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climateR

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climateR seeks to simplifiy the steps needed to get climate data into R. It currently provides access to the following gridded climate sources using a single parmaeter

Number Dataset Description Dates
1 GridMET Gridded Meteorological Data. 1979 - Yesterday
2 Daymet Daily Surface Weather and Climatological Summaries 1980 - 2019
3 TopoWX Topoclimatic Daily Air Temperature Dataset 1948 - 2016
4 PRISM Parameter-elevation Regressions on Independent Slopes 1981 - (Yesterday-1)
5 MACA Multivariate Adaptive Constructed Analogs 1950 - 2099
6 LOCA Localized Constructed Analogs 1950 - 2100
7 BCCA Bias Corrected Constructed Analogs 1950 - 2100
8 BCSD Bias Corrected Spatially Downscaled VIC: Monthly Hydrology 1950 - 2099
9 TerraClimate TerraClimate Monthly Gridded Data 1958 - 2019
10 TerraClimate Normals TerraClimate Normals Gridded Data Monthly for 1961-1990, 1981-2010, 2C & 4C
11 CHIRPS Climate Hazards Group InfraRed Precipitation with Station 1980 - Current month
12 EDDI Evaporative Demand Drought Index 1980 - Current year

Installation

remotes::install_github("mikejohnson51/AOI") # suggested!
remotes::install_github("mikejohnson51/climateR")

Usful Packages for climate data

library(AOI)
library(climateR)
library(sf)
library(raster)
library(rasterVis)

Examples

The climateR package is supplemented by the AOI framework established in the AOI R package.

To get a climate product, an area of interest must be defined:

AOI = aoi_get(state = "NC")
plot(AOI$geometry)

Here we are loading a polygon for the state of North Carolina More examples of constructing AOI calls can be found here.

With an AOI, we can construct a call to a dataset for a parameter(s) and date(s) of choice. Here we are querying the PRISM dataset for maximum and minimum temperature on October 29, 2018:

system.time({
 p = getPRISM(AOI, param = c('tmax','tmin'), startDate = "2018-10-29")
})
#>    user  system elapsed 
#>   0.329   0.118   1.027
r = raster::stack(p)

rasterVis::levelplot(r, par.settings = BuRdTheme, names.attr = names(p)) +
  layer(sp.lines(as_Spatial(AOI), col="gray30", lwd=3))

Data from known bounding coordinates

climateR offers support for sf, sfc, and bbox objects. Here we are requesting wind velocity data for the four corners region of the USA by bounding coordinates.

AOI = st_bbox(c(xmin = -112, xmax = -105, ymax = 39, ymin = 34), crs = 4326) %>% 
  getGridMET(param = "wind_vel", startDate = "2018-09-01")

rasterVis::levelplot(AOI$gridmet_wind_vel, margin = FALSE, main = "Four corners Wind Velocity")

Data through time …

In addition to multiple variables we can request variables through time, here let’s look at the gridMET rainfall for the Gulf Coast during Hurricane Harvey:

harvey = getGridMET(aoi_get(state = c("TX", "FL")), 
                  param = "prcp", 
                  startDate = "2017-08-20", endDate = "2017-08-31")

levelplot(harvey$gridmet_prcp, par.settings = BTCTheme, main = "Hurricane Harvey")

Climate Projections

Some sources are downscaled Global Climate Models (GCMs). These allow you to query forecasted ensemble members from different models and/or climate scenarios. One example is from the MACA dataset:

system.time({
m = getMACA(AOI = aoi_get(state = "FL"), 
            model = "CCSM4", 
            param = 'prcp', 
            scenario = c('rcp45', 'rcp85'), 
            startDate = "2080-06-29", endDate = "2080-06-30")
})
#>    user  system elapsed 
#>   0.403   0.113   1.150
r = raster::stack(m)
names(r) = paste(rep(names(m), each = 2), names(m[[1]]))
levelplot(r, par.settings = BTCTheme)

Getting multiple models results is also quite simple:

models = c("bnu-esm","canesm2", "ccsm4", "cnrm-cm5", "csiro-mk3-6-0")

temp =  getMACA(AOI = aoi_get(state = "conus"),
                  param = 'tmin', 
                  model = models, 
                  startDate = "2080-11-29")

s = stack(temp)
s = addLayer(s, mean(s))
names(s) = c(models, "Ensemble Mean")

# Plot
rasterVis::levelplot(s, par.settings = rasterVis::BuRdTheme)

If you don’t know your models, you can always grab a random set by specifying a number:

random = getMACA(aoi_get(state = "MI"), model = 3, param = "prcp", startDate = "2050-10-29")
random = stack(random) %>% setNames(names(random))
levelplot(stack(random), par.settings = BTCTheme)

Global Datasets

Not all datasets are USA focused either. TerraClimate offers global, monthly data up to the current year for many variables, and CHIRPS provides daily rainfall data:

kenya = aoi_get(country = "Kenya")
tc = getTerraClim(kenya, param = "prcp", startDate = "2018-01-01")
chirps = getCHIRPS(kenya, startDate = "2018-01-01", endDate = "2018-01-04" )

p1 = levelplot(tc$terraclim_prcp, par.settings = BTCTheme, main = "January 2018; TerraClim", margin = FALSE) +
  layer(sp.lines(as_Spatial(kenya), col="white", lwd=3))

p2 = levelplot(chirps,  par.settings = BTCTheme, main = "Janaury 1-4, 2018; CHIRPS", layout=c(2, 2)) +
  layer(sp.lines(as_Spatial(kenya), col="white", lwd=3))

gridExtra::grid.arrange(p1,p2, nrow = 1)

This raises the question “what is available for each resource?”. This can be checked in the appropriate meta_data objects. For example let’s see what parameter data is offered for gridMET, and what models and scenarios are offered for MACA.

head(param_meta$gridmet)
#>   common.name call                               description timestep
#> 1        prcp   pr                      precipitation_amount    daily
#> 2       rhmax rmax           daily_maximum_relative_humidity    daily
#> 3       rhmin rmin           daily_minimum_relative_humidity    daily
#> 4        shum  sph              daily_mean_specific_humidity    daily
#> 5        srad srad daily_mean_shortwave_radiation_at_surface    daily
#> 6    wind_dir   th                 daily_mean_wind_direction    daily
#>                          units
#> 1                           mm
#> 2                      Percent
#> 3                      Percent
#> 4                        kg/kg
#> 5                        W/m^2
#> 6 Degrees Clockwise from north

head(model_meta$maca)
#>           model ensemble scenario
#> 1       BNU-ESM   r1i1p1    rcp45
#> 2      CNRM-CM5   r1i1p1    rcp45
#> 3 CSIRO-Mk3-6-0   r1i1p1    rcp45
#> 4    bcc-csm1-1   r1i1p1    rcp45
#> 5       CanESM2   r1i1p1    rcp45
#> 6    GFDL-ESM2G   r1i1p1    rcp45

Point Based Data

Finally, data gathering is not limited to areal extents and can be retrieved as a time series at locations.

AOI = AOI::geocode('Colorado Springs', pt = TRUE)
ts  = getGridMET(AOI, param = 'srad', startDate = "2019-01-01", endDate = "2019-12-31")

ggplot(data = ts) + 
  aes(x = date, y = srad) + 
  geom_line() +
  stat_smooth(col = "red") + 
  theme_linedraw() + 
  labs(title = "Solar Radiation: Colorado Springs 2019", x = "Date", y = "Solar Radiation")

Point Based Ensemble

future = getMACA(geocode("UCSB", pt = TRUE), 
                 model = 5, param = "tmax", 
                 startDate = "2050-01-01", endDate = "2050-01-31")

future_long = future %>% 
  dplyr::select(-source, -lat, -lon) %>% 
  tidyr::pivot_longer(-date) 

ggplot(data = future_long, aes(x = date, y = value, col = name)) + 
  geom_line() + 
  theme_linedraw() + 
  scale_color_brewer(palette = "Dark2") + 
  labs(title = "UCSB Temperture: January, 2050",
       x = "Date",
       y = "Degree K",
       color = "Model")

Multi site extraction

Extracting data for a set of points is an interesting challenge. It turns it is much more efficient to grab the underlying raster stack and then extract time series as opposed to iterating over the locations:

  1. Starting with a set of locations in Brazil:
(sites = read.csv('./inst/extdata/example.csv') %>% 
  st_as_sf(coords = c("long", "lat"), crs = 4326))
#> Simple feature collection with 100 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -54.81975 ymin: -29.73627 xmax: -40.80975 ymax: -18.52627
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>         X     ID                    geometry
#> 1  190760 190760 POINT (-50.40975 -25.81627)
#> 2  267801 267801 POINT (-48.15975 -24.60627)
#> 3  219885 219885 POINT (-49.28975 -25.32627)
#> 4  200445 200445 POINT (-50.45975 -25.63627)
#> 5   74789  74789 POINT (-51.70975 -28.01627)
#> 6   18343  18343 POINT (-50.35975 -29.33627)
#> 7  143615 143615 POINT (-49.33975 -26.73627)
#> 8  588292 588292 POINT (-47.57975 -21.27627)
#> 9  371314 371314 POINT (-47.76975 -23.28627)
#> 10 638894 638894 POINT (-46.99975 -20.75627)
  1. climateR will grab the RasterStack underlying the bounding area of the points
sites_stack = getTerraClim(AOI   = sites, 
                           param = "tmax", 
                           startDate = "2018-01-01", 
                           endDate   = "2018-12-31")

plot(sites_stack$terraclim_tmax$X2018.01)
plot(sites$geometry, add = TRUE, pch = 16, cex = .5)

  1. Use extract_sites to extract the times series from these locations. The id parameter is the unique identifier from the site data with which to names the resulting columns.
sites_wide = extract_sites(sites_stack, sites, "ID")
sites_wide$terraclim_tmax[1:5, 1:5]
#>         date site_190760 site_267801 site_219885 site_200445
#> 1 2018-01-01        27.2        29.9        25.6        26.6
#> 2 2018-02-01        26.8        31.5        25.6        26.2
#> 3 2018-03-01        26.6        30.1        25.2        25.8
#> 4 2018-04-01        26.4        30.8        25.6        26.0
#> 5 2018-05-01        25.3        28.3        24.8        24.9

To make the data ‘tidy’ simply pivot on the date column:

tmax = tidyr::pivot_longer(sites_wide$terraclim_tmax, -date)
head(tmax)
#> # A tibble: 6 x 3
#>   date       name        value
#>   <date>     <chr>       <dbl>
#> 1 2018-01-01 site_190760  27.2
#> 2 2018-01-01 site_267801  29.9
#> 3 2018-01-01 site_219885  25.6
#> 4 2018-01-01 site_200445  26.6
#> 5 2018-01-01 site_74789   27.7
#> 6 2018-01-01 site_18343   24.1

ggplot(data = tmax, aes(x = date, y = value, color = name, group = name)) + 
  scale_color_viridis_d() +
  geom_line() + 
  theme_linedraw() + 
  theme(legend.position = "none") 

Fast Reprojection

This is a relatively new function (01-18-2020) that has not been extensively tested for how it scales with large requests. The aim is to provide fast projection of climateR gridded output. For point data use sf::st_transform. Starting with 2 days of precipitation data in 2080 from MACA:

cr = climateR::getMACA(
  AOI::aoi_get(state = "conus"), 
  model = "CCSM4", 
  param = 'prcp', 
  startDate = "2080-06-29", endDate = "2080-06-30")

levelplot(cr$maca_ccsm4_prcp_rcp45_mm, par.settings = BTCTheme)

Lets transform the projection system from the native WGS84 to the projected CONUS Albers Equal Area (EPSG:5070).

system.time({ cr2 = fast_reproject(cr, target_prj = 5070) })
#>    user  system elapsed 
#>   0.564   0.163   0.763
levelplot(cr2$maca_ccsm4_prcp_rcp45_mm, par.settings = BTCTheme)

Support:

climateR is written by Mike Johnson, a graduate Student at the University of California, Santa Barbara in Keith C. Clarke’s Lab.

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An R 📦 for getting point and gridded climate data by AOI

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