider was based.
#> Simple feature collection with 1 feature and 9 fields
#> geometry type: POLYGON
#> dimension: XY
#> bbox: xmin: -51.72274 ymin: -26.85503 xmax: -48.94853 ymax: -25.22428
#> geographic CRS: SIRGAS 2000
#> codONS codANA nome nomeOri adkm2
#> 1 74 7659 G_B_MUNHOZ UHE Governador Bento Munhoz da Rocha Neto 30207.57
#> volhm3 rio cobacia tpopera geometry
#> 1 5779 Rio Iguaçu 8625591 Regulariza_ONS POLYGON ((-51.56304 -26.259...
(area_poly_posto <- poly_posto[["adkm2"]])
#> [1] 30207.57
Digital elevation model for a watershed polygon
The digital elevation model for the station can be obtained with:
rawdem_br <- getData("alt", country = "BRA")
condem_posto <- extract_condem(
condem = rawdem_br,
poly_station = poly_posto,
dis.buf = 0.25 # buffer em degrees
)
#> Warning in st_buffer.sfc(st_geometry(x), dist, nQuadSegs, endCapStyle =
#> endCapStyle, : st_buffer does not correctly buffer longitude/latitude data
#> Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO"): Discarded
#> datum Unknown based on WGS84 ellipsoid in CRS definition
#> Warning in showSRID(SRS_string, format = "PROJ", multiline = "NO"): Discarded
#> datum Unknown based on WGS84 ellipsoid in CRS definition
plot(condem_posto)
plot(st_geometry(poly_posto), add = TRUE)
The extract_condem() function is designed to be used with a
hydrologically consistent ‘dem’, which required for hydrological
modeling purposes. But in the example above, a raw DEM was used for the
purpose of demonstrating the use of the function.
lhmet-ped/HEgis documentation built on July 23, 2022, 2:59 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
HEgis
The goal of HEgis is to prepare GIS data for use in fuse.prep. The main functions of are:
-
import_bhs_ons(): use it to import the shapefile with basins of major hydroelectric power dams. -
info_station(): it is useful to get basic information from a ONS station. -
extract_poly(): to extract a specific watershed polygon from the data set. -
extract_condem(): to crop and mask a geographic subset of a raster. In this specific case the raster is a hydrologically conditioned elevation model.
Installation
You can install {HEgis} from
github with:
library(devtools)
install_github("lhmet-ped/HEgis")
We load the required packages using:
library(sf)
#> Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
library(spData)
#> To access larger datasets in this package, install the spDataLarge
#> package with: `install.packages('spDataLarge',
#> repos='https://nowosad.github.io/drat/', type='source')`
library(HEgis)
library(raster)
#> Loading required package: sp
library(lhmetools)
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
#> ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
#> ✓ tibble 3.0.4 ✓ dplyr 1.0.2
#> ✓ tidyr 1.1.2 ✓ stringr 1.4.0
#> ✓ readr 1.4.0 ✓ forcats 0.5.0
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> x tidyr::extract() masks raster::extract()
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x dplyr::select() masks raster::select()
Watersheds
A shapefile with the watersheds of major Hydroelectric Power plants from
ONS is available with {HEgis} package automatically after you
install it. Import this data simply using:
(bhs_pols <- import_bhs_ons())
#> Using previously extracted data.
#> reading file from:
#> /home/hidrometeorologista/.R/libs/HEgis/extdata/BaciasHidrograficasONS_JUNTOS/BaciasHidrograifcasUHEsONS.shp
#> Simple feature collection with 87 features and 9 fields
#> geometry type: POLYGON
#> dimension: XY
#> bbox: xmin: -72.41788 ymin: -29.41284 xmax: -38.93867 ymax: -2.495375
#> geographic CRS: SIRGAS 2000
#> First 10 features:
#> codONS codANA nome nomeOri adkm2 volhm3 rio
#> 1 266 11735 ITAIPU UHE Itaipu 822904.333 29403.91 Rio Paraná
#> 2 291 62833 DARDANELOS <NA> 15332.607 NA Rio Aripuanã
#> 3 211 3581 FUNIL-GRANDE UHE Funil 15720.006 268.93 Rio Grande
#> 4 134 3875 SALTO GRANDE UHE Salto Grande 2476.716 78.00 Rio Guanhães
#> 5 245 8124 JUPIA UHE Jupiá 476527.733 3354.00 Rio Paraná
#> 6 197 3626 PICADA UHE Picada 1725.833 7.09 Rio do Peixe
#> 7 295 14588 JAURU UHE Jauru 2245.733 17.15 <NA>
#> 8 296 14589 GUAPORE UHE Guaporé 1344.294 21.00 Rio Guaporé
#> 9 240 8013 PROMISSAO UHE Promissão 57841.510 7408.00 Rio Tietê
#> 10 216 7607 CAMPOS NOVOS UHE Campos Novos 14445.832 1477.00 Rio Canoas
#> cobacia tpopera geometry
#> 1 8631311 Fio d'água POLYGON ((-43.60082 -21.168...
#> 2 46293331 <NA> POLYGON ((-59.35952 -11.975...
#> 3 86895773 Fio d'água POLYGON ((-44.50861 -22.224...
#> 4 7766211 Fio d'água POLYGON ((-43.04034 -18.603...
#> 5 865775 Fio d'água POLYGON ((-46.35946 -23.273...
#> 6 7788753 Fio d'água POLYGON ((-43.89604 -21.974...
#> 7 89969715 Fio d'água POLYGON ((-58.76821 -14.676...
#> 8 4699693 Fio d'água POLYGON ((-58.90869 -14.595...
#> 9 8661373 Regulariza_ONS POLYGON ((-46.10237 -22.999...
#> 10 829173 Regulariza_ONS POLYGON ((-49.44034 -28.132...
#> Simple feature collection with 87 features and 9 fields
#> geometry type: POLYGON
#> dimension: XY
#> bbox: xmin: -72.41788 ymin: -29.41284 xmax: -38.93867 ymax: -2.495375
#> geographic CRS: SIRGAS 2000
#> First 10 features:
#> codONS codANA nome nomeOri adkm2 volhm3 rio
#> 1 266 11735 ITAIPU UHE Itaipu 822904.333 29403.91 Rio Paraná
#> 2 291 62833 DARDANELOS <NA> 15332.607 NA Rio Aripuanã
#> 3 211 3581 FUNIL-GRANDE UHE Funil 15720.006 268.93 Rio Grande
#> 4 134 3875 SALTO GRANDE UHE Salto Grande 2476.716 78.00 Rio Guanhães
#> 5 245 8124 JUPIA UHE Jupiá 476527.733 3354.00 Rio Paraná
#> 6 197 3626 PICADA UHE Picada 1725.833 7.09 Rio do Peixe
#> 7 295 14588 JAURU UHE Jauru 2245.733 17.15 <NA>
#> 8 296 14589 GUAPORE UHE Guaporé 1344.294 21.00 Rio Guaporé
#> 9 240 8013 PROMISSAO UHE Promissão 57841.510 7408.00 Rio Tietê
#> 10 216 7607 CAMPOS NOVOS UHE Campos Novos 14445.832 1477.00 Rio Canoas
#> cobacia tpopera geometry
#> 1 8631311 Fio d'água POLYGON ((-43.60082 -21.168...
#> 2 46293331 <NA> POLYGON ((-59.35952 -11.975...
#> 3 86895773 Fio d'água POLYGON ((-44.50861 -22.224...
#> 4 7766211 Fio d'água POLYGON ((-43.04034 -18.603...
#> 5 865775 Fio d'água POLYGON ((-46.35946 -23.273...
#> 6 7788753 Fio d'água POLYGON ((-43.89604 -21.974...
#> 7 89969715 Fio d'água POLYGON ((-58.76821 -14.676...
#> 8 4699693 Fio d'água POLYGON ((-58.90869 -14.595...
#> 9 8661373 Regulariza_ONS POLYGON ((-46.10237 -22.999...
#> 10 829173 Regulariza_ONS POLYGON ((-49.44034 -28.132...
We can view the major watersheds (87) with:
sa <- world[world$continent == "South America", ]
br <- world[world$name_long == "Brazil", ]
set.seed(12)
ord_areas <- order(bhs_pols$adkm2, decreasing = TRUE)
cols <- sample(colors(), size = nrow(bhs_pols))
plot(st_geometry(sa),
axes = TRUE,
border = "grey",
xlim = c(-80, -30),
ylim = c(-40, 10)
)
plot(st_geometry(br), axes = TRUE, border = "grey60", lwd = 2, add = TRUE)
# from highest to lower drainage areas
for (i in ord_areas) {
# i <- ord_areas[1]
plot(st_geometry(bhs_pols)[i],
add = TRUE,
col = cols[i],
border = 1,
lwd = 0.7
)
}
Extraction from a specific watershed polygon
To extract a watershed polygon we first need the "station id" (posto).
We can get this information with the function info_station(), for
example for the HPP station of G.B. MUNHOZ we can use the following
code:
info_posto <- info_station(name_regex = "MUNHOZ")
info_posto
#> # A tibble: 1 x 10
#> num nome posto jus ree v_inic u_exis modif inic_hist fim_hist
#> <int> <chr> <int> <int> <int> <dbl> <chr> <int> <int> <int>
#> 1 74 G.B. MUNHOZ 74 76 11 29.2 EX 0 1931 2018
We now use the "station id" (posto) to select the polygon of interest.
(poly_posto <- extract_poly(station = info_posto$posto))
#> The data is not projected. We are taking CRS as SIRGAS 2000 (EPSG: 4674),
#> the same as that of BHO-ANA on which the provider was based.
#> Simple feature collection with 1 feature and 9 fields
#> geometry type: POLYGON
#> dimension: XY
#> bbox: xmin: -51.72274 ymin: -26.85503 xmax: -48.94853 ymax: -25.22428
#> geographic CRS: SIRGAS 2000
#> codONS codANA nome nomeOri adkm2
#> 1 74 7659 G_B_MUNHOZ UHE Governador Bento Munhoz da Rocha Neto 30207.57
#> volhm3 rio cobacia tpopera geometry
#> 1 5779 Rio Iguaçu 8625591 Regulariza_ONS POLYGON ((-51.56304 -26.259...
(area_poly_posto <- poly_posto[["adkm2"]])
#> [1] 30207.57
Digital elevation model for a watershed polygon
The digital elevation model for the station can be obtained with:
rawdem_br <- getData("alt", country = "BRA")
condem_posto <- extract_condem(
condem = rawdem_br,
poly_station = poly_posto,
dis.buf = 0.25 # buffer em degrees
)
#> Warning in st_buffer.sfc(st_geometry(x), dist, nQuadSegs, endCapStyle =
#> endCapStyle, : st_buffer does not correctly buffer longitude/latitude data
#> Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO"): Discarded
#> datum Unknown based on WGS84 ellipsoid in CRS definition
#> Warning in showSRID(SRS_string, format = "PROJ", multiline = "NO"): Discarded
#> datum Unknown based on WGS84 ellipsoid in CRS definition
plot(condem_posto)
plot(st_geometry(poly_posto), add = TRUE)
The extract_condem() function is designed to be used with a
hydrologically consistent ‘dem’, which required for hydrological
modeling purposes. But in the example above, a raw DEM was used for the
purpose of demonstrating the use of the function.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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